research papers of 2022

The top 10 journal articles of 2022

APA’s 89 journals published more than 5,500 articles in 2022. Here are the top 10 most read

Vol. 54 No. 1 Print version: page 26

1. Like this meta-analysis: Screen media and mental health

Ferguson, C. J., et al.

This meta-analysis in Professional Psychology: Research and Practice (Vol. 53, No. 2) suggests that exposure to screen time, including smartphones and social media, is not linked to mental health issues in adults or children. Researchers analyzed 37 data sets from 33 separate studies published between 2015 and 2019. They found no evidence that screen media contributes to suicidal ideation or other negative mental health outcomes. This result was also true when specifically investigating the use of smartphones or social media, and it was not affected by participants’ age or ethnicity. DOI: 10.1037/pro0000426

2. Conceptual and design thinking for thematic analysis

Braun, V., & Clarke, V.

Thematic analysis (TA) is a method used in qualitative research to examine themes or patterns of meaning within a data set, with the goal of answering a specific research question. This paper in Qualitative Psychology (Vol. 9, No. 1) aims to bolster researchers’ conceptual and design thinking when using TA to produce more methodologically sound results. Useful guidance includes best practices on research questions, data collection, participant/data item selection strategy and criteria, ethics, and quality standards and practices. The authors also outline the three primary approaches to TA: coding reliability, codebook, and reflexive. The first two approaches involve sorting data into pre-identified themes, while in the third, coding precedes theme development, and themes are built from coded data. The article ends with guidance on reporting standards for reflexive TA. DOI: 10.1037/qup0000196

3. Doomscrolling during Covid -19: The negative association between daily social and traditional media consumption and mental health symptoms during the Covid -19 pandemic

Price, M., et al.

The Covid -19 pandemic triggered a remarkable rise in doomscrolling, the consumption of an excessive amount of negative news resulting in significant negative affect. This article in Psychological Trauma: Theory, Research, Practice, and Policy (Vol. 14, No. 8) indicates that doomscrolling was associated with increased symptoms of depression and post-traumatic stress disorder (PTSD). Researchers assessed the psychopathology of 61 participants in the United States and asked them to track depression and PTSD symptoms and pandemic-related media consumption over 30 days in spring 2020. They found that an increase in social media use (but not traditional media use) was associated with more depression and PTSD symptoms, particularly in participants with a history of childhood maltreatment. Additionally, participants with more severe baseline psychopathology consumed more social media during this period. DOI: 10.1037/tra0001202

4. A comparison of emotion-focused therapy and cognitive-behavioral therapy in the treatment of generalized anxiety disorder: Results of a feasibility randomized controlled trial

Timulak, L., et al.

This study in Psychotherapy (Vol. 59, No. 1) indicates that emotion-focused therapy (EFT) may be as effective as cognitive behavioral therapy (CBT) in treating generalized anxiety disorder (GAD). Researchers randomly assigned 58 participants in Ireland with a GAD diagnosis to 16 to 20 sessions of either EFT or CBT, which were run by eight therapists trained in both therapies. The researchers assessed participants at baseline, week 16, the end of therapy, and 6 months after the end of therapy. Dropout from treatments was 10% for EFT and 27% for CBT. The two therapies showed large pre-to-post changes and similar outcomes across all measures, with the benefits of both lasting through the 6-month follow-up. Overall, the findings suggest that EFT is a potentially promising treatment for GAD, broadening the range of treatment choices available for this condition. DOI: 10.1037/pst0000427

5. Looking through a filtered lens: Negative social comparison on social media and suicidal ideation among young adults

Spitzer, E. G., et al.

This study in Psychology of Popular Media (online first publication) suggests that young adults who feel bad about themselves after comparing themselves with others on social networking sites such as Instagram and Facebook are more likely to contemplate suicide. Researchers surveyed 456 undergraduate students in the United States about their social media use as well as incidents of negative social comparison, suicidal ideation, and thwarted belongingness. They found an association between negative social comparison on social networking sites and suicidal ideation. Furthermore, on Instagram, the relationship between thwarted belongingness and suicidal ideation was stronger among those with high levels of negative social comparison. DOI: 10.1037/ppm0000380

6. Associations between young adults’ social media addiction, relationship quality with parents, and internalizing problems: A path analysis model

White-Gosselin, C.-É., & Poulin, F.

Young adults with social media addiction symptoms are more likely to be anxious and depressed and have worse relationships with their parents, indicates this study in the Canadian Journal of Behavioural Science (online first publication). Researchers asked 435 young adults in Canada with a median age of 19 to report their symptoms of anxiety, depression, and the quality of their relationships with their mothers and fathers (along the dimensions of conflict, satisfaction, and equality). The researchers found that the level of participants’ social media addiction was associated with high conflict, low satisfaction, and low equality with their fathers, and high conflict and low equality with their mothers. Furthermore, social media addiction was linked to symptoms of anxiety and depression. Finally, anxiety and depression exacerbated the negative impacts of social media addiction on conflict, satisfaction, and equality with participants’ mothers, as well as conflict and satisfaction with their fathers. DOI: 10.1037/cbs0000326

7. On the outside looking in: Social media intensity, social connection, and user well-being: The moderating role of passive social media use

Roberts, J. A., & David, M. E.

This study in the Canadian Journal of Behavioural Science (online first publication) suggests that intense social media use is sometimes linked with a weaker sense of social connection and well-being. In the first of three studies with 146 participants in the United States, researchers found that passive engagement (viewing social media but not regularly posting or interacting through the platform) was associated with less social connection and lower well-being. The second study with 80 participants revealed that the interaction between heavy social media use (across 11 social media platforms) and consistently passive use of these platforms was linked with lower perceived social connection and, subsequently, higher stress. In the third study, with 160 participants, the researchers manipulated the amount of social media use (heavy versus light) and type of social media use (passive versus active) and found that heavy social media use had a negative impact on social connection when used passively but a positive effect when used actively. DOI: 10.1037/cbs0000323

8. Effects of a statewide pre-kindergarten program on children’s achievement and behavior through sixth grade

Durkin, K., et al.

According to this study in Developmental Psychology (Vol. 58, No. 3) , children who attended a state-funded pre-K program were doing worse by the end of sixth grade than peers who did not attend a pre-K program. Researchers randomly assigned 2,990 children from low-income families who applied to pre-K program sites across Tennessee in 2009 and 2010 to an offer of admission or a waiting list control. State education records showed that at the end of their first year, the children who went to pre-K scored higher on school readiness. However, by third grade, the pre-K children had lower math and science test scores than the control group. Moreover, at the end of sixth grade, the pre-K children were doing even worse, with lower math, science, and reading scores. They also were more likely to be in special education and were more likely to be suspended for behavioral issues. DOI: 10.1037/dev0001301

9. The role of childhood traumas, interpersonal problems, and contrast avoidance model in development of the generalized anxiety disorder: A structural equation modeling

Shafiei, M., et al.

The contrast avoidance model supposes that people with generalized anxiety disorder (GAD) unconsciously worry as a way to sustain a negative emotional state to avoid the discomfort that comes with shifting from positive or neutral states into a negative state. In this study in Psychological Trauma: Theory, Research, Practice, and Policy (Vol. 14, No. 3) , researchers surveyed 457 participants in Iran with GAD and found that childhood trauma, interpersonal problems, and contrast avoidance of worry were all related to symptoms of GAD. Interpersonal problems and contrast avoidance of worry were able to directly predict symptoms of GAD. In addition, childhood trauma had an indirect effect on GAD by negatively influencing the relationship between interpersonal problems and contrast avoidance of worry. DOI: 10.1037/tra0001117

10. The continuing unfairness of death qualification: Changing death penalty attitudes and capital jury selection

Haney, C., et al.

To serve on a death penalty jury, potential jurors must declare under oath that they are willing to impose the death penalty. This study in Psychology, Public Policy, and Law (Vol. 28, No. 1) found numerous biases among such “death qualified” jurors. Researchers conducted in-depth surveys with a total of 1,792 representative individuals eligible for jury service in California, New Hampshire, and Florida. Despite differences in location, demographic makeup, and political ideology, the researchers observed very similar patterns of death-qualification bias. Specifically, they found that individuals whose death penalty attitudes qualified them to serve on a capital jury were more punitive overall, less well-informed about the processes involved in death sentencing, more willing to use aggravating factors to impose the death penalty, less willing to use mitigating factors to impose life in prison without the possibility of parole, and less racially diverse than people who would be unwilling to impose the death penalty. DOI: 10.1037/law0000335

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HBR’s Most-Read Research Articles of 2022

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Insights on equity, leadership, and becoming your best self.

The new year is a great time to set ambitious goals. But alongside our plans for the future, it’s also helpful to acknowledge all the challenges we’ve faced — and the progress we’ve made — in the last 12 months. In this end-of-year roundup, we share key insights and trends from HBR’s most-read research articles of 2022, exploring topics from embracing a new identity to fostering equity in the workplace and beyond.

For many of us, the arrival of a new year can be equal parts inspiring and daunting. While the promise of a fresh start is often welcome, it’s also a reminder of all the challenges we faced in the last 12 months — and all those still awaiting us, that we have yet to overcome.

• Dagny Dukach is a former associate editor at Harvard Business Review.

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The dizzying pace of research and innovation at MIT can make it hard to keep up. To mark the end of the year, MIT News is looking back at 10 of the research stories that generated the most excitement in 2022.

We’ve also rounded up the year’s  top MIT community-related stories .

• Designing a heat engine with no moving parts . In April, engineers at MIT and the National Renewable Energy Laboratory (NREL) designed a heat engine that might someday enable a fully decarbonized power grid. In demonstrations, the engine was able to convert heat to electricity with over 40 percent efficiency — a performance better than that of traditional steam turbines.
• Creating a lightweight material stronger than steel . In February, MIT chemical engineers used a new polymerization process to form a material that that is stronger than steel and as light as plastic, and can be easily manufactured in large quantities. The material could be used as a coating for car parts or as a building material for bridges and other structures.
• Enabling portable desalination at the push of a button . MIT researchers developed a suitcase-sized device that can remove particles and salts to generate drinking water. Unlike other desalination units that rely on filters, this device uses electrical power to purify the water. It requires less power to operate than a cell phone charger and can be driven by a small solar panel. Just push start.
• Linking human genes to function . A team of researchers created the first map tying every gene expressed in human cells to its job in the cell. The map, which is available for other scientists to use, makes it easier to study a range of biological questions. The map was created using a CRISPR-based single-cell sequencing method known as Perturb-seq.
• Improving supercomputing with a new programming language . A team of researchers based mainly at MIT invented a faster and more reliable programming language for high-performance computing. The language, which was tested on a number of small programs, could one day help computers with a number of deep learning tasks like image processing.
• Lifting people out of extreme poverty . A study co-authored by an MIT economist showed that a one-time capital boost (in this case, a cow) helped poor people in rural Bangladesh improve their lives in the long run. The study suggests the very poor are in a poverty trap, in which an initial lack of resources prevents them from improving their circumstances, and implies that large asset transfers are an effective way to reduce global poverty.
• Helping robots fly . Inspired by fireflies, MIT researchers created tiny actuators that emit light to allow insect-scale robots to communicate. Weighing barely more than a paper clip, the robots are too small to make use of traditional means of sensing and communication. Instead, the actuators that control the robots’ wings light up in different colors and patterns, which could enable them to do things like share their location and call for help.
• Detecting a radio signal in a far-off galaxy . In July, astronomers at MIT and elsewhere were surprised to find a periodic fast radio burst (FRB) originating billions of light-years from Earth. It is the longest lasting FRB pattern detected to date and is made up of intensely strong radio waves that repeat every 0.2 seconds, similar to a heartbeat. Astronomers suspect the signal is coming from a neutron star.
• Proposal for a new, low-cost battery design . Researchers at MIT developed a battery made from abundant, inexpensive materials to complement the rise of lithium-ion batteries. The new battery uses aluminum and sulfur as its two electrode materials and a molten salt electrolyte in between. It could be ideal for powering single homes or small to medium sized businesses, producing a few tens of kilowatt-hours of storage capacity.
• Immigrants as job creators . A study co-authored by an MIT economist found that compared to native-born citizens, immigrants are about 80 percent more likely to found a firm. The study, which looked at registered businesses of all types across the country, suggests that immigrants act more as "job creators" than "job takers" and play outsized roles in high-growth entrepreneurship in the U.S.

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The Top 17 ‘Must-Read’ AI Papers in 2022

We caught up with experts in the RE•WORK community to find out what the top 17 AI papers are for 2022 so far that you can add to your Summer must reads. The papers cover a wide range of topics including AI in social media and how AI can benefit humanity and are free to access.

Interested in learning more? Check out all the upcoming RE•WORK events to find out about the latest trends and industry updates in AI here .

Max Li, Staff Data Scientist – Tech Lead at Wish

Max is a Staff Data Scientist at Wish where he focuses on experimentation (A/B testing) and machine learning.  His passion is to empower data-driven decision-making through the rigorous use of data. View Max’s presentation, ‘Assign Experiment Variants at Scale in A/B Tests’, from our Deep Learning Summit in February 2022 here .

1. Boostrapped Meta-Learning (2022) – Sebastian Flennerhag et al.

The first paper selected by Max proposes an algorithm in which allows the meta-learner teach itself, allowing to overcome the meta-optimisation challenge. The algorithm focuses meta-learning with gradients, which guarantees improvements in performance. The paper also looks at how bootstrapping opens up possibilities. Read the full paper here .

2. Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces (2022) – Samuel Daulton et al.

Another paper selected by Max proposes MORBO, a scalable method for multiple-objective BO as it performs better than that of high-dimensional search spaces. MORBO significantly improves the sample efficiency, and where BO algorithms fail, MORBO provides improved sample efficiencies to the current BO approach used. Read the full paper here .

3. Tabular Data: Deep Learning is Not All You Need (2021) – Ravid Shwartz-Ziv, Amitai Armon

To solve real-life data science problems, selecting the right model to use is crucial. This final paper selected by Max explores whether deep models should be recommended as an option for tabular data. Read the full paper here .

Jigyasa Grover, Senior Machine Learning Engineer at Twitter

Jigyasa Grover is a Senior Machine Learning Engineer at Twitter working in the performance ads ranking domain. Recently, she was honoured with the 'Outstanding in AI: Young Role Model Award' by Women in AI across North America. She is one of the few ML Google Developer Experts globally. Jigyasa has previously presented at our Deep Learning Summit and MLOps event in San Fransisco earlier this year.

4. Privacy for Free: How does Dataset Condensation Help Privacy? (2022) – Tian Dong et al.

Jigyasa’s first recommendation concentrates on Privacy Preserving Machine Learning, specifically mitigating the leakage of sensitive data in Machine Learning. The paper provides one of the first propositions of using dataset condensation techniques to preserve the data efficiency during model training and furnish membership privacy. This paper was published by Sony AI and won the Outstanding Paper Award at ICML 2022. Read the full paper here .

5. Affective Signals in a Social Media Recommender System (2022) – Jane Dwivedi-Yu et al.

The second paper recommended by Jigyasa talks about operationalising Affective Computing, also known as Emotional AI, for an improved personalised feed on social media. The paper discusses the design of an affective taxonomy customised to user needs on social media. It further lays out the curation of suitable training data by combining engagement data and data from a human-labelling task to enable the identification of the affective response a user might exhibit for a particular post. Read the full paper here .

6. ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest (2022) – Paul Baltescu et al.

Jigyasa’s last recommendation is a paper by Pinterest that illustrates the aggregation of both textual and visual information to build a unified set of product embeddings to enhance recommendation results on e-commerce websites. By applying multi-task learning, the proposed embeddings can optimise for multiple engagement types and ensures that the shopping recommendation stack is efficient with respect to all objectives. Read the full article here .

Asmita Poddar, Software Development Engineer at Amazon Alexa

Asmita is a Software Development Engineer at Amazon Alexa, where she works on developing and productionising natural language processing and speech models. Asmita also has prior experience in applying machine learning in diverse domains. Asmita will be presenting at our London AI Summit , in September, where she will discuss AI for Spoken Communication.

7. Competition-Level Code Generation with AlphaCode (2022) – Yujia Li et al.

Systems can help programmers become more productive. Asmita has selected this paper which addresses the problems with incorporating innovations in AI into these systems. AlphaCode is a system that creates solutions for problems that requires deeper reasoning. Read the full paper here .

8. A Commonsense Knowledge Enhanced Network with Retrospective Loss for Emotion Recognition in Spoken Dialog (2022) – Yunhe Xie et al.

There are limits to model’s reasoning in regards to the existing ERSD datasets. The final paper selected by Asmita proposes a Commonsense Knowledge Enhanced Network with a backward-looking loss to perform dialog modelling, external knowledge integration and historical state retrospect. The model used has been shown to outperform other models. Read the full paper here .

Discover the speakers we have lined up and the topics we will cover at the London AI Summit.

Sergei Bobrovskyi, Expert in Anomaly Detection for Root Cause Analysis at Airbus

Dr. Sergei Bobrovskyi is a Data Scientist within the Analytics Accelerator team of the Airbus Digital Transformation Office. His work focuses on applications of AI for anomaly detection in time series, spanning various use-cases across Airbus. Sergei will be presenting at our Berlin AI Summit in October about Anomaly Detection, Root Cause Analysis and Explainability.

9. LaMDA: Language Models for Dialog Applications (2022) – Romal Thoppilan et al.

The paper chosen by Sergei describes the LaMDA system, which caused the furor this summer, when a former Google engineer claimed it has shown signs of being sentient. LaMDA is a family of large language models for dialog applications based on Transformer architecture. The interesting feature of the model is their fine-tuning with human annotated data and possibility to consult external sources. In any case, this is a very interesting model family, which we might encounter in many of the applications we use daily. Read the full paper here .

10. A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27 (2022) – Yann LeCun

The second paper chosen by Sergei provides a vision on how to progress towards general AI. The study combines a number of concepts including configurable predictive world model, behaviour driven through intrinsic motivation, and hierarchical joint embedding architectures. Read the full paper here .

11. Coordination Among Neural Modules Through a Shared Global Workpace (2022) – Anirudh Goyal et al.

This paper chosen by Sergei combines the Transformer architecture underlying most of the recent successes of deep learning with ideas from the Global Workspace Theory from cognitive sciences. This is an interesting read to broaden the understanding of why certain model architectures perform well and in which direction we might go in the future to further improve performance on challenging tasks. Read the full paper here .

12. Magnetic control of tokamak plasmas through deep reinforcement learning (2022) – Jonas Degrave et al.

Sergei chose the next paper, which asks the question of ‘how can AI research benefit humanity?’. The use of AI to enable safe, reliable and scalable deployment of fusion energy could contribute to the solution of pression problems of climate change. Sergei has said that this is an extremely interesting application of AI technology for engineering. Read the full paper here .

13. TranAd: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data (2022) – Shreshth Tuli, Giuliano Casale and Nicholas R. Jennings

The final paper chosen by Sergei is a specialised paper applying transformer architecture to the problem of unsupervised anomaly detection in multivariate time-series. Many architectures which were successful in other fields are at some points being also applied to time-series. The paper shows an improved performance on some known data sets. Read the full paper here .

Abdullahi Adamu, Senior Software Engineer at Sony

Abdullahi has worked in various industries including working at a market research start-up where he developed models that could extract insights from human conversations about products or services. He moved to Publicis, where he became Data Engineer and Data Scientist in 2018. Abdullahi will be part of our panel discussion at the London AI Summit in September, where he will discuss Harnessing the Power of Deep Learning.

14. Self-Supervision for Learning from the Bottom Up (2022) – Alexei Efros

This paper chosen by Abdullahi makes compelling arguments for why self-supervision is the next step in the evolution of AI/ML for building more robust models. Overall, these compelling arguments justify even further why self-supervised learning is important on our journey towards more robust models that generalise better in the wild. Read the full paper here .

15. Neural Architecture Search Survey: A Hardware Perspective (2022) – Krishna Teja Chitty-Venkata and Arun K. Somani

Another paper chosen by Abdullahi understands that as we move towards edge computing and federated learning, neural architecture search that takes into account hardware constraints which will be more critical in ensuring that we have leaner neural network models that balance latency and generalisation performance. This survey gives a birds eye view of the various neural architecture search algorithms that take into account hardware constraints to design artificial neural networks that give the best tradeoff of performance and accuracy. Read the full paper here .

16. What Should Not Be Contrastive In Contrastive Learning (2021) – Tete Xiao et al.

In the paper chosen by Abdullahi highlights the underlying assumptions behind data augmentation methods and how these can be counter productive in the context of contrastive learning; for example colour augmentation whilst a downstream task is meant to differentiate colours of objects. The result reported show promising results in the wild. Overall, it presents an elegant solution to using data augmentation for contrastive learning. Read the full paper here .

17. Why do tree-based models still outperform deep learning on tabular data? (2022) – Leo Grinsztajn, Edouard Oyallon and Gael Varoquaux

The final paper selected by Abdulliah works on answering the question of why deep learning models still find it hard to compete on tabular data compared to tree-based models. It is shown that MLP-like architectures are more sensitive to uninformative features in data, compared to their tree-based counterparts. Read the full paper here .

Sign up to the RE•WORK monthly newsletter for the latest AI news, trends and events.

Join us at our upcoming events this year:

·       London AI Summit – 14-15 September 2022

·       Berlin AI Summit – 4-5 October 2022

·       AI in Healthcare Summit Boston – 13-14 October 2022

·       Sydney Deep Learning and Enterprise AI Summits – 17-18 October 2022

·       MLOps Summit – 9-10 November 2022

·       Toronto AI Summit – 9-10 November 2022

·       Nordics AI Summit - 7-8 December 2022

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Blockchain booms, risks loom: the ai rescue mission in smart contract auditing, developing incident response plans for insider threats, weis wave: revolutionizing market analysis, top machine learning (ml) research papers released in 2022.

For every Machine Learning (ML) enthusiast, we bring you a curated list of the major breakthroughs in ML research in 2022.

Machine learning (ML) is gaining much traction in recent years owing to the disruption and development it brings in enhancing existing technologies. Every month, hundreds of ML papers from various organizations and universities get uploaded on the internet to share the latest breakthroughs in this domain. As the year ends, we bring you the Top 22 ML research papers of 2022 that created a huge impact in the industry. The following list does not reflect the ranking of the papers, and they have been selected on the basis of the recognitions and awards received at international conferences in machine learning.

• Bootstrapped Meta-Learning

Meta-learning is a promising field that investigates ways to enable machine learners or RL agents (which include hyperparameters) to learn how to learn in a quicker and more robust manner, and it is a crucial study area for enhancing the efficiency of AI agents.

This 2022 ML paper presents an algorithm that teaches the meta-learner how to overcome the meta-optimization challenge and myopic meta goals. The algorithm’s primary objective is meta-learning using gradients, which ensures improved performance. The research paper also examines the potential benefits due to bootstrapping. The authors highlight several interesting theoretical aspects of this algorithm, and the empirical results achieve new state-of-the-art (SOTA) on the ATARI ALE benchmark as well as increased efficiency in multitask learning.

• Competition-level code generation with AlphaCode

One of the exciting uses for deep learning and large language models is programming. The rising need for coders has sparked the race to build tools that can increase developer productivity and provide non-developers with tools to create software. However, these models still perform badly when put to the test on more challenging, unforeseen issues that need more than just converting instructions into code.

The popular ML paper of 2022 introduces AlphaCode, a code generation system that, in simulated assessments of programming contests on the Codeforces platform, averaged a rating in the top 54.3%. The paper describes the architecture, training, and testing of the deep-learning model.

• Restoring and attributing ancient texts using deep neural networks

The epigraphic evidence of the ancient Greek era — inscriptions created on durable materials such as stone and pottery —  had already been broken when it was discovered, rendering the inscribed writings incomprehensible. Machine learning can help in restoring, and identifying chronological and geographical origins of damaged inscriptions to help us better understand our past. 

This ML paper proposed a machine learning model built by DeepMind, Ithaca, for the textual restoration and geographical and chronological attribution of ancient Greek inscriptions. Ithaca was trained on a database of just under 80,000 inscriptions from the Packard Humanities Institute. It had a 62% accuracy rate compared to historians, who had a 25% accuracy rate on average. But when historians used Ithaca, they quickly achieved a 72% accuracy.

• Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer

Large neural networks use more resources to train hyperparameters since each time, the network must estimate which hyperparameters to utilize. This groundbreaking ML paper of 2022 suggests a novel zero-shot hyperparameter tuning paradigm for more effectively tuning massive neural networks. The research, co-authored by Microsoft Research and OpenAI, describes a novel method called µTransfer that leverages µP to zero-shot transfer hyperparameters from small models and produces nearly perfect HPs on large models without explicitly tuning them.

This method has been found to reduce the amount of trial and error necessary in the costly process of training large neural networks. By drastically lowering the need to predict which training hyperparameters to use, this approach speeds up research on massive neural networks like GPT-3 and perhaps its successors in the future.

• PaLM: Scaling Language Modeling with Pathways 

Large neural networks trained for language synthesis and recognition have demonstrated outstanding results in various tasks in recent years. This trending 2022 ML paper introduced Pathways Language Model (PaLM), a 780 billion high-quality text token, and 540 billion parameter-dense decoder-only autoregressive transformer.

Although PaLM just uses a decoder and makes changes like SwiGLU Activation, Parallel Layers, Multi-Query Attention, RoPE Embeddings, Shared Input-Output Embeddings, and No Biases and Vocabulary, it is based on a typical transformer model architecture. The paper describes the company’s latest flagship surpassing several human baselines while achieving state-of-the-art in numerous zero, one, and few-shot NLP tasks.

• Robust Speech Recognition via Large-Scale Weak Supervision

Machine learning developers have found it challenging to build speech-processing algorithms that are trained to predict a vast volume of audio transcripts on the internet. This year, OpenAI released Whisper , a new state-of-the-art (SotA) model in speech-to-text that can transcribe any audio to text and translate it into several languages. It has received 680,000 hours of training on a vast amount of voice data gathered from the internet. According to OpenAI, this model is robust to accents, background noise, and technical terminology. Additionally, it allows transcription into English from 99 different languages and translation into English from those languages.

The OpenAI ML paper mentions the author ensured that about one-third of the audio data is non-English. This helped the team outperform other supervised state-of-the-art models by maintaining a diversified dataset.

• OPT: Open Pre-trained Transformer Language Models

Large language models have demonstrated extraordinary performance f on numerous tasks (e.g., zero and few-shot learning). However, these models are difficult to duplicate without considerable funding due to their high computing costs. Even while the public can occasionally interact with these models through paid APIs, complete research access is still only available from a select group of well-funded labs. This limited access has hindered researchers’ ability to comprehend how and why these language models work, which has stalled progress on initiatives to improve their robustness and reduce ethical drawbacks like bias and toxicity.

The popular 2022 ML paper introduces Open Pre-trained Transformers (OPT), a suite of decoder-only pre-trained transformers with 125 million to 175 billion parameters that the authors want to share freely and responsibly with interested academics. The biggest OPT model, OPT-175B (it is not included in the code repository but is accessible upon request), which is impressively proven to perform similarly to GPT-3 (which also has 175 billion parameters)  uses just 15% of GPT-3’s carbon footprint during development and training.

• A Path Towards Autonomous Machine Intelligence

Yann LeCun is a prominent and respectable researcher in the field of artificial intelligence and machine learning. In June, his much-anticipated paper “ A Path Towards Autonomous Machine Intelligence ” was published on OpenReview. LeCun offered a number of approaches and architectures in his paper that might be combined and used to create self-supervised autonomous machines. 

He presented a modular architecture for autonomous machine intelligence that combines various models to operate as distinct elements of a machine’s brain and mirror the animal brain. Due to the differentiability of all the models, they are all interconnected to power certain brain-like activities, such as identification and environmental response. It incorporates ideas like a configurable predictive world model, behavior-driven through intrinsic motivation, and hierarchical joint embedding architectures trained with self-supervised learning. 

• LaMDA: Language Models for Dialog Applications 

Despite tremendous advances in text generation, many of the chatbots available are still rather irritating and unhelpful. This 2022 ML paper from Google describes the LaMDA — short for “Language Model for Dialogue Applications” — system, which caused the uproar this summer when a former Google engineer, Blake Lemoine, alleged that it is sentient. LaMDA is a family of large language models for dialog applications built on Google’s Transformer architecture, which is known for its efficiency and speed in language tasks such as translation. The model’s ability to be adjusted using data that has been human-annotated and the capability of consulting external sources are its most intriguing features.

The model, which has 137 billion parameters, was pre-trained using 1.56 trillon words from publicly accessible conversation data and online publications. The model is also adjusted based on the three parameters of quality, safety, and groundedness.

• Privacy for Free: How does Dataset Condensation Help Privacy?

One of the primary proposals in the award-winning ML paper is to use dataset condensation methods to retain data efficiency during model training while also providing membership privacy. The authors argue that dataset condensation, which was initially created to increase training effectiveness, is a better alternative to data generators for producing private data since it offers privacy for free. 

Though existing data generators are used to produce differentially private data for model training to minimize unintended data leakage, they result in high training costs or subpar generalization performance for the sake of data privacy. This study was published by Sony AI and received the Outstanding Paper Award at ICML 2022. 

• TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data

The use of a model that converts time series into anomaly scores at each time step is essential in any system for detecting time series anomalies. Recognizing and diagnosing anomalies in multivariate time series data is critical for modern industrial applications. Unfortunately, developing a system capable of promptly and reliably identifying abnormal observations is challenging. This is attributed to a shortage of anomaly labels, excessive data volatility, and the expectations of modern applications for ultra-low inference times. 

In this study , the authors present TranAD, a deep transformer network-based anomaly detection and diagnosis model that leverages attention-based sequence encoders to quickly execute inference while being aware of the more general temporal patterns in the data. TranAD employs adversarial training to achieve stability and focus score-based self-conditioning to enable robust multi-modal feature extraction. The paper mentions extensive empirical experiments on six publicly accessible datasets show that TranAD can perform better in detection and diagnosis than state-of-the-art baseline methods with data- and time-efficient training. 

• Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding 

In the last few years, generative models called “diffusion models” have been increasingly popular. This year saw these models capture the excitement of AI enthusiasts around the world. 

Going ahead of the current text to speech technology of recent times, this outstanding 2022 ML paper introduced the viral text-to-image diffusion model from Google, Imagen. This diffusion model achieves a new state-of-the-art FID score of 7.27 on the COCO dataset by combining the deep language understanding of transformer-based large language models with the photorealistic image-generating capabilities of diffusion models. A text-only frozen language model provides the text representation, and a diffusion model with two super-resolution upsampling stages, up to 1024×2014, produces the images. It employs several training approaches, including classifier-free guiding, to teach itself conditional and unconditional generation. Another important feature of Imagen is the use of dynamic thresholding, which stops the diffusion process from being saturated in specific areas of the picture, a behavior that reduces image quality, particularly when the weight placed on text conditional creation is large.

• No Language Left Behind: Scaling Human-Centered Machine Translation

This ML paper introduced the most popular Meta projects of the year 2022: NLLB-200. This paper talks about how Meta built and open-sourced this state-of-the-art AI model at FAIR, which is capable of translating 200 languages between each other. It covers every aspect of this technology: language analysis, moral issues, effect analysis, and benchmarking.

No matter what language a person speaks, accessibility via language ensures that everyone can benefit from the growth of technology. Meta claims that several languages that NLLB-200 translates, such as Kamba and Lao, are not currently supported by any translation systems in use. The tech behemoth also created a dataset called “FLORES-200” to evaluate the effectiveness of the NLLB-200 and show that accurate translations are offered. According to Meta, NLLB-200 offers an average of 44% higher-quality translations than its prior model.

• A Generalist Agent

AI pundits believe that multimodality will play a huge role in the future of Artificial General Intelligence (AGI). One of the most talked ML papers of 2022 by DeepMind introduces Gato – a generalist agent . This AGI agent is a multi-modal, multi-task, multi-embodiment network, which means that the same neural network (i.e. a single architecture with a single set of weights) can do all tasks while integrating inherently diverse types of inputs and outputs. 

DeepMind claims that the general agent can be improved with new data to perform even better on a wider range of tasks. They argue that having a general-purpose agent reduces the need for hand-crafting policy models for each region, enhances the volume and diversity of training data, and enables continuous advances in the data, computing, and model scales. A general-purpose agent can also be viewed as the first step toward artificial general intelligence, which is the ultimate goal of AGI. 

Gato demonstrates the versatility of transformer-based machine learning architectures by exhibiting their use in a variety of applications.  Unlike previous neural network systems tailored for playing games, stack blocks with a real robot arm, read words, and caption images, Gato is versatile enough to perform all of these tasks on its own, using only a single set of weights and a relatively simple architecture.

• The Forward-Forward Algorithm: Some Preliminary Investigations 

AI pioneer Geoffrey Hinton is known for writing paper on the first deep convolutional neural network and backpropagation. In his latest paper presented at NeurIPS 2022, Hinton proposed the “forward-forward algorithm,” a new learning algorithm for artificial neural networks based on our understanding of neural activations in the brain. This approach draws inspiration from Boltzmann machines (Hinton and Sejnowski, 1986) and noise contrast estimation (Gutmann and Hyvärinen, 2010). According to Hinton, forward-forward, which is still in its experimental stages, can substitute the forward and backward passes of backpropagation with two forward passes, one with positive data and the other with negative data that the network itself could generate. Further, the algorithm could simulate hardware more efficiently and provide a better explanation for the brain’s cortical learning process.

Without employing complicated regularizers, the algorithm obtained a 1.4 percent test error rate on the MNIST dataset in an empirical study, proving that it is just as effective as backpropagation.

The paper also suggests a novel “mortal computing” model that can enable the forward-forward algorithm and understand our brain’s energy-efficient processes.

• Focal Modulation Networks

In humans, the ciliary muscles alter the shape of the eye and hence the radius of the curvature lens to focus on near or distant objects. Changing the shape of the eye lens, changes the focal length of the lens. Mimicking this behavior of focal modulation in computer vision systems can be tricky.

This machine learning paper introduces FocalNet, an iterative information extraction technique that employs the premise of foveal attention to post-process Deep Neural Network (DNN) outputs by performing variable input/feature space sampling. Its attention-free design outperforms SoTA self-attention (SA) techniques in a wide range of visual benchmarks. According to the paper, focal modulation consists of three parts: According to the paper, focal modulation consists of three parts: 

a. hierarchical contextualization, implemented using a stack of depth-wise convolutional layers, to encode visual contexts from close-up to a great distance; 

b. gated aggregation to selectively gather contexts for each query token based on its content; and  

c. element-wise modulation or affine modification to inject the gathered context into the query.

• Learning inverse folding from millions of predicted structures

The field of structural biology is being fundamentally changed by cutting-edge technologies in machine learning, protein structure prediction, and innovative ultrafast structural aligners. Time and money are no longer obstacles to obtaining precise protein models and extensively annotating their functionalities. However, determining a protein sequence from its backbone atom coordinates remained a challenge for scientists. To date, machine learning methods to this challenge have been constrained by the amount of empirically determined protein structures available.

In this ICML Outstanding Paper (Runner Up) , authors explain tackling this problem by increasing training data by almost three orders of magnitude by using AlphaFold2 to predict structures for 12 million protein sequences. With the use of this additional data, a sequence-to-sequence transformer with invariant geometric input processing layers is able to recover native sequence on structurally held-out backbones in 51% of cases while recovering buried residues in 72% of cases. This is an improvement of over 10% over previous techniques. In addition to designing protein complexes, partly masked structures, binding interfaces, and numerous states, the concept generalises to a range of other more difficult tasks.

• MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge

Within the AI research community, using video games as a training medium for AI has gained popularity. These autonomous agents have had great success in Atari games, Starcraft, Dota, and Go. Although these developments have gained popularity in the field of artificial intelligence research, the agents do not generalize beyond a narrow range of activities, in contrast to humans, who continually learn from open-ended tasks.

This thought-provoking 2022 ML paper suggests MineDojo, a unique framework for embodied agent research based on the well-known game Minecraft. In addition to building an internet-scale information base with Minecraft videos, tutorials, wiki pages, and forum discussions, Minecraft provides a simulation suite with tens of thousands of open-ended activities. Using MineDojo data, the author proposes a unique agent learning methodology that employs massive pre-trained video-language models as a learnt reward function. Without requiring a dense shaping reward that has been explicitly created, MinoDojo autonomous agent can perform a wide range of open-ended tasks that are stated in free-form language.

• Is Out-of-Distribution Detection Learnable?

Machine learning (supervised ML) models are frequently trained using the closed-world assumption, which assumes that the distribution of the testing data will resemble that of the training data. This assumption doesn’t hold true when used in real-world activities, which causes a considerable decline in their performance. While this performance loss is acceptable for applications like product recommendations, developing an out-of-distribution (OOD) identification algorithm is crucial to preventing ML systems from making inaccurate predictions in situations where data distribution in real-world activities typically drifts over time (self-driving cars).

In this paper , authors explore the probably approximately correct (PAC) learning theory of OOD detection, which is proposed by researchers as an open problem, to study the applicability of OOD detection. They first focus on identifying a prerequisite for OOD detection’s learnability. Following that, they attempt to show a number of impossibility theorems regarding the learnability of OOD detection in a handful yet different scenarios.

• Gradient Descent: The Ultimate Optimizer 

Gradient descent is a popular optimization approach for training machine learning models and neural networks. The ultimate aim of any machine learning (neural network) method is to optimize parameters, but selecting the ideal step size for an optimizer is difficult since it entails lengthy and error-prone manual work. Many strategies exist for automated hyperparameter optimization; however, they often incorporate additional hyperparameters to govern the hyperparameter optimization process. In this study , MIT CSAIL and Meta researchers offer a unique approach that allows gradient descent optimizers like SGD and Adam to tweak their hyperparameters automatically.

They propose learning the hyperparameters by self-using gradient descent, as well as learning the hyper-hyperparameters via gradient descent, and so on indefinitely. This paper describes an efficient approach for allowing gradient descent optimizers to autonomously adjust their own hyperparameters, which may be layered recursively to many levels. As these gradient-based optimizer towers expand in size, they become substantially less sensitive to the selection of top-level hyperparameters, reducing the load on the user to search for optimal values.

• ProcTHOR: Large-Scale Embodied AI Using Procedural Generation 

Embodied AI is a developing study field that has been influenced by recent advancements in artificial intelligence, machine learning, and computer vision. This method of computer learning makes an effort to translate this connection to artificial systems. The paper proposes ProcTHOR, a framework for procedural generation of Embodied AI environments. ProcTHOR allows researchers to sample arbitrarily huge datasets of diverse, interactive, customisable, and performant virtual environments in order to train and assess embodied agents across navigation, interaction, and manipulation tasks. 

According to the authors, models trained on ProcTHOR using only RGB images and without any explicit mapping or human task supervision achieve cutting-edge results in 6 embodied AI benchmarks for navigation, rearrangement, and arm manipulation, including the ongoing Habitat2022, AI2-THOR Rearrangement2022, and RoboTHOR challenges. The paper received the Outstanding Paper award at NeurIPS 2022.

• A Commonsense Knowledge Enhanced Network with Retrospective Loss for Emotion Recognition in Spoken Dialog

Emotion Recognition in Spoken Dialog (ERSD) has recently attracted a lot of attention due to the growth of open conversational data. This is due to the fact that excellent speech recognition algorithms have emerged as a result of the integration of emotional states in intelligent spoken human-computer interactions. Additionally, it has been demonstrated that recognizing emotions makes it possible to track the development of human-computer interactions, allowing for dynamic change of conversational strategies and impacting the result (e.g., customer feedback). But the volume of the current ERSD datasets restricts the model’s development. 

This ML paper proposes a Commonsense Knowledge Enhanced Network (CKE-Net) with a retrospective loss to carry out dialog modeling, external knowledge integration, and historical state retrospect hierarchically. 

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Celebrating the most popular papers from 2022

As we count down to 2023, we’re celebrating the top 10 most popular articles of the year according to altmetric..

10. Do monkeys get drunk? 

This paper from Royal Society Open Science shows that spider monkeys regularly consume fermenting fruit, backing up the hypothesis that as humans we inherited a taste for alcohol from our primate ancestors. The research is the first to demonstrate that wild primates enjoy alcohol as a part of their natural diet, in the form of fermenting mango-like fruits. 

9. Facemasks can reduce severity of COVID-19

This research from Interface developed a model to investigate COVID-19 ‘variolation’ - an incidental, but potentially beneficial, form of immunization achieved by inhaling smaller doses of the virus than would be inhaled without a mask. The model suggests effective masking could drastically slow the spread of COVID-19 and reduce the prevalence of severe cases.

8. Could melting glaciers cause future pandemics?

These findings from Proceedings B suggest that as global temperatures rise due to climate change, it becomes more likely that viruses and bacteria locked up in glaciers could reawaken and infect local wildlife. The research investigated how climate change might affect spillover risk by examining samples from the Arctic landscape of Lake Hazen.

7. Does time spent playing video games impact well-being?

Major health bodies and national governments have made extensive policy decisions to address their potential risks, but current evidence tells us little about the links between video gaming and well-being. Research  in Royal Society Open Science addresses this lack of supporting data by studying a large sample of players to try to determine the impact of video gaming on players’ well-being.

6. Tree rings offer insight into destructive radiation storms

This Proceedings A paper has applied cutting-edge statistics to data from millennia-old trees to find out more about radiation ‘storms’. Researchers reconstructed the global carbon cycle using tree ring radiocarbon data - the results of this modeling gave researchers an extremely detailed picture of a number of radiation events. The research suggests that, based on available data, there's roughly a one percent chance of another radiation storm occurring within the next decade.

5. Kent was home to some of Britain’s earliest humans

This paper in Royal Society Open Science presents details of the first modern excavation and dating of artefacts discovered at Fordwich. These discoveries offer insight into the presence and behaviour of early humans in southern Britain and highlight the importance of Fordwich as an archaeological site.

4. What does successful reforestation look like?

While the main measurement used for many reforestation projects is the number of trees initially planted, this research from Philosophical Transactions B shows that many of these trees are not surviving long-term. This research is the first to bring together data to evaluate the long-term outcomes of restoration projects.

3. Ankylosaurs used their tail clubs to battle each other

This paper from Biology Letters has found new evidence for how armoured dinosaurs used their iconic tail clubs. The remarkable fossil has spikes that appear to have been broken and re-healed whilst the dinosaur was still alive. These injuries are thought to have been caused by a strike from another ankylosaur’s massive tail club, suggesting that these dinosaurs had complex behaviour, possibly battling for social and territorial dominance.

2. Discovery of world’s largest plant

This research from Proceedings B tested the DNA from meadows of ribbon weed seagrass in the shallow waters of the Shark Bay World Heritage Area in Western Australia. The results were extraordinary: it was all one plant. One single plant has expanded over a stretch of 180 km making it the largest known plant on Earth.

1. Can mushrooms communicate with one another?

Finally, the most popular paper of 2022 comes from Royal Society Open Science . Mathematical analysis of electrical signals fungi send to one another has identified patterns that hold a striking structural similarity to human speech. The research shows that these electrical signals often occurred in clusters which resemble a human vocabulary of up to 50 words.

This year our journal papers were downloaded over 40 million times! Thank you to everyone who chose to read our papers and publish in our journals this year - we look forward to publishing more insightful research in 2023.

Ciara Doyle

Marketing Executive, Publishing

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Top 10 Must-Read Data Science Research Papers in 2022

Data Science plays a vital role in many sectors such as small businesses, software companies, and the list goes on. Data Science understands customer preferences, demographics, automation, risk management, and many other valuable insights. Data Science can analyze and aggregate industry data. It has a frequency and real-time nature of data collection.

There are many data science enthusiasts out there who are totally into Data Science. The sad part is that they couldn't follow up with the latest research papers of Data Science. Here, Analytics Insight brings you the latest Data Science Research Papers. These research papers consist of different data science topics including the present fast passed technologies such as AI, ML, Coding, and many others. Data Science plays a very major role in applying AI, ML, and Coding. With the help of data science, we can improve our applications in various sectors. Here are the Data Science Research Papers in 2024

10DATA SCIENTISTS THAT TECH ENTHUSIASTS CAN FOLLOW ON LINKEDIN

ARE YOU A JOB SEEKER? KNOW THE IMPACT OF AI AND DATA SCIENCE

TOP 10 PYTHON + DATA SCIENCE COURSES YOU SHOULD TAKE UP IN 2022  

The Research Papers Includes

Documentation matters: human-centered ai system to assist data science code documentation in computational notebooks.

The research paper is written by April Yi Wang, Dakuo Wang, Jaimie Drozda, Michael Muller, Soya Park, Justin D. Weisz, Xuye Lui, Lingfei Wu, Casey Dugan.

This research paper is all about AMC transactions on Computer-Human Interaction. This is a combination of code and documentation. In this research paper, the researchers have Themisto an automated documentation generation system. This explores how human-centered AI systems can support data scientists in Machine Learning code documentation.

Assessing the effects of fuel energy consumption, foreign direct investment and GDP on CO2 emission: New data science evidence from Europe & Central Asia

The research paper is written by- Muhammad Mohsin, SobiaNaseem, Muddassar Sarfraz Tamoor, Azam

This research paper deals with how bad the effects of fuel consumption are and how data science is playing a vital role in extracting such huge information.

Impact on Stock Market across Covid-19 Outbreak

The research paper is written by-CharmiGotecha

This paper analyses the impacts of a pandemic from 2019-2022 and how it has affected the world with the help of data science tools. It also talks about how data science played a major role in recovering the world from covid losses.

Exploring the political pulse of a country using data science tools

The research paper is written by Miguel G. Folgado, Veronica Sanz

This paper deals with how data science tools/techniques are used to analyses complex human communication. This study paper is an example of how Twitter data and different types of data science tools for political analysis.

Situating Data Science

The research paper is written by-Michelle HodaWilkerson, Joseph L. Polman

This research paper gives detailed information about regulating procurement understanding the ends and means of public procurement regulation.

VeridicalFlow: a Python package for building trustworthy data science pipelines with PCS

The research paper is written by- James Duncan, RushKapoor, Abhineet Agarwal, Chandan Singh, Bin Yu

This research paper is more of a journal of open-source software than a study paper. It deals with the open-source software that is the programs available in the systems that are related to data science.

From AI ethics principles to data science practice: a reflection and a gap analysis based on recent frameworks and practical experience

The research paper is written by-IlinaGeorgieva, ClaudioLazo, Tjerk Timan, Anne Fleur van Veenstra

This study paper deals with the field of AI ethics, its frameworks, evaluation, and much more. This paper contributes ethical AI by mapping AI ethical principles onto the lifestyle of artificial intelligence -based digital services or products to investigate their applicability for the practice of data science.

Building an Effective Data Science Practice

The research paper is written by Vineet Raina, Srinath Krishnamurthy

This paper is a complete guide for an effective data science practice. It gives an idea about how the data science team can be helpful and how productive they can be.

Detection of Road Traffic Anomalies Based on Computational Data Science

The research paper is written by Jamal Raiyn

This research paper gives an idea about autonomous vehicles will have control over every function and how data science will be part of taking full control over all the functions. Also, to manage large amounts of data collected from traffic in various formats, a Computational Data Science approach is proposed by the researchers.

Data Science Data Governance [AI Ethics]

The research paper is written by Joshua A. Kroll

This paper analyses and gives brief yet complete information about the best practices opted by organizations to manage their data which encompass the full range of responsibilities borne by the use of data in automated decision making, including data security, privacy, avoidance of undue discrimination, accountability, and transparency.

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5 studies released in 2022 that might change how you think about health care

From racial bias in CPR to private equity price hikes, here’s what researchers learned about US health care this year.

by Dylan Scott

Though the pandemic and all its attendant health care crises remained the major health care story of 2022, churning all the while in the background has been the critical work of academic scholars, operating on longer timelines, who are still trying to make sense of US health care and of medicine itself, to get a better idea of what’s wrong and how to make it better.

To wrap up this year, I asked a couple dozen health policy experts what research released this year (though, as one of them reminded me, these papers are often years in the making) had surprised them, changed their thinking, or struck them as especially notable.

Here are five particularly interesting papers, at least in my view. Because many more than that warrant mention, I have tried to cram in as many references to other work as I could. One of my lessons from this exercise was that there are noteworthy new studies being produced all the time. The US health system certainly merits such extensive investigation, given the number and diversity of its flaws .

These studies cover a broad range of subjects, from the intricacies of Medicaid provider networks to prescription uptake by Medicare beneficiaries to how bystanders react when a person experiences a cardiac episode in public. But first, on the topic of the pandemic...

1) Vaccination education campaigns in nursing homes didn’t make much difference

Several experts pointed me to data sets related to Covid-19 vaccination in nursing homes, the scenes of so much illness and death in that frightening first year of the pandemic. Larry Levitt, executive vice president of the Kaiser Family Foundation, flagged one recent KFF survey that found less than half of nursing-home residents are up to date on their vaccines.

That put into sharp relief the findings of a study that Harvard Medical School’s David Grabowski cited as one of his favorites of the year. The paper, published in JAMA Internal Medicine in January 2022 , evaluated an effort to use educational campaigns and other incentives to improve vaccination rates among residents and staff in nursing homes.

They did not find a meaningful effect, despite three months of programming. There was plenty of room to grow, particularly among the staff, roughly half of whom were unvaccinated during the study period. (Vaccination rates among residents were already high at the time, though the experiment still did not find a significant effect of the multi-faceted campaign.)

“The conventional wisdom was that a big part of lagging vaccination rates was a lack of information and knowledge about the benefits of vaccination,” Grabowski said. But this study found instead that “these educational efforts were unsuccessful at encouraging greater vaccination. Although information campaigns sound like great policy, they really aren’t.”

He lamented that the study had been ignored by policymakers, noting federal efforts to increase vaccination rates among this population remain focused on education. Grabowski said the policies with the strongest evidence are vaccine clinics and vaccine mandates for staff.

2) Medicaid’s “phantom” networks may overstate patients’ access to health care

I love good jargon, and “ghost physicians” is one of my favorite new phrases of the year, which was recently brought to my attention by Harvard University professor (and former Vox contributor) Adrianna McIntyre.

It comes from this May 2022 Health Affairs paper , authored by Yale University’s Avital Ludomirsky and colleagues, which evaluated the provider networks of Medicaid managed-care plans. Those are privately administrated plans for low-income people that must follow state and federal Medicaid rules; they have grown in enrollment in recent years, with more states deciding to outsource their role in providing health coverage for the most vulnerable.

One of those rules is that enough doctors and hospitals accept those Medicaid plans, otherwise known as network adequacy. The Health Affairs study examined managed-care plans in four states over several years to get a better sense of providers’ participation in the program.

Their most striking finding is that while nominal participation was fairly high, about one-third of providers who technically accepted Medicaid actually saw less than 10 Medicaid beneficiaries in a given year. The responsibility for providing care to these patients was concentrated among a small number of practices, with 25 percent of primary care doctors providing 86 percent of the care and 25 percent of specialists providing 75 percent of those services.

In other words, while plenty of doctors might say they accept the plans, people who are enrolled in them may in practice find they have relatively few doctors to choose from. “Our findings suggest that current network adequacy standards might not reflect actual access,” the authors wrote in their conclusion. “New methods are needed that account for beneficiaries’ preferences and physicians’ willingness to serve Medicaid patients.”

The move toward more Medicaid-managed care has spurred a lot of debate and study. Matthew Fiedler, senior fellow at the Brooking Institution, sent me a Harvard study with its own surprising finding: In Texas, transitioning Medicaid enrollees to managed care had actually led to an increase in the use of prescription drugs and outpatient services, while the number of avoidable hospitalizations fell.

What seems to have happened, Fiedler said, is the state had been “penny-wise, pound-foolish” when running the program itself by imposing an unusually strict cap on the number of prescriptions patients could fill in a year. With that cap relaxed under the managed-care plans, patients filled more prescriptions and that corresponded to fewer episodes in which they ended up in the hospital when they didn’t have to.

Fiedler thought the study could also have implications for Medicare Advantage as well, another version of a government health insurance program that is being administered by private companies.

“To be clear, in many of these cases, it might well be better to fix this problem by just fixing the public program directly,” he told me. “But given that we often seem unwilling to do that, this does point to one very real advantage of involvement from private plans.”

3) Many Medicare beneficiaries don’t fill important prescriptions

Rachel Sachs, who studies prescription drug policy at Washington University in St. Louis, highlighted a study on a similar subject that was published in Health Affairs in April 2022 , led by Vanderbilt University’s Stacie Dusetzina. The researchers studied the difference in prescription uptake by Medicare beneficiaries who receive a government subsidy to offset their drug costs versus those who do not.

The latter group, who can sometimes be on the hook for hundreds of dollars for every prescription they fill, was substantially less likely to actually use their medication as it had been prescribed, even for serious diagnoses like cancer. People receiving subsidies were twice as likely to fill their prescriptions. The study found that 30 percent of prescriptions for anti-cancer drugs and 22 of prescriptions for hepatitis-C treatment were never started.

The problem of drug affordability for Medicare patients has been an ongoing concern. Because of how the program is currently structured, beneficiaries who are enrolled in the traditional Medicare program but do not qualify for subsidies have no upper limit on how much they can be asked to spend on pharmaceuticals in a given year.

As part of the Inflation Reduction Act , Congress is instituting such a cap for the first time, using savings from the new drug pricing controls also included in the law, capping out-of-pocket costs at $2,000 starting in 2025. That is the kind of intervention the Health Affairs authors were calling for when they published the paper. Now we’ll see if that policy change has an effect on seniors actually taking their medicine.

4) Black and Hispanic Americans are less likely to receive CPR in public spaces

One of the experts I asked for input is Harvard Medical School’s Michael Barnett , who publishes his own overview of the year’s best research every year on Twitter. He sent me a few studies, most of which I’ll let him share in his own overview, but one study in particular stuck out, looking at which people are more (or less) likely to get assistance when they have a health emergency in a public setting with other people around.

The paper, published in the New England Journal of Medicine in October by a collection of scholars across the country, examined more than 100,000 incidents of cardiac arrest that occurred outside of the hospital from 2013 to 2019. They focused in particular on whether the person experiencing cardiac arrest in public received CPR from a bystander.

The results were profound: Black and Hispanic people were significantly less likely to be given CPR than a white person, 45 percent versus 60 percent. And this pattern held across neighborhoods of different racial and socioeconomic make-ups. Whether the neighborhood was predominantly white, Black, or Hispanic, up and down the income ladder, in integrated neighborhoods, white people were more likely to receive CPR than someone who is Black and Hispanic.

“Racial and ethnic differences in bystander CPR in public locations raise additional concerns about implicit and explicit biases in layperson response to out-of-hospital cardiac arrests,” the authors wrote.

Racism within the medical system is well established. Black and Hispanic Americans tend to have less access to health care. They tend to have worse experiences with doctors. Clinical trials and other research does not prioritize them. Medical treatments tend to be developed and designed with white people in mind.

But this study would suggest that even the more casual forms of racism and internalized bias can have a real health effect for marginalized Americans.

5) Prices go up after private equity acquires a physician’s practice

Private equity’s deeper investment into US health care is one of the most important ongoing stories in the field right now. The stakes can be life or death, as one paper that I covered last year about mortality rates in nursing homes purchased by private equity firms found. (Of note this year, according to Harvard’s Amitabh Chandra, was another paper that documented the wide disparities in outcomes among nursing homes, even those who are located in the same area.)

A report published by scholars from Johns Hopkins, Harvard, and Oregon Science & Health University in September 2022 in JAMA Health Forum , which Emma Sandoe of North Carolina Medicaid and Duke University shared with me, examined what happened to costs and care utilization when private equity firms had purchased outpatient practices specializing in ophthalmology, dermatology, and gastroenterology, some of their favorite targets.

The study detected several effects: The cost of individual services went up and those practices were seeing more new patients and also charging more time for their existing patients. The amount billed by the private equity-acquired offices per service increased by 20 percent compared to the study’s control group. They saw 38 percent more new patients and there was a 9 percent increase in the number of existing patient visits that were billed as lasting longer than 30 minutes.

On the surface, that could look like partially good news, in spite of the price hikes, with more patients being seen and spending more time with their doctor. But some of the other findings call into question the value of those increases in service volume. In dermatology, the increase in outpatient visits did not correspond to an increase in biopsies ordered. In the gastroenterology practices, there were more visits and scopes but no notable increase in polyp removals. And likewise, at the eye clinics, there was more diagnostic imaging but not any effect on actual cataract removals.

The authors ended their paper with this warning. “Private equity ownership of physician practices has added a distinctly private and market-driven influence to the broader trends in corporate consolidation of physicians by health systems and insurers,” they wrote. “This study contributes evidence for potential overutilization and higher spending on care that will be important for policy makers to monitor.”

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Beyond safety: the 2022 WHO abortion guidelines and the future of abortion safety measurement

Caitlin gerdts.

1 Ibis Reproductive Health, Oakland, California, USA

Suzanne O Bell

2 Department of Population, Family, and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

Mridula Shankar

3 Department of General Practice, Monash University, Nottinghill, Victoria, Australia

Ruvani T Jayaweera

Onikepe owolabi.

4 Vital Strategies, New York, New York, USA

Associated Data

There are no data in this work.

Summary box

• In March 2022, the WHO released updated guidelines consolidating the current evidence and best practices for provision of quality abortion care, which, for the first time, include self-management of medical abortion as a fully recommended model of abortion care.
• The new guidelines have the potential to transform abortion access if international bodies, governments and health systems expand the availability of medication abortion pills and trained support. The guidelines also have important implications for the way we conceptualise and measure abortion safety.
• Abortion safety has historically been conceptualised from a purely biomedical (clinical and public health) perspective and relied on clinical process measures (method, provider and setting) for classification. This operationalisation required that the abortion involves a recommended method delivered by an appropriately trained provider, in an appropriate facility, to be considered ‘safe’.
• Over time, however, as knowledge, availablity, and use of abortion medications (misoprostol with or without mifepristone) increased outside of clinic settings, the process measures (method, provider, setting) that defined the abortion safety paradigm no longer correlated as directly with biomedical risk.
• This commentary discusses limitations of the current approach to the measurement of abortion safety, highlights the required reclassification of self-managed abortion as ‘safe’, and calls for a new paradigm to emerge for the measurement and understanding of global abortion experiences that is centred in a rights-based conceptualisation of quality care for all abortion experiences.

Introduction

In March 2022, the World Health Organization (WHO) released updated guidelines consolidating the current evidence and best practices for quality abortion care. 1 Undergirded by a framework of human rights standards and in recognition of the centrality of an enabling environment, the new set of recommendations span law, policy, clinical services, and mechanisms for service delivery. For the first time, WHO abortion service delivery recommendations include the self-management of medical abortion (Recommendation #50) and fully recommend trained community health workers, pharmacy workers, and pharmacists as providers for the medical management of abortion up to 12-weeks gestation (Recommendation #28). These shifts in WHO abortion care guidelines are the result of decades of work by grassroots activists 2 and researchers. Their innovative efforts to ensure access to evidence-based abortion care—regardless of legal setting—laid the groundwork for widespread experiential knowledge and scientific evidence regarding the safety and effectiveness of self-managed medical abortion. 3 Informed by this body of work, the recommendations for self-management of medical abortion in the new WHO guidelines have the potential to transform abortion access if international bodies, governments, and health systems expand the availability of abortion pills and access to trained support. The guidelines also have important implications for the way we conceptualise and measure abortion safety.

From legality to safety

Through the late 1980s, research on abortions obtained outside of clinical settings focused primarily on the quantification and reduction of ‘illegal abortions’—likely due to an observed correlation between restrictive abortion laws and high rates of maternal morbidity and mortality. In 1992, a WHO technical working group discussed the need to understand not only the legality but also the safety of abortion services. Their report, published in 1993, coined the term ‘unsafe abortion’—concluded that legality or illegality of services may not be the defining factor of abortion safety 4 . The report described the characteristics of ‘unsafe abortion’ as an abortion performed without adequate provider skills, using hazardous techniques, and/or occurring in unsanitary facilities. Following the publication of this report, the language and focus of research on abortions obtained outside of clinical settings made a distinct shift away from legality and towards the concept of abortion safety. 5

From the outset, the measurement of abortion safety was conceptualised from a purely biomedical (clinical and public health) perspective and relied on clinical process measures (method, provider and setting) for classification. This operationalisation required that a ‘safe’ abortion must involve a recommended method delivered by an appropriately trained provider, in an appropriate clinical facility. The early abortion safety paradigm emerged in a global context when abortions that occurred without meeting clinical process measures were highly correlated with complications such a haemmorrhage, infection and physical trauma. 6 Attention to abortion safety in research and advocacy was, therefore, deemed a public health imperative. For decades, the urgency of addressing maternal morbidity and mortality from unsafe abortion procedures drew the attention of governments, donors, and other influential stakeholders to the need for legal, policy, and health systems reforms to drive service improvements.

Over time, however, as knowledge, availability, and use of abortion medications (misoprostol with or without mifepristone) increased outside of clinic settings, the clinical process measures (method, provider, setting) that defined the abortion safety paradigm no longer correlated as directly with biomedical risk. 7 8 In recognition of this changing reality, in 2017, the WHO created a new, three-tier operationalisation of abortion safety measurement 9 —categorising an abortion as ‘safe’ if it involved recommended methods and providers with recommended levels of training, ‘less safe’ if only one of the two conditions were met, and ‘least safe’ when neither of the two conditions were met. In practice, the updated classification system continued to rely on clinical process measures. As such, nearly all surgical abortions were categorised as ‘safe’ because data (often from health facilities) existed to confirm that those abortions were performed using a ‘recommended’ method, by a ‘recommended’ provider, in a ‘recommended’ facility. This approach, however, fails to incorporate information about health system infrastructure, provider training and quality, and continuity of client–provider interactions necessary for the provision of high-quality abortion care. In addition, within the three-tier classification system, self-managed medical abortion—abortions which involved a recommended method (medical abortion) and a non-recommended provider (the person themself)—were classified as ‘less safe’ despite a substantial body of evidence observing across multiple settings that self-managed medical abortions do not correspond with higher biomedical health risks. 10 11

From safety to quality

The updated WHO abortion care guidelines now fully recognise the individual themself as an able provider who may ‘conduct some or all elements related to the abortion process (self-assessment of eligibility, self-administration of medicines and self-assessment of the success of the abortion) entirely on their own’. The guidelines also acknowledge that ‘It is the individual (ie, the “self”) who drives the process of deciding which aspects of the abortion care will be self-managed and which aspects will be supported or provided by trained health workers or in a health-care facility…from the perspective of the health system, self-management should not be considered a “last resort” option or a substitute for a non-functioning health system. Self-management must be recognized as a potentially empowering and active extension of the health system and task-sharing approaches’. 1 Furthermore, the large majority of abortions classified as ‘less safe’ in the 2017 assessment of global abortion safety were presumed to be self-managed medication abortions. From a purely methodological standpoint these abortions should now be reclassified as "safe". 2 Updates to the WHO abortion care guidelines provide a unique opportunity to rethink the very paradigm of abortion safety, and all but eliminate the necessity for the current three-tier classification.

Indeed, abortion remains a leading cause of maternal mortality in some settings, and significant work also remains to improve surgical abortion quality in low-resource settings where complication rates are higher than in high-resource settings. In addition, the self-management of medical abortion is, in many settings, far from achieving normative standards of self-care as delineated by a recently released WHO consolidated guideline and accompanying conceptual framework on self-care interventions in sexual and reproductive health. 12 As stipulated by the self-care framework, those self-managing an abortion must have access to quality pills, access to accurate and adequate information to ensure correct use, knowledge regarding possible complications, and safe linkage to a provider, if needed or wanted. Existing research suggests, however, that these criteria are often not met. 13

The updated WHO abortion care guidelines clearly articulate that ‘A person’s environment plays a crucial role in shaping their access to care and influencing their health outcomes. An enabling environment is the foundation of quality, comprehensive abortion care’. It is increasingly evident that access to legal abortion (a fundamental requirement for a supportive enabling environment) is under assault around the globe, and significant barriers remain to abortion access even where it is legally available. 14 Given the current landscape of abortion globally, the paradigm of evaluating ‘abortion safety’ using clinical process measures alone is no longer sufficient. The standard clinical process measures previously used to measure abortion safety simply fail to capture the elements of an abortion experience that are most salient to people’s lives and well-being. For example, documenting the method and provider for an abortion obscures the impact on health and well-being of the burdensome and often perilous routes that many abortion seekers must take to access recommended abortion methods or legal risks in settings where abortion is criminalised. Clinical process measures also provide no mechanism to document the experiences of those unsuccessful in obtaining a wanted abortion, whether due to the use of ineffective methods or insurmountable barriers to care (eg, lack of trained providers, exorbitant cost or denial of care). Research on abortion decision-making and preferences has repeatedly demonstrated that aspects of the abortion process like accessibility, cost, privacy, confidentiality and security, legal risk, and being treated with dignity and respect are paramount in peoples’ abortion decision-making and assessment of the quality of their abortion care. 15

In short, the current operationalisation of abortion safety—focused exclusively on abortion method, provider, and setting—does not adequately capture other essential elements of the abortion experience. A new paradigm for the measurement and understanding of global abortion experiences must emerge that moves us beyond documentation of the bare minimum (appropriate abortion method/provider/setting or ‘safety’) and towards a conceptualisation of abortion experiences that follow recommended protocols and occur within an enabling environment that ensures ‘respect for human rights, the availability and accessibility of information and a supportive, universally accessible, affordable and well-functioning health system’. 1

Contributors: CG envisioned and contributed to drafting and reviewing the manuscript. SOB, MS, RTJ and OO contributed to ideation, drafting and review of the manuscript.

Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests: None declared.

Provenance and peer review: Not commissioned; internally peer reviewed.

Data availability statement

Ethics statements, patient consent for publication.

Not applicable.

Ethics approval

10 trending open access articles 2022

Popular open access articles lead discussion on global topics.

Publishing open access (OA) means your research is free to read anywhere, anytime, and without barriers.

Below we explore some of the most popular open access articles published in 2022 by Taylor & Francis. With topics spanning COVID-19 treatments, artificial intelligence, and mental health – it’s clear to see that open access means wider discussion and impact for research.

1. Sex differences in sequelae from COVID-19 infection and in long COVID syndrome: a review

Shirley V. Sylvester, Rada Rusu, Biankha Chan, Martha Bellows, Carly O’Keefe & Susan Nicholson

Published in  Current Medical Research and Opinion

“Social media is a salient source for influencing consumers’ shopping and buying behaviors as they share product reviews, service experiences, and/or product usage tips. […] the findings illustrate the impact that social media can have on consumer decision-making processes.”

2. Structural and pragmatic language in young children with sex chromosome trisomy (XXX, XXY, XYY): predictive value for neurobehavioral problems one year later

Evelien Urbanus, Hanna Swaab, Nicole Tartaglia, Constance Stumpel & Sophie van Rijn

Published in The Clinical Neuropsychologist

“This study is one of the first studies that directly illustrates the relationship between language and behavioral outcomes in children with SCT. Our results stress the importance to closely monitor pragmatic language in addition to structural language in clinical care of children with SCT, as pragmatic language abilities could serve as an early marker for children at risk for developing behavioral problems.”

Post information

Related posts, insights topics, 3. probiotic improves symptomatic and viral clearance in covid19 outpatients: a randomized, quadruple-blinded, placebo-controlled trial.

Pedro Gutiérrez-Castrellón, Tania Gandara-Martí, Ana T. Abreu Y Abreu, Cesar D. Nieto-Rufino, Eduardo López-Orduña, Irma Jiménez-Escobar, Carlos Jiménez-Gutiérrez, Gabriel López-Velazquez & Jordi Espadaler-Mazo

Published in Gut Microbes

“Intestinal bacteria may influence lung homeostasis via the gut-lung axis. We conducted a single-center, quadruple-blinded, randomized trial in adult symptomatic Coronavirus Disease 2019 (Covid19) outpatients.”

4. Efficacy and safety of three new oral antiviral treatment (molnupiravir, fluvoxamine and Paxlovid) for COVID-19：a meta

Wen Wen, Chen Chen, Jiake Tang, Chunyi Wang, Mengyun Zhou, Yongran Cheng, Xiang Zhou, Qi Wu, Xingwei Zhang, Zhanhui Feng, Mingwei Wang & Qin Mao

Published in Annals of Medicine

“The coronavirus disease (COVID-19) epidemic has not been completely controlled. Although great achievements have been made in COVID-19 research and many antiviral drugs have shown good therapeutic effects against COVID-19, a simple oral antiviral drug for COVID-19 has not yet been developed. We conducted a meta-analysis to investigate the improvement in mortality or hospitalization rates and adverse events among COVID-19 patients with three new oral antivirals (including molnupiravir, fluvoxamine and Paxlovid).”

5. SARS-CoV-2 Omicron variant shows less efficient replication and fusion activity when compared with Delta variant in TMPRSS2-expressed cells  

Hanjun Zhao, Lu Lu, Zheng Peng, Lin-Lei Chen, Xinjin Meng, Chuyuan Zhang, Jonathan Daniel Ip, Wan-Mui Chan, Allen Wing-Ho Chu, Kwok-Hung Chan, Dong-Yan Jin, Honglin Chen, Kwok-Yung Yuen & Kelvin Kai-Wang To

Published in Emerging Microbes & Infections

“The novel SARS-CoV-2 Omicron variant (B.1.1.529), first found in early November 2021, has sparked considerable global concern and it has >50 mutations, many of which are known to affect transmissibility or cause immune escape. In this study, we sought to investigate the virological characteristics of the Omicron variant and compared it with the Delta variant which has dominated the world since mid-2021.”

6. Antibodies to watch in 2022

Hélène Kaplon, Alicia Chenoweth, Silvia Crescioli & Janice M. Reichert

Published in mAbs

“In this 13th annual installment of the annual ‘Antibodies to Watch’ article series, we discuss key events in commercial antibody therapeutics development that occurred in 2021 and forecast events that might occur in 2022.” 

7. Lessons learned from the fifth wave of COVID-19 in Hong Kong in early 2022 

Pak-Hin Hinson Cheung, Chi-Ping Chan & Dong-Yan Jin

“Before the fifth wave, Hong Kong had managed to remain free of local cases of COVID-19 for almost 100 days, ascribed to the enforcement of moderate non-pharmaceutical interventions (NPIs) and stringent border control measures, just to be balanced with relatively normal economic activities and social lives. […] From 31 December 2021 to 23 March 2022, the ongoing fifth wave of COVID-19 in Hong Kong has claimed 6356 lives.”

8. Omicron variant showed lower neutralizing sensitivity than other SARS-CoV-2 variants to immune sera elicited by vaccines after boost  

Jingwen Ai, Haocheng Zhang, Yi Zhang, Ke Lin, Yanliang Zhang, Jing Wu, Yanming Wan, Yanfang Huang, Jieyu Song, Zhangfan Fu, Hongyu Wang, Jingxin Guo, Ning Jiang, Mingxiang Fan, Yang Zhou, Yuanhan Zhao, Qiran Zhang, Qiang Liu, Jing Lv, Peiyao Li, Chao Qiu & Wenhong Zhang

“In this study, we explored the immunogenicity of COVID-19 breakthrough patients, BBIBP-CorV homologous booster group and BBIBP-CorV/ZF2001 heterologous booster group against SARS-CoV-2 pseudotypes corresponding to the prototype, Beta, Delta, and the emergent Omicron variant.”

9. A Research Ethics Framework for the Clinical Translation of Healthcare Machine Learning  

Melissa D McCradden, James A Anderson, Elizabeth A. Stephenson, Erik Drysdale, Lauren Erdman, Anna Goldenberg & Randi Zlotnik Shaul

Published in The American Journal of Bioethics

“The application of artificial intelligence and machine learning (ML) technologies in healthcare have immense potential to improve the care of patients. While there are some emerging practices surrounding responsible ML as well as regulatory frameworks, the traditional role of research ethics oversight has been relatively unexplored regarding its relevance for clinical ML.”

10. Clinician guidelines for the treatment of psychiatric disorders with nutraceuticals and phytoceuticals: The World Federation of Societies of Biological Psychiatry (WFSBP) and Canadian Network for Mood and Anxiety Treatments (CANMAT) Taskforce 

Jerome Sarris, Arun Ravindran, Lakshmi N. Yatham, Wolfgang Marx, Julia J. Rucklidge, Roger S. McIntyre, Shahin Akhondzadeh, Francesco Benedetti, Constanza Caneo, Holger Cramer, Lachlan Cribb, Michael de Manincor, Olivia Dean, Andrea Camaz Deslandes, Marlene P. Freeman, Bangalore Gangadhar, Brian H. Harvey, Siegfried Kasper, James Lake, Adrian Lopresti, Lin Lu, Najwa-Joelle Metri, David Mischoulon, Chee H. Ng, Daisuke Nishi, Roja Rahimi, Soraya Seedat, Justin Sinclair, Kuan-Pin Su, Zhang-Jin Zhang & Michael Berk

Published in The World Journal of Biological Psychiatry

“The therapeutic use of nutrient-based ‘nutraceuticals’ and plant-based ‘phytoceuticals’ for the treatment of mental disorders is common; however, despite recent research progress, there have not been any updated global clinical guidelines since 2015. To address this, the World Federation of Societies of Biological Psychiatry (WFSBP) and the Canadian Network for Mood and Anxiety Disorders (CANMAT) convened an international taskforce involving 31 leading academics and clinicians from 15 countries, between 2019 and 2021.”

Where to next?

Interested in choosing open access? Make sure you take a look at:

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• Conferences
• MachineHack
• Last updated October 25, 2022
• In AI Origins & Evolution

Top 10 Machine Learning Papers of 2022

• Published on October 25, 2022
• by Tasmia Ansari

The relevance of any field depends on the ongoing research and studies around it. This especially holds for advancing fields like machine learning. 

To bring you up to speed on the critical ideas driving machine learning in 2022, we handpicked the top 10 research papers for all AI/ML enthusiasts out there!

Let’s dive in!

• Artificial Replay: A Meta-Algorithm for Harnessing Historical Data in Bandits

Author(s) – Sean R. Sinclair et al .

Ways to incorporate historical data are still unclear: initialising reward estimates with historical samples can suffer from bogus and imbalanced data coverage, leading to computational and storage issues—particularly in continuous action spaces. The paper addresses the obstacles by proposing ‘Artificial Replay’, an algorithm to incorporate historical data into any arbitrary base bandit algorithm. 

Read the full paper here . 

• Bootstrapped Meta-Learning 

Author(s) – Sebastian Flennerhag et al.

The paper proposes an algorithm in which the meta-learner teaches itself to overcome the meta-optimisation challenge. The algorithm focuses on meta-learning with gradients, which guarantees performance improvements. Furthermore, the paper also looks at how bootstrapping opens up possibilities. 

Read the full paper here .

• LaMDA: Language Models for Dialog Applications

Author(s) – Romal Thoppilan et al.

The research describes the LaMDA system which caused chaos in AI this summer when a former Google engineer claimed that it had shown signs of sentience. LaMDA is a family of large language models for dialogue applications based on Transformer architecture. The interesting feature of the model is its fine-tuning with human-annotated data and the possibility of consulting external sources. This is a very interesting model family, which we might encounter in many applications we use daily. 

• Competition-Level Code Generation with AlphaCode

Author(s) – Yujia Li et al.

Systems can help programmers become more productive. The following research addresses the problems with incorporating innovations in AI into these systems. AlphaCode is a system that creates solutions for problems that require deeper reasoning. 

• Privacy for Free: How does Dataset Condensation Help Privacy?

Author(s) – Tian Dong et al.

The paper focuses on Privacy Preserving Machine Learning, specifically deducting the leakage of sensitive data in machine learning. It puts forth one of the first propositions of using dataset condensation techniques to preserve the data efficiency during model training and furnish membership privacy.

• Why do tree-based models still outperform deep learning on tabular data?

Author(s) – Léo Grinsztajn, Edouard Oyallon and Gaël Varoquaux

The research answers why deep learning models still find it hard to compete on tabular data compared to tree-based models. It is shown that MLP-like architectures are more sensitive to uninformative features in data compared to their tree-based counterparts. 

• Multi-Objective Bayesian Optimisation over High-Dimensional Search Spaces 

Author(s) – Samuel Daulton et al.

The paper proposes ‘MORBO’, a scalable method for multiple-objective BO as it performs better than that of high-dimensional search spaces. MORBO significantly improves the sample efficiency and, where existing BO algorithms fail, MORBO provides improved sample efficiencies over the current approach. 

• A Path Towards Autonomous Machine Intelligence Version 0.9.2

Author(s) – Yann LeCun

The research offers a vision about how to progress towards general AI. The study combines several concepts: a configurable predictive world model, behaviour driven through intrinsic motivation, and hierarchical joint embedding architectures trained with self-supervised

learning. 

• TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data

Author(s) –   Shreshth Tuli, Giuliano Casale and Nicholas R. Jennings

This is a specialised paper applying transformer architecture to the problem of unsupervised anomaly detection in multivariate time series. Many architectures which were successful in other fields are, at some point, also being applied to time series. The research shows improved performance on some known data sets. 

• Differentially Private Bias-Term only Fine-tuning of Foundation Models

Author(s) – Zhiqi Bu et al. 

In the paper, researchers study the problem of differentially private (DP) fine-tuning of large pre-trained models—a recent privacy-preserving approach suitable for solving downstream tasks with sensitive data. Existing work has demonstrated that high accuracy is possible under strong privacy constraints yet requires significant computational overhead or modifications to the network architecture.

Read the full paper here . 

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Reference management. Clean and simple.

The top list of academic search engines

1. Google Scholar

4. science.gov, 5. semantic scholar, 6. baidu scholar, get the most out of academic search engines, frequently asked questions about academic search engines, related articles.

Academic search engines have become the number one resource to turn to in order to find research papers and other scholarly sources. While classic academic databases like Web of Science and Scopus are locked behind paywalls, Google Scholar and others can be accessed free of charge. In order to help you get your research done fast, we have compiled the top list of free academic search engines.

Google Scholar is the clear number one when it comes to academic search engines. It's the power of Google searches applied to research papers and patents. It not only lets you find research papers for all academic disciplines for free but also often provides links to full-text PDF files.

• Coverage: approx. 200 million articles
• Abstracts: only a snippet of the abstract is available
• Related articles: ✔
• References: ✔
• Cited by: ✔
• Links to full text: ✔
• Export formats: APA, MLA, Chicago, Harvard, Vancouver, RIS, BibTeX

BASE is hosted at Bielefeld University in Germany. That is also where its name stems from (Bielefeld Academic Search Engine).

• Coverage: approx. 136 million articles (contains duplicates)
• Abstracts: ✔
• Related articles: ✘
• References: ✘
• Cited by: ✘
• Export formats: RIS, BibTeX

CORE is an academic search engine dedicated to open-access research papers. For each search result, a link to the full-text PDF or full-text web page is provided.

• Coverage: approx. 136 million articles
• Links to full text: ✔ (all articles in CORE are open access)
• Export formats: BibTeX

Science.gov is a fantastic resource as it bundles and offers free access to search results from more than 15 U.S. federal agencies. There is no need anymore to query all those resources separately!

• Coverage: approx. 200 million articles and reports
• Links to full text: ✔ (available for some databases)
• Export formats: APA, MLA, RIS, BibTeX (available for some databases)

Semantic Scholar is the new kid on the block. Its mission is to provide more relevant and impactful search results using AI-powered algorithms that find hidden connections and links between research topics.

• Coverage: approx. 40 million articles
• Export formats: APA, MLA, Chicago, BibTeX

Although Baidu Scholar's interface is in Chinese, its index contains research papers in English as well as Chinese.

• Coverage: no detailed statistics available, approx. 100 million articles
• Abstracts: only snippets of the abstract are available
• Export formats: APA, MLA, RIS, BibTeX

RefSeek searches more than one billion documents from academic and organizational websites. Its clean interface makes it especially easy to use for students and new researchers.

• Coverage: no detailed statistics available, approx. 1 billion documents
• Abstracts: only snippets of the article are available
• Export formats: not available

Consider using a reference manager like Paperpile to save, organize, and cite your references. Paperpile integrates with Google Scholar and many popular databases, so you can save references and PDFs directly to your library using the Paperpile buttons:

Google Scholar is an academic search engine, and it is the clear number one when it comes to academic search engines. It's the power of Google searches applied to research papers and patents. It not only let's you find research papers for all academic disciplines for free, but also often provides links to full text PDF file.

Semantic Scholar is a free, AI-powered research tool for scientific literature developed at the Allen Institute for AI. Sematic Scholar was publicly released in 2015 and uses advances in natural language processing to provide summaries for scholarly papers.

BASE , as its name suggest is an academic search engine. It is hosted at Bielefeld University in Germany and that's where it name stems from (Bielefeld Academic Search Engine).

CORE is an academic search engine dedicated to open access research papers. For each search result a link to the full text PDF or full text web page is provided.

Science.gov is a fantastic resource as it bundles and offers free access to search results from more than 15 U.S. federal agencies. There is no need any more to query all those resources separately!

Highly Cited Researchers 2023

Highly cited researchers have demonstrated significant and broad influence in their field(s) of research..

Each researcher selected has authored multiple Highly Cited Papers™ which rank in the top 1% by citations for their field(s) and publication year in the Web of Science™ over the past decade. However, citation activity is not the sole selection indicator. A preliminary list based on citation activity is then refined using qualitative analysis and expert judgement.

Of the world’s population of scientists and social scientists, Highly Cited Researchers™ are 1 in 1,000.

2023 Analysis

Experts from the Institute for Scientific Information™ provide their detailed insight into the list of Highly Cited Researchers 2023, including their geographical locations, primary tenured research institutes and a breakdown of their fields of research.

Read our analysis of the 2023 list.

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Multiple Authors

Last year brought no shortage of news. Frontpages around the world were dominated by Russia’s invasion of Ukraine , the death of Queen Elizabeth II and a UK prime minister outlasted by a lettuce .

But in yet another hectic year for news coverage, climate change still made headlines – not least because of the thousands of peer-reviewed journal papers about climate and energy that are published every year. 

These studies were picked up around the world by online news outlets and shared on social media platforms, including Twitter, Facebook and LinkedIn. Tracking all these “mentions” was Altmetric , an organisation that scores academic papers according to the media attention they receive.

Using Altmetric data for 2022, Carbon Brief has compiled its now-traditional list of the 25 most talked-about climate or energy-related papers that were published the previous year.

From megafloods to megadroughts and insects to polar bears, last year saw a broad range of headline-grabbing research – as well as a new record-high Altmetric score for a paper in a Carbon Brief annual review.

The infographic above shows which papers made it into the top 10, while the chart at the end of the article shows which journals feature most frequently in the top 25.

Pandemic prominence

As in 2020 and 2021 , the most talked-about scientific research of the past year has been about Covid-19. 

All but five of the 50 highest scoring papers of 2022 relate to the coronavirus. Those five include two papers on monkeypox (since renamed “mpox”), one on the global burden of drug-resistant bacteria , one that identifies the Epstein-Barr virus as “the leading cause of multiple sclerosis”, and one finding that, in 2020, firearms became the main cause of deaths in children in the US.

Continuing the theme, the most talked-about climate and energy paper of 2022 also relates to the Covid-19 pandemic.

Just outside the overall top 50, in 56th place, is “ Climate change increases cross-species viral transmission risk ”. Published in Nature, the study warns that mammals forced to move to cooler climes amid rising global temperatures are “already” spreading their viruses further – with “undoubtable” impacts for human health.

The research uses modelling to map how climate change could shift the geographic ranges of 3,100 mammal species and the viruses they carry by 2070. It finds that climate change is increasingly driving new encounters between mammal species, raising the risk of novel disease spread. The world’s “biodiversity hotspots” and densely populated parts of Asia and Africa are most likely to be affected.

Speaking to Carbon Brief when the paper was published in April, co-lead author Dr Colin Carlson , a global change biologist at Georgetown University in Washington DC, explained:

“Species are going to show up in new combinations because of climate change and, when they do, that’s an opportunity for them to share viruses with each other.” 

The relevance of the work was “reinforced by the Covid-19 pandemic”, the paper says, “which began only weeks after the completion of this study”. The authors note that Covid-19 “probably originated in south-east Asian horseshoe bats and may have spread to humans through an as-yet-unknown bridge host”. They add:

“Although we caution against over-interpreting our results as explanatory of the current pandemic, our findings suggest that climate change could easily become the dominant anthropogenic force in viral cross-species transmission, which will undoubtedly have a downstream effect on human health and pandemic risk.”

The paper clocks in with an Altmetric score of 7,803 – the highest score for any climate paper featured in Carbon Brief’s annual reviews and only the second time the top-scoring paper has surpassed 7,000. 

(For Carbon Brief’s previous Altmetric articles, see the links for 2021 , 2020 , 2019 , 2018 , 2017 , 2016 and 2015 .)

The study was covered in 716 news stories by 558 outlets around the world, including the Guardian , New York Times , Agence France-Presse , Al Jazeera and New Delhi Times . It was also picked up in 44 blog posts.

The research was the most talked about on Twitter of any of the top 25 climate papers included here. The paper’s URL was included in more than 10,000 tweets from more than 9,000 accounts, which collectively have almost 39 million followers.

Considering the paper’s timing, it is “not surprising” that connecting climate change with pandemics “gathered a bit of attention”, Carlson tells Carbon Brief. 

However, he says, “there’s still a weird pattern where climate gets left out of the conversation [around pandemics] a lot compared to biodiversity loss or wildlife trade”. He adds:

“I think I’ve started to see that change finally, and if we played a part in that, that’s wonderful. Most of my work focuses on the health impacts of climate change, and I think time and time again, we underestimate how bad they’ll be…Climate change is an existential threat to human and animal health, and I hope our study and the conversation around it helped push that envelope a bit.”

The second highest-scoring climate paper of 2022 – clocking in at 70th in the overall 2022 rankings – is, “ Exceeding 1.5C global warming could trigger multiple climate tipping points ”. Published in the journal Science, the study has an Altmetric score of 6,573.

The study’s lead author, Dr David McKay , told Carbon Brief in September that tipping points have been a keen area of interest in the climate community since 2008, when the study, “ Tipping elements in the Earth’s climate system ”, first “broke the ice” on the subject.

His work provides the first comprehensive assessment of climate-related tipping points since the 2008 paper. It identifies 16 climate tipping elements – shown below – and finds a “significant likelihood” that multiple tipping points will be crossed if global temperatures exceed 1.5C above pre-industrial levels.

The study was mentioned in 667 news stories from 397 outlets – including the Guardian , New Scientist and BBC News . It received the highest number of mentions in blog posts and Wikipedia pages of the top 25 climate papers – at 55 and 34, respectively. The study also featured in more than 6,000 tweets.

The study was strategically published just days before researchers, economists and civil society representatives gathered in McKay’s home town of Exeter for a conference on the topic of climate tipping points. Carbon Brief attended the conference, and summarised the key talking points, ideas and proposals that emerged.

Arctic warming

In third place with an Altmetric score of 6,201 is the Communications Earth and Environment study, “ The Arctic has warmed nearly four times faster than the globe since 1979 ”.

It is well known that Arctic temperatures are rising much faster than the global average. Previous estimates suggest that the region is warming twice or even three times as quickly as the rest of the world. However, this new study finds that the Arctic has warmed nearly four times faster than the global average over the past four decades.

Dr Mika Rantanen – a researcher at the Finnish Meteorological Institute and lead author on the study – tells Carbon Brief that “Arctic warming and its consequences have become one of the biggest manifestations of climate change”. As such, he says he and his co-authors expected the paper to attract media attention.

True to his expectations, the article was picked up in 765 news stories from 525 outlets, including the Independent , New York Times , Scientific American and Washington Post . “I think that choosing an attractive title was one reason why the paper was picked up so widely,” Rantanen tells Carbon Brief.

In fourth place, with an Altmetric score of 6,147, is the Nature Climate Change paper, “ Greenland ice sheet climate disequilibrium and committed sea level rise ”. 

The study finds that the “imbalance” of the Greenland ice sheet caused by global warming means it is already committed to contributing “at least” 274mm to global sea levels in future, “regardless of 21st-century climate pathways”.

The research also warned that if Greenland melt was consistently as large as the high-melt year of 2012, this would commit 782mm to sea levels, which serves as “an ominous prognosis for Greenland’s trajectory through a 21st century of warming”.

The study was picked up by 867 news stories from 658 outlets. These include, for example, a Guardian headline warning that major sea level rise from Greenland was now “inevitable”, and an Associated Press article leading on the idea of “zombie ice”:

“That’s doomed ice that, while still attached to thicker areas of ice, is no longer getting replenished by parent glaciers now receiving less snow. Without replenishment, the doomed ice is melting from climate change and will inevitably raise seas.”

Study co-author Dr William Colgan , a glaciologist at the Geological Survey of Denmark and Greenland , told the outlet:

“It’s dead ice. It’s just going to melt and disappear from the ice sheet…This ice has been consigned to the ocean, regardless of what climate (emissions) scenario we take now.”

The paper was also picked up by 37 blog posts and 1,671 tweets.

Completing the top five is another pandemic-related paper, “ Over half of known human pathogenic diseases can be aggravated by climate change ”, also published in Nature Climate Change.

It finds that nearly 60% of pathogen-caused diseases that affect humans have been “at some point aggravated” by climate-related “hazards”, such as warming and drought. 

Specifically, the researchers identify how 375 infectious diseases around the world have been affected by a range of climatic hazards. They find that while 16% of infectious diseases have “at times” been reduced by climate hazards, there are more than 1,000 “unique pathways” by which pathogenic diseases were exacerbated by climate change. 

The authors warn that these pathways are “too numerous for comprehensive societal adaptations” and say that this work highlights the “urgent need to work at the source of the problem: reducing [greenhouse gas] emissions”.

With an overall Altmetric tally of 6,079, the paper would have scored high enough to secure top spot last year . It was covered by 803 news stories from 582 outlets, 31 posts from 28 blogs, and 2,571 tweets from 2,277 users.

Just missing out on a spot in the top five is a “perspective” paper in the Proceedings of the National Academy of Sciences, “ Climate endgame: Exploring catastrophic climate change scenarios ”. This less-than-cheery article on “catastrophic” outcomes of climate change was referenced in 556 news stories from 428 outlets. The BBC News coverage of the study says:

“Catastrophic climate change outcomes, including human extinction, are not being taken seriously enough by scientists, a new study says. The authors say that the consequences of more extreme warming – still on the cards if no action is taken – are ‘dangerously underexplored’. They argue that the world needs to start preparing for the possibility of what they term the ‘climate endgame’.”

With an Altmetric score of 4,807, spot number seven goes to the Advances in Atmospheric Sciences paper, “ Another record: Ocean warming continues through 2021 despite La Niña conditions ”.

The annual Lancet Countdown on Health and Climate Change report clocks in at number eight. The paper – under the heading “health at the mercy of fossil fuels” – was mentioned in 521 news stories from 386 outlets. In a striking summary, the report says:

“Worldwide, people are seeing their health increasingly affected by climate change amidst the compounding impacts of Covid-19 and the cost of living and energy crises; governments and companies continue to prioritise fossil fuels over a healthy future despite climate commitments; and rapid, holistic action is the only route to ensuring a just and healthy future.”

At number nine is the “brief communication” Nature Climate Change paper, “ Rapid intensification of the emerging south-western North American megadrought in 2020-2021 ”. The study finds that the American west’s megadrought is the driest in at least 1,200 years.

The research was picked up in 1,187 new stories – more than any of the other 25 papers included here – including American publications the New York Times , the Washington Post , NBC News and NPR . The paper also topped the tables for blog post mentions, with 76.

Rounding off the top 10 climate articles of 2022 is the Nature Climate Change study, “ Pronounced loss of Amazon rainforest resilience since the early 2000s ”, with an Altmetric score of 4,195. This article was mentioned in 563 news stories from 434 outlets, including Carbon Brief , BBC News , the Independent , the New York Times and New Scientist . 

The study finds that three-quarters of the Amazon rainforest has lost “resilience” since 2003 – making it more vulnerable to extreme events such as droughts. The work shows we are “approaching a tipping point”, the lead author of the study told journalists at a press conference.

Elsewhere in the top 25

The rest of the top 25 contains a varied mix of papers, including how agriculture and climate change are “ reshaping ” insect biodiversity worldwide (12th) and the discovery of a “ genetically distinct and functionally isolated ” population of polar bears from south-eastern Greenland (19th).

Just missing out on the top 10 is, “ Global carbon budget 2022 ”, in 11th place. Published in the journal Earth System Science Data, the paper details the annually estimated “global carbon budget” as produced by the Global Carbon Project .

The paper’s lead author Prof Pierre Friedlingstein , chair of mathematical modelling of climate systems at the University of Exeter , co-wrote a Carbon Brief article to unpack their findings.

The latest data shows that global CO2 emissions from fossil fuels and cement increased by 1.0% in 2022, hitting a new record high of 36.6bn tonnes of CO2. This rise was “primarily driven by a strong increase in oil emissions as global travel continues to recover from the Covid-19 pandemic”, the article says, adding that “coal and gas emissions grew more slowly, though both had record emissions in 2022”.

There are several energy-related papers in the top 25. This includes a paper in 15th place from the Proceedings of the National Academy of Sciences , which finds that “corn-based ethanol in the US has failed to meet the policy’s own greenhouse gas emissions targets and negatively affected water quality, the area of land used for conservation, and other ecosystem processes”. Another is a Joule paper in 20th, which finds that “compared to continuing with a fossil fuel-based system, a rapid green energy transition is likely to result in trillions of net savings”.

The topic of public health features again in the top 25. For example, in 14th place is a Communications Earth & Environment study warning that, under 2C of warming, the exposure to dangerous heat stress “will likely increase by 50-100% across much of the tropics and increase by a factor of 3-10 in many regions throughout the mid-latitudes”.

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Research into extreme weather also appears in the top 25. This includes a Nature Communications study in 18th place on the 2020 North Atlantic hurricane season – the most active on record – which finds that “human-induced climate change increased the extreme three-hourly storm rainfall rates and extreme three-day accumulated rainfall amounts…for observed storms that are at least tropical storm strength”. A Science Advances paper in 23rd place warns that climate change has already doubled the likelihood of an event capable of producing a catastrophic “megaflood” in California.

Finally, rounding out the top 25 is a Nature Geoscience study showing that the Thwaites glacier in west Antarctica has seen “sustained pulses of rapid retreat” in the past two centuries and similar pulses “are likely to occur in the near future”.

All the final scores for the top 25 climate papers of 2022 can be found in this spreadsheet .

Top journals

Across the top 25 papers in Carbon Brief’s leaderboard this year, Nature Climate Change features most frequently with four papers. Nature Climate Change also took first place in 2021 (jointly with Nature) and 2016 (jointly with Science).  

In joint-second place is Nature and Science with three papers each. Nature is perennially high-placed in this analysis, taking first – or joint first – spot in Carbon Brief’s top 25 in 2021 , 2020 , 2019 , 2018 , 2017 and 2015 .

For the rest of the top 25, there are four journals that appear twice and seven that appear once.

Diversity of the top 25

The top 25 climate papers of 2022 cover a huge range of topics and scope. 

The “Lancet Countdown on health and climate change” and “Global Carbon Budget” reports are epic annual publications, which review vast swathes of literature and have around 100 authors each. Meanwhile, three of the climate papers in the list have only two authors, and focus on specific new frontiers of climate research.

In total, the top 25 climate papers of 2022 have more than 400 authors. However, despite the variety in the climate research the papers present, analysis of their authors reveals a distinct lack of diversity.

Carbon Brief recorded the gender and country of affiliation for each of these authors. (The  methodology used was developed by Carbon Brief for analysis presented in a special 2021 series on climate justice .) The analysis reveals that the authors of the climate papers most featured in the media in 2022 are predominantly men from the global north.

The chart below shows the institutional affiliations of all authors in this analysis, broken down by continent – Europe, North America , Oceania, Asia, South America and Africa.

The analysis shows that nine out of every 10 authors are affiliated with institutions from the global north – defined as North America, Europe and Oceania. Meanwhile, there are only two authors from South America, based in Peru, and a single author from Africa, based in South Africa.

Further data analysis shows that there are also inequalities within continents. The map below shows the percentage of authors from each country in the analysis, where dark blue indicates a higher percentage. Countries that are not represented by any authors in the analysis are shown in white.

The top-ranking countries on this map are the US and the UK, which together account for more than half of all authors in this analysis (32% and 19%, respectively). Almost half of all researchers from the global south are from China – which accounts for around 6% of all researchers in the analysis.

None of the authors of the top 25 climate papers of 2022 is from south Asia, central America or the Caribbean.

Meanwhile, only 27% of authors from the top 25 climate papers of 2022 are women. Similarly, only six of the 25 papers have a female lead author.

The plot below shows the number of male (purple) and female (orange) authors in this analysis from each continent.

The full spreadsheet showing the results of this data analysis can be found here. For more on the biases in climate publishing, see Carbon Brief’s article on the lack of diversity in climate-science research .

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Update: This article was updated on 09/01/2023 to add in the diversity analysis.

• Analysis: The climate papers most featured in the media in 2022

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Miller M , Azrael D. Firearm Storage in US Households With Children : Findings From the 2021 National Firearm Survey . JAMA Netw Open. 2022;5(2):e2148823. doi:10.1001/jamanetworkopen.2021.48823

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Firearm Storage in US Households With Children : Findings From the 2021 National Firearm Survey

• 1 Bouvé College of Health Sciences, Department of Health Sciences, Northeastern University, Boston, Massachusetts
• 2 Harvard Injury Control Research Center, Harvard T.H. Chan School of Public Health, Boston, Massachusetts

In 2015, one-third of all households with children contained firearms, 21% of which contained at least 1 firearm that was both loaded and unlocked. As a result, approximately 4.6 million children lived in a home with loaded and unlocked firearms. 1 The nationally representative survey study reported herein updates these estimates as of April 2021, 12 months into an unprecedented and sustained surge in firearm purchases. 2 , 3

Data are from a nationally representative survey of US adults conducted April 15 through 26, 2021, with respondents drawn from an online sampling frame of 55 000 US adults recruited using address-based sampling. A description of the survey methods is available elsewhere. 3 The institutional review boards at Northeastern University, Boston, Massachusetts, and Harvard’s School of Public Health, Boston, determined the survey did not require review because the deidentified data did not qualify as human participants research. We followed the American Association for Public Opinion Research ( AAPOR ) reporting guideline for survey studies.

Panel members were screened with questions about household firearm ownership, including whether they personally owned firearms. Panel members were asked (1) Do you personally own a working gun (yes/no)? and (2) Does anyone else in your household own a working gun (yes/no/don’t know)? Firearm owners were asked (1) Do you store any of the guns you keep in or around your home loaded (yes/no)? (2) Do you store any of the guns you keep in or around your home unlocked (yes/no)? and, if they answered yes to both questions, (3) Do you store any of the guns you keep in or around your home both loaded and unlocked (yes/no)? Race and ethnicity were self-reported, using profile categories supplied by the survey firm (Ipsos).

Poststratification weights were applied to adjust for nonresponse and undercoverage or overcoverage from the study-specific sample design relative to expected distributions from the US Census Current Population Survey and the American Community Survey. Analyses used the SVY suite of commands in Stata, version16 (StataCorp LLC) to produce prevalence estimates with 95% CIs.

Of the 29 985 adult panel members invited to participate, 19 049 (63.5%) did so. Among all respondents, 9144 (48.0%) were men, 9905 (52.0%) were women, 62.0% were White non-Hispanic, and 33.3% lived in households with children younger than 18 years. Of the adults with children, 40.4% (95% CI, 38.6%-42.2%) who responded to the survey lived in a household with firearms. Of these, 29.3% (95%, CI, 27.7%-30.9%) owned firearms, and 11.1% (95% CI, 9.9%-12.4%) lived in a home with firearms but did not personally own a firearm ( Table ). The mean number of children per firearm-owning household with children (1.9; 95% CI, 1.9-2.0) was comparable to that in households with children and no firearms (1.8).

Most of the firearm owners with children (n = 1363) were male, were White, were married, lived in either a rural or suburban area, and had attended some college ( Table ). Of these, 36.1% (95% CI, 32.0%-40.4%) had unlocked firearms, and 37.1% (95% CI, 33.1%-41.4%) had loaded firearms. Of these, 15.0% (95% CI, 12.3%-18.2%) stored at least 1 firearm loaded and unlocked (least safe), and 44.1% (95% CI, 39.8%-48.5%) stored all firearms unloaded and locked.

These results indicate that in April 2021, approximately 30 million children lived in households with firearms, 7 million more than in 2015. 1 , 2 Firearm owners with children were more likely to store all household firearms locked and unloaded in 2021 (44.1%) compared with 2015 (29%) 1 and were slightly less likely to have firearms that were both loaded and unlocked (15.0% vs 21% 1 ).

Nevertheless, the trend toward safer storage we observed in 2021 was offset by the increase in the proportion of adults with children who lived in households with firearms. As a result, our estimate of the number of children who lived in a household with loaded and unlocked firearms in 2021 (4.6 million) was not meaningfully different from the estimate reported in the 2015 National Firearms Survey.

Our study has limitations. This study’s results should be interpreted considering potential inaccuracies due to social desirability, with such bias likely underestimating the prevalence of unsafe storage.

The American Academy of Pediatrics recommends that all firearms in households with children should be unloaded and in locked storage, with ammunition stored separately. Our findings underscore the ongoing need for more effective efforts to reduce children’s exposure to unsafely stored firearms, especially considering recent increases in new firearm owners, including those with children. 2 - 5

Accepted for Publication: December 28, 2021.

Published: February 22, 2022. doi:10.1001/jamanetworkopen.2021.48823

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2022 Miller M et al. JAMA Network Open .

Corresponding Author: Matthew Miller, MD, MPH, ScD, Bouvé College of Health Sciences, Department of Health Sciences, Northeastern University, 360 Huntington Ave, Room 316 Robinson Hall, Boston, MA 02115 ( [email protected] ).

Author Contributions: Both authors had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Both authors.

Acquisition, analysis, or interpretation of data: Both authors.

Drafting of the manuscript: Both authors.

Critical revision of the manuscript for important intellectual content: Azrael.

Statistical analysis: Both authors.

Obtained funding: Both authors.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was supported in part by the Joyce Foundation.

Role of the Funder/Sponsor: The Joyce Foundation had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

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Scientific breakthroughs: 2024 emerging trends to watch

December 28, 2023

Across disciplines and industries, scientific discoveries happen every day, so how can you stay ahead of emerging trends in a thriving landscape? At CAS, we have a unique view of recent scientific breakthroughs, the historical discoveries they were built upon, and the expertise to navigate the opportunities ahead. In 2023, we identified the top scientific breakthroughs , and 2024 has even more to offer. New trends to watch include the accelerated expansion of green chemistry, the clinical validation of CRISPR, the rise of biomaterials, and the renewed progress in treating the undruggable, from cancer to neurodegenerative diseases. To hear what the experts from Lawrence Liverpool National Lab and Oak Ridge National Lab are saying on this topic, join us for a free webinar on January 25 from 10:00 to 11:30 a.m. EDT for a panel discussion on the trends to watch in 2024.

The ascension of AI in R&D

While the future of AI has always been forward-looking, the AI revolution in chemistry and drug discovery has yet to be fully realized. While there have been some high-profile set-backs , several breakthroughs should be watched closely as the field continues to evolve. Generative AI is making an impact in drug discovery , machine learning is being used more in environmental research , and large language models like ChatGPT are being tested in healthcare applications and clinical settings.

Many scientists are keeping an eye on AlphaFold, DeepMind’s protein structure prediction software that revolutionized how proteins are understood. DeepMind and Isomorphic Labs have recently announced how their latest model shows improved accuracy, can generate predictions for almost all molecules in the Protein Data Bank, and expand coverage to ligands, nucleic acids, and posttranslational modifications . Therapeutic antibody discovery driven by AI is also gaining popularity , and platforms such as the RubrYc Therapeutics antibody discovery engine will help advance research in this area.

Though many look at AI development with excitement, concerns over accurate and accessible training data , fairness and bias , lack of regulatory oversight , impact on academia, scholarly research and publishing , hallucinations in large language models , and even concerns over infodemic threats to public health are being discussed. However, continuous improvement is inevitable with AI, so expect to see many new developments and innovations throughout 2024.

‘Greener’ green chemistry

Green chemistry is a rapidly evolving field that is constantly seeking innovative ways to minimize the environmental impact of chemical processes. Here are several emerging trends that are seeing significant breakthroughs:

• Improving green chemistry predictions/outcomes : One of the biggest challenges in green chemistry is predicting the environmental impact of new chemicals and processes. Researchers are developing new computational tools and models that can help predict these impacts with greater accuracy. This will allow chemists to design safer and more environmentally friendly chemicals.
• Reducing plastics: More than 350 million tons of plastic waste is generated every year. Across the landscape of manufacturers, suppliers, and retailers, reducing the use of single-use plastics and microplastics is critical. New value-driven approaches by innovators like MiTerro that reuse industrial by-products and biomass waste for eco-friendly and cheaper plastic replacements will soon be industry expectations. Lowering costs and plastic footprints will be important throughout the entire supply chain.    
• Alternative battery chemistry: In the battery and energy storage space, finding alternatives to scarce " endangered elements" like lithium and cobalt will be critical. While essential components of many batteries, they are becoming scarce and expensive. New investments in lithium iron phosphate (LFP) batteries that do not use nickel and cobalt have expanded , with 45% of the EV market share being projected for LFP in 2029. Continued research is projected for more development in alternative materials like sodium, iron, and magnesium, which are more abundant, less expensive, and more sustainable.
• More sustainable catalysts : Catalysts speed up a chemical reaction or decrease the energy required without getting consumed. Noble metals are excellent catalysts; however, they are expensive and their mining causes environmental damage. Even non-noble metal catalysts can also be toxic due to contamination and challenges with their disposal. Sustainable catalysts are made of earth-abundant elements that are also non-toxic in nature. In recent years, there has been a growing focus on developing sustainable catalysts that are more environmentally friendly and less reliant on precious metals. New developments with catalysts, their roles, and environmental impact will drive meaningful progress in reducing carbon footprints.  
• Recycling lithium-ion batteries: Lithium-ion recycling has seen increased investments with more than 800 patents already published in 2023. The use of solid electrolytes or liquid nonflammable electrolytes may improve the safety and durability of LIBs and reduce their material use. Finally, a method to manufacture electrodes without solvent s could reduce the use of deprecated solvents such as N-methylpyrrolidinone, which require recycling and careful handling to prevent emissions.

Rise of biomaterials

New materials for biomedical applications could revolutionize many healthcare segments in 2024. One example is bioelectronic materials, which form interfaces between electronic devices and the human body, such as the brain-computer interface system being developed by Neuralink. This system, which uses a network of biocompatible electrodes implanted directly in the brain, was given FDA approval to begin human trials in 2023.

• Bioelectronic materials: are often hybrids or composites, incorporating nanoscale materials, highly engineered conductive polymers, and bioresorbable substances. Recently developed devices can be implanted, used temporarily, and then safely reabsorbed by the body without the need for removal. This has been demonstrated by a fully bioresorbable, combined sensor-wireless power receiver made from zinc and the biodegradable polymer, poly(lactic acid).
• Natural biomaterials: that are biocompatible and naturally derived (such as chitosan, cellulose nanomaterials, and silk) are used to make advanced multifunctional biomaterials in 2023. For example, they designed an injectable hydrogel brain implant for treating Parkinson’s disease, which is based on reversible crosslinks formed between chitosan, tannic acid, and gold nanoparticles.
• Bioinks : are used for 3D printing of organs and transplant development which could revolutionize patient care. Currently, these models are used for studying organ architecture like 3D-printed heart models for cardiac disorders and 3D-printed lung models to test the efficacy of drugs. Specialized bioinks enhance the quality, efficacy, and versatility of 3D-printed organs, structures, and outcomes. Finally, new approaches like volumetric additive manufacturing (VAM) of pristine silk- based bioinks are unlocking new frontiers of innovation for 3D printing.

To the moon and beyond

The global Artemis program is a NASA-led international space exploration program that aims to land the first woman and the first person of color on the Moon by 2025 as part of the long-term goal of establishing a sustainable human presence on the Moon. Additionally, the NASA mission called Europa Clipper, scheduled for a 2024 launch, will orbit around Jupiter and fly by Europa , one of Jupiter’s moons, to study the presence of water and its habitability. China’s mission, Chang’e 6 , plans to bring samples from the moon back to Earth for further studies. The Martian Moons Exploration (MMX) mission by Japan’s JAXA plans to bring back samples from Phobos, one of the Mars moons. Boeing is also expected to do a test flight of its reusable space capsule Starliner , which can take people to low-earth orbit.

The R&D impact of Artemis extends to more fields than just aerospace engineering, though:

• Robotics: Robots will play a critical role in the Artemis program, performing many tasks, such as collecting samples, building infrastructure, and conducting scientific research. This will drive the development of new robotic technologies, including autonomous systems and dexterous manipulators.
• Space medicine: The Artemis program will require the development of new technologies to protect astronauts from the hazards of space travel, such as radiation exposure and microgravity. This will include scientific discoveries in medical diagnostics, therapeutics, and countermeasures.
• Earth science: The Artemis program will provide a unique opportunity to study the Moon and its environment. This will lead to new insights into the Earth's history, geology, and climate.
• Materials science: The extreme space environment will require new materials that are lightweight, durable, and radiation resistant. This will have applications in many industries, including aerospace, construction, and energy.
• Information technology: The Artemis program will generate a massive amount of data, which will need to be processed, analyzed, and shared in real time. This will drive the development of new IT technologies, such as cloud computing, artificial intelligence, and machine learning.

The CRISPR pay-off

After years of research, setbacks, and minimal progress, the first formal evidence of CRISPR as a therapeutic platform technology in the clinic was realized. Intellia Therapeutics received FDA clearance to initiate a pivotal phase 3 trial of a new drug for the treatment of hATTR, and using the same Cas9 mRNA, got a new medicine treating a different disease, angioedema. This was achieved by only changing 20 nucleotides of the guide RNA, suggesting that CRISPR can be used as a therapeutic platform technology in the clinic.

The second great moment for CRISPR drug development technology came when Vertex and CRISPR Therapeutics announced the authorization of the first CRISPR/Cas9 gene-edited therapy, CASGEVY™, by the United Kingdom MHRA, for the treatment of sickle cell disease and transfusion-dependent beta-thalassemia. This was the first approval of a CRISPR-based therapy for human use and is a landmark moment in realizing the potential of CRISPR to improve human health.

In addition to its remarkable genome editing capability, the CRISPR-Cas system has proven to be effective in many applications, including early cancer diagnosis . CRISPR-based genome and transcriptome engineering and CRISPR-Cas12a and CRISPR-Cas13a appear to have the necessary characteristics to be robust detection tools for cancer therapy and diagnostics. CRISPR-Cas-based biosensing system gives rise to a new era for precise diagnoses of early-stage cancers.

MIT engineers have also designed a new nanoparticle DNA-encoded nanosensor for urinary biomarkers that could enable early cancer diagnoses with a simple urine test. The sensors, which can detect cancerous proteins, could also distinguish the type of tumor or how it responds to treatment.

Ending cancer

The immuno-oncology field has seen tremendous growth in the last few years. Approved products such as cytokines, vaccines, tumor-directed monoclonal antibodies, and immune checkpoint blockers continue to grow in market size. Novel therapies like TAC01-HER2 are currently undergoing clinical trials. This unique therapy uses autologous T cells, which have been genetically engineered to incorporate T cell Antigen Coupler (TAC) receptors that recognize human epidermal growth factor receptor 2 (HER2) presence on tumor cells to remove them. This could be a promising therapy for metastatic, HER2-positive solid tumors.

Another promising strategy aims to use the CAR-T cells against solid tumors in conjunction with a vaccine that boosts immune response. Immune boosting helps the body create more host T cells that can target other tumor antigens that CAR-T cells cannot kill.

Another notable trend is the development of improved and effective personalized therapies. For instance, a recently developed personalized RNA neoantigen vaccine, based on uridine mRNA–lipoplex nanoparticles, was found effective against pancreatic ductal adenocarcinoma (PDAC). Major challenges in immuno-oncology are therapy resistance, lack of predictable biomarkers, and tumor heterogenicity. As a result, devising novel treatment strategies could be a future research focus.

Decarbonizing energy

Multiple well-funded efforts are underway to decarbonize energy production by replacing fossil fuel-based energy sources with sources that generate no (or much less) CO2 in 2024.

One of these efforts is to incorporate large-scale energy storage devices into the existing power grid. These are an important part of enabling the use of renewable sources since they provide additional supply and demand for electricity to complement renewable sources. Several types of grid-scale storage that vary in the amount of energy they can store and how quickly they can discharge it into the grid are under development. Some are physical (flywheels, pumped hydro, and compressed air) and some are chemical (traditional batteries, flow batteries , supercapacitors, and hydrogen ), but all are the subject of active chemistry and materials development research. The U.S. government is encouraging development in this area through tax credits as part of the Inflation Reduction Act and a $7 billion program to establish regional hydrogen hubs.

Meanwhile, nuclear power will continue to be an active R&D area in 2024. In nuclear fission, multiple companies are developing small modular reactors (SMRs) for use in electricity production and chemical manufacturing, including hydrogen. The development of nuclear fusion reactors involves fundamental research in physics and materials science. One major challenge is finding a material that can be used for the wall of the reactor facing the fusion plasma; so far, candidate materials have included high-entropy alloys and even molten metals .

Neurodegenerative diseases

Neurodegenerative diseases are a major public health concern, being a leading cause of death and disability worldwide. While there is currently no cure for any neurodegenerative disease, new scientific discoveries and understandings of these pathways may be the key to helping patient outcomes.

• Alzheimer’s disease: Two immunotherapeutics have received FDA approval to reduce both cognitive and functional decline in individuals living with early Alzheimer's disease. Aducannumab (Aduhelm®) received accelerated approval in 2021 and is the first new treatment approved for Alzheimer’s since 2003 and the first therapy targeting the disease pathophysiology, reducing beta-amyloid plaques in the brains of early Alzheimer’s disease patients. Lecanemab (Leqembi®) received traditional approval in 2023 and is the first drug targeting Alzheimer’s disease pathophysiology to show clinical benefits, reducing the rate of disease progression and slowing cognitive and functional decline in adults with early stages of the disease.
• Parkinson’s disease: New treatment modalities outside of pharmaceuticals and deep brain stimulation are being researched and approved by the FDA for the treatment of Parkinson’s disease symptoms. The non-invasive medical device, Exablate Neuro (approved by the FDA in 2021), uses focused ultrasound on one side of the brain to provide relief from severe symptoms such as tremors, limb rigidity, and dyskinesia. 2023 brought major news for Parkinson’s disease research with the validation of the biomarker alpha-synuclein. Researchers have developed a tool called the α-synuclein seeding amplification assay which detects the biomarker in the spinal fluid of people diagnosed with Parkinson’s disease and individuals who have not shown clinical symptoms.
• Amyotrophic lateral sclerosis (ALS): Two pharmaceuticals have seen FDA approval in the past two years to slow disease progression in individuals with ALS. Relyvrio ® was approved in 2022 and acts by preventing or slowing more neuron cell death in patients with ALS. Tofersen (Qalsody®), an antisense oligonucleotide, was approved in 2023 under the accelerated approval pathway. Tofersen targets RNA produced from mutated superoxide dismutase 1 (SOD1) genes to eliminate toxic SOD1 protein production. Recently published genetic research on how mutations contribute to ALS is ongoing with researchers recently discovering how NEK1 gene mutations lead to ALS. This discovery suggests a possible rational therapeutic approach to stabilizing microtubules in ALS patients.

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Effective regulation is a powerful tool for addressing challenges, big and small. This is crucial now more than ever - for tackling climate change and unlocking the benefits from the technological transition while managing the risks, such as with Artificial Intelligence (AI). Having good processes for developing, implementing and reviewing regulation is vital to ensuring regulatory policies achieve policy goals that maximise benefits and minimise costs for all.

Yet across the world governments are failing to achieve their goals because of policies that are not working to their fullest capabilities. For example, ill-consulted reforms have led to undue burdens that have caused protests and voter dissatisfaction; poorly-designed digital regulations have stifled innovation whilst also failing to protect consumers; and action on environmental goals has been undermined by overly complex or existing regulations that are not fit for supporting contemporary policy goals.

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Good regulation helps economies succeed. It sets the “rules of the game,” aligning business practices with societal values and goals. It also gives businesses certainty to plan their operations long term, rewarding innovation and job creation.

Good regulation can enable technologies like Artificial Intelligence (AI) to flourish, while managing its risks and ensuring  ethical and transparent decision making. It can also encourage sustainable behaviours by businesses and consumers that benefit the environment and promotes green growth rather than locking in polluting fuels and practices. 

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Regulation is a primarily a tool of government, making trust in our public institutions an essential ingredient for success. If people do not trust governments, then it will be difficult to ensure businesses and citizens follow the rules. This is heightened by growing fiscal pressures, which constrain the use of other policy tools like spending and taxation, and by the urgent demands for action on pressing problems like stalling productivity, the climate crisis and rising cost of living.

With 51% of people in OECD countries expressing trust in their political leaders in 2021, focusing on a results-oriented regulatory approach is needed more than ever to maintain and restore trust in government.  Well-designed, transparent and effectively enforced rules can demonstrate the dividends of government and shore up support for elected representatives, ultimately enhancing stability and prosperity.

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While most OECD countries have a formal requirement to conduct an assessment of environmental impacts when designing a regulation, the existence of this requirement does not guarantee that impacts are effectively assessed in practice. For instance, assessments can be carried out superficially or inconsistently across different ministries and policy areas.

Governments must focus on bridging the gap between formal requirements to conduct evidence-based assessments and practical applications that guides decision making. This involves conducting granular, high-quality analysis that sufficiently considers environmental impacts on all groups in society. It also means addressing uneven use of these assessments across policy areas to achieve coherent, sustainable and whole-of-government approaches to environmental protection.

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No policy maker is an island – citizens, business activities, and policy issues like pollution or digital activities cross borders all the time. In our increasingly globalised world, regulation must adapt to cross-border challenges and opportunities. Rulemaking must consider international solutions, allowing policy makers to learn from each other and find common solutions to shared problems.

However, international regulatory co-operation is still not fully embedded into regulators’ DNA. Legal and administrative processes are still designed for yesterday’s economy, failing to make the best policies for addressing today’s complex policy challenges. 

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