Patterns, a Cell Press journal

Recalibrating academic expertise in the age of generative AI GenAI tools promise accelerated discovery but risk eroding scientific expertise. When researchers delegate to AI, they bypass the effortful processes through which competence develops. This perspectiv...

📣 New Perspective 📣

In our article "Recalibrating academic expertise
in the age of generative AI", Zhicheng Lin and I discuss how an over-reliance on generative AI in academia may erode key skills of scientific enquiry.

Out now in @cp-patterns.bsky.social, read it below! 👇

tinyurl.com/py38vnvb

On the cover: The central brain in this image represents IoT-LLM, a framework for processing real-world sensor data with large language models, which is described by An et al. in this issue of Patterns. IoT-LLM provides a central reasoning core that connects and interprets heterogeneous sensor inputs grounded in the physical world such as electrocardiography, temperature, motion, camera images, and robotic system sensors. In the image, these data streams converge on the central model, highlighting how IoT-LLM performs retrieval-augmented fusion and structured reasoning across different data types. Systems like IoT-LLM are laying a foundation for future embodied robots operating in real-world environments. Image credit: An Tuo, Nanyang Technological University.

Our first issue of 2026 is now live!
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This month's cover image highlights a framework, IoT-LLM, for applying large language model driven reasoning to real-world sensor data www.cell.com/patterns/ful...

Text on image reads: Patterns, A Cell Press journal, Editors' Pick, Best of 2025, Explore the collection

Check out some of our best papers from 2025, as selected by the journal's editors info.cell.com/collection-r...

IoT-LLM: A framework for enhancing large language model reasoning from real-world sensor data IoT-LLM enables language models to understand and reason about the physical world through real-world sensors. By converting diverse signals, such as motion and electrocardiography data, into meaningful insights, it bridges data and understanding, advancing explainable and human-aware intelligence for future embodied AI.

Online Now: IoT-LLM: A framework for enhancing large language model reasoning from real-world sensor data #datascience

A label masked autoencoder for image-guided segmentation label completion Many segmentation datasets carry gaps or noise in their annotations, which blunts model training. Here, the authors present a label-image fusion approach that learns to fill missing or corrupted regions. By turning imperfect labels into dependable supervision, it upgrades existing datasets and lifts accuracy without fresh hand labeling. The idea offers a simple, scalable approach to maintaining and expanding datasets across benchmarks and application domains.

Online Now: A label masked autoencoder for image-guided segmentation label completion #datascience

Covered by @gizmodo.com gizmodo.com/ai-image-gen...

Online Now: Autonomous language-image generation loops converge to generic visual motifs #datascience

AI’s water and electricity use soars in 2025 It’s guzzling up even more water than expected.

Check out the coverage @theverge.com www.theverge.com/news/845831/...

The carbon and water footprints of data centers and what this could mean for artificial intelligence Company-wide metrics from the environmental disclosure of data center operators suggest that AI systems may have a carbon footprint equivalent to that of New York City in 2025, while their water footprint could be in the range of the global annual consumption of bottled water. Further disclosures from data center operators are urgently required to improve the accuracy of these estimates and to responsibly manage the growing environmental impact of AI systems.

Online Now: The carbon and water footprints of data centers and what this could mean for artificial intelligence #datascience

Online Now: Mainzelliste: Ten years of pseudonymization, record linkage, and informed consent management #datascience

On the cover: The image, related to the work by Fernandez Bonet et al., shows a series of spatially coherent networks, explored with breadth-first search to differing depths. The emerging predictable scaling pattern revealed by the progression is indicative of geometric consistency, and this feature can be detected in networks as an indication of how “spatial” a network is. Image credit to Ian Hoffecker and David Fernandez Bonet.

Our December issue is live!
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On the cover this month is an image highlighting the work by David Fernandez Bonet and co-authors, which introduces a set of geometry-based metrics for assessing the quality of DNA barcode networks
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Node persistence from topological data analysis reveals changes in brain functional connectivity This study employs persistent homology to investigate alterations in brain functional connectivity associated with healthy aging and autism spectrum disorder (ASD). Node persistence, a scalable local measure based on persistent homology introduced in this study, identifies brain regions linked to these conditions, including those with clinical evidence from non-invasive brain stimulation.

Online Now: Node persistence from topological data analysis reveals changes in brain functional connectivity #datascience

A self-supervised framework for emphysema anomaly detection and staging in computed tomography scans This study introduces a self-supervised learning framework that detects and stages emphysema from non-emphysema chest CT scans without manual labels. By modeling from non-emphysema lungs and using knowledge-guided synthetic patterns, the approach enables accurate, interpretable, and scalable assessment of emphysema in clinical imaging practice.

Online Now: A self-supervised framework for emphysema anomaly detection and staging in computed tomography scans #datascience

Spatial coherence in DNA barcode networks This study introduces spatial coherence, a way to check whether DNA barcode networks follow conventional geometry rules. Three geometry tests detect network quality, help with filtering corrections, and improve sequencing-based microscopy reconstructions in both simulations and published datasets.

Online Now: Spatial coherence in DNA barcode networks #datascience

Is AI overhyped? In this People of Data, we asked five researchers, including three members of the journal’s advisory board, whether they feel AI technologies are currently overhyped. Their responses reveal both optim...

Really enjoyed this Q&A @cp-patterns.bsky.social with five AI researchers, asking whether AI is overhyped. www.cell.com/patterns/ful...

Leveraging protein language models and a scoring function for indel characterization and transfer learning Insertions and deletions (indels) are difficult to study because they change protein sequence length, limiting existing tools. Here, IndeLLM is presented: a framework that leverages protein language models to assess indel effects in a more interpretable and generalizable way. The method maps indel impact to protein regions, improves predictive accuracy with a Siamese network, and provides guidelines for transfer learning. IndeLLM offers new opportunities for indel annotation and disease-related variant analysis.

Online Now: Leveraging protein language models and a scoring function for indel characterization and transfer learning #datascience

Confidence-weighted integration of human and machine judgments for superior decision-making When AI surpasses human performance, what can humans offer? We demonstrate that the performance of teams increases by integrating human judgments with those of machines. Integration is achieved by a straightforward regression approach that combines team members' confidence-weighted judgments.

Online Now: Confidence-weighted integration of human and machine judgments for superior decision-making #datascience

Predictive remapping and allocentric coding as consequences of energy efficiency in recurrent neural network models of active vision This study gives an example of how complex computations in neural networks can emerge from simple physical principles. Training a model to optimize internal energy efficiency while performing eye movements suffices for predictive remapping to emerge. The model learns to translate eye movements into an allocentric reference frame. Based on this reference frame, it learns to predict and inhibit the next fixation.

Online Now: Predictive remapping and allocentric coding as consequences of energy efficiency in recurrent neural network models of active vision #datascience

I've received some questions recently about the kinds of literature reviews we consider at @cp-patterns.bsky.social. Given also changes at the CS section of arXiv (blog.arxiv.org/2025/10/31/a...), I thought it would be timely to share some tips on submitting review papers to the journal. 1/n

Dignity, properly used, could be a useful construct in AI ethics Rueda et al. argue that the concept of dignity is problematic for AI ethics due to its complexity, ambiguity, and biased usage. While agreeing on many points, we propose that adding the necessary prec...

Great paper here from Cait Lamberton and colleagues replying to our previous publication in @cp-patterns.bsky.social

Dignity, properly used, could be a useful construct in AI ethics
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Is #AI overhyped? We asked five researchers, including three from @cp-patterns.bsky.social's Advisory Board. Here's what they thought:
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These two papers are examples of our commitment to covering research in non-English AI and natural language processing. For more, see our 2023 editorial
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Tucano: Advancing neural text generation for Portuguese Recent natural language processing (NLP) advances have favored high-resource languages while leaving many others underrepresented. This imbalance challenges global AI inclusivity. Addressing it requir...

Also in this issue, is a new resource for advancing natural language processing AI tools for Portuguese
www.cell.com/patterns/ful...

A network of nodes and edges encircles the globe, reflecting the worldwide reach of natural language processing as it spreads across languages and regions. From this global mesh, Greek letters emerge. When viewed horizontally, they are mirrored onto a continuously evolving background, suggesting the transformation of language technologies over time. The image highlights the work by Bakagianni et al. in this month’s issue, which offers a perspective on how machine learning for language has expanded dramatically across the world, affecting many languages, and captured here through the lens of Greek. Cover design by Smart Graphics, Ntina Gkanti. Image credit: Artistdesign.13 (shutterstock.com).

Our November issue is now live!
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On the cover this month is a colorful image highlighting the work by Bakagianni et al. that surveys natural language processing capabilities and tools for the Greek language
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New paper out! 🚨
“Assessing the adoption of the FAIR principles in Italian environmental research infrastructures”
We explore how 14 RIs are putting #FAIRdata into practice — key to building more open, reusable, and connected environmental science.
🧪 🌊 🧬 🪨

Contrastive learning enables epitope overlap predictions for targeted antibody discovery Antibodies are important for human health, but their diversity makes computational prediction of their binding properties challenging. Through contrastive fine-tuning of antibody language models on millions of antibody pairs, the authors enable accurate prediction of epitope overlap even among sequence-diverse antibodies, providing powerful new tools for therapeutic antibody discovery.

Online Now: Contrastive learning enables epitope overlap predictions for targeted antibody discovery #datascience

Assessing the adoption of the FAIR principles in Italian environmental research infrastructures The authors analyze how 14 environmental research infrastructures (RIs) implement the FAIR (Findable, Accessible, Interoperable, and Reusable) principles in Italy. The study uncovers diverse practices across the atmosphere, marine, biosphere, and geosphere subdomains. Results reveal not only strong heterogeneity but also emerging convergence, particularly in the marine subdomain. By highlighting transferable FAIR strategies, this work can guide other scientific communities interested in FAIR compliance and support the development of new digital ecosystems within and beyond Italy.

Online Now: Assessing the adoption of the FAIR principles in Italian environmental research infrastructures #datascience

Three-factor learning in spiking neural networks: An overview of methods and trends from a machine learning perspective This perspective surveys three-factor learning, a key mechanism for brain-inspired spiking neural networks. Adding a third, global signal, analogous to neuromodulators, allows these AI systems to learn and adapt more effectively. This overview connects recent advances in neuroscience and machine learning, outlining a path toward more powerful AI.

Online Now: Three-factor learning in spiking neural networks: An overview of methods and trends from a machine learning perspective #datascience

Sustainability challenges demand social science insights We live in an era of escalating sustainability challenges, such as exceeding the global 1.5°C threshold in 2024, the collapse of talks to address the widespread plastic crisis, and increasing wildfire...

In this month’s editorial, led by our social science editor Lu Liu, we highlight the importance of social sciences for sustainability. Social science has too often been seen as a soft science, a nice-to-have element or a useful add-on rather than a fundamental pillar

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Are individuals who are positive about artificial intelligence also more unsure? As artificial intelligence matures, the impact it might have on how society functions is being actively pondered. In this opinion, through uniform-binomial mixtures, the author sheds quantitative ligh...

Are individuals who are positive about artificial intelligence also more unsure?: Applications of uniform-binomial mixed densities. Patterns www.cell.com/patterns/ful...