Accurately predicting the state of health for sodium-ion batteries is crucial for managing battery modules, playing a vital role in ensuring operational safety. However, highly accurate models available thus far are rare due to a lack of aging data for sodium-ion batteries. In this study, we experimentally collected 53 single cells at four temperatures (0, 25, 35, and 45 {\deg}C), along with two battery modules in the lab. By utilizing the charging profiles, we were able to predict the SOC, capacity, and SOH simultaneously. This was achieved by designing a new framework that integrates the neural ordinary differential equation and 2D convolutional neural networks, using the partial charging profile as input. The charging profile is partitioned into segments, and each segment is fed into the network to output the SOC. For capacity and SOH prediction, we first aggregated the extracted features corresponding to segments from one cycle, after which an embedding block for temperature is concatenated for the final prediction. This novel approach eliminates the issue of multiple outputs for a single target. Our model demonstrated an $R^2$ accuracy of 0.998 for SOC and 0.997 for SOH across single cells at various temperatures. Furthermore, the trained model can be employed to predict single cells at temperatures outside the training set and battery modules with different capacity and current levels. The results presented here highlight the high accuracy of our model and its capability to predict multiple targets simultaneously using a partial charging profile.

Developing computer vision for high-content screening is challenging due to various sources of distribution-shift caused by changes in experimental conditions, perturbagens, and fluorescent markers. The impact of different sources of distribution-shift are confounded in typical evaluations of models based on transfer learning, which limits interpretations of how changes to model design and training affect generalisation. We propose an evaluation scheme that isolates sources of distribution-shift using the JUMP-CP dataset, allowing researchers to evaluate generalisation with respect to specific sources of distribution-shift. We then present a channel-agnostic masked autoencoder Campfire which, via a shared decoder for all channels, scales effectively to datasets containing many different fluorescent markers, and show that it generalises to out-of-distribution experimental batches, perturbagens, and fluorescent markers, and also demonstrates successful transfer learning from one cell type to another. Phenotypic drug discovery, in which cells or animal models are subject to a perturbation and monitored for a desired change in phenotype, has seen a resurgence due to its success in finding compounds that meet regulatory approval (Zheng et al., 2013; Boutros et al., 2015; Zanella et al., 2010). To quantify the effect of perturbations, it is common to use high content screening (HCS), a method in which batches of cells are stimulated with thousands of compounds in parallel, and multiple markers of changes in phenotype are measured simultaneously. In comparison with modalities based on sequencing technologies, imaging is more time-and cost-effective at scale and has been the main modality of HCS data. This necessitated the development of automated pipelines that extract biologically relevant features from cellular imaging data. Typically, this has involved traditional methods based on cell-segmentation and feature extraction and has been applied in various applications including protein sub-cellular localisation (P arnamaa & Parts, 2017), quantitative structure-activity relationship modelling (Nguyen et al., 2023) and identifying mechanism of action (D urr & Sick, 2016; Wong et al., 2023) and markers of drug resistance (Kelley et al., 2023).

This image shows the three-dimensional genome structures of several chromosomes reported in a Dip-C study, which were used to train the new ChromoGen model. Every cell in your body contains the same genetic sequence, yet each cell expresses only a subset of those genes. These cell-specific gene expression patterns, which ensure that a brain cell is different from a skin cell, are partly determined by the three-dimensional structure of the genetic material, which controls the accessibility of each gene. MIT chemists have now come up with a new way to determine those 3D genome structures, using generative artificial intelligence. Their technique can predict thousands of structures in just minutes, making it much speedier than existing experimental methods for analyzing the structures.

Large language models excel at interpreting complex natural language instructions, enabling them to perform a wide range of tasks. In the life sciences, single-cell RNA sequencing (scRNA-seq) data serves as the "language of cellular biology", capturing intricate gene expression patterns at the single-cell level. However, interacting with this "language" through conventional tools is often inefficient and unintuitive, posing challenges for researchers. To address these limitations, we present InstructCell, a multi-modal AI copilot that leverages natural language as a medium for more direct and flexible single-cell analysis. We construct a comprehensive multi-modal instruction dataset that pairs text-based instructions with scRNA-seq profiles from diverse tissues and species. Building on this, we develop a multi-modal cell language architecture capable of simultaneously interpreting and processing both modalities. InstructCell empowers researchers to accomplish critical tasks--such as cell type annotation, conditional pseudo-cell generation, and drug sensitivity prediction--using straightforward natural language commands. Extensive evaluations demonstrate that InstructCell consistently meets or exceeds the performance of existing single-cell foundation models, while adapting to diverse experimental conditions. More importantly, InstructCell provides an accessible and intuitive tool for exploring complex single-cell data, lowering technical barriers and enabling deeper biological insights.

Recent developments in single-cell omics technologies have enabled the quantification of molecular profiles in individual cells at an unparalleled resolution. Deep learning, a rapidly evolving sub-field of machine learning, has instilled a significant interest in single-cell omics research due to its remarkable success in analysing heterogeneous high-dimensional single-cell omics data. Nevertheless, the inherent multi-layer nonlinear architecture of deep learning models often makes them `black boxes' as the reasoning behind predictions is often unknown and not transparent to the user. This has stimulated an increasing body of research for addressing the lack of interpretability in deep learning models, especially in single-cell omics data analyses, where the identification and understanding of molecular regulators are crucial for interpreting model predictions and directing downstream experimental validations. In this work, we introduce the basics of single-cell omics technologies and the concept of interpretable deep learning. This is followed by a review of the recent interpretable deep learning models applied to various single-cell omics research. Lastly, we highlight the current limitations and discuss potential future directions. We anticipate this review to bring together the single-cell and machine learning research communities to foster future development and application of interpretable deep learning in single-cell omics research.

Recent advances such as Open AI's GPT-4 chatbot have awakened the world to how sophisticated artificial intelligence has become and how rapidly the field is advancing. Could this powerful new technology help save the world? We asked five leading AI researchers to lay out their best-case scenarios. In 1999, I predicted that computers would pass the Turing test [and be indistinguishable from human beings] by 2029. Stanford university found that alarming, and organised an international conference – experts came from all over the world.

In single cells of the cat striate cortex, lateral inhibition across orienta(cid:173) tion and/or spatial frequency is found to enhance pre-existing biases. A contrast-dependent but spatially non-selective inhibitory component is also found. Stimulation with ascending and descending contrasts reveals the latter as a response hysteresis that is sensitive, powerful and rapid, sug(cid:173) gesting that it is active in day-to-day vision. Both forms of inhibition are not recurrent but are rather network properties. These findings suggest two fundamental inhibitory mechanisms: a global mechanism that limits dynamic range and creates spatial selectivity through thresholding and a local mechanism that specifically refines spatial filter properties.