Materials discovery and design are essential for advancing technology across various industries by enabling the development of application-specific materials. Recent research has leveraged Large Language Models (LLMs) to accelerate this process. We explore the potential of LLMs to generate viable hypotheses that, once validated, can expedite materials discovery. Collaborating with materials science experts, we curated a novel dataset from recent journal publications, featuring real-world goals, constraints, and methods for designing real-world applications. Using this dataset, we test LLM-based agents that generate hypotheses for achieving given goals under specific constraints. To assess the relevance and quality of these hypotheses, we propose a novel scalable evaluation metric that emulates the process a materials scientist would use to evaluate a hypothesis critically. Our curated dataset, proposed method, and evaluation framework aim to advance future research in accelerating materials discovery and design with LLMs.

Live-cell imaging of multiple subcellular structures is essential for understanding subcellular dynamics. However, the conventional multi-color sequential fluorescence microscopy suffers from significant imaging delays and limited number of subcellular structure separate labeling, resulting in substantial limitations for real-time live-cell research applications. Here, we present the Adaptive Explainable Multi-Structure Network (AEMS-Net), a deep-learning framework that enables simultaneous prediction of two subcellular structures from a single image. The model normalizes staining intensity and prioritizes critical image features by integrating attention mechanisms and brightness adaptation layers. Leveraging the Kolmogorov-Arnold representation theorem, our model decomposes learned features into interpretable univariate functions, enhancing the explainability of complex subcellular morphologies. We demonstrate that AEMS-Net allows real-time recording of interactions between mitochondria and microtubules, requiring only half the conventional sequential-channel imaging procedures. Notably, this approach achieves over 30% improvement in imaging quality compared to traditional deep learning methods, establishing a new paradigm for long-term, interpretable live-cell imaging that advances the ability to explore subcellular dynamics.

Advances in microscopy imaging enable researchers to visualize structures at the nanoscale level thereby unraveling intricate details of biological organization. However, challenges such as image noise, photobleaching of fluorophores, and low tolerability of biological samples to high light doses remain, restricting temporal resolutions and experiment durations. Reduced laser doses enable longer measurements at the cost of lower resolution and increased noise, which hinders accurate downstream analyses. Here we train a denoising diffusion probabilistic model (DDPM) to predict high-resolution images by conditioning the model on low-resolution information. Additionally, the probabilistic aspect of the DDPM allows for repeated generation of images that tend to further increase the signal-to-noise ratio. We show that our model achieves a performance that is better or similar to the previously best-performing methods, across four highly diverse datasets. Importantly, while any of the previous methods show competitive performance for some, but not all datasets, our method consistently achieves high performance across all four data sets, suggesting high generalizability.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. What do Lady Gaga, Barack Obama, Bill Gates, Paul McCartney and Justin Bieber have in common with Ronald Reagan, Jimi Hendrix, Judy Garland, Fidel Castro and David Bowie? They are all left-handed, a trait shared by roughly 10% of people. But why are some people left-handed while most are righties?

Work related musculoskeletal disorders (WMSDs) are often caused by repetitive lifting, making them a significant concern in occupational health. Although wearable assist devices have become the norm for mitigating the risk of back pain, most spinal assist devices still possess a partially rigid structure that impacts the user comfort and flexibility. This paper addresses this issue by presenting a smart textile actuated spine assistance robotic exosuit (SARE), which can conform to the back seamlessly without impeding the user movement and is incredibly lightweight. The SARE can assist the human erector spinae to complete any action with virtually infinite degrees of freedom. To detect the strain on the spine and to control the smart textile automatically, a soft knitting sensor which utilizes fluid pressure as sensing element is used. The new device is validated experimentally with human subjects where it reduces peak electromyography (EMG) signals of lumbar erector spinae by around 32 percent in loaded and around 22 percent in unloaded conditions. Moreover, the integrated EMG decreased by around 24.2 percent under loaded condition and around 23.6 percent under unloaded condition. In summary, the artificial muscle wearable device represents an anatomical solution to reduce the risk of muscle strain, metabolic energy cost and back pain associated with repetitive lifting tasks.

Virtually all cells use energy and ion-specific membrane pumps to maintain large transmembrane gradients of Na$^+$, K$^+$, Cl$^-$, Mg$^{++}$, and Ca$^{++}$. Although they consume up to 1/3 of a cell's energy budget, the corresponding evolutionary benefit of transmembrane ion gradients remain unclear. Here, we propose that ion gradients enable a dynamic and versatile biological system that acquires, analyzes, and responds to environmental information. We hypothesize environmental signals are transmitted into the cell by ion fluxes along pre-existing gradients through gated ion-specific membrane channels. The consequent changes of cytoplasmic ion concentration can generate a local response and orchestrate global or regional responses through wire-like ion fluxes along pre-existing and self-assembling cytoskeleton to engage the endoplasmic reticulum, mitochondria, and nucleus. Here, we frame our hypothesis through a quasi-physical (Cell-Reservoir) model that treats intra-cellular ion-based information dynamics as a sub-cellular process permitting spatiotemporally resolved cellular response that is also capable of learning complex nonlinear dynamical cellular behavior. We demonstrate the proposed ion dynamics permits rapid dissemination of response to information extrinsic perturbations that is consistent with experimental observations.

Volume electron microscopy is the method of choice for the in-situ interrogation of cellular ultrastructure at the nanometer scale. Recent technical advances have led to a rapid increase in large raw image datasets that require computational strategies for segmentation and spatial analysis. In this protocol, we describe a practical and annotation-efficient pipeline for organelle-specific segmentation, spatial analysis, and visualization of large volume electron microscopy datasets using freely available, user-friendly software tools that can be run on a single standard workstation. We specifically target researchers in the life sciences with limited computational expertise, who face the following tasks within their volume electron microscopy projects: i) How to generate 3D segmentation labels for different types of cell organelles while minimizing manual annotation efforts, ii) how to analyze the spatial interactions between organelle instances, and iii) how to best visualize the 3D segmentation results. To meet these demands we give detailed guidelines for choosing the most efficient segmentation tools for the specific cell organelle. We furthermore provide easily executable components for spatial analysis and 3D rendering and bridge compatibility issues between freely available open-source tools, such that others can replicate our full pipeline starting from a raw dataset up to the final plots and rendered images. We believe that our detailed description can serve as a valuable reference for similar projects requiring special strategies for single- or multiple organelle analysis which can be achieved with computational resources commonly available to single-user setups.

Human consciousness is one of the grand mysteries of our time on earth. How do you know that you are "you"? Does your sense of being aware of yourself come from your mind or is it your body that is creating it? What really happens when you enter an "altered" state of consciousness with the help of some chemical or plant? While you would think this basic enigma of our self-awareness would be at the forefront of scientific inquiry, science does not yet have strong answers to these questions.

The protein tau is implicated in several brain disorders, including Alzheimer's disease, suggesting that it could be a target of therapeutics. However, because it is unclear how the pleiotropic roles of tau lead to neural pathology in different brain diseases, drug development remains challenging. Chang et al. review the possible mechanisms of tau in brain diseases and possible paths forward to improving research and drug development. Science , this issue p. [eabb8255][1] ### BACKGROUND The microtubule-associated protein tau has been implicated in the pathogenesis of Alzheimer’s disease and a range of other neurodegenerative disorders (called “tauopathies”). As the number of people with tauopathies is rising in aging populations across the world, interest in the fundamental biology of this protein and in the development of tau-targeting treatments has been expanding rapidly. Recent insights into the complexity of this intrinsically disordered protein suggest that tau is a worthy but challenging target whose multifaceted nature will likely require a multipronged therapeutic approach. Derived from a single gene by alternative splicing, six major isoforms of tau have been identified in the human brain. In addition, tau is subject to many different posttranslational modifications, further indicating that it may be regulated by multiple processes and may participate in diverse functions. ### ADVANCES Tau is widely presumed to stabilize microtubules. However, the experimental reduction or ablation of tau in vivo does not alter many neural properties and processes that likely depend on microtubules, including neuronal integrity, axonal transport, synapse formation, and complex brain functions. Although tau reduction seems to have minimal effects on otherwise unmanipulated brains, it can prevent or diminish aberrant cell signaling, neural network dysfunctions (e.g., epileptic activity), and behavioral alterations caused by diverse disease processes, which suggests that tau activities are needed for other pathogenic triggers to cause these derangements. In addition to this “enabling bystander” role, tau’s interactions with a large number of other proteins can cause adverse gains of function, which are associated with—and possibly caused by—the formation of abnormal tau structures and assemblies. Because abnormal forms of tau trigger a plethora of pathomechanisms, targeting individual downstream mechanisms may have limited therapeutic impact, unless the relative pathogenic importance of the specific mechanism has been well established in experimental models that allow for conclusive validation of cause-and-effect relationships. Although much attention has focused on the abnormal aggregation of tau in tauopathies and on the ability of tau “seeds” to spread from neuron to neuron, internalization of propagating tau does not appear to impair neuronal survival or brain functions. Moreover, tau reduction prevents or diminishes neural network dysfunction and behavioral abnormalities also in disease models that do not have abnormal tau inclusions, which suggests that there is more to tau than aggregation and propagation. A promising diversification of tau-targeting therapeutic strategies is beginning to address this complexity. Lowering overall tau levels may have the greatest potential, as this strategy bypasses the unresolved questions of which forms of tau and which downstream mechanisms are most detrimental in any given condition. ### OUTLOOK Many efforts to develop better treatments for neurodegenerative diseases have failed, in large part because of an inadequate understanding of disease mechanisms and, perhaps, because too many fundamental knowledge gaps, alternative interpretations of data, and methodological complexities did not receive the attention they deserved. This Review highlights important gaps in the understanding of tau and the methodological advances needed to fill them. It also pinpoints obstacles that could complicate the translation of tau-related scientific discoveries into better therapeutics and offers pragmatic strategies to overcome these challenges. Despite the extraordinary progress that has been made to date, the main physiological functions that tau fulfills in the adult and aging brain remain to be defined. Another critical objective is to develop better experimental models and technologies to rigorously compare different tau species and pathomechanisms, particularly their relative impacts on neuronal functions and survival in vivo. For the development of truly informative biomarkers and effective therapeutics, it will be critical to rigorously differentiate between associations and cause-and-effect relationships. Until the main drivers of neuronal dysfunction and demise have been identified for Alzheimer’s disease and other conditions in which tau has a causal or enabling role, it seems prudent to focus on pragmatic strategies, such as overall tau reduction, while also expanding efforts to further validate the importance of more-specific targets and approaches. Investigational approaches to lower overall tau levels include tau-targeting antisense oligonucleotides, which have advanced into a clinical trial for early Alzheimer’s disease, and the development of small-molecule drugs that can modulate the production or degradation of tau. The most desirable tau-targeting therapeutics would be efficacious across diverse tauopathies, as well as affordable, easy to access, and well tolerated when administered over long periods of time to fragile groups of people who likely take multiple other medications. ![Figure][2] Potential tau pathomechanisms. Developing effective tau-targeting therapeutics will require a better understanding of how exactly tau contributes to Alzheimer’s disease and other disorders of the central nervous system. Potential mechanisms likely fall into the three broad categories shown. However, the relative pathogenic impact and overall importance of individual mechanisms have yet to be defined in truly disease-relevant contexts and may differ among diseases and even patients. The blue box on the right indicates tau activities that do not directly mediate but indirectly promote or facilitate pathogenic processes. Several lines of evidence implicate the protein tau in the pathogenesis of multiple brain disorders, including Alzheimer’s disease, other neurodegenerative conditions, autism, and epilepsy. Tau is abundant in neurons and interacts with microtubules, but its main functions in the brain remain to be defined. These functions may involve the regulation of signaling pathways relevant to diverse biological processes. Informative disease models have revealed a plethora of abnormal tau species and mechanisms that might contribute to neuronal dysfunction and loss, but the relative importance of their respective contributions is uncertain. This knowledge gap poses major obstacles to the development of truly impactful therapeutic strategies. The current expansion and intensification of efforts to translate mechanistic insights into tau-related therapeutics should address this issue and could deliver better treatments for a host of devastating conditions. [1]: /lookup/doi/10.1126/science.abb8255 [2]: pending:yes

We define a novel type of ensemble Graph Convolutional Network (GCN) model. Using optimized linear projection operators to map between spatial scales of graph, this ensemble model learns to aggregate information from each scale for its final prediction. We calculate these linear projection operators as the infima of an objective function relating the structure matrices used for each GCN. Equipped with these projections, our model (a Graph Prolongation-Convolutional Network) outperforms other GCN ensemble models at predicting the potential energy of monomer subunits in a coarse-grained mechanochemical simulation of microtubule bending. We demonstrate these performance gains by measuring an estimate of the FLOPs spent to train each model, as well as wall-clock time. Because our model learns at multiple scales, it is possible to train at each scale according to a predetermined schedule of coarse vs. fine training. We examine several such schedules adapted from the Algebraic Multigrid (AMG) literature, and quantify the computational benefit of each. Finally, we demonstrate how under certain assumptions, our graph prolongation layers may be decomposed into a matrix outer product of smaller GCN operations.