Intracortical brain-computer interfaces (iBCIs) can restore movement and communication abilities to individuals with paralysis by decoding their intended behavior from neural activity recorded with an implanted device. While this activity yields high-performance decoding over short timescales, neural data are often nonstationary, which can lead to decoder failure if not accounted for. To maintain performance, users must frequently recalibrate decoders, which requires the arduous collection of new neural and behavioral data. Aiming to reduce this burden, several approaches have been developed that either limit recalibration data requirements (few-shot approaches) or eliminate explicit recalibration entirely (zero-shot approaches). However, progress is limited by a lack of standardized datasets and comparison metrics, causing methods to be compared in an ad hoc manner. Here we introduce the FALCON benchmark suite (Few-shot Algorithms for COnsistent Neural decoding) to standardize evaluation of iBCI robustness. FALCON curates five datasets of neural and behavioral data that span movement and communication tasks to focus on behaviors of interest to modern-day iBCIs. Each dataset includes calibration data, optional few-shot recalibration data, and private evaluation data. We implement a flexible evaluation platform which only requires user-submitted code to return behavioral predictions on unseen data.

However, associating small molecules with changes in cell state is challenging.

Drugs For what purpose was the dataset created? that do not have a current FDA-approved label UniTox was created as a unified toxicity dataset (e.g., withdrawn or discontinued drugs) are not across eight types of drug toxicities Each instance is a single drug. For each We generated information across all toxicities for instance, there are eight toxicities, and for each the same set of 2,418 drugs with the same toxicity, there is an LLM-generated summary of methodology of applying LLMs. For each drug, the relevant sections of the drug label, a ternary for each toxicity, we provide an LLM-generated prediction (No/Less/Most), and a binary summary of the relevant portions of the drug prediction (No/Yes). Each instance also provides label, as well as ternary (No/Less/Most) the unique SPL ID, allowing users to find the predictions and binary (No/Yes) predictions for exact text used to generate the instance data. Is there a label or target associated with each Who created the dataset (e.g., which team, instance?

Drug-induced toxicity is one of the leading reasons new drugs fail clinical trials. Machine learning models that predict drug toxicity from molecular structure could help researchers prioritize less toxic drug candidates. However, current toxicity datasets are typically small and limited to a single organ system (e.g., cardio, renal, or liver). Creating these datasets often involved time-intensive expert curation by parsing drug labelling documents that can exceed 100 pages per drug.

Contrastive analysis (CA) refers to the exploration of variations uniquely enriched in a target dataset as compared to a corresponding background dataset generated from sources of variation that are irrelevant to a given task. For example, a biomedical data analyst may wish to find a small set of genes to use as a proxy for variations in genomic data only present among patients with a given disease (target) as opposed to healthy control subjects (background). However, as of yet the problem of feature selection in the CA setting has received little attention from the machine learning community.

Wandercraft's Personal Exoskeleton is about helping people stand tall, connect with others and live life on their own terms. For Caroline Laubach, being a Wandercraft test pilot is about more than just trying out new technology. It's about reclaiming a sense of freedom and connection that many wheelchair users miss. Laubach, a spinal stroke survivor and full-time wheelchair user, has played a key role in demonstrating the personal AI-powered prototype exoskeleton's development, and her experience highlights just how life-changing this device can be. "When I'm in the exoskeleton, I feel more free than I do in my daily life," said Laubach.

The news: A bill that allows clinics to sell unproven treatments has been passed in Montana. Under the legislation, doctors can apply for a license to open an experimental treatment clinic and recommend and sell therapies not approved by the Food and Drug Administration (FDA) to their patients. Why it matters: Once it's signed by the governor, the law will be the most expansive in the country in allowing access to drugs that have not been fully tested. The bill allows for any drug produced in the state to be sold in it, providing it has been through phase I clinical trials--but these trials do not determine if the drug is effective. The big picture: The bill was drafted and lobbied for by people interested in extending human lifespans.