The FDA Says It Didn't Apologize to Supplier Linked to Diarrhea Outbreak, Actually The agency stressed that the July 17 voluntary recall from Taylor Farms still stands. The supplier has been linked to the ongoing cyclospora outbreak. In the middle of a diarrhea shitstorm, the Food and Drug Administration and lettuce supplier Taylor Farms appear to be facing off in a passive aggressive battle of technicalities. On July 17, the California-based company issued a voluntary recall on iceberg lettuce sourced from Central Mexico. The recall came after people across at least five states were sickened with cyclosporiasis, a parasitic infection that causes explosive diarrhea.

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US fast-food chain Taco Bell is removing lettuce from its menu in some states after investigations found it could be linked to an outbreak of explosive diarrhoea caused by a parasite. The decision was taken out of an abundance of caution following discussions with health officials, Taco Bell told the BBC. The US Food and Drug Administration (FDA) says 1,644 people in five states that reported exposure to Taco Bell have been infected by cyclosporiasis, a parasitic infection that spreads through contaminated food or water. Do not eat food items with shredded iceberg lettuce from Mexico served at Taco Bell locations in Indiana, Kentucky, Michigan, Ohio, and West Virginia, the FDA said. No deaths have been reported but 94 people have been hospitalised due to cyclosporiasis infections, which were first detected on 13 May, the FDA added.

Midjourney announces a full-body ultrasound scanner that uses water and sound waves to create body composition maps in about 60 seconds with no radiation or MRI magnets.

We examine hypothesis testing within a principal-agent framework, where a strategic agent, holding private beliefs about the effectiveness of a product, submits data to a principal who decides on approval. The principal employs a hypothesis testing rule, aiming to pick a p-value threshold that balances false positives and false negatives while anticipating the agent's incentive to maximize expected profitability. Building on prior work, we develop a game-theoretic model that captures how the agent's participation and reporting behavior respond to the principal's statistical decision rule. Despite the complexity of the interaction, we show that the principal's errors exhibit clear monotonic behavior when segmented by an efficiently computable critical p-value threshold, leading to an interpretable characterization of their optimal p-value threshold.

Out-of-distribution (OOD) node detection in graphs is a critical yet challenging task. Most existing approaches rely heavily on fine-grained labeled data to obtain a pretrained supervised classifier, inherently assuming the existence of a well-defined pretext classification task. However, when such a task is ill-defined or absent, their applicability becomes severely limited. To overcome this limitation, there is an urgent need to propose a more scalable OOD detection method that is independent of both pretext tasks and label supervision. We harness a new phenomenon called Feature Resonance, focusing on the feature space rather than the label space. We observe that, ideally, during the optimization of known ID samples, unknown ID samples undergo more significant representation changes than OOD samples, even when the model is trained to align arbitrary targets. The rationale behind it is that even without gold labels, the local manifold may still exhibit smooth resonance. Based on this, we further develop a novel graph OOD framework, dubbed Resonance-based Separation and Learning (RSL), which comprises two core modules: (i)-a more practical micro-level proxy of feature resonance that measures the movement of feature vectors in one training step.

The ViT-L/14 encoder processes images into visual tokens, which the LLaMA-2-7B decoder converts into text.

This position paper argues that post-deployment monitoring in clinical AI is underdeveloped and proposes statistically valid and label-efficient testing frameworks as a principled foundation for ensuring reliability and safety in real-world deployment. A recent review found that only 9% of FDA-registered AI-based healthcare tools include a post-deployment surveillance plan [1]. Existing monitoring approaches are often manual, sporadic, and reactive, making them ill-suited for the dynamic environments in which clinical models operate. We contend that post-deployment monitoring should be grounded in label-efficient and statistically valid testing frameworks, offering a principled alternative to current practices. We use the term "statistically valid" to refer to methods that provide explicit guarantees on error rates (e.g., Type I/II error), enable formal inference under pre-defined assumptions, and support reproducibility--features that align with regulatory requirements. Specifically, we propose that the detection of changes in the data and model performance degradation should be framed as distinct statistical hypothesis testing problems. Grounding monitoring in statistical rigor ensures a reproducible and scientifically sound basis for maintaining the reliability of clinical AI systems. Importantly, it also opens new research directions for the technical community--spanning theory, methods, and tools for statistically principled detection, attribution, and mitigation of post-deployment model failures in real-world settings.

Recent studies in Machine Learning (ML) for biological research focus on investigating molecular properties to accelerate drug discovery. However, limited labeled molecular data often hampers the performance of ML models. A common strategy to mitigate data scarcity is leveraging auxiliary learning tasks to provide additional supervision, but selecting effective auxiliary tasks requires substantial domain expertise and manual effort, and their inclusion does not always guarantee performance gains. To overcome these challenges, we introduce Automatic Auxiliary Task Selection (AUTAUT), a fully automated framework that seamlessly retrieves auxiliary tasks using large language models and adaptively integrates them through a novel gradient alignment weighting mechanism. By automatically emphasizing auxiliary tasks aligned with the primary objective, AUTAUT significantly enhances predictive accuracy while reducing negative impacts from irrelevant tasks. Extensive evaluations demonstrate that AUTAUT outperforms 10 auxiliary task-based approaches and 18 advanced molecular property prediction models.

This is health care's Uber moment. Every knowledge-based profession may one day reach the point when AI outperforms the human experts. In medicine, that day appeared to come in April. A group of primarily Harvard and Stanford researchers announced the results of a study that pitted ChatGPT against hundreds of physicians in a diagnostic obstacle course involving written medical mysteries and information from real-world patients. The bot had won, and the humans weren't entirely happy about it.