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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.

On today's, we unpack OpenAI's ongoing drama, both legal and reputational, and whether these developments could further hurt the company--particularly in its fight against Anthropic. This week on, the team discusses the details behind Apple suing OpenAI over alleged stolen hardware secrets. And the company's headaches don't stop there--a group of OpenAI employees just started a super PAC to advocate for stronger AI guardrails. Plus, New York's first-in-the-nation data center moratorium draws Donald Trump's ire, DOGE stonewalls FOIA requests on its AI use at HUD, and WIRED's Emily Mullin explains the cyclosporiasis outbreak spreading across more than 30 states. DOGE Used AI for Housing Policy. The Government Won't Say How Write to us at [email protected] . You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link . Last Friday, Apple sued the company basically alleging that OpenAI has been stealing confidential hardware secrets. And now this week, WIRED learned that some OpenAI employees are launching a super PAC to push for AI guardrails. We'll get into all those details and whether these developments could further hurt OpenAI, particularly in its fight against Anthropic. We'll dive into what that means exactly and whether this move could pave the way for other states to follow suit. And we're going to check in on the disease sweeping the nation, cyclosporiasis, which is causing turbo diarrhea across the country . So basically what happened was last Friday, Apple filed a lawsuit against OpenAI for allegedly stealing information like unreleased iPhone parts and prototypes, confidential designs, documents about secret projects. That accusation sucks for OpenAI. It's pretty intense, but it gets really messy because Apple is basically saying that this theft, this alleged theft, mostly happened through former employees.

Bryan Johnson, the biohacking and longevity guru, reveals he has autoimmune gastritis, a condition attacking his stomach's parietal cells and causing B12 and iron deficiency.

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Omit references to the index or number of the sub-images, such as (xx), left, right, etc.3. There might be a common prefix or suffix caption shared among all sub-images at the beginning, end, or within the caption. Please incorporate the prefix or suffix into each sub-image's caption. If one subcaption contains context for multiple other subcaptions, add that context to each of the relevant subcaptions.4. The final output should be in JSON format, with an outer field'subcaptions', with a value that is a list of'subfigure' and'subcaption' dictionaries.5. If a subfigure contains more nested figures, i.e. subfigure (A) contains references to (left) and (right), add a field called "location" that stores the "left" or "right".6. If there are no references to sub-images, give a single subcaption with label "A".User Prompt:You are a research paper processor which splits the captions of figures into sub-captions that correspond with subfigures.System Prompt:"(a) H&E image of a breast tumor tissue. Fluorescently labeled markers superimposed as green color on the H&E image, (b) \u03b2-catenin, (c) pan-keratin, and (d) smooth muscle \u03b1-actin, markers.":{"subcaptions":

Clinical reasoning in medicine is a hypothesis-driven process where physicians refine diagnoses from limited information through targeted history, physical examination, and diagnostic investigations. In contrast, current medical benchmarks for large language models (LLMs) primarily assess knowledge recall through single-turn questions, where complete clinical information is provided upfront. To address this gap, we introduce VivaBench, a multi-turn benchmark that evaluates sequential clinical reasoning in LLM agents. Our dataset comprises 1152 physiciancurated clinical vignettes structured as interactive scenarios that simulate a viva voce examination in medical training, requiring agents to actively probe for relevant findings, select appropriate investigations, and synthesize information across multiple steps to reach a diagnosis. We evaluated several state-of-the-art LLMs and found that while models demonstrate competence in diagnosing conditions within well-described clinical presentations, their performance degrades significantly when required to navigate diagnostic uncertainty. Our analysis identified several failure modes that mirror common issues in clinical practice, including: (1) fixation on initial hypotheses, (2) excessive investigation ordering, (3) premature diagnostic closure, and (4) missing critical conditions. These patterns reveal fundamental limitations in how current LLMs manage uncertainty and gather information sequentially. Through VivaBench, we provide a standardized benchmark for evaluating conversational medical AI systems for real-world clinical decision support. Beyond medical applications, we contribute to the larger corpus of research on agentic AI by demonstrating how sequential reasoning trajectories can diverge in complex decision-making environments.

Motivated by the privacy, sensitivity and sharing limitations of health data, we present a comprehensive pipeline for inference of Bayesian mixture models within a federated learning setting, i.e. when data cannot be fully shared or pooled across compute nodes. We adopt a Consensus Monte Carlo (CMC) approach, in which an MCMC algorithm is run independently within each data silo to estimate local posterior distributions, which are then aggregated to approximate the posterior over the full data. The variational CMC approach of Rabinovich, Angelino and Jordan (2015) [1] frames the aggregation step as a variational inference problem, but their application to mixtures assumes the number of clusters and key mixture parameters to be known. Our main methodological contributions are: (i) an extension of variational CMC to over-fitted Bayesian mixture models that infer the number of clusters and all model parameters, without requiring conjugacy; (ii) novel cluster-matching algorithms suitable for cross-silo settings in which not every cluster appears in each local dataset; (iii) a number of inference strategies for the aggregation step, matched to different federated learning constraints; and (iv) guidelines for choosing among these in practice. A comprehensive simulation study validates the framework and allows us to compare to state-of-the-art federated learning alternatives. Notably, we show that when the composition of local datasets reflects the underlying clustering structure in the data, our approach can recover small clusters with greater accuracy than standard MCMC applied to the pooled data. We illustrate the framework on large-scale electronic health record data, identifying multi-morbidity patterns in a British geriatric population.