Causal mediation analysis provides techniques for defining and estimating effects that may be endowed with mechanistic interpretations. With many scientific investigations seeking to address mechanistic questions, causal direct and indirect effects have garnered much attention. The natural direct and indirect effects, the most widely used among such causal mediation estimands, are limited in their practical utility due to stringent identification requirements. Accordingly, considerable effort has been invested in developing alternative direct and indirect effect decompositions with relaxed identification requirements. Such efforts often yield effect definitions with nuanced and challenging interpretations. By contrast, relatively limited attention has been paid to relaxing the identification assumptions of the natural direct and indirect effects. Motivated by a secondary aim of a recent non-randomized vaccine prospective cohort study (NCT05168813), we present a set of relaxed conditions under which the natural direct effect is identifiable in spite of unobserved baseline confounding of the exposure-mediator pathway; we use this result to investigate the effect mediated by putative immune correlates of protection. Relaxing the commonly used but restrictive cross-world counterfactual independence assumption, we discuss strategies for evaluating the natural direct effect in non-randomized settings that arise in the analysis of vaccine studies. We revisit prior studies of semi-parametric efficiency theory to demonstrate the construction of flexible, multiply robust estimators of the natural direct effect and discuss efficient estimation strategies that do not place restrictive modeling assumptions on nuisance functions.

The Download: an exclusive chat with Jim O'Neill, and the surprising truth about heists Over the past year, Jim O'Neill has become one of the most powerful people in public health. As the US deputy health secretary, he holds two roles at the top of the country's federal health and science agencies. He oversees a department with a budget of over a trillion dollars. And he signed the decision memorandum on the US's deeply controversial new vaccine schedule. In an exclusive interview with earlier this month, O'Neill described his plans to increase human healthspan through longevity-focused research supported by ARPA-H, a federal agency dedicated to biomedical breakthroughs. Fellow longevity enthusiasts said they hope he will bring attention and funding to their cause.

The effectiveness of existing defenses is susceptible to the latest model poisoning attacks, leading to a decrease in prediction accuracy.

Experts worry it will be used to further Robert F. Kennedy Jr.'s anti-vaccine agenda. Get your news from a source that's not owned and controlled by oligarchs. The US Department of Health and Human Services (HHS) is developing a generative artificial intelligence tool to find patterns across data reported to a national vaccine monitoring database and to generate hypotheses on the negative effects of vaccines, according to an inventory released last week of all use cases the agency had for AI in 2025. The tool has not yet been deployed, according to the HHS document, and an AI inventory report from the previous year shows that it has been in development since late 2023. But experts worry that the predictions it generates could be used by HHS secretary Robert F. Kennedy Jr. to further his anti-vaccine agenda.

Experts worry Robert F. Kennedy Jr.'s Health Department will use an internal AI tool to analyze vaccine injury claims in a way that furthers his anti-vaccine agenda. The US Department of Health and Human Services is developing a generative artificial intelligence tool to find patterns across data reported to a national vaccine monitoring database and to generate hypotheses on the negative effects of vaccines, according to an inventory released last week of all use cases the agency had for AI in 2025. The tool has not yet been deployed, according to the HHS document, and an AI inventory report from the previous year shows that it has been in development since late 2023. But experts worry that the predictions it generates could be used by Health and Human Services secretary Robert F. Kennedy Jr. to further his anti-vaccine agenda. A long-standing vaccine critic, Kenedy has upended the childhood vaccination schedule in his year in office, removing several shots from a list of recommended immunizations for all children, including those for Covid-19, influenza, hepatitis A and B, meningococcal disease, rotavirus, and respiratory syncytial virus, or RSV.

RFK's Overhauled Autism Committee Is Even Worse Than It Looks Kennedy has stacked another HHS panel with his fellow travelers in the anti-vaccine and pseudoscience world. Get your news from a source that's not owned and controlled by oligarchs. Last April, Health and Human Services Secretary Robert F. Kennedy, Jr. promised that his agency would find the cause of autism "by September." That didn't pan out, but this week he appears to be trying again--by stacking a decades-old committee devoted to "innovations in autism research, diagnosis, treatment, and prevention" with his friends and fellow travelers in the anti-vaccine and pseudoscience world. Much like the Centers for Disease Control and Prevention's Advisory Committee on Immunization Practices, which Kennedy overhauled last fall with a full slate of new appointees after firing all the old members, he filled the Interagency Autism Coordinating Committee (IACC), which was first established in 2000 to help set the federal agenda for autism research, with Kennedy's allies in the anti-vaccine movement.

The new paradigm of fine-tuning-as-a-service introduces a new attack surface for Large Language Models (LLMs): a few harmful data uploaded by users can easily trick the fine-tuning to produce an alignment-broken model. We conduct an empirical analysis and uncovera \textit{harmful embedding drift} phenomenon, showing a probable cause of the alignment-broken effect. Inspired by our findings, we propose Vaccine, a perturbation-aware alignment technique to mitigate the security risk of users fine-tuning. The core idea of Vaccine is to produce invariant hidden embeddings by progressively adding crafted perturbation to them in the alignment phase. This enables the embeddings to withstand harmful perturbation from un-sanitized user data in the fine-tuning phase. Our results on open source mainstream LLMs (e.g., Llama2, Opt, Vicuna) demonstrate that Vaccine can boost the robustness of alignment against harmful prompts induced embedding drift while reserving reasoning ability towards benign prompts.

AI-based molecule generation provides a promising approach to a large area of biomedical sciences and engineering, such as antibody design, hydrolase engineering, or vaccine development. Because the molecules are governed by physical laws, a key challenge is to incorporate prior information into the training procedure to generate high-quality and realistic molecules. We propose a simple and novel approach to steer the training of diffusion-based generative models with physical and statistics prior information. This is achieved by constructing physically informed diffusion bridges, stochastic processes that guarantee to yield a given observation at the fixed terminal time. We develop a Lyapunov function based method to construct and determine bridges, and propose a number of proposals of informative prior bridges for both high-quality molecule generation and uniformity-promoted 3D point cloud generation. With comprehensive experiments, we show that our method provides a powerful approach to the 3D generation task, yielding molecule structures with better quality and stability scores and more uniformly distributed point clouds of high qualities.