At WIRED Health, immunologist Daniel Davis detailed the ways in which new technologies are enabling a better understanding of the human immune system. The immune system operates at a scale scientists are only just beginning to be able to see. That new view could change how diseases like cancer are tackled. Speaking at WIRED Health on April 16, Daniel Davis, an immunologist at Imperial College London, detailed how researchers are using advanced microscopes to uncover previously invisible dynamics in the human immune system, showing that there are multiple processes happening on a "nanoscale" that was previously out of reach. That new view is already reshaping how immunity is understood.

Deadly pincers and tails make them nature's answer to cyborgs. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Paratuthus scorpions' venom is quick-acting, so they do not need to rely as much on their pincers to capture prey. Breakthroughs, discoveries, and DIY tips sent six days a week. Scorpions are optimized hunters, whose skills have been honed through millions of years of evolution.

Plus: OpenAI has ended its exclusive partnership with Microsoft. Elon Musk and Sam Altman are going to court over OpenAI's future Ahead of OpenAI's IPO, the court could rule on whether the company can exist as a for-profit enterprise. It could even oust its leadership. Musk, an OpenAI co-founder, claims he was deceived into bankrolling the firm under false pretenses. Find out how the trial could upend the global AI race . In a celebrated episode, a community of gnomes sneak out at night to steal underpants.

Environment Animals Wildlife Bats People keep trespassing near cave filled with bats infected by Ebola's cousin The Marburg virus disease can reach a nearly 90 percent mortality rate. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Epidemiologists believe the Marburg virus disease is primarily transmitted to humans through Egyptian fruit bats. Breakthroughs, discoveries, and DIY tips sent six days a week. You do not want to contract Marburg virus disease (MVD).

Japanese scientist Makoto Kuroda faces felony charges after allegedly poisoning a colleague's water bottle with toxic chemicals at UW-Madison's Influenza Research Institute.

This paper investigates the off-policy evaluation (OPE) problem from a distributional perspective. Rather than focusing solely on the expectation of the total return, as in most existing OPE methods, we aim to estimate the entire return distribution. To this end, we introduce a quantile-based approach for OPE using deep quantile process regression, presenting a novel algorithm called Deep Quantile Process regression-based Off-Policy Evaluation (DQPOPE). We provide new theoretical insights into the deep quantile process regression technique, extending existing approaches that estimate discrete quantiles to estimate a continuous quantile function. A key contribution of our work is the rigorous sample complexity analysis for distributional OPE with deep neural networks, bridging theoretical analysis with practical algorithmic implementations. We show that DQPOPE achieves statistical advantages by estimating the full return distribution using the same sample size required to estimate a single policy value using conventional methods. Empirical studies further show that DQPOPE provides significantly more precise and robust policy value estimates than standard methods, thereby enhancing the practical applicability and effectiveness of distributional reinforcement learning approaches.

Selection bias arises when the probability that an observation enters a dataset depends on variables related to the quantities of interest, leading to systematic distortions in estimation and uncertainty quantification. For example, in epidemiological or survey settings, individuals with certain outcomes may be more likely to be included, resulting in biased prevalence estimates with potentially substantial downstream impact. Classical corrections, such as inverse-probability weighting or explicit likelihood-based models of the selection process, rely on tractable likelihoods, which limits their applicability in complex stochastic models with latent dynamics or high-dimensional structure. Simulation-based inference enables Bayesian analysis without tractable likelihoods but typically assumes missingness at random and thus fails when selection depends on unobserved outcomes or covariates. Here, we develop a bias-aware simulation-based inference framework that explicitly incorporates selection into neural posterior estimation. By embedding the selection mechanism directly into the generative simulator, the approach enables amortized Bayesian inference without requiring tractable likelihoods. This recasting of selection bias as part of the simulation process allows us to both obtain debiased estimates and explicitly test for the presence of bias. The framework integrates diagnostics to detect discrepancies between simulated and observed data and to assess posterior calibration. The method recovers well-calibrated posterior distributions across three statistical applications with diverse selection mechanisms, including settings in which likelihood-based approaches yield biased estimates. These results recast the correction of selection bias as a simulation problem and establish simulation-based inference as a practical and testable strategy for parameter estimation under selection bias.

Technology Engineering Woman builds EpiPen cannon, because why not? It's not the most efficient way to deliver the life-saving medicine, but it's definitely the most entertaining. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The bolt-action device can hold up to four pens. Breakthroughs, discoveries, and DIY tips sent six days a week.

Data-driven operations management often relies on parameters estimated from costly human-generated labels. Recent advances in large language models (LLMs) and other AI systems offer inexpensive auxiliary data, but introduce a new challenge: AI outputs are not direct observations of the target outcomes, but could involve high-dimensional representations with complex and unknown relationships to human labels. Conventional methods leverage AI predictions as direct proxies for true labels, which can be inefficient or unreliable when this relationship is weak or misspecified. We propose Generative Augmented Inference (GAI), a general framework that incorporates AI-generated outputs as informative features for estimating models of human-labeled outcomes. GAI uses an orthogonal moment construction that enables consistent estimation and valid inference with flexible, nonparametric relationship between LLM-generated outputs and human labels. We establish asymptotic normality and show a "safe default" property: relative to human-data-only estimators, GAI weakly improves estimation efficiency under arbitrary auxiliary signals and yields strict gains whenever the auxiliary information is predictive. Empirically, GAI outperforms benchmarks across diverse settings. In conjoint analysis with weak auxiliary signals, GAI reduces estimation error by about 50% and lowers human labeling requirements by over 75%. In retail pricing, where all methods access the same auxiliary inputs, GAI consistently outperforms alternative estimators, highlighting the value of its construction rather than differences in information. In health insurance choice, it cuts labeling requirements by over 90% while maintaining decision accuracy. Across applications, GAI improves confidence interval coverage without inflating width. Overall, GAI provides a principled and scalable approach to integrating AI-generated information.

Antibody lead optimization is inherently a multi-objective challenge in drug discovery. Achieving a balance between different drug-like properties is crucial for the development of viable candidates, and this search becomes exponentially challenging as desired properties grow. The ever-growing zoo of sophisticated in silico tools for predicting antibody properties calls for an efficient joint optimization procedure to overcome resource-intensive sequential filtering pipelines. We present BOAT, a versatile Bayesian optimization framework for multi-property antibody engineering. Our `plug-and-play' framework couples uncertainty-aware surrogate modeling with a genetic algorithm to jointly optimize various predicted antibody traits while enabling efficient exploration of sequence space. Through systematic benchmarking against genetic algorithms and newer generative learning approaches, we demonstrate competitive performance with state-of-the-art methods for multi-objective protein optimization. We identify clear regimes where surrogate-driven optimization outperforms expensive generative approaches and establish practical limits imposed by sequence dimensionality and oracle costs.