Closing the Empirical Loop: Autonomous AI Agents Conduct End‐to‐end Research With Human Participants
A multi‐agent AI system autonomously executes the complete scientific workflow, from hypothesis to manuscript, across three psychological studies involving 288 participants. The system designs experiments, collects real world data, develops analysis pipelines, and writes manuscripts with theoretical rigor comparable to experienced researchers.
Gabrielle Wehr +6 more
wiley +1 more source
Transposable Element–Driven PIEZO Mutation Enhances Locust Flight in Plateau Hypoxia
Why transposable elements (TEs) persisted or expanded in genomes remains a mystery. Using integrated analysis of TE macro‐ and microevolution in locusts, our results showed that thousands of TE insertions promoted widespread adaptive variation. Subfamilies of candidate adaptive TEs amplified and reshaped species‐level genomic architecture.
Xuanzhao Li +8 more
wiley +1 more source
Bayesian inference is facilitated by modular neural networks with different time scales.
Various animals, including humans, have been suggested to perform Bayesian inferences to handle noisy, time-varying external information. In performing Bayesian inference by the brain, the prior distribution must be acquired and represented by sampling ...
Kohei Ichikawa, Kunihiko Kaneko
doaj +1 more source
Gaussian mixture models and semantic gating improve reconstructions from human brain activity
Better acquisition protocols and analysis techniques are making it possible to use fMRI to obtain highly detailed visualizations of brain processes. In particular we focus on the reconstruction of natural images from BOLD responses in visual cortex.
Sanne eSchoenmakers +3 more
doaj +1 more source
Systematic Multi‐Level Analyses Decode the Arthritis‐Neurodegeneration Axis With In Vivo Validation
Arthritis and neurodegeneration are usually studied as separate disorders, but this study connects them through population evidence, genetic inference, transcriptomic mapping, and mouse models. It highlights RNF40 as a context‐dependent joint‐brain candidate, induced in inflammatory joints yet functionally linked to dopamine‐neuron vulnerability ...
Jinwen Wang +7 more
wiley +1 more source
Objective Bayesian Inference for a Generalized Marginal Random Effects Model
An objective Bayesian inference is proposed for the generalized marginal random effects model p(x|μ, σλ) = f((x − μ1) T (V + σ2 λI) −1 (x − μ1))/ det(V + σ2 λI).
Bodnar, Olha +5 more
core +1 more source
Bayesian parametric bootstrap for models with intractable likelihoods [PDF]
In this paper it is demonstrated how the Bayesian parametric bootstrap can be adapted to models with intractable likelihoods. The approach is most appealing when the computationally efficient semi-automatic approximate Bayesian computation (ABC) summary ...
Christopher C. Drovandi +8 more
core +1 more source
Perceived Time Shapes Physical Fatigue Accumulation and Its Neural Oscillatory Correlates
Can a clock change the course of physical fatigue? By covertly manipulating the clock calibration during repeated isometric contractions, we showed that perceived time shapes fatigue accumulation and frontal oscillatory dynamics. While fatigue accumulation was only reduced when the clock was slowed down, frontal oscillatory dynamics followed perceived ...
Pierre‐Marie Matta +3 more
wiley +1 more source
Canonical Antibodies Adopt Distinct Binding Modes to Recognize Viral Glycan Shields
Canonical Y‐shaped antibodies recognize viral glycan shields through adaptive Fab assembly states shaped by glycan organization and somatic hypermutation. Structural analyses ofbroadly neutralizing antibodies VRC35 and VRC36 across glycoproteins of HIV‐1, influenza, SARS‐CoV‐2, and Lassa viruses reveal distinct Fab assembly states, spanning monovalent,
Jiaxuan Cheng +71 more
wiley +1 more source
Bayesian factorial linear Gaussian state-space models for biosignal decomposition [PDF]
We discuss a method to extract independent dynamical systems underlying a single or multiple channels of observation. In particular, we search for one-dimensional subsignals to aid the interpretability of the decomposition. The method uses an approximate
Chiappa, S, Barber, D
core

