The development of deep learning approaches for modeling these multifactorial effects of GVs is still in its nascent stages, primarily due to the lack of comprehensive datasets that capture the intricate relationships between GVs and their downstream effects on complex traits.

Among those Robert F. Kennedy Jr. recently named to a federal autism committee are people who tout dangerous treatments and say vaccine manufacturers are "poisoning children." US health secretary Robert F. Kennedy Jr. has filled an autism committee with friends, associates, and former colleagues who believe that autism is caused by vaccines. Autism advocates are now worried the group could pave the way for dangerous pseudoscientific treatments going mainstream. Last week, Kennedy announced an entirely new lineup for the Interagency Autism Coordinating Committee (IACC), a group that recommends what types of autism research the government should fund and provides guidance on the services the autism community requires. The group is typically composed of experts in the area of autism research, along with policy experts and autistic people advocating for their own community.

Predicting critical transitions in complex systems, such as epileptic seizures in the brain, represents a major challenge in scientific research. The high-dimensional characteristics and hidden critical signals further complicate early-warning tasks. This study proposes a novel early-warning framework that integrates manifold learning with stochastic dynamical system modeling. Through systematic comparison, six methods including diffusion maps (DM) are selected to construct low-dimensional representations. Based on these, a data-driven stochastic differential equation model is established to robustly estimate the probability evolution scoring function of the system. Building on this, a new Score Function (SF) indicator is defined by incorporating Schrödinger bridge theory to quantify the likelihood of significant state transitions in the system. Experiments demonstrate that this indicator exhibits higher sensitivity and robustness in epilepsy prediction, enables earlier identification of critical points, and clearly captures dynamic features across various stages before and after seizure onset. This work provides a systematic theoretical framework and practical methodology for extracting early-warning signals from high-dimensional data.

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.

Pathophysiolpgical modelling of brain systems from microscale to macroscale remains difficult in group comparisons partly because of the infeasibility of modelling the interactions of thousands of neurons at the scales involved. Here, to address the challenge, we present a novel approach to construct differential causal networks directly from electroencephalogram (EEG) data. The proposed network is based on conditionally coupled neuronal circuits which describe the average behaviour of interacting neuron populations that contribute to observed EEG data. In the network, each node represents a parameterised local neural system while directed edges stand for node-wise connections with transmission parameters. The network is hierarchically structured in the sense that node and edge parameters are varying in subjects but follow a mixed-effects model. A novel evolutionary optimisation algorithm for parameter inference in the proposed method is developed using a loss function derived from Chen-Fliess expansions of stochastic differential equations. The method is demonstrated by application to the fitting of coupled Jansen-Rit local models. The performance of the proposed method is evaluated on both synthetic and real EEG data. In the real EEG data analysis, we track changes in the parameters that characterise dynamic causality within brains that demonstrate epileptic activity. We show evidence of network functional disruptions, due to imbalance of excitatory-inhibitory interneurons and altered epileptic brain connectivity, before and during seizure periods.

AI model from Google's DeepMind reads recipe for life in DNA An AI model developed by Google's DeepMind could transform our understanding of DNA - the complete recipe for building and running the human body - and its impact on disease and medicine discovery, according to researchers. Called AlphaGenome, the model could help scientists discover why subtle differences in our DNA put us at risk of conditions such as high blood pressure, dementia and obesity. It could also dramatically accelerate our understanding of genetic diseases and cancer. The developers of the model acknowledge it's not perfect, but experts have described it as an incredible feat and a major milestone. We see AlphaGenome as a tool for understanding what the functional elements in the genome do, which we hope will accelerate our fundamental understanding of the code of life, says Natasha Latysheva, research engineer at DeepMind.

He Went to Prison for Gene-Editing Babies. Now He's Planning to Do It Again Chinese scientist He Jiankui wants to end Alzheimer's and thinks Silicon Valley is conducting a "Nazi eugenic experiment." In 2018, a nervous-looking He Jiankui took the stage at a scientific conference in Hong Kong. A hush settled over the packed auditorium as the soft-spoken Chinese scientist adjusted his microphone and confirmed the circulating media reports: He had created the world's first gene-edited babies . Three little girls were born with modifications to their genomes that were intended to protect them against HIV. The changes he'd made to their DNA were permanent and heritable, meaning they could be passed down to future generations.

Why personalized gene editing, genetic resurrections and embryo scoring made our list. Earlier this week, published its annual list of Ten Breakthrough Technologies. As always, it features technologies that made the news last year, and which--for better or worse--stand to make waves in the coming years. They're the technologies you should really be paying attention to. This year's list includes tech that's set to transform the energy industry, artificial intelligence, space travel --and of course biotech and health. Our breakthrough biotechnologies for 2026 involve editing a baby's genes and, separately, resurrecting genes from ancient species.

Aurora Therapeutics' first target is the rare inherited disease phenylketonuria, also known as PKU. Here at we've been writing about the gene-editing technology CRISPR since 2013, calling it the biggest biotech breakthrough of the century. Yet so far, there's been only one gene-editing drug approved. It's been used commercially on only about 40 patients, all with sickle-cell disease. It's becoming clear that the impact of CRISPR isn't as big as we all hoped. In fact, there's a pall of discouragement over the entire field--with some journalists saying the gene-editing revolution has " lost its mojo ."