The site, a few kilometers from her own hut in Ipatsé, a Kuikuro village in the Xingu Indigenous territory, was once the backyard of her great-grandparents' house. As she scrapes the brown earth with a trowel, she soon spots a black ceramic shard. It is only about the size of her palm, and this is her first day ever on an archaeological excavation. But she immediately recognizes what the object once was. "It's an alato," she says, showing the piece to a group of archaeologists and other Kuikuro who have gathered to watch the excavation in the village of Anitahagu. An alato, Yamána explains, is a large pan used to cook beiju, a white flatbread made with yucca flour that's eaten almost every day in her village. Her grandmother still has one in the backyard fire pit where she prepares most meals, just as countless Kuikuro women did before her. This alato likely belonged to her great-grandmother on her mother's side.

As the promise of artificial intelligence (AI) captivates biomedicine, few people are riding the wave like Priscilla Chan--because few people have her resources. Trained as a pediatrician, Chan and her husband, Facebook creator Mark Zuckerberg, co-run a philanthropy that launched in 2015 with the wildly ambitious--some would say quixotic--goal of curing, preventing, or managing every disease by the end of the century. The couple pledged nearly their entire fortune-- 45 billion then and more than 200 billion today--to the Chan Zuckerberg Initiative (CZI), which would also support their education and progressive causes. Recently, however, the foundation has wound down support for almost everything but science. And this week, CZI announced it is increasing its research spending, doubling down on AI, and vowing to meet Chan and Zuckerberg's biomedical goal even earlier--although CZI won't set a specific target.

What makes us who we are? Is it our DNA, passed down through generations, or the environment that shapes our lives? This question--how nature and nurture combine to influence health and behavior--has long captured my curiosity. As I grew up in a multigenerational household, I was struck by the story of my two uncles, identical twins who were genetically indistinguishable but who lived out very different health journeys. One developed severe cardiovascular disease by his early forties; the other stayed healthy into his sixties. What separated them was not biology--it was environment.

Neuronal architectures comprise synaptically connected neurons distributed throughout the central nervous system, the coordinated activities of which orchestrate neurological functions ranging from breathing to movement and cognition. Disentangling these neuronal architectures and how they are disrupted in disease is a fundamental goal of neuroscience. Historically, this challenge has been addressed with a reductionist framework that translated hypotheses into the interrogation of discrete neuronal subpopulations based on a priori expectations. The advent of high-throughput methodologies, including whole–central nervous system imaging in rodent models and single-cell transcriptomic readouts, now enable the visualization and characterization of neuronal subpopulations throughout the central nervous system. Increases in scale further enable comparative experimental designs that can be navigated with computational frameworks. These advances augur a new era wherein neuronal architectures implicated in diverse neurological functions, yet obscured by the complexity of the central nervous system, can be exposed without bias and interrogated with genetically guided experimental manipulations.

Although the functional roles of phospho-Ser2 and phospho-Ser5 and the kinases that place those marks have been extensively studied, the functions of the three so-called "orphan" residues (Tyr1, Thr4, and Ser7) and the identity of kinases that modify those sites remain poorly defined. Unbiased mass spectrometric mapping of the CTD revealed that the orphan sites are phosphorylated, and non-CDK kinases, such as HRR25, PLK3, and ABL1, modify those residues. Notably, unlike Ser2 or Ser5, whose phosphorylation has broad effects, mutations of the orphan residues or inhibition of kinases that act on them selectively disrupt the expression of limited sets of genes. The pathways mapped to those genes suggest that of the 250 phospho-acceptor residues that are densely packed within 360 residues of the human CTD, nearly 150 orphan sites may be used by other kinases to selectively regulate distinct sets of functionally coherent genes. To bridge the knowledge gap and identify previously unknown CTD-active kinases, we used three orthogonal kinome testing platforms in conjunction with machine learning algorithms to predict kinase-substrate pairings.

For a long time, many climate scientists and advocates held onto an optimistic belief that once the impacts of climate change became undeniable, people and governments would act. But whereas the predictions of climate models have increasingly borne out, the assumptions about human behavior have not. Even as disasters mount, climate change remains low on voters' priority lists, and policy responses remain tepid. To me, this gap reflects a deeper failure--not just in policy or communication, but in how we understand human adaptability. When I began my career as a computational cognitive scientist, I was drawn to a defining strength of human cognition--a marked ability to adapt.

Using dual-color optogenetics combined with intracellular somatic and dendritic recordings, we mapped the functional connectivity from LECGLU and LECGABA inputs to CA3. LECGLU drove excitation in CA3 PNs but also substantial feedforward inhibition that prevented somatic and dendritic spikes. LECGABA exclusively inhibited a subset of soma-targeting local CA3 inhibitory neurons recruited by LECGLU, thereby reducing feedforward inhibition onto CA3 PNs. This LECGABA-mediated disinhibition selectively increased CA3 PN somatic, but not dendritic, spiking in response to integrated LECGLU and CA3 feedback but not DG feedforward inputs. Using dual-chemogenetic silencing of CA3-projecting LECGLU or LECGABA and two-photon calcium imaging of CA3 PN somas and dendrites, we tested how this circuit supports memory-related ensemble activity and behavior.

Earlier today, OpenAI completed a controversial restructuring of its for-profit arm into a public benefit corporation: the latest gust in a whirlwind that has swept up hundreds of billions of dollars of global investment for artificial intelligence (AI) tools. But even as the AI company--founded as a nonprofit, now valued at 500 billion--completes its long-awaited restructuring, a nagging issue with its core offering remains unresolved: hallucinations. Large language models (LLMs) such as those that underpin OpenAI's popular ChatGPT platform are prone to confidently spouting factually incorrect statements. These blips are often attributed to bad input data, but in a preprint posted last month, a team from OpenAI and the Georgia Institute of Technology proves that even with flawless training data, LLMs can never be all-knowing--in part because some questions are just inherently unanswerable. However, that doesn't mean hallucinations are inevitable.

The ultimate objective of biomolecular modeling is to provide molecular dynamics simulations with quantum-mechanical accuracy at system sizes and timescales relevant to realistic applications. Current approaches often face trade-offs among efficiency, accuracy, scalability, and transferability. Machine learning force fields have successfully bridged the gap between some of these factors, but achieving truly general molecular simulations remains elusive. Kabylda et al. report a pretrained neural network and universal pairwise force fields that demonstrated robust performance in nanosecond-long simulations of small biomolecular systems, high transferability throughout biochemical space, and scalability to hundreds of thousands of atoms. Although many biomolecular processes are currently beyond reach, the proposed method is a promising step toward the long-standing goal of accurate large-scale modeling across extended chemical space.

Understanding how life is adapting to urban environments represents an important challenge in evolutionary biology. In this work, we investigate a widely cited example of urban adaptation, Culex pi...