Browse Articles

Author Correction: Cross-population GWAS and proteomics improve risk prediction and reveal mechanisms in atrial fibrillation

A deep single cell mass cytometry approach to capture canonical and noncanonical cell cycle states

Abstract The cell cycle (CC) underpins diverse cell processes like cell differentiation, cell expansion, and tumorigenesis but current single-cell (sc) strategies study CC as: coarse phases, rely on transcriptomic signatures, use imaging modalities limited to adherent cells, or lack high-throughput multiplexing. To solve this, we develop an expanded, Mass Cytometry (MC) approach with 48 CC-related molecules that deeply phenotypes the diversity of scCC states. Using Cytometry by Time of Flight, we quantify scCC states across suspension and adherent cell lines, and stimulated primary human T cells. Our approach captures the diversity of scCC states, including atypical CC states beyond canonical definitions. Pharmacologically-induced CC arrest reveals that perturbations exacerbate noncanonical states and induce previously unobserved states. Notably, primary cells escaping CC inhibition demonstrated aberrant CC states compared to untreated cells. Our approach enables deeper phenotyping of CC biology that generalizes to diverse cell systems with simultaneous multiplexing and integration with MC platforms.

A stable monoclinic variant and resultant robust ferroelectricity in single-crystalline hafnia-based films

AI-embodied multi-modal flexible electronic robots with programmable sensing, actuating and self-learning

Abstract Achieving robust environmental interaction in small-scale soft robotics remains challenging due to limitations in terrain adaptability, real-time perception, and autonomous decision-making. Here, we introduce Flexible Electronic Robots constructed from programmable flexible electronic components and setae modules. The integrated platform combines multimodal sensing/actuation with embedded computing, enabling adaptive operation in diverse environments. Applying modular design principles to configure structural topologies, actuation sequences, and circuit layouts, these robots achieve multimodal locomotion, including vertical surface traversal, directional control, and obstacle navigation. The system implements proprioception (shape and attitude) and exteroception (vision, temperature, humidity, proximity and pathway shape recognition) under dynamic conditions. Onboard computational units enable autonomous behaviors like hazard evasion and thermal gradient tracking through adaptive decision-making, supported by embodied artificial intelligence. In this work, we establish a framework for creating small-scale soft robots with enhanced environmental intelligence through tightly integrated sensing, actuation, and decision-making architectures.

All-optical voltage interrogation for probing synaptic plasticity in vivo

Abstract Measuring synaptic efficacy and defining the rules for induction of synaptic plasticity at identified connections in the mammalian brain is essential for understanding how synapses contribute to learning and memory. This requires new approaches to selectively evoke presynaptic activity and measure postsynaptic responses with high spatiotemporal resolution and high sensitivity over long periods in vivo. Here we develop an all-optical approach to probe synaptic plasticity at identified cerebellar synapses in awake, behaving mice. We developed and applied JEDI-2Psub, a genetically encoded voltage indicator with increased sensitivity around resting membrane potentials, to record subthreshold and suprathreshold activity in Purkinje cell (PC) dendrites while selectively activating their granule cell (GrC) inputs using optogenetics and their climbing fiber (CF) inputs using sensory stimulation. We measured synaptic potentials and complex spike signals across the dendrites of multiple neighboring PCs, enabling us to examine correlations in voltage signals within and between neurons. We show how pairing GrC activity with sensory-evoked CF inputs can trigger long-term plasticity of inhibitory responses in PCs. These results provide a blueprint for defining the rules for plasticity induction at identified synapses in awake animals during behavior.

Tellurium-assisted growth of large-scale atom-thin insulating amorphous carbon on insulating substrates

A physiologically-relevant intermediate state structure of a voltage-gated potassium channel

Template Learning: Deep learning with domain randomization for particle picking in cryo-electron tomography

Abstract Cryo-electron tomography (cryo-ET) enables three-dimensional visualization of biomolecules and cellular components in their near-native state. A key challenge in cryo-ET data analysis is particle picking, often performed by template matching, which relies on cross-correlating tomograms with known structural templates. Current deep learning-based methods improve accuracy but require labor-intensive annotated datasets for supervised training. Here, we present Template Learning, a technique that combines deep learning accuracy with the convenience of training on biomolecular templates via domain randomization. Template Learning automates synthetic dataset generation, modeling molecular crowding, structural variability, and data acquisition variation, thereby reducing or eliminating the need for annotated experimental data. We show that models trained using Template Learning, and optionally fine-tuned with experimental data, outperform those trained solely on annotations. Furthermore, Template Learning provides higher precision and more uniform orientation detection than template matching, particularly for small non-spherical particles. Template Learning software is open-source, Python-based, and GPU/CPU parallelized.

SFPQ-TFE3 reciprocally regulates mTORC1 and induces lineage plasticity in a mouse model of renal tumorigenesis

Abstract MiT/TFE gene fusions like SFPQ-TFE3 drive both epithelial (translocation RCC) and mesenchymal (PEComas) neoplasms. However, no mouse models for SFPQ-TFE3-related tumors exist and the underlying mechanisms of lineage plasticity remain unclear. Here, we demonstrate that constitutive murine renal expression of SFPQ-TFE3 disrupts kidney development with early neonatal renal failure and death, while post-natal induction induces infiltrative epithelioid tumors, that morphologically and transcriptionally resemble human PEComas, with strong activation of mTORC1 signaling via increased V-ATPase expression. Remarkably, SFPQ-TFE3 expression is sufficient to induce lineage plasticity, with down-regulation of the PAX2/PAX8 nephric lineage factors and tubular epithelial markers, and up-regulation of PEComa differentiation markers in transgenic mice, cell lines and human tRCC. mTOR inhibition downregulates SFPQ-TFE3 expression and rescues PAX8 expression and transcriptional activity in vitro. These data provide evidence of an epithelial cell-of-origin for TFE3-driven PEComas, highlighting a reciprocal role for SFPQ-TFE3 and mTOR in driving lineage plasticity in the kidney.

Creative experiences and brain clocks

Abstract Creative experiences may enhance brain health, yet metrics and mechanisms remain elusive. We characterized brain health using brain clocks, which capture deviations from chronological age (i.e., accelerated or delayed brain aging). We combined M/EEG functional connectivity (N = 1,240) with machine learning support vector machines, whole-brain modeling, and Neurosynth metanalyses. From this framework, we reanalyzed previously published datasets of expert and matched non-expert participants in dance, music, visual arts, and video games, along with a pre/post-learning study (N = 232). We found delayed brain age across all domains and scalable effects (expertise>learning). The higher the level of expertise and performance, the greater the delay in brain age. Age-vulnerable brain hubs showed increased connectivity linked to creativity, particularly in areas related to expertise and creative experiences. Neurosynth analysis and computational modeling revealed plasticity-driven increases in brain efficiency and biophysical coupling, in creativity-specific delayed brain aging. Findings indicate a domain‑independent link between creativity and brain health.

AI has designed thousands of potential antibiotics. Will any work?

Daily briefing: The Nobel prizes’ most prestigious rivals

How AI agents will change research: a scientist’s guide

The child of humanity becomes the mother of all

These science prizes want to rival the Nobels: how do they compare?

Clinician perspectives on explainability in AI-driven closed-loop neurotechnology

Abstract Artificial Intelligence (AI) holds promise for advancing the field of neurotechnology and accelerating its clinical translation. AI-driven clinical neurotechnologies leverage the power of non-linear algorithms to analyze complex brain data and enable adaptive, closed-loop neurostimulation. Despite these promises, the integration of AI into clinical practice remains limited, with lack of explainability being commonly cited as one main obstacle. This raises the question of whether opacity and lack of explainability also hinder the adoption of AI in closed-loop medical neurotechnologies. We investigated the attitudes, informational needs and preferences of clinicians regarding AI-driven closed-loop neurotechnologies and explored what forms of explanation they consider necessary for clinical use. We conducted semi-structured expert interviews with twenty clinicians (including neurologists, neurosurgeons, and psychiatrists) from Germany and Switzerland. Using reflexive thematic analysis, we explored their understanding of and expectations for explainability in the context of AI-driven closed-loop neurotechnology systems. Clinicians consistently emphasized the importance of context-sensitive, clinically meaningful forms of explainability such as understanding what input data were used to train the system and how the output relates to clinically relevant outcomes. By contrast, detailed knowledge of the model’s inner architecture or technical mechanics were of limited interest. Several participants specifically called for Explainable AI (XAI) techniques, particularly feature importance and relevance measures, to support their interpretation of system outputs. Our findings suggest that the clinical utility of AI-driven neurotechnologies can be improved by focusing on intuitive, user-centered and clinically meaningful forms of explainability rather than full algorithmic transparency. Designing systems that meet these pragmatic needs may help bridge the translational gap between AI development and clinical implementation.

A retrospective investigation for the seroprevalence of Chikungunya virus and its co-existence with Dengue virus in Pakistani population, 2014–2015

Gastropexy device impaction in children with push percutaneous endoscopic gastrostomy

The applicability of fluorescent optotracers for in vitro and in vivo Staphylococcus aureus detection and quantification

Abstract A sensitive and specific method for assessing microbial contamination is crucial in many sectors of our society like the medical field. Optotracers that trigger fluorescence upon binding to bacterial cell surfaces offer a novel approach. Several studies have highlighted limitations in the specificity of these optotracers with respect to their molecular targets, but, to the best of our knowledge, none did in vivo studies with the same bacterial strain as the in vitro studies. In this study, we compared the activatable optotracer EbbaBiolight 680 for bacterial detection, both in vitro and in vivo with the same Staphylococcus aureus bacterial strain, while analyzing the sensitivity and specificity of the probe against this strain. In vitro the probe’s fluorescence correlated strongly with the number of bacterial colony-forming units, both in planktonic suspension and biofilms. However, in vivo results from a mouse model demonstrated limited specificity for S. aureus, as the probe also binds to repetitive component motifs in the extracellular matrix of the tissue. This resulted in a substantial background signal that obscured bacterial detection. In conclusion, while EbbaBiolight 680 effectively detects S. aureus in planktonic suspension and biofilms in vitro, the probe has unfortunately limited specificity in vivo, which can hinder accurate bacterial detection.

CryoEMNet driven symmetry-aware molecular reconstruction through deep learning enhanced electron microscopy