Iontronic pressure sensors featuring high sensitivity are promising across different emerging fields. In applications requiring high-fidelity sensing such as surgeries, reliable pressure monitoring necessitates sensitivity and stability at the same time to ensure safety. However, existing iontronic sensors often face a trade-off between them-soft ionic materials with high conductivity often present pronounced viscoelasticity, resulting in a low sensitivity-to-drift-rate (S/D) ratio. Here, we report a polyelectrolyte-based iontronic pressure sensor that achieves a nearly one order-of-magnitude improvement in S/D ratio over state-of-the-art designs using a polarity-balance strategy in the polyelectrolyte to simultaneously regulate ionic conductivity and viscoelasticity. The resulting sensor exhibits ultrahigh sensitivity (119.5 per kilopascal) and exceptional stability (drift rate < 1%) under prolonged pressures up to 450 kilopascals, enabling nearly drift-free pressure detection for high-precision surgical operations in both surgeon-operated and robotic-assisted procedures. This work provides an extensible material design strategy to enhance sensitivity while suppressing signal drift in soft pressure sensors, with broad relevance to robotics, medical devices, and wearables.
Catalyst design principles for generating multicarbon (C2+) products from electrochemical carbon dioxide (CO2) reduction (CO2R) and carbon monoxide (CO) reduction (COR) are often similar and used interchangeably. However, here we demonstrate how a copper bismuth (CuBi) catalyst generates a drastically different product output for CO2R versus COR, offering a valuable case study. During CO2R, we observe predominant formate production, due to selective adsorption of the CO2 reactant onto Bi sites rather than Cu sites. On the other hand, acetate is observed as the primary product for COR because formate production on Bi sites is shut down. Specifically, with the optimal CuBi catalyst, we achieve a high acetate Faradaic efficiency of 68.2% at 100 milliamperes per square centimeter under COR conditions. Experiments involving the coreduction of 13CO2/12CO feeds of varying mixing ratios show that acetate has the highest carbon-12 content among the products. This suggests that CO-selective sites (CuCO) could play a role in facilitating selective acetate production. Notably, analysis of the acetate isotopologues also reveals a preference for the formation of 12CH313COO- over 13CH312COO-. We propose this to be due to an acetate-selective pathway involving asymmetric hydrogenation of *12CO13CO to *12CH213CO (ketene). This pathway is promoted at high pH and is postulated to occur at the interfaces between CuCO and CO2-selective sites (CuCO2). Together, our results provide mechanistic insights into acetate formation under COR conditions.
Predicting single-cell transcriptional responses to perturbations is central to dissecting gene regulation and accelerating therapeutic design, yet the field lacks a rigorous, task-spanning assessment of model behavior. We present a large-scale benchmark of 13 representative methods and baselines across 25 datasets spanning diverse perturbation modalities and species, including two primary immune-cell drug-response resources. We evaluated three core tasks—generalization to unseen single-gene perturbations, prediction of combinatorial interactions, and transfer across cell types—using 24 metrics covering expression-level accuracy, relative changes, differential expression (DE) recovery, and distributional similarity. Across tasks, performance depended strongly on perturbation effect size and evaluation perspective: Expression-level agreement was the highest for small-effect perturbations resembling controls, whereas delta- and DE-based metrics improved with larger effects, providing clearer signals. Models shared a conservative bias, with fine-tuned foundation models compressing variance and underestimating synergistic effects in combinations. PerturbNet showed superior recovery of DE signatures in Tasks 1 and 2, while no method consistently generalized across cell types in Task 3, where biological consistency dominated outcomes. This benchmark establishes current methodological limits, clarifies that different metrics probe distinct biological signals rather than redundant summaries of the same prediction problem, and provides a foundation for developing virtual-cell models that more faithfully capture heterogeneous perturbation responses.
Changes in past subsistence strategies reflect the adaptation of human societies to climatic, environmental, and cultural settings. Despite being a resistant, flexible, and nutritive crop, millet only sporadically became a staple crop in Europe and is nearly forgotten today. This raises the question of the cultural dimensions associated with the adoption or rejection of millet. We use a multi-isotope dataset from Bronze Age Poland (2200 to 1000 BCE) coupled with radiocarbon dates to identify dietary practices and associate them with cultural and environmental conditions. We (i) revise the earliest evidence for millet consumption in Europe to 1590 ± 50 calibrated years (cal) BCE instead of 1460 ± 40 cal BCE, (ii) reconstruct dietary shifts at the group and individual level, (iii) suggest environmental instead of cultural mechanisms behind millet adoption, and (iv) reveal land-use shifts associated with millet cultivation, showing no strong evidence for a demic diffusion model. The gained insights into crop integration processes can help in discussing coping strategies for present-day challenges.
Hepatocellular carcinoma (HCC) originates from premalignant disease-associated hepatocytes (daHeps) that emerge during the progression of metabolic dysfunction-associated steatotic liver disease (MASLD) to metabolic dysfunction-associated steatohepatitis (MASH). As daHeps are compensatorily primed by metabolic stress, we reproduced the accelerated progression of MASLD-associated HCC in mice by phenocopying the decreased expression of a metabolic regulator, protein arginine methyltransferase 1 (PRMT1). In Prmt1 liver-specific knockout (LKO), m6A-mediated changes in mRNA stability reprogram the transcriptome via paralog compensation; increased PRMT6 activates m6A methyltransferases by inducing asymmetric arginine dimethylation of METTL3. This event enhances global m6A deposition that leads to the down-regulation of Keap1, which would trigger the NRF2 axis, promoting premalignancy; under diet- and chemical-induced stress, the incidence of steatohepatitic HCC was increased, clinically correlating with the PRMT-METTL3-NRF2 pathway. Together, we propose that compensatory arginine methylation primes MASLD-associated HCC by modulating m6A-mediated transcriptome and NRF2 regulatory pathways as adaptive defenses against metabolic and oxidative stress.
