Browse Articles

Global modules robustly emerge from local interactions and smooth gradients

Childhood maltreatment influences coping in youths with major depression and bipolar depression through resilience and impulsivity

Mathematician who reshaped theory of symmetry wins Abel Prize

Comparing large Language models and human annotators in latent content analysis of sentiment, political leaning, emotional intensity and sarcasm

Pregnancy’s true toll on the body: huge birth study paints most detailed picture yet

The sixth finger illusion induced by palm outside stroking shows stable ownership and independence

Efficient 22 nm GNRFET PTLA using low power trimode technique for high speed processor

Robust and reproducible human intestinal organoid-derived monolayer model for analyzing drug absorption

Evaluation of the efficacy of P11-4 and CCP-ACPF in the prevention and treatment of white spot lesions: a multi-technique approach

Assignment of hybrid laser and microwave inter-satellite links for navigation satellite systems

Evaluation of correctness and reliability of GPT, Bard, and Bing chatbots’ responses in basic life support scenarios

De novo design of transmembrane fluorescence-activating proteins

Neural activation in a septal area is related to intrinsic motivation for non-courtship singing in adult zebra finches

Synaptic and neural behaviours in a standard silicon transistor

Abstract Hardware implementations of artificial neural networks (ANNs)—the most advanced of which are made of millions of electronic neurons interconnected by hundreds of millions of electronic synapses—have achieved higher energy efficiency than classical computers in some small-scale data-intensive computing tasks1. State-of-the-art neuromorphic computers, such as Intel’s Loihi2 or IBM’s NorthPole3, implement ANNs using bio-inspired neuron- and synapse-mimicking circuits made of complementary metal–oxide–semiconductor (CMOS) transistors, at least 18 per neuron and six per synapse. Simplifying the structure and size of these two building blocks would enable the construction of more sophisticated, larger and more energy-efficient ANNs. Here we show that a single CMOS transistor can exhibit neural and synaptic behaviours if biased in a specific (unconventional) manner. By connecting one additional CMOS transistor in series, we build a versatile 2-transistor-cell that exhibits adjustable neuro-synaptic response (which we named neuro-synaptic random access memory cell, or NS-RAM cell). This electronic performance comes with a yield of 100% and an ultra-low device-to-device variability, owing to the maturity of the silicon CMOS platform used—no materials or devices alien to the CMOS process are required. These results represent a short-term solution for the implementation of efficient ANNs and an opportunity in terms of CMOS circuit design and optimization for artificial intelligence applications.

Molecular hydrogen as a potential mediator of the antitumor effect of inulin consumption

Choice history biases in dyadic decision making

Abstract How do we interact with our environment and make decisions about the world around us? Empirical research using psychophysical tasks has demonstrated that our perceptual decisions are influenced by past choices, a phenomenon known as the “choice history bias” effect. This decision-making process suggests that the brain adapts to environmental uncertainties based on history. However, single-subject experiment task design is prevalent across the work on choice history bias, thus limiting the implications of the empirical evidence to individual decisions. Here, we explore the choice history bias effect using a dual-participant approach, where dyads perform a shared perceptual decision-making task. We first propose two competing hypotheses: the participants equally weigh their own and their partner’s decision history, or the participants do not weigh equally their own and their partner’s decision history. We then use a statistical modeling approach to fit generalized linear models to the choice data in a series of steps and arrive at a model that best fits the observed data. Our results indicated that the own and partner’s trial history cannot be treated independently. The findings suggest an interaction of actor and decision at 1-back, leading to a choice alternation bias after a partner’s decision in contrast to a choice repetition bias after an own decision. A similar effect is observed at 2-back, in addition to an additive choice repetition bias of similar size. The effects of actor and decision at 2-back do not depend on the properties of the 1-back trial. Together, these findings support the idea that the participants do not ignore their partner’s decisions but treat these qualitatively differently from their own.

An interpretable deep learning model for the accurate prediction of mean fragmentation size in blasting operations

Thermal behavior and conversion of agriculture biomass residues by torrefaction and pyrolysis

Author Correction: Prediction of the transient coolant jet released from the nose cone at supersonic flow via machine learning

Solanum pan-genetics reveals paralogues as contingencies in crop engineering

Abstract Pan-genomics and genome-editing technologies are revolutionizing breeding of global crops1,2. A transformative opportunity lies in exchanging genotype-to-phenotype knowledge between major crops (that is, those cultivated globally) and indigenous crops (that is, those locally cultivated within a circumscribed area)3–5 to enhance our food system. However, species-specific genetic variants and their interactions with desirable natural or engineered mutations pose barriers to achieving predictable phenotypic effects, even between related crops6,7. Here, by establishing a pan-genome of the crop-rich genus Solanum 8 and integrating functional genomics and pan-genetics, we show that gene duplication and subsequent paralogue diversification are major obstacles to genotype-to-phenotype predictability. Despite broad conservation of gene macrosynteny among chromosome-scale references for 22 species, including 13 indigenous crops, thousands of gene duplications, particularly within key domestication gene families, exhibited dynamic trajectories in sequence, expression and function. By augmenting our pan-genome with African eggplant cultivars9 and applying quantitative genetics and genome editing, we dissected an intricate history of paralogue evolution affecting fruit size. The loss of a redundant paralogue of the classical fruit size regulator CLAVATA3 (CLV3)10,11 was compensated by a lineage-specific tandem duplication. Subsequent pseudogenization of the derived copy, followed by a large cultivar-specific deletion, created a single fused CLV3 allele that modulates fruit organ number alongside an enzymatic gene controlling the same trait. Our findings demonstrate that paralogue diversifications over short timescales are underexplored contingencies in trait evolvability. Exposing and navigating these contingencies is crucial for translating genotype-to-phenotype relationships across species.