Browse Articles

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.

Cathepsin D inhibits AGEs-induced phenotypic transformation in vascular smooth muscle cells

Systematic bone tool production at 1.5 million years ago

A two-sample Mendelian randomization study of type 1 diabetes and the risk of 22 site-specific cancers

Abstract Previous observational studies have suggested a potential link between Type 1 Diabetes (T1D) and site-specific cancer risk. However, the nature of this association remains uncertain due to confounding factors, reverse causation, and biases inherent in observational research. To address this gap, we conducted a two-sample Mendelian randomization (MR) study to assess the causal relationship between T1D and 22 site-specific cancers. Using summary statistics from large-scale genome-wide association studies of European ancestry, comprising data on T1D (N = 520,580) and the 22 site-specific cancers, we selected single nucleotide polymorphisms strongly associated with T1D as instruments for our analysis. Causal relationships were primarily evaluated through inverse-variance weighting-based analyses, supplemented by three additional methods: MR-Egger, weighted median, and mode-based estimate. Sensitivity analyses were performed, excluding genetic variants with potential pleiotropic effects. The finding demonstrated a causal association between T1D and increased risks of lung cancer (OR = 1.018, 95% CI 1.004–1.033, p = 0.011), colorectal cancer (OR = 1.022, 95% CI 1.003–1.041, p = 0.019), and prostate cancer (OR = 1.018, 95% CI 1.005–1.030, p = 0.006). Conversely, T1D was associated with decreased risks of breast cancer (OR = 0.989, 95% CI 0.981–0.998, p = 0.016), lymphoma (OR = 0.999, 95% CI 0.974–0.999, p = 0.003), malignant melanoma (OR = 0.999, 95% CI 0.989–0.999, p = 0.001), and non-melanoma skin cancer (OR = 0.999, 95% CI 0.899–0.999, p = 0.003). Our MR study provides an evidence of causal association between T1D and altered risks of various site-specific cancers. Further research is recommended to validate this finding in diverse populations to enhance the generalizability of findings across different ethnic groups.

Phosphate-enabled mechanochemical PFAS destruction for fluoride reuse

Abstract Perfluoroalkyl and polyfluoroalkyl substances (PFASs) are persistent, bioaccumulative and anthropogenic pollutants that have attracted the attention of the public and private sectors because of their adverse impact on human health1. Although various technologies have been deployed to degrade PFASs with a focus on non-polymeric functionalized compounds (perfluorooctanoic acid and perfluorooctanesulfonic acid)2–4, a general PFAS destruction method coupled with fluorine recovery for upcycling is highly desirable. Here we disclose a protocol that converts multiple classes of PFAS, including the fluoroplastics polytetrafluoroethylene and polyvinylidene fluoride, into high-value fluorochemicals. To achieve this, PFASs were reacted with potassium phosphate salts under solvent-free mechanochemical conditions, a mineralization process enabling fluorine recovery as KF and K2PO3F for fluorination chemistry. The phosphate salts can be recovered for reuse, implying no detrimental impact on the phosphorus cycle. Therefore, PFASs are not only destructible but can now contribute to a sustainable circular fluorine economy.

Influential nodes identification for complex networks based on multi-feature fusion

Protein sequence modelling with Bayesian flow networks

Optimizing structural integrity of a pressure vessel via finite element analysis and machine learning based XGBoost approaches

Solidification of Earth’s mantle led inevitably to a basal magma ocean

Abstract One of the main interpretations of deep-rooted geophysical structures in the mantle1 is that they stem from the top-down solidification of the primitive basal magma ocean of Earth above the core2–6. However, it remains debated whether solids first formed at the bottom of the mantle, solidifying upward, or above the melts, solidifying downward. Here we show that gravitational segregation of dense, iron-rich melts from lighter, iron-poor solids drives mantle evolution, regardless of where melting curves and geotherms intersect. This process results in the accumulation of iron-oxide-rich melts above the core, forming a basal magma ocean. We numerically model mantle solidification using a new multiphase fluid dynamics approach that integrates melting phase relations and geochemical models. This enables estimating the compositional signature and spatial distribution of primordial geochemical reservoirs, which may be directly linked to the isotopic anomalies measured in Archean rocks7–11. We find that a substantial amount of solids is produced at the surface of the planet, not at depth, injecting geochemical signatures of shallow silicate fractionation in the deep mantle. This work could serve as a foundation for re-examining the intricate interplay between mantle dynamics, petrology and geochemistry during the first thousand million years of the evolution of rocky planets.