Browse Articles

Methylation-based ctDNA monitoring in metastatic breast cancer during CDK4/6 inhibitor therapy

Antioxidant lipid nanoparticles enhance mRNA stability for regeneration therapy and gene editing

An in-sensor communication electronic textile for imperceptible and ultrarobust silent speech

Why a synthetic human genome is still worth building

A computational model of reward learning and habits on social media

Abstract Social media have fundamentally transformed how we live and communicate. However, the methods to study how our cognitive systems interact with technology platforms are very limited. Computational modelling represents a new avenue to uncover the finegrained cognitive processes driving social media behaviour. Here, we develop a computational model of real-world social media posting data, adapted from the animal reward learning literature. Using a Twitter (currently X) dataset ( n  = 2696 users), including a preregistered replication, we show that a hybrid reinforcement learning and habitual cognitive process underlies social media posting behaviour. More frequent posters show more signs of habitual behaviour. Further, younger people and women are more driven by reinforcement learning – updating their strategy more adaptively to maximise social media rewards – while older users and men are more habitual.

Can an army of babies and dogs rescue psychology from its reproducibility crisis?

Implications of regional variations in climate change vulnerability and mitigation behaviour for social-climate dynamics

The size of tropical vegetation gross primary production

DNA damage drives antigen diversification in Trypanosoma brucei

Abstract Antigenic variation, using large genomic repertoires of antigen-encoding genes, allows pathogens to evade host antibody. Many pathogens, including the African trypanosome Trypanosoma brucei , extend their antigenic repertoire through genomic diversification. Although evidence suggests that T. brucei depends on the generation of new variant surface glycoprotein (VSG) genes to maintain a chronic infection 1–4 , a lack of experimentally tractable tools for studying this process has obscured its underlying mechanisms. Here we present a highly sensitive targeted sequencing approach for measuring VSG diversification. Using this method, we demonstrate that a Cas9-induced DNA double-strand break within the VSG coding sequence can induce RAD51- and BRCA2-dependent VSG recombination with patterns identical to those observed during infection. These newly generated VSGs are antigenically distinct from parental clones and thus capable of facilitating immune evasion. Together, these results provide insight into the mechanisms of VSG diversification and an experimental framework for studying the evolution of antigen repertoires in pathogenic microorganisms.

Analysis and research on multilevel video game addiction characteristics among children and adolescents based on association rules

Non-contact on-device detection of obstructive sleep apnea from infrared video

Nanostructure of tooth enamel casts light on dietary shifts as humans evolved

Attention U-Net with differential privacy in federated learning framework for brain stroke lesion segmentation

Multimodal deep-learning optimization of chiroptical properties in all-inorganic perovskite-coated TiO2 nanohelices and inverse-design transfer to organic chiral luminophores

Abstract Circularly polarized luminescence (CPL) has been catching increasing attention for developing advanced photonic displays, quantum communication, bioimaging, and chiral sensing. All-inorganic chiral luminophores are superior to their organic or organic-inorganic hybrid counterparts in thermal stability, environmental robustness and device compatibility, but limited by the difficulty in fabrication and low luminescence dissymmetry factor ( g lum  < 0.1), whereby g lum is generally applied to evaluate the purity of circular polarization of CPL. Herein, chiral TiO 2 nanohelices (NHs) act as chiral templates that are conformally coated with achiral perovskite luminophores composed of cesium lead bromides, to form all-inorganic chiral core@shell nano-luminophores. Chirality transmission from TiO 2 NHs to perovskites accounts for the generation of CPL. Given by the complex and multifactorial experimental conditions, the manual engineering of fabrication procedure leads to an optimized g lum  = 0.2. To further optimize g lum , we develop OptiCPL, a few-shot multimodal deep-learning framework that integrates spectral and morphological features, to boost g lum from 0.20 to 0.35 through model prediction and experimental validation. In addition, the OptiCPL model is transferrable to polymer F8BT-based chiral organic luminophores, achieving g lum  = 0.87. This work establishes a synergistic chiral core@shell approach and offers a transferable deep-learning framework for designing high- g lum CPL materials.

Physiological homeostasis, yield, and sugar profile of salt-stressed sugar beet plants as influenced by nano-structured mixture of zinc, boron, and molybdenum application

Abstract Sugar beet is a key industrial crop valued for its sucrose content, but its yield and sugar profile are greatly affected by soil salinity. In this study, we introduce novel nano-structured zinc (Zn), boron (B), and molybdenum (Mo) mixture designed to mitigate the adverse effects of salt stress. While individual nano-micronutrient applications have been investigated, the potential of combined nano-micronutrient formulations still need to be clarified. A two-season field experiments evaluated the effect of a nano-structured Zn, B, and Mo (MMNPs) mixture on salt-stressed sugar beet. Treatments included applied once (MMNPs1) or twice (MMNPs2) applications of MMNPs, their bulk counterparts (MMB1 or MMB2), and a control treatment (CK). Physiological traits, yield attributes, and sugar profile were assessed. The findings of this study pointed out that application of MMNPs2 and MMB2 treatments improved growth, photosynthetic efficiency, productivity, and juice quality compared with CK, MMNPs1, and MMB1, with MMNPs2 showing the greatest effects. Relative to CK, MMNPs2 increased net assimilation rate, absolute crop growth rate, taproot dry weight, taproot length, and taproot diameter by 3.87-, 6.14-, 1.98-, 1.24-, and 1.20-fold, respectively. It also enhanced chlorophyll a by 78.3%, chlorophyll b by 163.1%, carotenoids by 50.4%, F v /F ₀ by 19.7%, F v /F m by 3.6%, and the photosynthetic performance index by 77.7%. The MMNPs2 treatment yielded the highest sugar content and lowest non-sugar impurities. Briefly, foliar applying a nano-structured Zn, B, and Mo mixture at 165 mg L⁻¹ twice during the growth development is an effective strategy to maintain high sugar yield and quality in nutrient-deficient, sandy, salt-affected soils.

Carbon mesopore depth engineering boosts the performance of low-platinum fuel cells

Mechanophore cross-linking enhances ballistic energy dissipation of polymers

Prediction of unpowered diving and floating for large-depth manned submersible based on geometric similarity

Abstract Large-depth manned submersibles generally conduct diving and floating operations in an unpowered mode. On the premise of guaranteeing satisfactory diving and floating speed, this mode can significantly reduce energy consumption and prolong underwater operating time, which serves as one of the key foundations for the underwater operational performance of manned submersibles. Therefore, the study on prediction methods for unpowered diving and floating motion is of great engineering significance. At present, existing prediction methods are mainly established on the standard motion model, whose results are extremely sensitive to the handling of hydrodynamic terms in the model, thus limiting the prediction accuracy. In this paper, a prediction method for unpowered diving and floating motion of manned submersibles is proposed based on geometric similarity theory, by deriving the relationship between the submersible’s diving and floating speed and its net weight in water. Taking the “Jiao Long” manned submersible as a research object, the established model is employed to predict the 7000 m unpowered diving and floating motion using the 5000 m deep-sea trial data. Comparative analysis indicates that the proposed method achieves higher prediction accuracy than the conventional standard motion model, which preliminarily verifies its feasibility and effectiveness and its general applicability remains to be further examined in follow-up investigations. Meanwhile, many assumptions are adopted in the development of the proposed method. Therefore, this method is only applicable to the quasi-steady segments of the same manned submersible, and is not suitable for transient phases such as water entry, ballast release transitions, or motions involving significant attitude coupling. Furthermore, the method is only intended for operational application phases, rather than the demonstration and design phases of manned submersibles.

GWAS on short tandem repeats identifies genetic mechanisms in Alzheimer’s disease

Abstract GWAS typically focus on SNPs, often excluding complex genetic variants, such as short tandem repeats. Here, we report the results of GWAS analyses systematically assessing the role of short tandem repeats, both imputed and directly genotyped by whole genome sequencing, on risk for Alzheimer’s disease in a large collection of ~330,000 individuals (3287 cases; 47,048 Alzheimer’s disease-by-proxy cases, 283,111 controls) from the UK biobank. Using short tandem repeat genotype data, we identify 15 independent loci showing evidence for genome-wide significant association with Alzheimer’s disease risk. While most identified loci had already been highlighted by SNP-based GWAS, we detect short tandem repeat-based signals near the genes SNX32 (chr. 11q13) and WSB1 (chr. 17q11). In addition, we delineate several other loci where short tandem repeats (and not SNPs) either represent the lead signal ( ABCA7 ) or make substantial contributions to the SNP-driven associations ( HLA-DRB1, MINDY/ADAM10 , and APOE ). Heritability analyses estimate that short tandem repeats account for at least 3% of the total phenotypic variance of Alzheimer’s disease in this dataset. Aligning our top short tandem repeats with DNA methylation and transcriptome profiles from human brain samples suggests that several short tandem repeats may unfold their effects by impacting gene expression.

Demographic-aware temporal graph attention for fair and accurate cardiac abnormality detection in 12-lead ECG

Abstract Automated 12-lead ECG interpretation has achieved strong diagnostic accuracy through deep learning, yet systematic demographic disparities—particularly between male and female patients—undermine the equitable deployment of these systems in clinical practice. Existing fairness approaches treat bias as a post-hoc correction, frequently at the cost of diagnostic performance. This paper introduces DA-GAT-v2, a Demographic-Aware Graph Attention Network designed to simultaneously advance diagnostic accuracy and algorithmic fairness in multi-label cardiac abnormality detection. Three clinically motivated architectural innovations are integrated: a lead-wise Temporal Convolutional Encoder (TCE) replacing coarse statistical node features with 128-dimensional morphologically rich embeddings capturing sex- and age-specific PQRST characteristics; a dynamic α-Net that predicts patient-specific inter-lead graph topologies by adaptively balancing anatomical adjacency and signal correlation, reflecting demographic-dependent cardiac geometry; and Feature-wise Linear Modulation (FiLM) integrated into every graph attention layer, enabling independent feature-wise demographic conditioning with substantially greater expressiveness than prior scalar gating approaches. These innovations are optimized through a three-stage curriculum training strategy incorporating a composite fairness regularization loss combining equalized odds and demographic parity constraints. Evaluated on PTB-XL (21,507 recordings), DA-GAT-v2 achieves macro F1 of 0.8952 and AUROC of 0.9762, surpassing all compared baselines. The male–female diagnostic performance gap is reduced from 15.42% to 1.75%, with an equalized odds difference (EO) of 0.0423—well within the clinical acceptance threshold of 0.10. Cross-dataset validation on Chapman-Shaoxing (10,646 recordings) confirms generalization with a minimal F1 degradation of 0.0150. Ablation studies quantify each component’s independent contribution, and attention maps reveal clinically coherent demographic-dependent lead prioritization. These results establish DA-GAT-v2 as a technically sound candidate for further clinical evaluation, demonstrating that diagnostic accuracy and demographic fairness in ECG AI are complementary objectives achievable through principled architectural design. Fairness evaluation is currently limited to sex and age subgroups owing to the absence of race and ethnicity metadata in both utilized datasets.