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An integrative multi-omics approach points to membrane composition as a key factor in E. coli persistence
Many diverse bacteria can enter non- or slow-growing states where they are transiently tolerant to antibiotics. Despite its medical importance, the genetic mechanisms underlying this ‘persistence’ remain largely unknown, especially for spontaneous (type II) persistence that arises during exponential growth. To address this challenge, here we combine genomic, transcriptomic, and lipidomic analysis to explore the persistence mechanisms. We first analyzed the genome of the high-persistence mutant Escherichia coli DS1 ( hipQ ) to identify candidate genes for the high-persistence phenotype. We then compared the gene expression profile of these isolated persisters to that of normally growing cells with RNA-Seq and found that the activation of stress response mechanisms is likely not important in the entrance into hipQ -driven spontaneous persistence during exponential growth. Transcriptomic results also suggest that modifications in the cell membrane are closely linked to persistence, as further corroborated by lipidomic profiles showing a higher level of unsaturated fatty acids in persisters compared to normally growing cells. Taken together, our results indicate that changing membrane composition is associated with persistence, and further our understanding of spontaneous persister cells from the DS1 ( hipQ ) context.
Plant-based prebiotics to modulate skin microbiota: a novel approach for next-generation cosmeceuticals
Hydroxyl-rich nanocavities on perovskite enable nearly barrierless intramolecular hydrogen transfer for nitrate electroreduction to ammonia
Abstract Electrocatalytic nitrate reduction to ammonia (NITRR) provides a sustainable avenue for simultaneous nitrate mitigation and ammonia synthesis, but the sluggish surface hydrogen migration during NITRR remains a major bottleneck. Here, we show a barrierless hydrogen transfer pathway along intramolecular hydrogen bonds between hydroxyls of hydroxyl-rich nanocavities for efficient nitrate electroreduction to ammonia. This nanocavity is constructed via electrochemical reduction-assisted selective Sr ions leaching on the La 0.4 Sr 0.6 FeO 3-δ perovskites. Combined experimental and theoretical investigations reveal that the nanocavity features nanocavity-like architecture with hydroxyl-enriched walls, boosting active hydrogen generating and hopping for NO 3 - hydrogenation. Benefiting from such unusual intramolecular hydrogen transfer, the surface nanoconcaved La 0.4 Sr 0.6 FeO 3-δ achieves a Faradaic efficiency of 97.81 % and an ammonia yield rate of 51.37 mg h −1 cm −2 at −0.8 V versus reversible hydrogen electrode (RHE), surpassing nanocavity-free counterpart and ranking among superior NITRR catalysts. Ampere-level current density of nitrate-to-ammonia conversion are further realized in a renewable-energy-powered electrolyzer at a very low cell voltage of 2.23 V. Techno-economic analysis underscores dual benefits of this process including economic viability in ammonia synthesis and environmental impact in nitrate remediation.
Forecasting COVID-19 new cases using NBEATS deep learning and mobility data
COVID-19 is a highly contagious disease transmitted primarily through human contact. Therefore, understanding population mobility is essential for predicting COVID-19 case trends. In this paper, we propose a novel deep learning approach for forecasting new COVID-19 cases using a neural architecture called Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS). The N-BEATS model effectively handles long input sequences and large output horizons without information loss or increased computational complexity. We compare the performance of N-BEATS with a state-of-the-art benchmark model, LSTM-Markov, across four major countries: the United States, the United Kingdom, Russia, and Brazil. Three distinct COVID-19 datasets from Google, Apple, and Our World in Data (OWID) were used in this study. Incorporating Google and Apple mobility data as covariates enhances both the accuracy and interpretability of the N-BEATS model. Our results show that N-BEATS consistently outperforms LSTM-Markov across all datasets and countries, consistently yielding lower Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Furthermore, the N-BEATS model with covariates outperforms its counterpart without covariates, indicating that mobility data provide substantial value for forecasting new COVID-19 cases. Overall, this study demonstrates the effectiveness of the N-BEATS architecture in capturing pandemic dynamics and offers valuable insights for policymakers and public health officials in managing future outbreaks.
A degradation-aware and boundary-constrained network for coal-rock interface recognition
Retraction Note: Targeting 17q23 amplicon to overcome the resistance to anti-HER2 therapy in HER2+ breast cancer
Phylogeography of the Black Kite (Milvus migrans) in Punjab Wetlands: Assessing genetic connectivity and lineage admixture at a migratory crossroads
The Milvus genus presents a taxonomic challenge due to the complex delineation of its evolutionary units. Research on the potentially divergent mitochondrial lineages within Milvus migrans in Pakistan remains sparse, leaving the regional phylogeny largely uncharacterized. This study utilized partial COI gene sequences to evaluate genetic relationships and identify the presence of divergent lineages within Pakistan’s M. migrans populations. Samples collected from three Punjab wetlands (Chashma Barrage, Taunsa Barrage, and Patisar Lake) were integrated with global GenBank reference sequences to evaluate maternal diversity and reconstruct demographic histories. Maternal genetic distances among regional conspecifics were remarkably low, ranging from 0.00% to 1.00%. However, comparisons across more divergent mitochondrial lineages revealed distances reaching 2.00%, particularly between West African and European isolates; notably, these intraspecific values remained distinctly lower than the significant divergence observed with outgroup taxa. Neutrality tests indicated signatures consistent with population expansion or purifying selection. A hierarchical AMOVA confirmed strong continental-scale genetic structuring (65.39% of total variation) contrasting with high gene flow within Punjab’s panmictic local populations. Principal Coordinates Analysis (PCoA) revealed lineage-driven clustering, supporting a homogeneous admixture of three mitochondrial lineages within the Indus Flyway. These results collectively reveal a significant intersection of divergent lineages within Pakistan’s M. migrans populations, highlighting the Punjab region as a critical zone of genetic admixture. While the data indicate high maternal connectivity and extensive haplotype sharing with distant global lineages, they also hint at complex evolutionary histories. These findings underscore the ecological importance of Punjab wetlands as a migratory hub, though the hypothesis of cryptic diversity remains tentative and requires future validation via multi-locus nuclear genomic markers.
Characteristic of PM2.5 potentially toxic elements from the Plateau City (Kunming), Southwest China
Replication-independent eviction of the histone variant H2B.8 reveals chromatin reprogramming during seed imbibition
Abstract The transition from seed to seedling involves major changes in nuclear organization and gene expression, yet the extent to which this developmental transition requires chromatin reprogramming remains largely unexplored. Here, we report that Arabidopsis dry seed embryos accumulate the histone variant H2B.8, which contributes to higher-order chromatin organization by forming spatial clusters that structure the 3D nuclear space. H2B.8 predominantly assembles into heterotypic nucleosomes, is enriched at euchromatic transposons and lowly expressed genes and, during imbibition, modulates the transcriptional activation of a subset of these genes. Water uptake triggers a decrease in H2B.8 transcripts and the eviction of the H2B.8 variant from chromatin, in a process that operates independently of DNA replication but requires de novo transcription. Histone eviction is not restricted to H2B.8, as imbibition also induces turnover of the H3.3 histone variant and therefore initiates a replication-independent chromatin reprogramming process. These findings highlight a fundamental mechanism of epigenetic regulation during early plant development.
Incidence of depression in patients with psoriasis and psoriatic arthritis treated with biologic therapy: Protocol for a systematic review and meta-analysis
Background Psoriasis and psoriatic arthritis are chronic immune-mediated diseases with both physical and psychological consequences. Depression is a common comorbidity associated with impaired quality of life and lower treatment adherence. Biologics, monoclonal antibodies targeting key cytokines, may favorably influence depressive symptoms by reducing systemic inflammation and improving disease activity. However, current evidence is heterogeneous and has not been comprehensively synthesized. Methods This systematic review and meta-analysis will evaluate whether biologic therapy reduces the risk of depression among adults with psoriasis or psoriatic arthritis compared with non-biologic treatment or no therapy. Electronic searches will include MEDLINE, Embase, Cochrane Library, PsycINFO, and ClinicalTrials.gov. Eligible designs are randomized controlled trials (RCTs), cohort, and case–control studies reporting depression incidence or prevalence. Depression may be ascertained through clinical diagnosis or validated instruments. Study selection, data extraction, and risk-of-bias assessment will be performed independently by two reviewers. Using the Cochrane Risk of Bias 2.0 tool and the Newcastle–Ottawa Scale, we will assess quality of each study. Data permitting, random-effects meta-analysis will estimate pooled risk ratios (RRs) or odds ratios (ORs) with 95% confidence intervals (CIs). Certainty of evidence will be rated using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. Discussion This review will synthesize the impact of biologic therapy on incident depression in psoriasis and psoriatic arthritis, informing integrated care for patients with psoriatic disease. Systematic review registration PROSPERO (submitted; registration number to be confirmed prior to data extraction).
Integrated multi-assessment and structural performance index framework for stacking-sequence optimisation of natural fibre reinforced laminates
Cellular replisomes are powered by flex-fuel motors for unwinding DNA
Correction: The contagion effect of heterogeneous investor groups
Metaheuristic hyperparameter optimization of deep neural networks for demographic-aware autism spectrum disorder classification
Abstract Autism Spectrum Disorder (ASD) classification from neuroimaging data poses significant challenges due to high data heterogeneity and the complexity of learning robust representations across demographic subgroups. While recent deep learning approaches have shown promise, most existing studies rely on binary classification and limited model tuning strategies, overlooking demographic variability and the role of systematic optimization in model design. To address these challenges, this study formulates demographic-aware ASD classification as an optimization-driven learning problem and proposes a deep learning framework based on structural MRI (sMRI) data. The proposed framework employs three customized Convolutional Neural Network (CNN) models targeting gender-based classification, age-group-based classification, and joint age–gender classification using an octal class structure. Model architecture and training hyperparameters are automatically optimized using the Optimized Artificial Bee Colony (OptABC) algorithm, enabling task-specific adaptation without manual tuning. A dedicated preprocessing pipeline incorporating structural localization and controlled data augmentation is applied to improve robustness across heterogeneous imaging sites. All models are evaluated using five-fold cross-validation on the multi-site ABIDE dataset. Experimental results demonstrate that the proposed optimization-driven framework achieves accuracies of 84.25%, 88.07%, and 71.58% for gender-based, age-based, and joint age–gender classification tasks, respectively, yielding competitive performance relative to widely used pre-trained transfer learning models under comparable experimental settings. The results further indicate that age-aware modeling offers stronger discriminative capability than gender-based classification, while joint demographic stratification introduces increased task complexity due to finer class granularity. Overall, the findings highlight the effectiveness of metaheuristic optimization for enhancing deep learning models in complex, demographic-aware neuroimaging classification tasks. Further evaluation on independent datasets is planned to assess robustness across broader settings.
Reverse Tesla valve modulated efficient water evaporation and cooling
Evaluation of urban community digital landscape and runoff regulation effect by GIS and InVEST model
In high-density urban communities, the expansion of impervious surfaces and the fragmentation of green spaces increase surface runoff. Accurate identification of runoff regulation effects at the community scale has therefore become an important issue in landscape optimization. This study examined a typical high-density urban community. A Geographic Information System (GIS) was used to process high-resolution remote sensing images, terrain data, and meteorological records. An eight-class digital landscape classification system was then established. The Integrated Valuation of Ecosystem Services and Trade-offs–Sediment Delivery Ratio (InVEST-SDR) module was combined with landscape pattern metrics to construct a community-scale runoff regulation assessment framework. Model parameters were calibrated using observed runoff data. Runoff regulation results under current conditions and optimized scenarios were subsequently compared. The results showed that the model achieved a coefficient of determination of 0.89 and an average relative error of 1.3%, indicating reliable performance at the community scale. When impervious surfaces accounted for 37.2% of the area, the mean annual runoff depth reached 41.8 mm and the runoff coefficient was 0.52. After increasing green-space coverage by 15% and patch aggregation by 23%, the mean annual runoff depth decreased to 27.3 mm. Runoff regulation efficiency increased by 34.7%. Landscape fragmentation showed a significant positive correlation with runoff volume. Patch aggregation showed a significant positive correlation with runoff reduction rate. A dispersed green-space layout improved regulation efficiency by 18.6% compared with a centralized layout. These results indicate that the spatial structure of digital landscapes influences runoff regulation at the community scale. The GIS–InVEST coupling framework provides quantitative support for landscape optimization and spatial planning of sponge communities.
Explainable artificial intelligence reveals divergent learning in pharmacophore-based hierarchical pooling graph neural networks
Abstract Hierarchical pooling is a promising mechanism to enhance graph neural networks (GNNs) by enabling multi-scale representation learning. Rationalization of hierarchical GNN predictions remains an underexplored area. In this work, we investigate the impact of hierarchical pooling on GNNs for molecular property prediction. We designed architectural variants integrating pharmacophore features with pooling GNNs at different levels. GNN models with pharmacophore-based graph reduction or hierarchical pooling achieved comparable compound classification performance. Explainable artificial intelligence (XAI) methods were applied to compare feature importance and substructure attribution for the different model architectures. Qualitative and quantitative analyses of the resulting explanations demonstrated that the GNN variants had different internal learning characteristics. GNN models based on reduced graphs matched the prediction accuracy of models based on complete graph representations following different variant-dependent learning strategies.
Hundreds of cardiac MRI traits derived using 3D diffusion autoencoders share a common genetic architecture
Learning in a noisy world: How lucky successes and unlucky failures shape learning consequences
Learning is noisy. People can face unlucky failure , in which they fail despite using effective strategies, or experience lucky success , in which they succeed in spite of using suboptimal strategies. Both situations can serve as potentially valuable learning opportunities, as long as performance outcomes are interpreted accurately. This report examines people’s ability to recognize and adjust for these errors during the learning process. Our pilot data demonstrated that people assumed that noise affected failure at greater rates, and thereby believed successful outcomes were more informative learning cues. Based on these findings, our research aims to investigate whether experiencing a lucky success is more detrimental to future learning than experiencing unlucky failure, especially when it occurs at the beginning of a learning process. In other words, we seek to understand whether learners sufficiently discount the role of noise in successful outcomes or place too much weight on the informative value of their success, thereby hindering their learning when these outcomes are noisy.