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Background Longevity and muscle strength are heritable traits, and age‐related muscle weakness is a major contributor to disability in older adults. However, the susceptibility genes and shared genetic mechanisms underlying lifespan and sarcopenia remain unclear. This study aimed to identify genes associated with longevity and muscle weakness and to characterize their shared genetic architecture. Methods We integrated the largest genome‐wide association studies (GWAS) on longevity (age > 90th: n = 11 262 cases; age > 99th: n = 3484 cases) and muscle weakness (European Working Group on Sarcopenia in Older People (EWGSOP): n = 48 596 cases; Foundation for the National Institutes of Health (FNIH): n = 20 335 cases) with Genotype‐Tissue Expression (GTEx) v8 multi‐tissue expression quantitative trait locus (eQTL) data. Gene–trait associations were evaluated using multi‐tissue and single‐tissue TWAS, and validated using Multi‐marker Analysis of GenoMic Annotation (MAGMA). Mendelian randomization (MR) and colocalization were applied to test causality and shared variants. Cross‐trait genetic correlation was estimated with LDSC, and pleiotropic loci were identified by pleiotropy analysis under the composite null hypothesis (PLACO) followed by Functional Mapping and Annotation (FUMA)/MAGMA annotation. Results Across TWAS approaches, APOC1 and TOMM40 were identified as longevity‐associated genes, while DYM and TGFA were susceptibility genes for muscle weakness. In MR analysis, higher expression of APOC1 and TOMM40 increased the odds of longevity (OR > 1, p < 0.05), whereas higher expression of DYM and TGFA reduced the risk of muscle weakness (OR < 1, p < 0.05). Colocalization supported shared causal variants for APOC1 (rs429358, PP.H4 = 0.81) and TOMM40 (rs429358, PP.H4 = 0.85) with longevity (age > 90th survival percentile), and for DYM and TGFA with muscle weakness defined by both EWGSOP and FNIH (PP.H4 > 0.80). A significant negative genetic correlation was observed between longevity and muscle weakness (Rg < 0, p < 0.05). Cross‐trait pleiotropy analysis identified several pleiotropic genes (PVRL2, PPP1R9A, SLC39A8 and the TOMM40/APOE/APOC1 gene cluster) that influence both longevity and muscle weakness. Conclusions We identified susceptibility genes for longevity (APOC1, TOMM40) and muscle weakness (DYM, TGFA) and uncovered shared pleiotropic loci linking aging and muscle decline. These findings improve the understanding of the genetic architecture underlying aging‐related phenotypes and provide potential molecular targets for promoting healthy aging and reducing late‐life disability.

• Leaf area (LA), mass per area (LMA), nitrogen per unit area (Narea) and the leaf-internal to ambient CO₂ ratio (χ) are fundamental traits for plant functional ecology and vegetation modelling. Here we aimed to assess how their variation, within and between species, tracks environmental gradients. • Measurements were made on 705 species from 116 sites within a broad north–south transect from tropical to temperate Australia. Trait responses to environment were quantified using multiple regression; within- and between-species responses were compared using analysis of covariance and trait-gradient analysis. • Leaf area, the leaf economics spectrum (indexed by LMA and Narea) and χ (from stable carbon isotope ratios) varied almost independently among species. Across sites, however, χ and LA increased with mean growing-season temperature (mGDD₀) and decreased with vapour pressure deficit (mVPD₀) and soil pH. LMA and Narea showed the reverse pattern. Climate responses agreed with expectations based on optimality principles. Within-species variability contributed < 10% to geographical variation in LA but > 90% for χ, with LMA and Narea intermediate. • These findings support the hypothesis that acclimation within individuals, adaptation within species and selection among species combine to create predictable relationships between traits and environment. However, the contribution of acclimation/adaptation vs species selection differs among traits.

Background and Aims The molecular processes involved in the progression of neuropsychiatric and liver disorders in some patients who have achieved sustained virologic response after successful DAA treatment are still unclear. To understand these processes, we investigated alterations in the transcription patterns of genes associated with neural and immune functions after DAA therapy. Methods A total of six microarray gene expression datasets related to patients who had received DAA treatment were downloaded from the Gene Expression Omnibus. Three groups comprising pretreatment and posttreatment CHC patients, as well as healthy subjects, were considered for the analysis. A weighted gene co‐expression network analysis was then performed to identify the gene groups (modules) implicated in chronic hepatitis C before and after DAA treatment. Differential gene expression (DEG) analysis and protein–protein interaction network (PPIN) analysis were then used to determine the major dysregulated genes before and after treatment. Results The common genes identified between the DEGs and selected modules, as well as further PPIN analysis, revealed the non‐normalization of novel neural‐related genes, including IRF3, FYN, CFL1, TGFβ1, DPYSL2, CDK5, and GIT1, as well as novel immune‐related genes, including IκBα, CD14, IL‐1β, IRAK1, TBK1, and CEBPB, after DAA treatment. Conclusions Our findings suggest that DAA treatment does not lead to the normalization of gene transcription patterns in CHC patients up to 6 months after HCV clearance. The non‐normalization of neuronal and immune gene expression, along with subsequent changes in the activity of related pathways, may contribute to the persistence or progression of HCV‐induced neuropsychiatric disorders and liver injuries after DAA treatment. The identified genes and their altered expression patterns provide novel insights into potential molecular mechanisms underlying disease progression following successful DAA therapy. Furthermore, these genes may serve as candidate biomarkers for monitoring disease progression or as potential targets for therapeutic intervention.

Background The extent to which earlier age of onset (AO) is a reflection of increased genetic risk for major depression (MD) is still unknown. Previous biometrical research has provided mixed empirical evidence for the genetic overlap of AO with MD. If AO is demonstrated to be relevant to molecular polygenic risk for MD, incorporation of AO as a phenotype could enhance future genetic studies. Methods This research estimated the SNP‐based heritability of AO in the China, Oxford and VCU Experimental Research on Genetic Epidemiology (CONVERGE) case‐control sample (N = 9,854; MD case, n = 4,927). Common single nucleotide polymorphism heritability of MD was also examined across both high and low median‐split AO groups, and best linear unbiased predictor (BLUP) scores of polygenic risk, in split‐halves, were used to predict AO. Distributions of genetic risk across early and late AO were compared, and presence of self‐reported family history (FH) of MD was also examined as a predictor of AO. Results AO was not significantly heritable and polygenic risk derived from the aggregated effects of common genetic variants did not significantly predict AO in any analysis. AO was modestly but significantly lower in cases with a first‐degree genetic FH of MD. Conclusions Findings indicate that AO is associated with greater self‐reported genetic risk for MD in cases, yet not associated with common variant polygenic risk for MD. Future studies of early MD may benefit more from the examination of important moderating variables such as early life events.

Social networks have attracted the attention of psychologists, as the behavior of users can be used to assess personality traits, and to detect sentiments and critical mental situations such as depression or suicidal tendencies. Recently, the increasing amount of image uploads to social networks has shifted the focus from text to image-based personality assessment. However, obtaining the ground-truth requires giving personality questionnaires to the users, making the process very costly and slow, and hindering research on large populations. In this paper, we demonstrate that it is possible to predict which images are most associated with each personality trait of the OCEAN personality model, without requiring ground-truth personality labels. Namely, we present a weakly supervised framework which shows that the personality scores obtained using specific images textually associated with particular personality traits are highly correlated with scores obtained using standard text-based personality questionnaires. We trained an OCEAN trait model based on Convolutional Neural Networks (CNNs), learned from 120K pictures posted with specific textual hashtags, to infer whether the personality scores from the images uploaded by users are consistent with those scores obtained from text. In order to validate our claims, we performed a personality test on a heterogeneous group of 280 human subjects, showing that our model successfully predicts which kind of image will match a person with a given level of a trait. Looking at the results, we obtained evidence that personality is not only correlated with text, but with image content too. Interestingly, different visual patterns emerged from those images most liked by persons with a particular personality trait: for instance, pictures most associated with high conscientiousness usually contained healthy food, while low conscientiousness pictures contained injuries, guns, and alcohol. These findings could pave the way to complement text-based personality questionnaires with image-based questions.

Summary Among above‐ and below‐ground traits, specific leaf area (SLA, cm2 g−1) and specific root length (SRL, m g−1) are the two key traits reflecting species resource acquisition strategies. However, patterns of variation in SLA and SRL have rarely been examined simultaneously across evolutionary history and environmental gradients, and the SLA–SRL relationship is still controversial on several grounds. We examined the inter‐ and intraspecific variations in SLA and SRL of different root branching orders and the SLA–SRL relationship across 55 species and 21 plant communities of four vegetation types along a 2000‐km transect in the Inner Mongolia grassland. With increasing root branching order, the interspecific variation in SRL increased, but the intraspecific variation in SRL decreased considerably, and the form of SLA–SRL relationship shifted from positive to negative. This indicates that acquisition of soil resources (e.g. water and nutrients) is a fundamental strategy for plant investment to root length. When inter‐ and intraspecific variations in SLA and SRLs were partitioned into alpha (within‐community) and beta (among‐community) components, the alpha component exhibited substantially greater inter‐ and intraspecific variations than the beta component. Across the transect, the evolutionarily late diverged species in phylogenetic tree evolved towards low SLA and SRL‐1 (SRL for first‐order roots) and tended to distribute in resource‐poor conditions along the environmental gradient. The early diverged species, in contrast, had high SLA and SRL‐1 and mostly distributed in resource‐rich conditions. Our findings suggest that patterns of inter‐ and intraspecific variations in SLA and SRL of different root branching orders and the form of SLA–SRL relationship could be well explained by within‐ and among‐community filtering processes and species divergence time. Coordination and trade‐offs between leaves and roots do not mutually exclude but operate simultaneously at different scales and among different root branching orders in arid and semi‐arid grasslands. Lay Summary

Premise Determining which traits characterize strategies of coexisting species is important to developing trait‐based models of plant communities. First, global dimensions may not exist locally. Second, the degree to which traits and trait spectra constitute independent dimensions of functional variation at various scales continues to be refined. Finally, traits may be associated with existing categorical groupings. Methods We assessed trait integration and differentiation across 57 forest understory plant species in Douglas‐fir forests of western Oregon, United States. We combined measurements for a range of traits with literature‐based estimates of seed mass and species groupings. We used network analysis and nonmetric multidimensional scaling ordination (NMS) to determine the degree of integration. Results We observed a strong leaf economics spectrum (LES) integrated with stem but not root traits. However, stem traits and intrinsic water‐use efficiency integrated LES and root traits. Network analyses indicated a modest grouping of a priori trait dimensions. NMS indicated that multivariate differences among species were related primarily to (1) rooting depth and plant height vs. specific root length, (2) the LES, and (3) leaf size vs. seed mass. These differences were related to species groupings associated with growth and life form, leaf lifespan and seed dispersal mechanisms. Conclusions The strategies of coexisting understory plant species could not be reduced to a single dimension. Yet, species can be characterized efficiently and effectively for trait‐based studies of plant communities by measuring four common traits: plant height, specific leaf area, leaf size, and seed mass.

Aim Extreme droughts, intensified by climate change, threaten floodplain ecosystems. However, the mechanisms underlying biotic responses remain insufficiently understood. We assessed drought‐induced changes in the taxonomic and functional structure of fish communities, identified key environmental and biotic drivers during the drought period, and evaluated post‐drought community recovery trajectories. Location Poyang Lake, Yangtze River Basin, China. Methods Fish assemblages were surveyed across four floodplain lakes of Poyang Lake from 2022 to 2024 during the same seasonal window to isolate drought effects. Community composition and traits were analysed in multivariate trait space to assess changes in functional identity and the abundance of key species, and diversity indices at multiple scales were calculated. Key environmental drivers were identified using ordination and trait‐environment association analyses. Results Extreme drought induced substantial shifts in fish community structure and functional composition, favouring small‐bodied, benthopelagic omnivores and causing a 94% decline in the abundance of the apex predator Culter alburnus. Simultaneously, taxonomic and functional α‐diversity declined significantly, while β‐diversity increased, driven primarily by species turnover. The mechanisms underlying community assembly also shifted, with physicochemical factors predominant before the drought, but macrophyte growth form and macrobenthic biomass becoming the dominant drivers during drought conditions. Although hydrological connectivity was restored in 2024, community recovery remained incomplete, characterised by only partial functional trait rebound and persistent suppression of taxonomic richness, particularly among habitat specialists. Main Conclusions Extreme drought functioned as a basin‐scale filter, selectively favouring fish assemblages with drought‐tolerant trait combinations while leading to the decline or local extinction of intolerant taxa. The resulting habitat contraction and fragmentation reduced the local capacity to support biodiversity and promoted niche partitioning along gradients of habitat structure and food availability. Even after the restoration of hydrological connectivity, drought‐induced habitat legacies and priority effects constrained specialist recovery, impeding the re‐establishment of pre‐drought community composition.

Aims Existing observational studies examining the effect of body fat on the risk of Parkinson disease (PD) have yielded inconsistent results. We aimed to investigate this causal relationship at the genetic level. Methods We employed two‐sample Mendelian randomization (TSMR) to investigate the causal effects of body fat on PD, with multiple sex‐specific body fat measures being involved. We performed Bayesian colocalization analysis and cross‐trait meta‐analysis to reveal pleiotropic genomic loci shared between body mass index (BMI) and PD. Finally, we used the MAGMA tool to perform tissue enrichment analysis of the genome‐wide association study hits of BMI. Results TSMR analysis suggests that except waist circumference, higher measures of body fatness are associated with a decreased risk of PD, including BMI (OR: 0.83), body fat percentage (OR: 0.69), body fat mass (OR: 0.77), and hip circumference (OR: 0.83). The observed effects were slightly more pronounced in females than males. Colocalization analysis highlighted two colocalized regions (chromosome 3p25.3 and chromosome 17p12) shared by BMI and PD and pointed to some genes as possible players, including SRGAP3, MTMR14, and ADORA2B. Cross‐trait meta‐analysis successfully identified 10 novel genomic loci, involving genes of TOX3 and MAP4K4. Tissue enrichment analysis showed that BMI‐associated genetic variants were enriched in multiple brain tissues. Conclusions We found that nonabdominal body fatness exerts a robust protective effect against PD. Our colocalization analysis and cross‐trait meta‐analysis identified pleiotropic genetic variation shared between BMI and PD, providing new clues for understanding the association between body fat and PD. Our two‐sample Mendelian randomization analysis suggested that except waist circumference, higher body mass index (BMI), body fat percentage, body fat mass, and hip circumference were protective against Parkinson disease (PD), and were more pronounced in females than in males. Colocalization analysis highlighted two colocalized regions shared by BMI and PD. Cross‐trait meta‐analysis successfully identified 10 novel genomic loci. They pointed to some genes as possible players, including SRGAP3, MTMR14, ADORA2B, TOX3, and MAP4K4. Enrichment analysis showed that BMI‐associated genetic variants were enriched in multiple brain tissues.

• Premise of the study: Continuous within-plant variation in quantitative traits of reiterated, homologous structures is a component of intraspecific variation, but its contribution to functional diversity remains largely unexplored. For the perennial Helleborus foetidus, we measured functional leaf traits to quantify the contribution of within-plant variation to intraspecific functional variance and evaluate whether within-plant variability itself deserves separate consideration. • Methods: Within-individual variation in eight leaf traits was quantified for 138 plants sampled from 10 widely spaced locations in the Sierra de Cazorla, southeastern Spain. An amplified fragment length polymorphism (AFLP) technique was used to look for associations between within-plant variability and specific AFLP markers. • Key resulrs: Leaflets from basal positions in ramets were longer, heavier, had greater surface area and larger stomata, and lower specific area, stomatal index, and stomatal density than those from distal positions. Continuous variation between leaves from the same ramet was the main source of population-wide variance for most traits. Within-plant variability differed among populations. Individuals differed in within-plant variability, which was largely independent of trait means and associated with genetic characteristics. Up to four AFLP markers were associated with the within-plant variability level of a given leaf trait. • Conclusions: Subindividual variability in continuous leaf traits was independent of plant means and related to genetic features. The within-individual component generally exceeded the between-individual component of intraspecific variance. Withinplant variation may broaden the ecological breadth and enhance stability and persistence of plant populations and communities and may provide novel insights when incorporated in trait-based community ecology models.