Biology

Background: Bladder cancer has entered an era in which immune checkpoint blockade (ICB) and antibody-drug conjugate (ADC)-based combinations are reshaping clinical management. However, transcriptomic scores that connect prognosis, tumor microenvironment state, and treatment response are incompletely defined. Methods: Open-access TCGA-BLCA RNA-seq, clinical, mutation, copy-number, and RPPA data were downloaded from the Genomic Data Commons (GDC). Tumor-normal differential expressions, survival screening, LASSO-Cox modeling, train-test validation, GEO validation, pathway enrichment, immune signature scoring, mutation/CNV/RPPA support, drug sensitivity prediction, single-cell/spatial localization, and ICB validation were performed using reproducible Python and R scripts. A reduced model was derived using only genes shared by TCGA, GSE13507, and GSE31684. The fixed formula was then applied without refitting to IMvigor210 and GSE176307. Results: A five-gene model composed of EMP1, AHNAK, TNFRSF14, CLEC2D, and GSDMB retained TCGA internal prognostic value (train C-index 0.693, test C-index 0.605, all-sample C-index 0.667; TCGA test log-rank p = 0.015), although GEO survival validation in GSE13507 and GSE31684 was modest. High-risk tumors were enriched for epithelial-mesenchymal transition (EMT), TNF-alpha/NF-kB signaling, inflammatory response, hypoxia, complement, CAF, macrophage, checkpoint, and cytotoxic programs. Single-cell and spatial analyses localized the score to basal tumor, endothelial, fibroblast, and perivascular compartments. In IMvigor210, risk scores were higher in ICB non-responders than responders (Wilcoxon p = 0.044; AUC for non-response = 0.580), high-risk tumors had a lower responder rate (17.6% vs. 28.0%), and high risk predicted poorer OS (log-rank p = 0.016; multivariate continuous risk HR = 3.15, p = 0.044). GSE176307 showed directionally consistent but non-significant response results (AUC = 0.576). Conclusions: The five-gene score is best interpreted not as a standalone universal prognostic classifier, but as a compact stromal-EMT and immune-suppression phenotype associated with inferior ICB response. These findings support a framework linking prognosis, microenvironment biology, immunotherapy resistance, and therapeutic hypotheses in bladder cancer.

Motivation: With the scaling frequency and intensity of extreme heat events across the globe, it is critical for public institutions to develop early detection systems and continuous monitoring of these events and their impacts. In Brazil, Rio de Janeiro was the first city to publish its heat protocol, with the Rio Heat Dashboard as a central component of this system. Implementation: The dashboard was implemented using R/Shiny and integrates climatic and health data from multiple sources. General features: The application comprises real-time heat exposure monitoring and automatic alert level classification, which is monitored daily by multiple municipal actors and supports activation of actions specified in the heat protocol. It also features a health impact module, which lists each heat event and its impact on mortality, and primary care and emergency visits. Availability: The source for full reproducibility is available through https://github.com/joaohmorais/RioHeatDashboard.

Background: Biochemical recurrence (BCR) occurs in 20-40% of men after radical prostatectomy. Existing postoperative recurrence risk tools based on PSA and pathology are clinically useful but show only moderate and variable discrimination, highlighting the need for biomarkers that improve risk stratification and consequent treatment decisions. We hypothesized that the prostate microenvironment, including both the tumor and non-cancerous adjacent tissue, may contain prognostic features associated with adverse postoperative PSA outcomes. Methods: We assembled a cohort of matched tumor-adjacent benign and tumor prostate tissue from 243 men across three institutions to establish a discovery cohort (n=123; 43 postoperative PSA events, 35%) and validation cohort (n=120; 46 events, 38%). For primary binary analyses, a postoperative PSA event included BCR, defined as two consecutive postoperative PSA values >=0.2 ng/mL, or PSA persistence. We performed RNA sequencing of matched tumor-adjacent benign and tumor tissues, quantified immune signatures, and developed an integrated model combining the adjacent-tissue B-cell signature, preoperative PSA, and radical prostatectomy Gleason score (BRIGADE). CAPRA-S-adjusted Cox analyses excluding recurrence-time-0 cases evaluated time to BCR, and CD19 multiplex immunofluorescence provided tissue-level confirmation (n=10). Results: In prostatectomy specimens, tumors from patients without a postoperative PSA event were enriched for B-cell transcriptional programs, whereas tumors from event-positive patients showed elevated proliferation signatures. B-cell-related transcriptional programs were correlated between tumor and adjacent tissue. Tumor-adjacent benign B-cell scores were higher in no-event cases and discriminated postoperative PSA-event status in PCBN discovery (AUC 0.63) and BM validation (AUC 0.81) cohorts, outperforming numerous other immune-related signatures. In CAPRA-S-adjusted Cox sensitivity analyses excluding recurrence-time-0 cases, higher adjacent-tissue B-cell activity was associated with reduced recurrence risk in PCBN (HR 0.42, 95% CI 0.19-0.94; BH-adjusted p=0.035) and BM (HR 0.54, 95% CI 0.30-0.95; BH-adjusted p=0.034). Tissue-based validation showed that CD19+ B-cell density in adjacent benign tissue was higher in no-event than event-positive patients (median 0.1145 vs 0.0471; p=0.008). BRIGADE achieved an AUC of 0.68 in cross-validation and 0.83 in independent validation, compared to AUCs of 0.54-0.63 and 0.44-0.78 for the tested clinical predictors, respectively. At the fixed classification threshold, the validation-cohort odds ratio for BRIGADE was 2.75. The adjacent B-cell score remained associated with lower odds of a postoperative PSA event after adjustment for PSA and Gleason score. Conclusions: B-cell infiltration in tumor-adjacent benign prostate tissue may complement existing clinicopathologic models for stratifying adverse postoperative PSA outcomes and subsequent BCR after radical prostatectomy. The transcriptomic signal was recapitulated by CD19-based tissue staining, supporting further development of a pathology-based assay.

Brook trout (Salvelinus fontinalis) exhibit strain-level variation in growth performance, environmental tolerance, and survival, yet the biochemical mechanisms underlying these differences remain poorly understood. We developed and applied a ratiometric biochemical framework integrating the pentose-phosphate pathway (PPP) and glutathione metabolism to characterize strain-specific hepatic metabolic organization in brook trout. Five strains reared under standardized conditions differed significantly in hepatic soluble protein density, glutathione pool size, total NADP(H) concentration, and activities of glucose-6-phosphate dehydrogenase (G6PDH), glutathione reductase (GR), and transketolase (TKT). These differences were not uniformly coordinated across pathways, demonstrating that metabolic phenotype cannot be inferred from individual biomarkers alone. Derived ratios describing oxidative-to-non-oxidative PPP capacity (G6PDH/TKT) and glutathione buffering relative to recycling capacity ((GSH+GSSG)/GR) resolved distinct patterns of metabolic allocation among strains. Despite shared ancestry, the Temiscamie (TEM) strain and its domestic x TEM hybrid (TXD) exhibited markedly divergent metabolic phenotypes, demonstrating that closely related strains can differ substantially in hepatic metabolic organization. Together, these findings identify relative allocation among interconnected metabolic pathways as an axis of physiologic diversity and establish a ratiometric approach for comparing metabolic organization across populations and species. Graphical abstractHepatic metabolic phenotypes of brook trout strains were characterized by integrating pentose phosphate pathway enzyme capacities, glutathione metabolism, NADP(H) availability, and soluble protein into a ratiometric framework. Ratios distinguish investment in oxidative versus non-oxidative PPP capacity (G6PDH/TKT), antioxidant buffering versus glutathione recycling capacity (total glutathione/GR), and hepatic protein density (soluble protein/liver mass), revealing distinct metabolic organization among strains. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=88 SRC="FIGDIR/small/743818v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1694676org.highwire.dtl.DTLVardef@90f2d4org.highwire.dtl.DTLVardef@365327org.highwire.dtl.DTLVardef@8d56ca_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIA ratiometric framework was developed to characterize hepatic metabolic organization in brook trout C_LIO_LIGlutathione buffering and recycling capacity distinguish alternative redox phenotypes C_LIO_LIInvestment in oxidative and non-oxidative PPP capacity varies independently among strains C_LIO_LIG6PDH/TKT and total glutathione (GSH+GSSG)/GR reveal distinct metabolic phenotypes C_LIO_LIRatiometric indices provide a framework for interpreting redox metabolism and carbon allocation C_LI

Summary: Agentic AI platforms enable the engineering of autonomous workflows but are not designed for experimentation and hypothesis testing. ASAREE (Analytical Sandbox for Agentic AI Research, Engineering, and Experimentation), is an open-source platform to address this gap. ASAREE creates agents, connects to MCP servers and tools, and designs factorial experiments through a visual interface or Python SDK. It records a full provenance trace for every run and routes all model calls through a provider-agnostic bridge that supports local deployments, ensuring data privacy. As a use-case, we use ASAREE to evaluate key design choices in a mutli-agent machine learning pipeline. Across a 2 x 2 x 2 factorial design, more advanced models, greater reasoning effort, and critic agent use significantly increased compute time, token use, cost, and feature count without improving predictive performance. The lowest-cost baseline, Claude Sonnet 5 with medium effort and no critic, achieved the highest mean PR AUC while Claude Opus 5 with extra high effort and a critic agent cost 15.5x more (USD) and ran 13.1x longer while performing worse on average. These findings highlight ASAREE as a robust framework for evaluating agentic system performance and resource efficiency.

Prolonged low-temperature exposure may extend the preservation window of mammalian cells but can also disrupt cellular homeostasis and ultimately compromise cell viability. This study investigated the time-dependent phenotypic and transcriptomic responses of primary canine dermal fibroblasts to sustained hypothermic stress. Passage-three fibroblasts were continuously maintained at 15 for up to 15 days, with samples collected on Days 0, 3, 6, 9, 12, and 15. Cellular morphology, metabolic activity and viability, and apoptosis were evaluated using bright-field microscopy, Cell Counting Kit-8 assays, and Annexin V-FITC/propidium iodide flow cytometry, respectively. RNA sequencing was performed to characterize dynamic transcriptional changes throughout the exposure period. Early low-temperature exposure was associated with relatively preserved cellular morphology and viability, suggesting a transient adaptive response. With increasing exposure duration, fibroblasts exhibited progressive morphological deterioration, reduced metabolic activity, loss of adhesion, and increased apoptosis. Time-series transcriptomic analysis further revealed temporally coordinated and stage-dependent gene-expression programs associated with metabolic regulation, cellular stress responses, structural homeostasis, and cell survival. Integration of phenotypic and transcriptomic data demonstrated that the response of primary canine dermal fibroblasts to 15 was dynamic rather than linear, progressing from early adaptation to cumulative dysfunction during prolonged exposure. These findings provide a framework for defining the low-temperature tolerance of primary canine dermal fibroblasts and may inform the optimization of protocols for their short- to medium-term preservation and transportation.

Abstract The gayal (Bos frontalis) is an endangered semi-domesticated bovine species renowned for its high-quality beef. However, its semi-feral lifestyle, ongoing habitat fragmentation, and extensive genetic introgression from sympatric local cattle have led to dramatic population decline and severe erosion of purebred genetic integrity, posing substantial challenges to its conservation and utilization. To address the urgent demand for rapid, non-invasive, and field-compatible germplasm identification, we developed an integrated artificial intelligence (AI) framework that predicts genomic admixture composition from external morphological images. We constructed a comprehensive dataset comprising 6,245 morphological images and matched genomic sequences from 52 gayals maintained at the Yunnan Provincial Gayal Conservation Farms. Following a preliminary evaluation of nine deep learning models, five were incorporated into a anatomical segment-based multi-modal pipeline, among which Inception_V3 delivered the optimal overall performance. To enhance simultaneous extraction of local fine-grained features and global structural information, we further designed an innovative HybridInceptionViT model by integrating the multi-scale Inception module with the Vision Transformer (ViT) framework. This hybrid model significantly outperformed the baseline Inception_V3, boosting the accuracy of phenotype-derived prediction against genomic admixture estimate from 69.69% to 87.87% (absolute error <15%). This study establishes a practical, low-cost "phenotype-to-genotype" tool for rapid on-site gayal germplasm screening, offering a scalable strategy for the conservation and breeding management of endangered livestock, and holds broad application prospects for agricultural and livestock production systems.

Spodoptera frugiperda is one of the most important agronomical pests due to its migratory capacity and broad host range. Since it is resistant to several insecticides, novel control strategies are being explored to control it. In this way, Spodoptera frugiperda Multiple Nucleopolyhedrovirus, a natural pathogen, has been proposed for its biocontrol. In this work, we performed a small RNA-seq on uninfected larvae and larvae infected with SfMNPV to identify expressed miRNA, characterize them, and identify differentially expressed (DE) miRNA in the infected condition. We identified several known and putative novel miRNAs, some of which are encoded in multiple copies and may be expressed within miRNA clusters. We also found 13 DE miRNA, most of them previously reported, two of them are putative novel miRNAs identified in this work. We predicted miRNA targets and found that their putative biological role could be related with processes relevant to the infection such as proliferative and apoptotic pathways, cell cycle regulation, autophagy, DNA damage response (DDR), vesicle transport, cytoskeleton remodelling, JAK/STAT and Toll signaling pathway, and immune response activation, among others. Moreover, we observed that several of the putative targets were hub genes in a predicted protein - protein interaction network. Finally, we found DE miRNA putatively associated with the regulation of viral gene expression, suggesting they might have a role in modulating the infection. Our results contribute to better understanding the miRNA landscape in S. frugiperda, and their putative role upon SfMNPV infection.

Background: Chronic inflammation is the most common histological feature in Benign Prostatic Hyperplasia (BPH), and T cells are a key component of immune infiltrate. Advanced BPH is commonly associated with the formation of nodules, but it remains unclear whether a link exists among T cell infiltration, nodular development, and BPH progression. Using a Toxoplasma gondii (T. gondii) model and human specimens, we characterize the subtypes of T cells present during prostatic hyperplasia and their association with nodular development of the prostate. Methods: Male CBA/j mice were intraperitoneally infected with T. gondii parasites, and flow cytometry was performed on the prostate to quantify the number of CD4+ and CD8+ T cells. Histology was used to score microglandular hyperplasia (MGH), and immunofluorescence was used to quantify and examine the locality of CD4+ and CD8+ T cells and compared that to human BPH tissue. Results: We found that infecting male mice with T. gondii resulted in an increase of both CD4+ and CD8+ T cells in the prostate acutely and that CD8+ cells remained sustained at chronically. We also established the presence of glandular nodule formation at this timepoint through hematoxylin and eosin (H&E) staining. Immunofluorescence revealed that CD8+ cells were found proximal to forming glandular nodules relative to non-nodular glands. We also found more CD8+ cells localized to non-nodular glands in nodular BPH tissue versus non-nodular BPH tissue. Finally, we discovered a higher prevalence of CD8+ cells in T. gondii IgG+ patients than in IgG- patients. All T. gondii IgG+ patients exhibited nodular BPH, whereas all but one IgG- patient exhibited non-nodular BPH. Conclusions: This study is the first to investigate the presence and location of CD4+ and CD8+ T cells within nodular and non-nodular BPH glands. We found an association of the presence of CD8+ T cells with nodular progression. This association held true in human prostate tissue. Translationally, CD8+ T cells may enhance nodular BPH progression, and T. gondii infection may promote this CD8+ T cell-mediated response.

Human Papillomavirus (HPV) infection among men who have sex with men (MSM) has become a significant public health concern, particularly in countries where male vaccination is unavailable. Given the high susceptibility of MSM to HPV and anal cancer, and the unavailability of HPV vaccination for males in low- and middle-income countries (LMICs), there is a need to identify alternative interventions for reducing disease transmission and burden in this population. The novel mathematical model presented in this article couples smoking behavior dynamics with HPV transmission and anal cancer progression among MSM. Smoking reduction is introduced as an intervention to assess its effects on disease transmission and burden. The basic reproduction number (R0) is derived using the next-generation matrix method, and a global sensitivity analysis is performed using partial rank correlation coefficients (PRCC) to identify the influence of model parameters on RR0. Further, the theoretical analysis of the model reveals a backward bifurcation, implying that RR0 < 1 is necessary but not sufficient to eradicate the disease. The study finds that smoking reduction among MSM reduces HPV infection and anal cancer burden relative to baseline projections without intervention. The joint effect of smoking reduction and vaccination shows that the critical vaccination coverage needed to achieve RR0 <1 decreases as the level of smoking reduction increases. A similar outcome is observed for contact reduction. These findings highlight the importance of concurrent interventions, which can significantly curtail the spread of HPV and reduce disease burden in both the high-risk group and the general population.

Traditional animal models are often inbred and genetically uniform. This makes them powerful for controlled experiments, but it limits how well they represent the patient-to-patient variation seen in real-world disease. Comparative oncology seeks to address this gap by studying naturally occurring cancers in outbred companion animals, especially dogs. Canine medicine offers two important advantages: first, prospective trials can often be completed faster than in humans and second, dogs are already part of the translational pipeline through pharmacokinetic and toxicology studies. Here, we assessed the transcriptional fidelity of human and canine invasive urothelial carcinoma in primary tumors and patient-derived organoids. We then used single-cell and spatial data to resolve the underlying cellular organization. Despite strong species and platform differences, human and canine tumors preserved the same major luminal-basal structure and a similar tumor microenvironment. The two species reached this shared biology through different recurrent mutations. These included FGFR3 alterations in humans and BRAF alterations in dogs, which converged on overlapping pathways and a luminal phenotype. Human and canine organoids also underwent a similar shift in culture. Both became more proliferative and metabolic while losing inflammatory programs. Thus, organoids preserved important tumor biology while introducing predictable platform effects. Single-cell and spatial analyses showed that the luminal-basal axis reflects a gradient of cell states organized around the tumor-stroma boundary, rather than two discrete tumor types. This helps explain why bulk RNA-sequencing subtypes are reproducible but coarse. Together, these findings define where canine and human bladder cancer agree, where they differ, and how dogs can support parallel therapeutic and diagnostic development.

Multi-Cancer Detection (MCD) tests can detect more than 50 different types of cancer using a blood test. Recently passed law in the U.S. guarantees that Medicare will pay for these tests when they are FDA approved and show evidence for clinical benefit. This manuscript provides estimates of the cost of MCD tests to Medicare under different assumptions of cost per test, eligibility, and screening uptake in the eligible population. This manuscript additionally estimates the cost of follow-up testing resulting from false positive results, which are considered avoidable costs caused by the screening test.

Tomato production, yield and fruit quality face major challenges due to several factors, including the complex polygenic inheritance of agronomically relevant traits, biotic and abiotic stresses, and increasingly stringent regulations limiting the use of phytosanitary products. In this context, bioinoculants have emerged as a sustainable strategy capable of enhancing yield without compromising fruit quality, conferring protection against different stresses and exerting a minimal or no impact on environment and human health. In this study, we evaluated the effects and the underlying mechanisms by which Streptomyces sp. N2A, an actinobacteria isolated from soybean rhizosphere, promotes seed germination, vegetative growth and yield in tomato, without modifying fruit quality. The obtained results demonstrated that the bacterial treatment significantly improved seedlin[g]s emergence and growth and development in vegetative stage. At harvest, yield was also significantly enhanced, mainly driven by increased individual fruit weight, which was positively correlated with a thicker pericarp in fruits from N2A-treated plants. Transcriptional analysis during fruit development revealed a coordinated induction of auxin and cytokinin signaling pathways before and after anthesis, providing a hormonal framework that underlies the promotion of pericarp growth. This study provides evidence of the beneficial effect of inoculation with Streptomyces sp. N2A on tomato yield and constitutes the first report describing the modification of fruit morphology and expression of genes involved in phytohormonal modulation during early growth and development, induced by a plant growth-promoting Streptomyces.

Volatile organic compounds (VOCs) define the distinctive aroma of cannabis and critically influence consumer preference, cultivar authentication, and breeding programs. However, systematic characterization of VOC diversity across commercial drug-type cultivars remains limited. This study presents a comprehensive volatilomics-based phenotypic characterization of 165 commercial drug-type cannabis accessions using gas chromatography with flame ionization detection and mass spectrometry (GC-FID/MS). We identified 61 high-confidence VOCs assigned to three biosynthetic classes: terpenoids (n = 45), fatty acid-derived volatiles (n = 12) and amino acid-derived volatiles (n = 4), resolved into 12 subclasses. Analysis of variance revealed highly significant among-accession differences for all compounds (p < 0.001; 2 = 0.67-0.97), with repeatability estimates averaging 0.81 (range 0.50-0.95). Unsupervised clustering partitioned accessions into three distinct chemotypes (n = 90, 53, and 22), supported by principal component and t-SNE analyses. Machine learning-based feature selection identified a consensus panel of 12 discriminative compounds (camphene, -fenchene, sabinene, -terpinene, ({+/-})-limonene, -humulene, linalool, endo-fenchol, {Delta}3-carene, -thujene, {gamma}-terpinene and -phellandrene) that recovered the chemotype assignment of 32 of 33 held-out accessions. Estimated odor-activity screening ranked prenylthiol, -pinene, ({+/-})-limonene, linalool and myrcene highest among the odor-active compounds. All three chemotypes shared a prenylthiol-dominated core (67-77% of summed OAV) and were distinguished by the extent and nature of terpenoid modulation of that core: minimally modulated (Cluster ZERO), citrus-floral modulated (Cluster ONE) and pine-terpenic modulated (Cluster TWO). These findings indicate that volatile diversity in this panel can be summarized by three reproducible chemotypes, providing a quantitative basis for accession characterization and a foundation for future breeding and quality-assessment studies.

Mulberry trees have high economic and ecological value, and a robust molecular marker system plus germplasm genetic diversity analysis is critical for innovative utilization of high-quality medicinal and economic mulberry germplasm. Here, 51 mulberry samples were used to develop SNP primers via genome resequencing, with the SNP-PCR system optimized by single-factor and orthogonal assays. The phenotypic diversity and SNP molecular marker genetic diversity of 1-deoxynojirimycin (1-DNJ) in mulberry leaves were analyzed respectively, and the genetic correlation between molecular markers and phenotypic traits was evaluated by Mantel test. Tested germplasm showed marked 1-DNJ variation (0.4805-2.5300 mg/g, CV=0.4241), reflecting rich genetic diversity. The optimal SNP-PCR system included Buffer (containing Mg{superscript 2}+) 2.2 L, 2.5 mM dNTP 0.4 L, forward and reverse primers (10 mol{middle dot}L-1) totaling 2.75 L, Taq DNA polymerase (5 U{middle dot}L-1) 0.3 L, DNA (50 ng{middle dot}L-1) 1.1 L, and ddH2O 13.65 L. 23 highly polymorphic ones amplified 91 loci (81 polymorphic, 89.10% polymorphism rate). Genetic diversity analysis showed that the average genetic distance was 0.3010, and the average expected heterozygosity (H) and Shannon information index (I) reached 0.4667 and 0.3104 respectively, indicating that the genetic differentiation among the tested mulberry germplasms was significant and the population had a moderate to upper level of genetic diversity. UPGMA clustering divided 51 germplasms into 6 major groups at a genetic similarity coefficient of about 0.7, while phenotypic clustering based on 1-DNJ content divided them into 2 major categories and 4 subcategories, with high 1-DNJ germplasm clustered independently. Mantel correlation analysis showed that 6 SNP sites were significantly weakly correlated with 1-DNJ content (r < 0.3, p < 0.05), and can be used as candidate molecular markers for subsequent genetic analysis of 1-DNJ content.This study established a stable mulberry SNP-PCR system, Analyze the molecular genetic characteristics of mulberry germplasm and DNJ phenotypic variation rules respectively, and provide basic data for cluster comparison. and provided a scientific basis for marker database improvement, germplasm identification and molecular-assisted breeding.

Temperature plays a central role in marine phytoplankton biogeographical dynamics, physiology and gene expression. Nonetheless, the transcriptional regulatory mechanisms controlling temperature acclimation in marine phytoplankton are yet to be characterized. Ostreococcus tauri was chosen as a model species for green marine phytoplankton due to its cellular and genomic simplicity, as well as its evolutionary position within the green lineage. In this study, epigenomic and transcriptomic data were integrated to characterize changes induced by temperature in the trimethylation of histone 3 at lysines 27 and 4 (H3K27me3 and H3K4me3) epigenetic marks established by the Polycomb (PcG) and Trithorax group (TrxG) complexes, respectively. H3K27me3 was found to be a repressive mark responding to temperature, showing predominantly significant increased levels at high temperatures. While H3K4me3 was associated with active transcription, presenting less evident variations in cultures acclimated to different temperatures. H3K27me3 was found only marginally associated with transposable elements, being mostly involved in the repression of specific biological processes, such as gene expression control by transcription factors, meiosis, motors proteins and cytoskeletal structures. No significant conservation was found between the H3K27me3 gene targets in the model plant Arabidopsis thaliana and Ostreococcus tauri. Nonetheless, transcriptions factors belonging to the MADS-box, WRKY and AP2 families were consistently repressed by H3K27me3 in both species, unveiling that, although the specific downstream targets of this epigenetic mark have diversified during evolution, its role in modulating higher order regulatory nodes remains evolutionary conserved.

All-season mortality surveillance can inform public-health planning under climate change, but attribution is complicated by overlapping effects of temperature, respiratory infections and other non-environmental factors. Here we develop a component-based neural-network model for daily all-cause mortality that combines high-resolution temperature and dew-point data, severe acute respiratory infection (SARI) hospitalization incidence, demographic structure and an adaptive mortality baseline. In Germany, inputs included non-COVID SARI and COVID-19-associated SARI. Using 2014-2025 mortality data, the hybrid model reduced daily root-mean-square error to 80.2 deaths, compared with 170.2 for a weather-only model and 117.1 for an infection-only model, and enabled age- and sex-resolved examination of fitted components. Including respiratory-infection indicators reduced the increase in risk assigned by the model to cold exposure, suggesting that temperature-only models may partly assign winter infection-related variation to cold. Component magnitudes represent conditional model decompositions rather than causal estimates of deaths due to SARI or temperature.

A protein whose removal or deletion causes significant disruption or collapse of a protein-protein interaction (PPI) network is referred to as a vulnerable protein. Such proteins may serve as valuable therapeutic or diagnostic targets in disease-associated networks. In this study, two PPI networks were constructed, one for HPV-positive and the other for HPV-negative head and neck squamous cell carcinoma (HNSCC), and the vulnerable proteins of these networks were identified by the node deletion approach. After analyzing the networks, 27 unique vulnerable proteins in HPV-positive and 72 unique vulnerable proteins in HPV-negative HNSCC were identified. Among them, one HPV-positive and seven HPV-negative HNSCC vulnerable proteins were further chosen by integrating multi-omics data. To exploit the vulnerabilities of these proteins, candidate synthetic lethal (SL) partners were predicted whose inhibition may selectively impair tumor survival. Subsequently, drug-gene interaction analysis was performed to identify inhibitors targeting the SL partners of these vulnerable proteins. Notably, in HPV-positive HNSCC, TOP2A, CHEK1, and CHEK2 genes were identified as SL partners of TTN, and their inhibitors were already clinically approved. While in HPV-negative HNSCC, ADA and MMP19 were identified as an SL partner of LMO7; TMEM45B, CDH3, and ELF3 genes were identified as an SL partner of CGN; and ZNF433 was identified as an SL partner of FLNC. However, MMP19, ZNF433, and TMEM45B inhibitors were not reported. Thus, these vulnerable proteins, including their SL partners, provide novel avenues to explore and develop more efficient and precise therapeutic and diagnostic strategies.

Epigenetic clocks based on DNA methylation patterns are among the most accurate molecular correlates of chronological age, yet widely used clocks are predominantly empirical models with limited explicit characterization of the underlying methylation variability, lacking a direct connection to the physical mechanisms of aging. In this work, we bridge this gap by introducing an information-theoretic framework for DNA methylation dynamics combined with nonlinear machine learning to develop a competitive and interpretable age predictor. We model the population distribution of methylation {beta}-values at each CpG site using a reparameterized three-parameter Generalized Gamma Distribution (GGD) and derive a closed-form expression for its differential Shannon entropy. The resulting CpG-level entropy is used to characterize methylation variability and as a criterion for locus filtering. We introduce the Stacy Gradient Boosting Clock (Stacy-GB), which combines this GGD-based representation with a LightGBM regressor. The model was evaluated across independent cohorts using the ComputAgeBench epigenetic clock benchmark. Stacy-GB achieved a mean absolute error (MAE) of 3.74 years and a median error (bias) of 2.41 years, significantly outperforming state-of-the-art epigenetic clock baselines. Furthermore, age acceleration estimated by Stacy-GB was associated with several clinical pathologies, including ischemic heart disease, HIV infection, multiple sclerosis, and Werner syndrome, supporting its potential as an accurate and biophysically grounded tool for clinical aging research.

A better understanding factors regulating agricultural production is a research priority, given the pace of environmental change and global human population growth. Fruit orchards may be particularly vulnerable to climate change owing to the strong temperature-dependency of reproduction in temperate trees. In this study, we explored climate influences on the reproductive dynamics of apple (Malus domestica), one of the most widely-grown, economically-important fruit crops worldwide. Observations of individual-tree fruit-set, a key indicator of final yield, were acquired for three apple cultivars in an eight-year census of ~44,900 trees in >5,500 orchards spanning wide climate gradients across the Republic of Korea. With maximum-likelihood models, we quantified temporal and climate-driven patterns in fruit-set, investigated evidence for spatially-synchronized production, and conducted simulations of orchard vulnerability to climate change. We found annual fruit-set oscillated around modal levels and was synchronized between orchards within 25 km. Higher temperatures had contrasting influences, reducing fruit-set during the cold-season (consistent with climate-induced phenological shifts), while increasing fruit-set during the season of bud initiation (prior spring). Simulations predict that higher future temperatures during bud initiation increase average fruit-set, despite the constraints of cold-season warming, but that such enhancements level-off by late-century and are accompanied by heightened volatility. Decelerating fruit-set and greater instability in production have implications for future societal needs, as robust supply systems depend as much on consistent production as on high average output. Our analyses also imply that future climate patterns promoting synchrony in fruit-set among orchards may accentuate the negative consequences of fluctuating production.