Search Results (438)

Understanding global precipitation trends is critical for managing water resources, anticipating extreme events, and assessing the impacts of climate change. This study analyzes spatial and temporal patterns of precipitation from 1998 to 2024 using NASA’s Global Precipitation Measurement Mission (GPM) Integrated Multi-satellite Retrievals for (IMERG) Version 7, which merges satellite observations with rain-gauge data at 0.1° resolution. A total of 324 monthly datasets were aggregated into annual and seasonal composites to evaluate annual and seasonal trends in global precipitation. The non-parametric Mann–Kendall test was applied at the pixel scale to detect statistically significant monotonic trends, and Sen’s slope estimator method was used to quantify the magnitude of change in mean annual and seasonal global precipitation. Results reveal robust and geographically consistent patterns: significant wetting trends are evident in high-latitude regions, with the Arctic and Southern Oceans showing the strongest increases across multiple seasons, including +0.04 mm/day in December–January–February for the Arctic Ocean and +0.04 mm/day in June–July–August for the Southern Ocean. Northern China also demonstrates persistent increases, aligned with recent intensification of extreme late-season precipitation. In contrast, significant drying trends are detected in the tropical East Pacific (up to −0.02 mm/day), northern South America, and some areas in central-southern Africa, highlighting regions at risk of sustained hydroclimatic stress. The North Atlantic south of Greenland emerges as a summer drying hotspot, consistent with Greenland Ice Sheet melt enhancing stratification and reducing precipitation. Collectively, the findings underscore a dual pattern of wetting at high latitudes and drying in tropical belts, emphasizing the role of polar amplification, ocean–atmosphere interactions, and climate variability in shaping Earth’s precipitation dynamics. Full article

Colorectal cancer (CRC) exhibits profound molecular heterogeneity between left-sided and right-sided tumors with distinct therapeutic responses that current static genomic analyses incompletely explain. We developed Dynamic Functional Influence Computation (DynaFIC), a computational framework modeling time-resolved signal propagation through biological networks to quantify functional influence beyond static expression levels. Using the GSE39582 dataset comprising 583 primary CRC samples, we performed confounder-adjusted differential expression analysis controlling for microsatellite instability status, BRAF mutations, Tumor Node Metastasis (TNM) stage, age, and sex, identifying 105 laterality-associated genes that underwent DynaFIC temporal network analysis. Right-sided tumors exhibited dramatically higher network connectivity density despite fewer nodes, creating distributed vulnerability patterns with HOXC6 as the dominant regulator, achieving 200-fold influence through network amplification. Left-sided tumors showed compartmentalized, hierarchical organization with PRAC1 as the primary regulator and predictable expression-influence scaling. Temporal clustering revealed distinct propagation kinetics: right-sided tumors demonstrated rapid signal saturation requiring early intervention, while left-sided tumors exhibited sustained propagation permitting sequential approaches. Stability Volatility Index analysis showed right-sided tumors maintain significantly higher systemic vulnerability. These findings establish anatomical location as a fundamental network organizational principle, suggesting that incorporating temporal dynamics into cancer analysis reveals therapeutically relevant differences for precision medicine applications in colorectal cancer. Full article

Backgrounds: purple pakchoi (Brassica rapa subsp. chinensis (L.) Hanelt) is rich in anthocyanins, which contribute to its significant edible, ornamental, and potential health-promoting value. Fine mapping of the genes responsible for the purple-leaf trait is essential for establishing molecular marker-assisted breeding and facilitating genetic improvement. Methods: In this study, we used the inbred purple-leaf line ‘PQC’ and green-leaf line ‘HYYTC’ as parents to construct a six-generation genetic segregation population. We analyzed the inheritance pattern of the purple-leaf trait and combined Bulked Segregant Analysis Sequencing (BSA-Seq) with penta-primer amplification refractory mutation system (PARMS) to map the causal gene. Results: the main findings are as follows: the purple-leaf trait is controlled by a single dominant gene. Using BSA-Seq and PARMS, the genes were mapped to a 470 kb region (31.18–31.65 Mb) on chromosome A03. Within this interval, 29 candidate genes were identified, Bra017888 which encoding trehalose phosphate synthase 10 (TPS10), was highlighted as a potential regulator of anthocyanin biosynthesis. A developed molecular marker, SNP31304070, based on the final candidate region, successfully distinguished between purple homozygous and purple heterozygous plants in the F₂ and F₃ populations. Conclusions: the candidate gene controlling purple-leaf trait was finally located to A03 chromosome 31.18–31.65 Mb. The SNP31304070 marker and trait were co-separated, This marker could be applied to molecular-assisted breeding in purple pakchoi. Full article

In recent years, Mamba has gained increasing importance in the field of image restoration, gradually outperforming traditional convolutional neural networks (CNNs) and Transformers. However, the existing Mamba-based networks mainly focus on capturing global contextual relationships and neglect the crucial impact of local feature interactions on restoration performance in low-light environments. These environments inherently require the joint optimization of multi-scale spatial dependencies and frequency-domain characteristics. The traditional CNNs and Transformers face challenges in modeling long-range dependencies, while State Space Models (SSMs) in Mamba demonstrate proficiency in sequential modeling yet exhibit limitations in fine-grained feature extraction. To address the limitations of existing methods in capturing global degradation patterns, this paper proposes a novel global-to-local feature extraction framework through systematic Mamba integration. The Low-Frequency Mamba Block (LFMBlock) is introduced first to perform refined feature extraction in the low-frequency domain. The High-Frequency Guided Enhancement Block (HFGBlock) is used, which utilizes low-frequency priors to compensate for texture distortions in high-frequency components. Comprehensive experiments on multiple benchmark datasets show that the Global-to-Local Mamba architecture achieves superior performance in low-light restoration and image enhancement. It significantly outperforms state-of-the-art methods in both quantitative metrics and visual quality preservation, especially in recovering edge details and suppressing noise amplification under extreme illumination conditions. The hierarchical design effectively bridges global structural recovery with local texture refinement, setting a new paradigm for frequency-aware image restoration. Full article

Background: Most immunologically “cold” tumors do not respond durably to checkpoint blockade because tumor antigen (TA) release and presentation are insufficient to prime effective T-cell immunity. While prior work demonstrated synergy between cisplatin and a TLR7/8/9 agonist (CR108) in 4T1 tumors, the underlying mechanism—particularly whether chemotherapy functions as a broad antigen-releasing agent enabling TLR-driven immune amplification—remained undefined. Methods: Using murine models of breast (4T1), melanoma (B16-F10), and colorectal cancer (CT26), we tested multiple chemotherapeutic classes combined with CR108. We quantified intratumoral and systemic soluble TAs, antigen presentation and cross-priming by antigen-presenting cells, tumor-infiltrating lymphocytes, and cytokine production by flow cytometry/ICS. T-cell receptor β (TCRβ) repertoire dynamics in tumor-draining lymph nodes were profiled to assess amplitude and breadth. Tumor microenvironment remodeling was analyzed, and public datasets (e.g., TCGA basal-like breast cancer) were interrogated for expression of genes linked to TA generation/processing and peptide loading. Results: Using cisplatin + CR108 in 4T1 as a benchmark, we demonstrate that diverse chemotherapies—especially platinum agents—broadly increase the repertoire of soluble tumor antigens available for immune recognition. Across regimens, chemotherapy combined with CR108 increased T-cell recognition of candidate TAs and enhanced IFN-γ⁺ CD8⁺ responses, with platinum agents producing the largest expansions in soluble TAs. TCRβ sequencing revealed increased clonal amplitude without loss of repertoire breadth, indicating focused yet diverse antitumor T-cell expansion. Notably, therapeutic efficacy was not predicted by canonical damage-associated molecular pattern (DAMP) signatures but instead correlated with antigen availability and processing capacity. In human basal-like breast cancer, higher expression of genes involved in TA generation and antigen processing/presentation correlated with improved survival. Conclusions: Our findings establish an antigen-centric mechanism underlying chemo–TLR agonist synergy: chemotherapy liberates a broadened repertoire of tumor antigens, which CR108 then leverages via innate immune activation to drive potent, T-cell-mediated antitumor immunity. This framework for rational selection of chemotherapy partners for TLR7/8/9 agonism and support clinical evaluation to convert “cold” tumors into immunologically responsive disease. Full article

Rainfalls are the result of complex cloud microphysical processes. Trying to estimate their intensity and duration is a key task necessary for assessing precipitation magnitude. Across mountains, extreme rainfalls may cause several side effects on the ground, triggering severe geo-hydrological issues (floods and landslides) which impact people, human activities, buildings, and infrastructure. Therefore, having a tool able to reconstruct rainfall processes easily and understandably is advisable for non-expert stakeholders and researchers who deal with rainfall management. In this work, an evolution of the LUME (Linear Upslope Model Experiment), designed to simplify the study of the rainfall process, is presented. The main novelties of the new version, called LUME 2D, regard (1) the 2D domain extension, (2) the inclusion of warm-rain and cold-rain bulk-microphysical schemes (with snow and hail categories), and (3) the simulation of convective precipitations. The model was completely rewritten using Python (version 3.11) and was tested on a heavy rainfall event that occurred in Piedmont in April 2025. Using a 2D spatial and temporal interpolation of the radiosonde data, the model was able to reconstruct a realistic rainfall field of the event, reproducing rather accurately the rainfall intensity pattern. Applying the cold microphysics schemes, the snow and hail amounts were evaluated, while the rainfall intensity amplification due to the moist convection activation was detected within the results. The LUME 2D model has revealed itself to be an easy tool for carrying out further studies on intense rainfall events, improving understanding and highlighting their peculiarity in a straightforward way suitable for non-expert users. Full article

With the widespread integration of Artificial Intelligence-Generated Content (AIGC) into e-commerce platforms, understanding how users perceive, evaluate, and respond to such content has become a critical issue for both academia and industry. This study examines the influence mechanism of AIGC Content Quality (AIGCQ) on users’ Purchase Intention (PI) by constructing a cognitive model centered on Trust (TR) and Perceived Risk (PR). Additionally, it introduces two moderating variables—Ethical Concern (EC) and Perceived Platform Responsibility (PLR)—to explore higher-order psychological influences. The research variables were identified through a systematic literature review and expert interviews, followed by structural equation modeling based on data collected from 507 e-commerce users. The results indicate that AIGCQ significantly reduces users’ PR and enhances TR, while PR negatively and TR positively influence PI, validating the fundamental dual-pathway structure. However, the moderating effects reveal unexpected complexities: PLR simultaneously amplifies both the negative effect of PR and the positive effect of TR on PI, presenting a “dual amplification” pattern; meanwhile, EC weakens the strength of both pathways, manifesting a “dual attenuation” effect. These findings highlight the nonlinear cognitive mechanisms underlying users’ acceptance of AIGC, suggesting that PLR and EC influence decision-making in more intricate ways than previously anticipated. By uncovering the unanticipated patterns in moderation, this study extends the boundary conditions of the trust–risk theoretical framework within AIGC contexts. In practical terms, it reveals that PLR acts as a “double-edged sword,” providing more nuanced guidance for platform governance of AI-generated content, including responsibility frameworks and ethical labeling strategies. Full article

This study examines the effects of partial wakes caused by upstream turbine yaw control on the trailing-edge noise of a downstream turbine under stable and neutral atmospheric conditions. Using a combined model coupling the unsteady vortex lattice method (UVLM) with the Curl wake model, calibrated with large eddy simulation data, wake behavior and noise characteristics were analyzed for yaw angles from −30° to +30°. Results show that partial wakes slightly raise overall noise levels and lateral asymmetry of trailing-edge noise, while amplitude modulation (AM) strength is more strongly influenced by yaw control. AM varies linearly with wake deflection at moderate yaw angles but behaves nonlinearly beyond a threshold due to large wake deflection and deformation. Findings reveal that yaw control can significantly increase the lateral asymmetry in the AM strength directivity pattern of the downstream turbine, and that AM characteristics depend on the complex interplay between inflow distribution and convective amplification effects, highlighting the importance of accurate wake prediction, along with appropriate consideration of observer point location and blade rotation, for evaluating AM characteristics of a wind turbine influenced by a partial wake. Full article

The emergence of H5N1 highly pathogenic avian influenza virus (HPAIV) clade 2.3.4.4b in dairy cattle across multiple U.S. states in early 2024 marks a major shift in the virus’s host range and epidemiological profile. Traditionally limited to bird species, the ongoing detection of H5N1 in cattle, a mammalian host not previously considered vulnerable, raises urgent animal and human health concerns about zoonoses and mammalian adaptation. We assessed the feasibility of using commercially available pasteurized milk as a sentinel matrix for the molecular detection and genetic characterization of H5N1 HPAIV. Our aim was to determine whether retail milk could serve as a practical tool for virological monitoring and to evaluate the use of full-length genome segment amplification for extracting genomic sequence information from this highly processed matrix. Our results link HPAIV sequences in store-bought milk to the cattle outbreak and highlight both the potential and the limitations of retail milk as a surveillance window. Together, these findings provide evidence that influenza A virus RNA can be repeatedly detected in retail milk in patterns linked to specific supply chains, with genomic data confirming close relationships with the viruses circulating in cattle. Full article

Vibration monitoring plays a crucial role in assessing the condition and operational safety of electric drive systems. In many industrial applications, scalar control is widely used due to its simplicity and reliability, yet its influence on vibration transmission within interconnected machines remains insufficiently explored. This study addresses the problem of understanding how mechanical vibrations are transmitted between a scalar-controlled induction motor coupled with an AC generator. A comparative experimental investigation was conducted using two different configurations of drive units, incorporating either an induction or a synchronous generator. Vibrations were measured at various operating speeds and analysed using different sensor types to ensure repeatability and reliability of the results. The findings have revealed distinct patterns of vibration transmission between the motor and generator, highlighting the importance of drive system configuration and measurement methodology. A novel approach to data presentation is proposed by normalising vibration levels between machines, offering a clearer interpretation of vibration amplification or damping effects. The results contribute to the development of diagnostic techniques and the optimisation of scalar-controlled drive designs. Full article

Bacillus cereus sensu lato is widespread and causes significant food spoilage that alters the flavour and structure of milk. The present study investigated the prevalence, enterotoxigenic genes, and resistant profiles of B. cereus strains isolated from raw milk of Bos taurus in South Africa (the Eastern Cape Province). One thousand four hundred samples were obtained from commercial dairy farms and were evaluated for the presence of B. cereus using B. cereus selective agar, and genomic DNA was isolated from B. cereus colonies with specific characteristics. PCR was used to evaluate the presence of enterotoxigenic genes, and antibacterial susceptibility was carried out using the Kirby-Bauer Disc diffusion method. The result revealed that B. cereus was detected in 250 raw milk samples. In addition, 67.2% of the isolates grew when incubated at 4 °C. Among the enterotoxigenic genes studied, ces showed the highest occurrence (88.8%), but hblABC (0%) did not demonstrate amplification from any isolate. Our analysis found two significant patterns (III and V): nheABC-entFM (27.2% and 24.4%) and the ces gene. Total (100%) sensitivity was observed for six of the twelve antibiotics tested, while organisms showed complete resistance to penicillin and rifampicin. This study marks the initial documentation of B. cereus and its enterotoxigenic genes in Bos taurus raw milk sourced from the Eastern Cape Province, South Africa. Enterotoxin FM should be considered the second most crucial enterotoxin, after non-hemolytic enterotoxin, and should be included in the molecular approach used to classify pathogenic B. cereus in nutrimental products. These findings underscore the urgent need for public health awareness, particularly in rural communities where raw milk consumption is prevalent. The high prevalence of antibiotic resistance and toxigenic strains of B. cereus calls for improved milk pasteurization practices to mitigate the risk of foodborne illness. Full article

This paper aims to address the possibility of parametric resonance effects in microtubules via tryptophan qubits, using the Hamiltonian of the cavity quantum electrodynamics (QED) model involving photons in a waveguide and the surrounding environment. The time evolution equations for qubits and photons are derived using the input–output formulation. Input signals with a 560 nm wavelength are amplified by Rabi oscillations for tryptophan qubits in excited states. Here, the qubits organized in multiple layers are all in excited states. When an appropriate decay to the environment occurs as internal loss, which is prepared in multiple layers, we find binary patterns of the parametric amplification of input signals and the reduction of output signals. This property might help us to understand the information processing of optical signals by filtering them with the use of tryptophan residues in microtubules and diffused nonlocal processing spreading over the whole brain in the form of holograms. Full article

Graph Neural Networks (GNNs) have become a cornerstone for graph classification, yet their vulnerability to backdoor attacks remains a significant security concern. While clean-label attacks provide a stealthier approach by preserving original labels, they tend to be less effective in graph settings compared to traditional dirty-label methods. This performance gap arises from the inherent dominance of rich, benign structural patterns in target-class graphs, which overshadow the injected backdoor trigger during the GNNs’ learning process. We demonstrate that prior strategies, such as adversarial perturbations used in other domains to suppress benign features, fail in graph settings due to the amplification effects of the GNNs’ message-passing mechanism. To address this issue, we propose two strategies aimed at enabling the model to better learn backdoor features. First, we introduce a long-distance trigger injection method, placing trigger nodes at topologically distant locations. This enhances the global propagation of the backdoor signal while interfering with the aggregation of native substructures. Second, we propose a vulnerability-aware sample selection method, which identifies graphs that contribute more to the success of the backdoor attack based on low model confidence or frequent forgetting events. We conduct extensive experiments on benchmark datasets such as NCI1, NCI109, Mutagenicity, and ENZYMES, demonstrating that our approach significantly improves attack success rates (ASRs) while maintaining a low clean accuracy drop (CAD) compared to existing methods. This work offers valuable insights into manipulating the competition between benign and backdoor features in graph-structured data. Full article

Background: Centrosome amplification, a hallmark of cell cycle dysregulation, drives carcinogenesis through aneuploidy induction and invasive phenotype acquisition. In pancreatic adenocarcinoma—a malignancy characterized by profound genomic instability—the molecular circuitry of centrosome amplification remains enigmatic. Critical gaps persist in understanding its spatiotemporal dynamics in tumor microenvironment remodeling and therapy resistance. Methods: This study integrated centrosome amplification-related genes from TCGA and Genecards, established a prognostic risk model through univariate Cox regression–LASSO penalized Cox regression–multivariate Cox regression analyses, and validated it using GEO datasets. Single-cell sequencing analyses dissected transcriptional heterogeneity and intercellular communication networks, while spatially resolved transcriptomics unveiled spatiotemporal expression patterns and molecular regulatory mechanisms of core genes. With further experimental validation via PCR analysis of patient-derived tissue samples confirming key gene expression patterns. Results: This study identified 23 centrosome amplification-related prognostic genes in pancreatic adenocarcinoma, establishing IFI27, KIF20A, KLK10, SPINK7, and TOP2A as highly specific diagnostic and prognostic biomarkers. The constructed signature was established as an independent prognostic indicator correlating with aggressive clinicopathological characteristics and chemoresistance. Mechanistically linked to enhanced DNA repair capacity and accelerated cell cycle progression, also synergizes with KRAS mutational profiles. Tumor microenvironment analysis revealed significant associations with immunosuppressive. Single-cell resolution demonstrated cellular specificity of IFI27/KLK10 in ductal epithelial cells and fibroblasts, with intercellular communication networks exhibiting multidimensional regulatory features. Spatially resolved transcriptomics delineated tumor-region-specific expression patterns of core genes. While PCR validation on matched tumor/normal tissues confirmed significant differential expression of IFI27, KIF20A, KLK10, and TOP2A. Conclusions: This study deciphers the multidimensional clinic–molecular network orchestrated by centrosome amplification in PDAC, revealing its dual-pathogenic mechanism in fueling tumor aggressiveness through coordinated induction of genomic instability and immunosuppressive microenvironment reprogramming. These findings establish a translational framework for developing centrosome dynamics-based prognostic stratification and molecularly targeted therapeutic strategies. Full article

Gene duplications are considered to be the major evolutionary resource of novel functions. The gene family Esf2/ABP1 is conserved in metazoan organisms from yeast to humans. Here we performed a search and characterization of Esf2/ABP1 homologs in the Drosophila genus. Whereas in the majority of Drosophila species this gene family is represented by only a single gene, in the melanogaster and suzukii subgroups recurrent gene duplications arose, providing 47 homologous genes located on the X chromosome. To study the evolutionary history of duplicates, we performed phylogenetic, functional domain, and tissue-specific expression analyses. We revealed a male-specific and testis-biased transcription pattern of duplicated copies in Drosophila melanogaster and Drosophila sechellia compared to ubiquitous expression of the parental gene. The amplification of 21 repeated paralogs within the heterochromatic piRNA cluster resulted in the ovarian-specific transformation of these repeats into piRNAs in D. melanogaster. In three species of the suzukii subgroup, Esf2/ABP1 genes evolved with domain diversification: in addition to RNA-binding ABT1-like domain preservation, all homologous proteins acquired expanded intrinsically disordered regions. By studying the duplicated copies of the Esf2/ABP1 family in Drosophila, we offer insight into how novel gene functions emerge and are maintained, contributing to life’s diversity and complexity. Full article