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How Congress can restore the independence of US science

Chemical and hydrostatic pressure induced metallization in $$\hbox {NiS}_{2-x}$$ $$\hbox {Se}_x$$ single crystals

Preoperative expiratory muscle training for swallowing function in patients with esophageal cancer undergoing esophagectomy: A randomized controlled phase II trial protocol

Esophagectomy is a highly invasive and curative procedure for esophageal cancer. Although minimally invasive techniques reduce the incidence of pulmonary complications, postoperative dysphagia remains a common and clinically significant issue. Preoperative expiratory muscle training (EMT) may improve swallowing function by strengthening the relevant muscles; however, its effectiveness in patients with esophageal cancer has not been widely studied. This phase II randomized, controlled, double-blind trial has been designed to evaluate the effects of preoperative EMT on postoperative swallowing function in patients undergoing esophagectomy for thoracic esophageal squamous cell carcinoma. Forty patients will be randomly assigned (1:1) to either the EMT or sham-EMT group. EMT will be performed using the EX-1 Medic ® device at 50–70% maximal expiratory pressure, whereas the control group will receive minimal resistance (10 cmH₂O). The primary outcome is the Penetration-Aspiration Scale score on the postoperative video-fluoroscopic swallowing study, targeted at postoperative days 10 (allowable window: postoperative days 6–14). The secondary outcomes will be assessed perioperatively (preoperative and/or postoperative, depending on the measure) and include tongue pressure, the Repetitive Saliva Swallowing Test, T he eating assessment test-10, respiratory muscle strength, appendicular skeletal muscle index, and exercise tolerance. This study has been registered at University Hospital Medical Information Network (UMINID:000057795). Recruitment began on June 10, 2025, is expected to continue until September 30, 2028. This trial will clarify whether preoperative EMT can improve swallowing function and reduce postoperative dysphagia in patients with esophageal cancer, potentially establishing a novel prehabilitation strategy in surgical care. Trial registration : UMIN Clinical Trials Registry (UMIN-CTR), UMIN000057795. Registered on May 8, 2025. https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000065994 .

The world’s salt lakes are drying up, but solutions are hard to come by

Subchondral bone alterations in temporomandibulat joint arthralgia negatively affect the functional results of stabilization appliance therapy. A retrospective exploratory cohort study

Integrative transcriptomics unravels the central role of fatty acid elongation in early cotton fiber development

Cotton fiber initiation is a critical determinant of yield and quality. To decipher the genetic networks underlying this process, we conducted a time-series transcriptome analysis of a wild-type cotton (142-WT) and its fuzzless-lintless mutant (142- fl ) at 0, 1, and 2 days post-anthesis (DPA). Phenotypic characterization confirmed the absence of fiber protrusion in the mutant. Through integrated analysis of differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA), we identified a key module highly correlated with fiber development. This module is overwhelmingly enriched for lipid metabolism pathways, notably fatty acid elongation. Furthermore, we identified 83 high-connectivity hub genes within this network, including key enzymes like 3-ketoacyl-CoA synthases ( KCS ) and regulatory transcription factors. Our findings propose a novel model in which the coordinated activation of fatty acid metabolism, particularly the biosynthesis of very-long-chain fatty acids, is essential for the initiation and early development of cotton fibers. This study provides crucial insights and valuable genetic resources for the molecular breeding of cotton with improved fiber traits.

Monthly HIV-drug injections offer potent alternative to daily tablets

PAF15–PCNA exhaustion governs the strand-specific control of DNA replication

Abstract Eukaryotic genome replication is surveyed by the S-phase checkpoint, which coordinates sequential origin activation to prevent the exhaustion of poorly defined, rate-limiting replisome components 1–3 . Here we show that excessive origin firing saturates chromatin-bound proliferating cell nuclear antigen (PCNA)—a sliding clamp for DNA polymerase processivity and Okazaki fragment processing 4 —thereby restricting further PCNA loading and lagging-strand synthesis when checkpoint control is lost. PCNA-associated factor 15 (PAF15) emerges as a dosage-sensitive regulator of this process 5–9 . During unperturbed S phase, the entire soluble PAF15 pool binds to chromatin, leaving no reserve to stabilize PCNA under conditions of excessive origin activation. PAF15 binds to PCNA specifically on the lagging strand through a high-affinity PIP motif and occupies the DNA-encircling channel, protecting the clamp and associated enzymes from premature unloading by the ATAD5–RFC complex. Conversely, overexpression of PAF15 or forced redistribution to the leading strand disrupts replisome progression and induces cell death. These detrimental effects are mitigated by Timeless–Claspin, which blocks PAF15–PCNA binding on the leading strand. E2F4-mediated repression fine-tunes PAF15 expression to ensure optimal dosage and strand specificity. These findings reveal a previously unrecognized replisome constraint: when PAF15–PCNA assemblies are exhausted, the S-phase checkpoint globally restricts origin activation, linking a strand-specific rate-limiting mechanism to global replication dynamics.

Development and physicochemical evaluation of nutritional drink using underutilized Caralluma tuberculata L.

Exploring the aesthetic cognition and artistic acceptance of AIGC-generated urban sculptures: A structural equation modeling and visual content analysis approach

As artificial intelligence–generated content (AIGC) becomes increasingly integrated into creative practices, its application in public art—particularly in urban sculpture—raises fundamental questions regarding aesthetic cognition, emotional engagement, and artistic acceptance. This study proposes and empirically tests a conceptual model to explain how general audiences perceive and evaluate AIGC-generated urban sculptures. Drawing upon Leder et al.’s aesthetic appreciation framework and theories of human–AI trust, we develop a structural equation model (SEM) comprising seven latent constructs: visual aesthetic features, cognitive mastery, emotional arousal, perceived artistic value, trust in AIGC, artistic acceptance intention, and familiarity control. A total of 24 AI-generated sculpture stimuli were produced using Midjourney v6 and evaluated along five aesthetic dimensions through expert visual content analysis. Questionnaire data were collected from 326 respondents across sculpture parks, art plazas, and university campuses in China. SEM results reveal that both cognitive mastery and emotional arousal significantly mediate the relationship between aesthetic features and perceived artistic value. Moreover, trust in AIGC and perceived artistic value jointly predict acceptance intentions, highlighting the intertwined roles of perceptual, affective, and attitudinal factors in the legitimation of AI-generated art. This research extends classical aesthetic theory to non-human creative contexts and provides practical implications for the design, deployment, and public communication of algorithmically generated urban artworks. By demonstrating that audiences can cognitively and emotionally resonate with AI-generated sculptures—contingent on visual coherence, symbolic richness, and technological trust—this study offers a novel empirical foundation for future investigations into the cultural and spatial integration of artificial creativity. However, the ecological validity of the study is inherently limited, as the stimuli consisted of digital renderings rather than physical public sculptures. Therefore, the findings represent preliminary insights into audience responses to conceptual AIGC artworks.

Fermentation quality and nutritional value of silage from sweet sorghum and mung bean grown under different planting patterns

A transfer learning approach for automatic conflicts detection in software requirement sentence pairs based on dual encoders

Software Requirement Document (RD) typically contains tens of thousands of individual requirements, and ensuring consistency among these requirements is a critical prerequisite for the success of software engineering projects. Automated detection methods can significantly enhance efficiency and reduce costs; however, existing approaches still face several challenges, including low detection accuracy on imbalanced data, limited semantic extraction due to the use of a single encoder, and poor performance in cross-domain transfer learning. To address these issues, this paper proposes a Transferable Software Requirement Conflicts Detection Framework based on SBERT and SimSCE, termed TSRCDF-SS. First, the framework employs two independent encoders named Sentence-BERT (SBERT) and Simple Contrastive Sentence Embedding (SimCSE) to generate sentence embeddings for requirement pairs, followed by a six-element concatenation strategy. Furthermore, the classifier is enhanced by incorporating a two-layer fully connected, alongside a hybrid loss function optimization strategy for feedforward neural network (FFNN) that integrates a variant of Focal Loss, domain-specific constraints, and a confidence-based penalty term. Finally, the framework synergistically integrates sequential and cross-domain transfer learning. Experimental results demonstrate that, compared with other advanced classical methods, our framework achieves an improvement ranging from 4.9% to 12.1% in macro-F1 and weighted-F1 under non-cross-domain conditions, and an average enhancement of 6% in macro-F1 under optimal cross-domain scenarios.

Granularity-guided fusion for multi-modal sentiment understanding

Comparative analysis of the characteristics, care pathways, and outcomes of English and Welsh major trauma patients injured by high versus low energy transfer mechanisms in 2019

Background Recent trends in high-income countries indicate a shift in the causes of major trauma, with low-energy transfer mechanisms, particularly falls from less than two meters, becoming increasingly prevalent. This study aimed to compare the demographics, care processes, and outcomes of major trauma patients injured by low and high-energy transfer mechanisms. Methods This comparative cohort study utilized anonymized data from adult patients recorded in the Trauma Audit and Research Network in 2019. Patients were categorized into low-energy (falls less than 2 meters) and high-energy (other mechanisms) groups. The study focused on patients with an Injury Severity Score (ISS) greater than 15. Data from up to 179 English and Welsh hospitals were included. Results In 2019, 53.6% (n = 16,087) of major trauma patients were injured by low-energy falls. When compared to the high-energy cohort, these affected older patients (median age 80 vs. 47 years; p < 0.001), with a higher prevalence of pre-existing comorbidities (90.4% [95%CI 89.9–90.8] vs. 56.2% [95%CI 55.4–57.0]; p < 0.001) and traumatic brain injuries (74.0% [95%CI 73.3–74.7] vs. 49.8% [95%CI 48.9–50.6]; p < 0.001). Low-energy fall patients were more likely to be initially treated in Trauma Units rather than Major Trauma Centres and received fewer interventions such as surgery and critical care admission. Low-energy falls patients had a higher in-hospital mortality rate (14.0% [95%CI 13.5% − 14.6%] vs. 10.3% [95%CI 9.8% − 10.8%]; p < 0.0001). Conclusions The increasing burden of major trauma from low-energy falls necessitates a re-evaluation of current trauma care systems and injury prevention strategies to better serve this distinct and growing patient population. Future research should focus on optimizing care pathways, defining patient orientated outcomes and improving outcomes for patients injured by low-energy falls.

Association between air pollution exposure and multimorbidity among middle-aged and older adults in China: a cross-sectional study

Combining lenalidomide with IL-2 family of cytokines enhances activating receptor and perforin/granzyme expression in NK cells

Background Lenalidomide is an immunomodulatory drug approved in the treatment of autoimmune disease, inflammation, and cancer. Its impact continues to grow due to its diverse spectrum of effects hampered only by toxicities and reduced efficacy. Therefore, development of strategies that enhance function while reducing drawbacks remains a prime goal. Objective and Hypothesis The mechanisms of action of lenalidomide on the activity of natural killer cells (NK cells) remains understudied yet could be critical for the development of strategies to enhance its efficacy. These cells are critical drivers of anti-tumor immune responses which are often functionally suppressed in malignancies. NK cell and T cell survival and function is driven by the IL-2 family of cytokines (IL-2 or IL-15) and work has shown that lenalidomide potentially works by increasing the secretion of IL-2 by other lymphocytes, such as CD4 + T helper cells. Thus, we hypothesized that improving NK activity with IL-2 family of cytokines could lead to enhanced lenalidomide-induced responses of these cells. Results We show that lenalidomide does not affect NK cell viability but reduces their proliferation through cell cycle arrest which could be overcome by exogenous addition of IL-2 family of cytokines. Moreover, lenalidomide induced the secretion of IL-2 on isolated NK cells although it also modulated NK receptor expression, such as NKp46, trough downregulation of PI3K/AKT pathway reduction. This was overcome by exogeneous addition of IL-2 family of cytokines increasing natural cytotoxicity, through higher perforin and granzyme expression. Mechanistically, this increased gene and protein expression occurred through the activation of STAT5 by lenalidomide which was also enhanced through the exogenous addition of IL-2 family of cytokines and modulation of IL-2R subunit changes. Conclusions These data provide a rationale for the combination of lenalidomide with IL-2 family of cytokines to enhance the effectiveness of NK cells.

Spatial transcriptomic landscape of invasion patterns in human papillomavirus-associated endocervical adenocarcinoma

Wealth, health, and happiness: An inverse story of the Easterlin Paradox in China

One popular explanation for the Easterlin paradox is that income growth over time is usually accompanied by industrialization and pollution, which cause damage to happiness that cannot be reflected by income change. We examine this explanation by exploring the effects of a large-scale environmental regulation program -the “Two Control Zones (TCZ)” Policy- on subjective well-being (SWB) using data from a series of household surveys in China. We find that, the regulation has successfully mitigated air pollution in the implemented area, although at the cost of local income. Overall, the environmental effect dominates the income effect and TCZ policy increases the SWB of affected people. In particular, despite its negative effect on income, by controlling air pollution, the TCZ policy brought a net increase in residential happiness with a money value of ￥59.04 per month in terms of 2009 CNY. This finding supports the environmental explanation of the Easterlin paradox.

Peanut-processing microbes ward off dangerous allergic shock

Topology constrained nonnegative matrix factorization for time varying omic expression

Abstract Deciphering disease-specific progression from low sample size, high-dimensional omic profiles remains challenging. Traditional biomarker discovery methods are costly and limited, while Nonnegative Matrix Factorization (NMF), though popular, suffers from instability and lack of biologically relevant solutions. This study aims to overcome these limitations by introducing a more robust framework. This article proposes TopConNMF , a topology-constrained extension of NMF which incorporates structural constraints, ensures stability, accuracy, and faster performance while maintaining biological interpretability. The method was evaluated on two publicly available time-varying omic datasets with established ground truths and compared against other state-of-the-ar t approaches. The TopConNMF consistently demonstrated stable performance across both the datasets, delivering superior accuracy and biologically relevant factorization compared to conventional NMF and other benchmark methods. The exhaustive evaluation confirmed its robustness in capturing disease-specific profiles and its efficiency in handling complex, high-dimensional data. Thus, TopConNMF provides a deeper understanding of complex biological systems by producing stable and interpretable factorization. Its broad applicability across multiple disease manifestations highlights its potential as a valuable tool for advancing omic data analysis and biomarker discovery. Clinical Impact : TopConNMF enables reliable biomarker discovery from limited omic data, supporting early diagnosis, patient stratification, and personalized treatment, thereby bridging computational findings with clinical applications.