Browse Articles

Correction for Meireles et al., Aging populations threaten conservation goals of zoos

Genome-wide rare copy number variations potentially associated with drug resistance in epilepsy

What’s the science behind acupuncture?

Cold exposure increases aortic dissection risk through extracellular cold inducible RNA binding protein and toll like receptor 4 signaling

Repetitions trigger illusory awareness in implicit statistical learning

The present study examined the accuracy of conscious representations that emerge from implicit statistical learning (ISL), a fundamental cognitive process through which we extract regularities in the environment. While ISL has several characteristics of unconscious processing (e.g., it operates unintentionally, produces subjectively unconscious knowledge), participants in ISL experiments always report some fragmentary conscious knowledge. Thus, the notion that ISL is truly an unconscious process has been the subject of perpetual debates. In the present study, we challenge the assumption that these conscious reports reflect direct access to the acquired knowledge. Combining previously collected and novel data, we tested the hypothesis that participants’ conscious reports in ISL reflect a post hoc conscious model of their nonconscious knowledge. Across two experiments, participants were exposed to sequences of stimuli (letters, faces, or body movements in VR) generated by different regularities (artificial grammars and grammar-like bigram regularities). In a subsequent test, they decided whether novel strings were grammatical or not and reported their subjective awareness trial-by-trial. In both experiments, we found extreme Bayesian evidence that repetitions embedded in the testing strings made participants more aware of the knowledge driving their grammaticality decisions, above and beyond their influence on responses or accuracy. This suggests that, lacking access to the true basis of their decisions, participants attributed their responses to the most salient feature available: the repetitions. Thus, we find evidence that our conscious experience can misrepresent not only the external world but also our own unconsciously learned representations.

The E3 ubiquitin ligase RNF180 modulates the EGFR/PI3K/AKT pathway to reduce cisplatin resistance in non-small cell lung cancer

How to quantify immigration from community abundance data using the neutral community model

Biological communities are connected through dispersal, which regulates diversity across local and regional scales. However, dispersal is difficult to measure directly, limiting what is known about dispersal’s impact on species composition in complex communities. One method to measure dispersal employs the Neutral Community Model (NCM) to quantify how a local community is influenced by the immigration of individuals from a larger source community. Conveniently, the immigration rate N T m of the NCM can be fit from biological sequence abundance datasets, which are plentiful. Yet it is neither known if these estimated values reflect the ground truth, nor what sampling effort is required to yield accurate estimates. In this study, we introduce two inference methods, a variance-based and a Dirichlet-multinomial log-likelihood (DM-LL) method, to complement the established occupancy-based inference method. In simulations of communities that resemble activated sludge microbiomes, all inference methods were capable of estimating N T m within 10% of ground-truth, with the variance-based and DM-LL methods requiring less sampling effort. Accurate inferences require read depths greater than N T m in each sample. The three methods agree in their inferred N T m in simulations of communities experiencing weak non-neutral effects (e.g., selection) and in applications to abundance datasets from wastewater activated sludge, tropical trees, and coral reefs. Based on these findings, we propose practical sampling and methodological guidelines for quantifying immigration between highly diverse, complex communities using the NCM.

Ecological flow guarantee rate along the Xijiang River mainstream at different scales based on multiple probability distributions

NMI promotes the secretion of IL-17 and exacerbates psoriasis

Damage-associated molecular patterns (DAMPs) are well established as key mediators of innate immune activation; however, their functions in modulating adaptive immunity, particularly T cell responses, remain incompletely characterized. Here, we report the identification and functional characterization of NMI—a recently identified DAMP—as a regulator of T helper 17 (Th17) cell activity and its involvement in psoriasis pathogenesis. Elevated expression of NMI was observed in psoriatic skin lesions and correlated significantly with heightened IL-17 levels. In a murine model of psoriasis, Nmi deficiency ( Nmi −/− ) resulted in a marked attenuation of skin inflammation, accompanied by significantly reduced expression of IL-17A and IL-17F. In vitro studies further demonstrated that NMI promotes the secretion of IL-17A and IL-17F from differentiated Th17 cells through its interaction with Toll-like receptor 4. Moreover, therapeutic neutralization of NMI using specific antibodies effectively ameliorated psoriatic symptoms in mice. Collectively, these results identify NMI as an endogenous enhancer of Th17-mediated immunity and highlight its potential as a therapeutic target in psoriasis.

Optimizing abrasive wear in sustainable MCC reinforced hemp bamboo epoxy composites for tribological applications

Abstract This study investigates the two-body abrasive wear characteristics of hybrid hemp and bamboo fibers in woven form epoxy (H/B F-Ep) composites reinforced with micro-crystalline cellulose (MCC) using a response surface methodology (RSM) framework and microstructural analysis. The effects of MCC content, emery paper grit, load, and abrading distance, on weight loss, coefficient of friction (CoF), and surface roughness (Ra) were assessed using four factors and three levels using Box–Behnken design. Analysis of variance (ANOVA) was used to develop and statistically validate quadratic regression models, which demonstrated strong predictive ability, a non-significant lack-of-fit, and high coefficients of determination (R² = 95.84–97.06%). Emery paper grit and abrading distance dominate wear loss, MCC content controls frictional response, and both MCC and grit have a substantial impact on surface roughness, according to an ANOVA. Strong nonlinear wear behavior under severe abrasion is indicated by significant interaction and quadratic terms, especially grit 2 and filler–grit coupling. Optimized MCC loading reduces micro-cutting and stabilizes tribo-layer development, as indicated by main-effects and interaction plots. The statistical results were supported by SEM measurements, which showed a shift from severe micro-ploughing and fiber pull-out in unfilled composites to moderate abrasion and compacted tribo-films at the optimal MCC content. To minimize wear loss (0.0385 g), CoF (0.27), and Ra (1.62 μm), with an overall desirability of 0.96, multi-response desirability optimization determined that 3 wt% MCC, 400-grit abrasive, 150 m abrading distance, and 10 N load were the optimal settings. A strong framework for customizing natural fiber hybrid composites for tribological applications is provided by the combined RSM–SEM technique.

Coupled machine learning–ecosystem ensemble models substantially improve predictions of nitrous oxide (N ₂ O) fluxes from US croplands

Nitrous oxide (N 2 O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N 2 O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high-emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N 2 O fluxes from US cropland. Trained and validated on ~12,000 N 2 O chamber measurements at 17 US Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N 2 O at both training (R 2 = 0.84, RMSE = 16.4 g N ha −1 d −1 ) and held-out testing sites (R 2 = 0.84, RMSE = 6.2 g N ha −1 d −1 ). Analyses identified six dominant N 2 O drivers: soil organic carbon (SOC), NH 4 + , NO 3 - , water-filled pore space, temperature, and aboveground biomass production. Wet, warm soils produced large N 2 O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N 2 O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.

Assessing the applicability of big data driven urban vibrancy analysis in mixed urbanized-depopulated contexts: a case study of a Japanese city

Abstract Understanding the relationship between the built environment and urban vibrancy is crucial for effective urban planning and policy development. While recent research using big data and regression analysis has identified built environment factors associated with vibrancy, most studies focus on densely populated urban cores. However, many cities now contain both thriving centers and depopulated areas, which pose sustainability challenges and require targeted strategies. This study investigates whether conventional big data driven approaches can provide reliable, context-sensitive insights when applied to a city encompassing both urbanized and depopulated areas, particularly under data limitations. Using Toyota City, Japan, as a case study, we employed large-scale GPS trajectory data as a proxy for human activity and constructed built environment factors from readily available Geographic Information System data, including land-use maps, building-use/stock information, road and railway networks, and point of interest (POI) locations, to quantify key dimensions of the built environment, namely diversity, density, and accessibility. Global and local regression models were applied to analyze spatial variation in relationships between vibrancy and built environment factors. The results show that these relationships differ markedly between urbanized and depopulated areas; for example, POI density correlates strongly with vibrancy in urbanized areas, whereas residential density is more critical in depopulated areas. These findings demonstrate that big data driven vibrancy analysis can yield meaningful insights even in data-scarce contexts, extending its applicability to diverse urbanized-depopulated settings.

Med14 phosphorylation shapes genomic response to GLP-1 agonists

Binding of GLP-1 to its receptor in pancreatic beta cells triggers activation of the cAMP pathway and phosphorylation of CREB, leading to induction of cellular target genes containing CREB binding sites. By contrast with their acute effects on beta cell gene expression, chronic exposure of beta cells to stable GLP-1 analogs like Exendin-4 stimulates sustained expression of beta cell-specific genes, leading to increases in beta cell viability and insulin secretion. In a proteomic screen for transcriptional coregulators that contribute to the transcriptional effects of GLP-1, we identified Med14, the scaffolding subunit of the conserved 30 subunit Mediator complex. Exposure to Exendin-4 and other GLP-1 receptor agonists stimulates sustained phosphorylation of Med14 at Ser983, which corresponds to a conserved PKA recognition site. Mutation of Med14 at Ser983 blocked Exendin-4 effects on cellular gene expression by interfering with CREB-mediated activation of beta cell-specific enhancers. Med14 mutation results in higher alpha-to-beta cell ratios and blunted gene regulation in response to Exendin-4 in Ser983-mutant primary mouse islets. Our work reveals how phosphorylation of a general transcription factor in response to GLP-1 analogs triggers a broad genomic response with salutary effects on beta cell function.

Awareness and attitudes toward cytoreductive surgery and hyperthermic intraperitoneal chemotherapy among surgical and medical oncologists

Stress-dependent growth in breast cancer arises from a mechano-osmotic coupling and cell-sizing checkpoint

Mechanoresponsive cell proliferation is a feature of growing tumors, despite the suppression of many other regulatory checkpoints in cancer, but the underlying cell-scale mechanisms driving this behavior have not yet been established. In this study, we propose a biophysical model for cell growth as governed by actively controlled osmolarity, which we integrate with a discrete particle framework to simulate growth and remodeling of breast cancer spheroids. Confinement and biomechanical feedback from the extracellular environment are analyzed through a neural-network-accelerated finite element solver. Combining the framework with experiments, our model reveals that stress-dependent spheroid growth can arise from a sizing checkpoint for mitosis. Under sufficient extracellular loading, cell growth is restricted by high hydrostatic forces in competition with osmotic pressure from biomolecule synthesis, which prevents cells from surpassing a critical volume. Our model provides insight into mechanosensitive growth arrest in breast cancer, potentially serving as a computational tool for analyzing growth in a wider range of normal and malignant biological tissues.

Lived experience and perceived barriers to self-care among patients with type 2 diabetes mellitus in South Ethiopia: a descriptive phenomenological study

Extracellular heme:DNA complexes promote oxidative stress and inflammation during lupus-associated hemolysis

Hemolysis is associated with the release of damage-associated molecular patterns, including free heme and extracellular DNA (ecDNA). Using several mouse models of bleeding anemia and hemolysis, we demonstrate a significant increase in plasma ecDNA, independent of neutrophil extracellular trap formation. This ecDNA forms G-quadruplex (G4) structures, which we detected in both mice and patients with systemic lupus erythematosus. Catalytic complexes (DNAzymes) formed by G4 ecDNA and heme drive oxidative stress, tissue injury, and inflammation. In anemic mice lacking deoxyribonuclease 1L3 ( DNase1l3 −/− ), we found elevated polynucleosomal ecDNA in the plasma, reduced expression of the heme-degrading enzyme heme oxygenase-1 in macrophages, but also increased plasma creatinine, renal iron accumulation, and complement C3 deposition along elevated apoptosis and DNA damage. ecDNA isolated from these mice also triggered toll-like receptor 9-dependent inflammatory responses in vitro and in vivo. In summary, these findings suggest that concurrent release of heme and ecDNA during hemolysis promotes inflammation and tissue damage, contributing to lupus pathogenesis.

Evaluation of rainwater harvesting system in university buildings for non-potable water demand

Correction for Cheng and Yu, Integrating generative AI and physical tests to strengthen claims about emergent phenomena

Study on enhanced sludge dewatering and mechanism by modified corn straw powder in conjunction with tannic acid

Abstract In sludge treatment, high moisture content presents a core challenge. Traditional flocculants often release toxic substances that can harm the environment, leading to a growing interest in natural polymer flocculants due to their eco-friendly properties. This study explores the modification of corn straw using sodium hydroxide and 3-chloro-2-hydroxypropyl trimethyl ammonium chloride, aiming to develop a natural and environmentally friendly dewatering agent. The research indicates that the modified corn straw powder can effectively disrupt the structure of sludge flocs, resulting in a significant reduction of approximately 20% in sludge moisture content. Additionally, it was found that the modified corn straw powder, in conjunction with tannic acid, can notably decrease the particle size of sludge, effectively reducing the moisture content by about 30%.