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Ultrasensitive bioluminescent reporters of protein kinase A based on luciferase activity modulated by phosphorylation (PKA LAMP)

Knowledge transfer, knowledge gaps, and knowledge silos in citation networks

The advancement of science relies on the exchange of ideas across disciplines and the integration of diverse knowledge domains. However, tracking knowledge flows and interdisciplinary integration in rapidly evolving, multidisciplinary fields remains a significant challenge. This work introduces a novel network analysis framework to study the dynamics of knowledge transfer directly from citation data. By applying dynamic community detection to cumulative, time-evolving citation networks, we can identify research areas as groups of papers sharing knowledge sources and outputs. Our analysis characterises the life-cycles and knowledge transfer patterns of these dynamic communities over time. We demonstrate our approach through a case study of eXplainable Artificial Intelligence (XAI) research, an emerging interdisciplinary field at the intersection of machine learning, statistics, and psychology. Key findings include: (i) knowledge transfer between these important foundational topics and the contemporary topics in XAI research is limited, and the extent of knowledge transfer varies across different contemporary research topics; (ii) certain application domains exist as isolated “knowledge silos”; (iii) significant “knowledge gaps” are identified between related XAI research areas, suggesting opportunities for cross-pollination and improved knowledge integration. By mapping interdisciplinary integration and bridging knowledge gaps, this work can inform strategies to synthesise ideas from disparate sources and drive innovation. More broadly, our proposed framework enables new insights into the evolution of knowledge ecosystems directly from citation data, with applications spanning literature review, research planning, and science policy.

Enhancing CAR-T cell efficacy through IFN ablation

‘Congress has your back’: US senators tell scientists they want to protect NIH budget

Sal is a proteobacterial bile acid aldolase that repurposes key thiolase catalytic residues for retroaldol cleavage of C5 steroid side chains

Burden of gram-positive bacteria from urinary tract infection: A four-year retrospective study in a tertiary health setting in southern Saudi Arabia

Background Antibiotic resistance is a worldwide problem causing significant health-related and economic losses. Gram-positive causes of urinary tract infections (UTIs) are usually underestimated or overlooked by physicians. Aim To examine the prevalence of antibiotic resistance among major gram-positive bacteria from UTIs in a tertiary care health hospital in southern Saudi Arabia. Method A cross-sectional retrospective study was done in a tertiary health setting in southern Saudi Arabia between 2019 and 2022, to identify the major gram-positive bacteria and antibiotic resistance. Data were collected from the hospital records and was analyzed using the SPSS statistical package. Results The most common gram-positive species were Enterococcus faecalis (44.7%), Staphylococcus aureus (15.1%), and Enterococcus faecium (12.9%), beta-hemolytic streptococci (8.4%), and methicillin-resistant Staphylococcus aureus (MRSA) (1.8%). The 1540 isolates showed an overall susceptibility of 71.0%, compared to a resistance of 29.0%. The most resistance was among Enterococcus faecium (54.5%), Enterococcus gallinarum (42.4%), Enterococcus faecalis (34.3%), and MRSA (27.2%). The most common resistance was to erythromycin (75.7%), followed by cefotaxime (73.9%), tetracycline (70.5%), ciprofloxacin (54.3%), and Synercid (53.6%). The prediction model indicates an increase in the prevalence of resistance in MRSA and, to a lesser extent, with E. faecalis, E. faecium, and beta-hemolytic streptococci. Conclusions Enterococcus faecalis was the predominant gram-positive species, surpassing Staphylococcus aureus. Almost remarkable resistance was observed to most of the antibiotics that are frequently used in the study area, mainly erythromycin, cefotaxime, and tetracycline. Performing continuous monitoring of drug susceptibility may help with the empirical treatment of bacterial agents in the region.

A neonatal window for in vivo gene therapy in haematopoietic stem cells

I could not have become an astronomer on my own

Silencing PIM1 inhibits ENO1-induced AKT activation and attenuates fibrillogenesis during spinal cord injury-induced skeletal muscle atrophy

Hard input, soft management and student engagement: How institutional actions promote innovation ability and academic performance among top innovative talent

The training and cultivation of innovative talent, especially top-quality talent, cannot be explained fully by reference to a simple linear input‒output model. Research that can open the “black box” that characterizes this process has important theoretical and practical significance with respect to efforts to promote the development of high-quality education. By reference to a sample consisting of 1,500 graduating doctoral students who participated in a national stratified sampling survey in China, we develop a structural equation model with the goal of investigating the different roles played by the hard inputs and soft management employed by institutions in the task of cultivating top-quality innovative doctoral students as well as the specific paths associated with this influence. Our analysis reveals that promoting learning initiative among doctoral students and regulating their level of scientific research engagement are important ways in which various institutional inputs, especially soft management measures, can affect the intrinsic innovation ability of doctoral students, whereas efforts to encourage doctoral students to pursue top-quality academic achievements on the basis of their acquisition of relevant capabilities are related mainly to the hard inputs provided by institutions.

The impact of RNA chemical probing reagents on RNA-binding proteins

The YoungFitT project: Study protocol for a randomized mixed-methods trial of physical exercise and mind-body interventions, with or without virtual reality, in university students

Background Mental health issues among young university students have increased in recent years, driven by academic stress and sedentary lifestyles. The YoungFitT Project aims to explore well-being strategies and the psychobiological mechanisms behind their effects on university students. The project includes two studies: the first evaluates the effectiveness of High-Intensity Functional Training (HIFT), Mindfulness-Based Stress Reduction (MBSR), and Qigong (QG) on psychological well-being and cognitive functions, and also explores whether socio-demographic, mental (mindful thinking, sleep quality), physical (physical fitness, physical activity), physiological (heart rate variability), and biological (microbiota) factors mediate or moderate intervention effects on university students. Given that immersive virtual reality (VR) can enhance adherence and provide additional benefits, the second study will explore the feasibility and efficacy of HIFT-VR, MBSR-VR, and QG-VR on university students’ psychological well-being and cognitive functions. Methods Two mixed-methods randomized controlled trials will be conducted. In Study 1, participants will be randomly assigned to one of three groups (HIFT, MBSR, QG) using a 1:1:1 ratio. Psychological, cognitive, physical, physiological, and biological measures will be evaluated two weeks before and after the interventions. The interventions include three weekly sessions for 12 weeks. Subsequently, a follow-up will be conducted 12 weeks after the intervention to assess psychological well-being. Study 2 is a proof-of-concept study in which VR interventions will be co-designed with input from university students and professionals. Twelve participants from each study will also complete semi-structured interviews to explore their experiences and perceived impact. Discussion The proposed interventions are expected to produce differential effects on psychological well-being and cognitive function. VR environments may enhance adherence and offer added benefits over conventional training. Findings will inform effective, personalized strategies for the mental and physical health of university youth. Trial registration www.ClinicalTrials.gov, identifier NCT06406283; Registration date: 2024/05/06.

NMR-guided identification of CYP11A1–Adrenodoxin interactions that differentially govern cholesterol and vitamin D3 metabolism

An analysis of spatial distribution characteristics and driving factors of traditional villages in the Loess Plateau

By revealing the spatial distribution characteristics and driving mechanisms of 1,027 traditional Chinese villages in the Loess Plateau, as announced in six batches up to 2023, this article provides a theoretical basis for formulating scientific and differentiated protection and development strategies. Utilizing the ArcGIS 10.8 platform and the GeoDetector model, this study comprehensively applies methods including the nearest neighbor index, standard deviation ellipse, kernel density estimation, and spatial autocorrelation to systematically analyze the spatial pattern of traditional villages, and quantitatively reveals their key driving factors and interactions through the GeoDetector. The results show: (1) Traditional villages in the Loess Plateau present a significant clustered spatial distribution (Nearest Neighbor Index R = 0.47, Moran’s I = 0.189), with an overall pattern of “dense in the east and west, sparse in the center,” primarily concentrated within an elliptical area with an flattening ratio of 0.664 and a directional angle of 84.5°, and with high-density areas located in Shanxi Province, the Yellow River basin along the Shanxi-Shaanxi border, and the Hehuang Valley. (2) Spatially, five major core clusters are formed, presenting a layered structure of “core clustering, gradient transition,” and exhibiting a non-stable diffusion trend towards the periphery. (3) The spatial differentiation is a result of the synergistic driving of human and natural factors, with human factors having a stronger overall explanatory power. Among them, the density of cultural heritage sites (q = 0.546) is the primary driving factor, followed by annual precipitation (q = 0.458), population size (q = 0.457), and urbanization rate (q = 0.354). The interaction between factors is significant and mostly shows non-linear enhancement, for instance, the interactive explanatory power of population size and the density of cultural heritage sites is as high as 0.852.

Understanding semaglutide action in the brain

Molecular basis for Gβγ-SNARE-mediated inhibition of synaptic vesicle fusion

Correction: Exploring a method for extracting concerns of multiple breast cancer patients in the domain of patient narratives using BERT and its optimization by domain adaptation using masked language modeling

Mosquito-targeted malaria control

The structural characteristics of cellular phospholipid acyl chains required for ABCA1-mediated HDL formation

Association between physical activity level and cardiovascular disease: An empirical analysis based on CHARLS data in 2018

Objective Cardiovascular disease (CVD) was a global public health challenge. This study aimed to investigate the association between physical activity level (PAL) and CVD among middle-aged and older adults in China, to provide evidence to inform strategies for CVD prevention and management. Methods The diagnosis of CVD was based on self-reported physician-diagnosed heart disease or stroke, while PAL were classified using calculations derived from a structured questionnaire. Both datasets were obtained from the 2018 China Health and Retirement Longitudinal Study (CHARLS) survey. A multivariate logistic regression model was used for the primary correlation analysis. Additionally, restricted cubic spline (RCS) regression was employed to examine the potential nonlinear association between PAL and CVD. Results The final analysis included 9,015 participants, 1,069 of whom were diagnosed with CVD, yielding a prevalence of 11.86%. After adjusting for all covariates, the multivariate-adjusted odds ratios (ORs) for the moderate PAL group (600−3000 MET-minutes/week) and the high PAL group (>3000 MET-minutes/week) were 0.79 (95% CI: 0.64–0.97) and 0.72 (95% CI: 0.60–0.87), respectively, compared to the low PAL group (<600 MET-minutes/week). Furthermore, restricted cubic spline analysis revealed a significant linear relationship between PAL and CVD (nonlinear P > 0.05), indicating that the OR for CVD decreased with higher levels of PAL. Conclusions This study revealed a negative correlation between PAL and the prevalence of CVD. Furthermore, our findings suggested that middle-aged and older adults should maintain moderate to high levels of physical activity, as this was associated with a lower risk of CVD.