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Copper-catalyzed C(sp3)−H amination and etherification of unactivated hydrocarbons via photoelectrochemical pathway

Molecular insights into the role of Estrogen Receptor Beta in Ecdysterone Mediated Anabolic Activity

Ecdysterone, often dubbed a “natural steroid,” has garnered significant attention among athletes for its reputed growth-promoting and anabolic properties. Unlike synthetic anabolic steroids, which are classified as controlled substances, ecdysteroids remain largely unregulated in many countries and are widely marketed as dietary supplements. Notably, ecdysterone has been included in the World Anti-Doping Agency (WADA) monitoring program, highlighting its potential impact on athletic performance and raising questions about its regulation. Emerging evidence indicates that, unlike traditional anabolic steroids that act primarily via the Androgen Receptor (AR), ecdysterone’s anabolic effects may be mediated through Estrogen Receptors (ERs), particularly Estrogen Receptor beta (ERβ). Despite these insights, the precise molecular mechanisms underlying ecdysterone’s biological activity remain poorly characterized, particularly from an in-silico perspective. This paper aims to address these gaps by exploring ecdysterone’s mechanism of action through computational and molecular modeling approaches. This study employs an advanced computational framework to unravel the binding dynamics and interaction mechanisms of ecdysterone with Androgen Receptor (AR), Estrogen Receptor alpha (ERα), and Estrogen Receptor beta (ERβ). Using chemical descriptor analysis, inter-molecular interaction mapping, and all-atom molecular dynamics simulations spanning 250 ns for each system, the study reveals that ecdysterone preferentially binds to ERβ, forming stable and compact complexes characterized by minimal per-residue fluctuations as evident in the average RMSD, RMSF, and Rg values observed for ERβ - Ecdysterone as 1.98 ± 0.31 Å, 1.07 ± 0.52 Å, and 18.44 ± 0.08 Å respectively which are significantly comparable with the ERβ - native complex, while high hydrogen bond occupancy was also observed for ERβ - Ecdysterone complex. Although binding free energy calculations suggest stronger interactions with ERα, the associated high fluctuations diminish its binding efficacy. In contrast, interactions with ERβ remain consistent and robust. Machine learning-based principal component analysis highlights coordinated motion patterns, while free energy profiles demonstrate stable energy basins with minimal variation. These findings underscore the pivotal role of ERβ in mediating ecdysterone’s anabolic effects, distinguishing it from traditional androgenic steroids, and provide critical insights into its unique mechanism of action. This work lays the foundation for further exploration of ecdysterone as a potential anabolic agent.

Author Correction: Multi-institutional atlas of brain metastases informs spatial modeling for precision imaging and personalized therapy

Effects of organic fertilizer replacing chemical fertilizer on organic carbon mineralization and active carbon fractions in yellow paddy soil of Guizhou Province

The aim was to decrease chemical fertilizer use and improve soil carbon sequestration. Replacing 50% chemical nitrogen fertilizer with organic fertilizer can inhibit the mineralization of organic carbon in yellow paddy soil by increasing the active organic carbon components. Four fertilization treatments (no fertilization, conventional fertilization, 50% organic fertilization and 50% chemical nitrogen fertilization, and organic fertilization instead of chemical nitrogen addition) were used to investigate the effects of using organic fertilizer instead of chemical fertilizer on soil organic carbon mineralization and active organic carbon components in paddy fields. The soil organic carbon, total nitrogen, available phosphorus, and available potassium contents were markedly higher for the organic fertilizer treatment than the no fertilization treatment. Compared with the application of chemical fertilizer alone, the substitution of chemical fertilizer with organic fertilizer significantly increased soil pH and significantly decreased the content of available potassium. The cumulative soil organic carbon mineralization rates for all treatments decreased during the incubation period. The ROC, dissolved organic carbon, and MBC contents were in 24.46%, 55.45%, and 17.60% higher, respectively, before and 19.34%, 74.98%, and 66.83%, respectively, after mineralization for 50% organic fertilization than no fertilization. Compared with the single application of chemical fertilizer, the ROC and DOC in the 1/2NPKM treatment increased significantly by 10.32% and 56.03% respectively after mineralization (p < 0.05), while the MBC in the M treatment decreased significantly by 12.05% before and 27.05% after mineralization (p < 0.05). The decrease in ROC was the most significant. Soil organic carbon mineralization was negatively correlated with SOC and active carbon fractions, and SOC was positively correlated with active carbon fractions. In summary, replacing 50% of chemical fertilizer with organic fertilizer inhibited soil organic carbon mineralization, which would improve carbon sequestration and fertilization. ROC and MBC were the main organic carbon sources mineralized.

Cell-cell communication-mediated cell-type-specific parent-of-origin effects in mammals

Examination of the psychometric properties of Arabic version of the Body Vigilance Scale

Introduction The Body Vigilance Scale (BVS) was designed and validated as a short and concise measure to assess attentional focus on bodily sensations and related processes. The BVS is available in the English language, but no Arabic version have been developed, and no validation of the scale exists in Lebanon. The current study aimed to determine the reliability, validity and factor structure of the Arabic version of the Body Vigilance Scale. Methods This study has a cross-sectional design. It was conducted from October 2 to November 20, 2024, enrolling Lebanese adults. The study was carried out in the Arabic language and included the BVS, the Patient Health Questionnaire, the Insomnia Severity Index and the Freiburg Mindfulness Inventory. Results In total, 641 participants participated in this study, with a mean age of 35.11 ± 12.67 years and 70.5% females. Internal reliability of BVS was adequate (ω = .87/ α = .86). Invariance was shown at the metric and scalar levels in terms of genders. A significantly higher mean BVS score was found in females compared to males. Higher depression (r = 0.26; p < 0.001), anxiety (r = 0.29; p < 0.001), insomnia (r = 0.29; p < 0.001) and mindfulness (r = 0.27; p < 0.001) correlated significantly with higher body vigilance scores. Conclusion The Arabic version of the BVS is a reliable and valid tool for assessing somatic attention in Arabic-speaking populations. Its psychometric robustness, demonstrated measurement invariance across genders, and associations with psychological distress measures underscore its utility in both clinical and research settings.

Concept transfer of synaptic diversity from biological to artificial neural networks

Abstract Recent developments in artificial neural networks have drawn inspiration from biological neural networks, leveraging the concept of the artificial neuron to model the learning abilities of biological nerve cells. However, while neuroscience has provided new insights into the mechanisms of biological neural networks, only a limited number of these concepts have been directly applied to artificial neural networks, with no guarantee of improved performance. Here, we address the discrepancy between the inhomogeneous and dynamic structures of biological neural networks and the largely homogeneous and fixed topologies of artificial neural networks. Specifically, we demonstrate successful integration of concepts of synaptic diversity, including spontaneous spine remodeling, synaptic plasticity diversity, and multi-synaptic connectivity, into artificial neural networks. Our findings reveal increased learning speed, prediction accuracy, and resilience to gradient inversion attacks. Our publicly available drop-in replacement code enables easy incorporation of these proposed concepts into existing networks.

Risk factors for perioperative nerve injury associated with total knee arthroplasty: Analysis of a national administrative database

Introduction Nerve injury related to total knee arthroplasty (TKA) is a rare but serious complication. Previous studies identifying risk factors for nerve injury related to TKA have been constrained by institutional data or small cohorts. The current study utilized a comprehensive, national, administrative database to investigate independent risk factors for nerve injury associated with TKA. Materials and Methods The PearlDiver M161 database was queried for adult TKA procedures performed between 2010 and 2022. Cases with postoperative nerve injury within 90 days of surgery were identified. Factors such as patient age, sex, body mass index (BMI), Elixhauser Comorbidity Index (ECI), fracture indication, and type of surgery (primary vs. revision) were evaluated for their correlation with nerve injury using multivariate analyses. Results Out of 1,517,637 TKA procedures, nerve injury was identified for 4,480 (0.3%). Multivariate analysis identified the following independent risk factors for nerve injury, listed in decreasing order of odds ratio (OR): revision surgery (OR: 1.68), female sex (OR: 1.31), ECI ≥ 5 (OR: 1.27), and younger age (OR: 1.02 per decreasing decade) (P < 0.05 for each). Factors not significantly associated with nerve injury included underweight BMI (<20 kg/m2) and fracture indication. A decreased risk of nerve injury was observed in individuals with a BMI ≥ 35 kg/m2 (OR: 0.80, P = 0.002). Discussion As expected, the incidence of nerve injury following TKA was low at 0.3%. Independent risk factors were identified for this adverse outcome, with the highest risk associated with revision surgeries. These findings, drawn from the largest cohort studied to date, offer valuable insights for risk stratification, and should inform patient discussions.

Inflammatory diseases and risk of lung cancer among individuals who have never smoked

Abstract Lung cancer in never-smokers (LCINS) is a leading cause of cancer death globally, but no screening programs for LCINS exist. To identify medical conditions that could serve as markers of LCINS risk, we conducted a nested case-control study within the United Kingdom’s Clinical Practice Research Datalink (CPRD-GOLD), consisting of 1581 LCINS cases and 14,318 never-smoking controls. Conditions significantly associated with LCINS 1-10 years before the index date were validated in an independent dataset, CPRD-Aurum (2188 LCINS cases, 19,597 never-smoking controls). These conditions include Chronic Obstructive Pulmonary Disease/Emphysema (COPD); gastroesophageal reflux disease (GERD); bronchitis and tracheitis; diabetes mellitus type 1; and gastritis and non-infective gastroenteritis and colitis. Adjusting for medication use only slightly attenuated these associations. Overall, inflammatory diseases appear to be important in LCINS pathogenesis although further studies need to confirm these associations. Conditions such as GERD or COPD could be considered as part of eligibility criteria for future LCINS screening programs.

Intelligent and precise auxiliary diagnosis of breast tumors using deep learning and radiomics

Background Breast cancer is the most common malignant tumor among women worldwide, and early diagnosis is crucial for reducing mortality rates. Traditional diagnostic methods have significant limitations in terms of accuracy and consistency. Imaging is a common technique for diagnosing and predicting breast cancer, but human error remains a concern. Increasingly, artificial intelligence (AI) is being employed to assist physicians in reducing diagnostic errors. Methods We developed an intelligent diagnostic model combining deep learning and radiomics to enhance breast tumor diagnosis. The model integrates MobileNet with ResNeXt-inspired depthwise separable and grouped convolutions, improving feature processing and efficiency while reducing parameters. Using AI-Dhabyani and TCIA breast ultrasound datasets, we validated the model internally and externally, comparing it to VGG16, ResNet, AlexNet, and MobileNet. Results: The internal validation set achieved an accuracy of 83.84% with an AUC of 0.92, outperforming other models. The external validation set showed an accuracy of 69.44% with an AUC of 0.75, demonstrating high robustness and generalizability. Conclusions: We developed an intelligent diagnostic model using deep learning and radiomics to improve breast tumor diagnosis. The model combines MobileNet with ResNeXt-inspired depthwise separable and grouped convolutions, enhancing feature processing and efficiency while reducing parameters. It was validated internally and externally using the AI-Dhabyani and TCIA breast ultrasound datasets and compared with VGG16, ResNet, AlexNet, and MobileNet.

Spectral physical unclonable functions: downscaling randomness with multi-resonant hybrid particles

Mathematical modelling of inflammatory process and obesity in osteoarthritis

Osteoarthritis (OA) is prevalent in obese people due to the inflamed adipose tissue surrounding the joints. The increase in obesity level upregulates adipokines enhancing inflammation. Whilst a few main inflammatory mediators including cytokines and adipokines have been identified, the multi-effects of obesity and exercise on OA inflammation are elusive. This study aimed to develop a five-variable mathematical model elucidating the dynamics of OA inflammation associated with obesity and physical activity. Within this model, pro- and anti-inflammatory cytokines, adipokines, matrix metalloproteinases and fibronectin fragments interact to regulate the inflammatory process. The damage of cartilage is considered crucial to stimulate the production of fibronectin fragments, subsequently leading to chronic inflammation. The adipokine production is dependent on the obesity level measured by body mass index (BMI). Hill functions are used to describe the interactions (stimulation and inhibition) between mediators and the nonlinear impacts of physical activity level on adiposity. The dynamics of this inflammation system was verified and analysed through bifurcation diagrams. Results indicate that a high BMI reduces the bistability of the system up to a BMI value of 33 for which inflammation is persistent in the non-dimensionalised model. In codimension-2 bifurcations, parameters of adipokine production can govern the transition of system behaviours. This shows the variability of individuals susceptible to OA inflammation related to obesity. The minimum damage leading to persistent inflammation is decreased as BMI increases and the correlation is nonlinear, which suggests a significant rise in OA risk with a high level of obesity. Additionally, the simulations of multiple physical activity intervention strategies suggest that physical activity can minimise and postpone inflammation by downregulating adipokines within a window period after injury. This novel computational model describes the roles of obesity and physical activity in OA inflammation, providing a mathematical framework to evaluate the risk of OA inflammation from the perspective of obesity.

Author Correction: Dietary fiber content in clinical ketogenic diets modifies the gut microbiome and seizure resistance in mice

PCB-YOLO: Enhancing PCB surface defect detection with coordinate attention and multi-scale feature fusion

Nowadays, industrial electronic products are integrated into all aspects of life, with PCB quality playing a decisive role in their performance. Ensuring PCB factory quality is thus crucial. Common PCB defects serve as key references for evaluating quality. To address low detection accuracy and the bulky size of existing models, we propose an improved PCB-YOLO model based on YOLOv8n.To reduce model size, we introduce a novel CRSCC module combining SCConv convolution and C2f, enhancing PCB defect detection accuracy and significantly reducing model parameters. For feature fusion, we propose the FFCA attention module, designed to handle PCB surface defect characteristics by fusing multi-scale local features. This improves spatial dependency capture, detail attention, feature resolution, and detection accuracy. Additionally, the WIPIoU loss function is developed to calculate IoU using auxiliary boundaries and address low-quality data, improving small-target recognition and accelerating convergence. Experimental results demonstrate significant improvements in PCB defect detection, with mAP50 increasing by 5.7%, and reductions of 13.3% and 14.8% in model parameters and computational complexity, respectively. Compared to mainstream models, PCB-YOLO achieves the best overall performance. The model’s effectiveness and generalization are further validated on the NEU-DET steel surface defect dataset, achieving excellent results. The PCB-YOLO model offers a practical, efficient solution for PCB and steel defect detection, with broad application prospects.

Constraints on sea-level rise during meltwater pulse 1B from the Great Barrier Reef

The effects of low-load resistance training combined with blood flow restriction on knee rehabilitation in middle-aged and elderly patients: A systematic review and meta-analysis

This meta-analysis evaluates the effectiveness of low-load resistance training combined with blood flow restriction in knee rehabilitation. Methods: Randomized controlled trials investigating the effects of blood flow restriction training on knee injury rehabilitation were systematically searched in the PubMed, EBSCO, and Web of Science databases for studies published between January 2000 and May 2024. The Cochrane Risk of Bias Tool was used to assess study quality, and statistical analyses were performed using Review Manager 5.3 software. Results: (1) Compared to low-load control training, blood flow restriction training showed no significant difference in pain scores (standardized mean difference = -0.10, P = 0.46) but significantly improved muscle strength (standardized mean difference = 1.11, P < 0.00001). (2) When compared to high-intensity resistance training, blood flow restriction training demonstrated no significant differences in muscle strength (standardized mean difference = -0.11, P = 0.74) or pain scores (standardized mean difference = -0.84, P = 0.17). (3) Preoperative blood flow restriction training did not significantly improve postoperative pain scores (standardized mean difference = 0.77, P = 0.37); however, among 241 patients undergoing preoperative training, blood flow restriction training significantly enhanced postoperative muscle strength (standardized mean difference = 0.97, P = 0.03). Conclusions: Although blood flow restriction training has limited effects on reducing pain, it significantly improves muscle strength, particularly in preoperative rehabilitation and low-load training settings, making it a valuable alternative in clinical knee rehabilitation strategies.

Mitigating alcohol inhibition of oxide chemiresistors: bilayer sensors with HZSM-5 zeolite overlayers

Multiuser wireless network enhancement via an innovative rime optimization search strategy

This paper introduces an Improved Rime Optimization Algorithm (IROA) designed to maximize achievable rates in multiuser wireless communication networks equipped with Reconfigurable intelligent surfaces (RISs). The proposed technique incorporates the Quadratic Interpolation Method (QIM) into the classic Rime Optimization Algorithm (ROA), which improves solution diversity, facilitates broader exploration of the search space, and enhances robustness against local optima. Finding the ideal quantity and positioning of RIS components to optimize system performance is the main goal of the optimization framework. Two objective models are taken into consideration: one that maximizes the lowest achievable rate in order to prioritize fairness, and another that maximizes the average achievable rate for all users. The performance of IROA is evaluated on systems with 20 and 50 users and compared against established algorithms such as Differential Evolution (DE), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Augmented Jellyfish Search Optimization Algorithm (AJFSOA), and Jellyfish Search Optimization Algorithm (JFSOA). Results demonstrate that the proposed IROA achieves relative performance improvements ranging from 5% to 46% across different scenarios and objective models. In the 20-user case with the first objective model, IROA achieves improvements of 28.02%, 42.07%, 46.54%, 1.74%, 35.46%, and 25.95% compared to AJFSOA, JFSOA, PSO, ROA, GWO, and DE, respectively, in terms of average achievable rate. Similarly, for the second objective model, IROA achieves relative improvements of 5.94%, 13.29%, 14.55%, 7.1%, 15.97%, and 46.26% over ROA, DE, PSO, AJFSOA, JFSOA, and GWO, respectively, in terms of minimum achievable rate. On contrary, the IROA shows lower standard deviation compared to the current ROA. However, the proposed IROA achieves superior performance over ROA in terms of the best, mean and worst objective outcomes. These findings demonstrate that in RIS-assisted wireless communication networks, the suggested IROA achieves strong flexibility and reliable performance benefits across a range of multiuser optimization tasks.

Machine learning-powered activatable NIR-II fluorescent nanosensor for in vivo monitoring of plant stress responses

Student perceptions of COVID-19 challenges affecting student motivation, well-being, and success in undergraduate education

Our objectives in this study were to understand the impact of COVID-19 disruptions on the academic and personal experiences of undergraduate students at a state land-grant institution in the Western United States, and to use those insights to identify actionable ways to improve student success. We used a mixed method survey to assess strategies used by undergraduates to adapt to COVID-19 disruptions. Results revealed that despite challenges, the majority of students continued toward their academic goals. Face-to-face classes yielded the greatest student satisfaction, and students reported great dissatisfaction with separation from peers and instructors. These insights will be especially helpful to educators and administrators in responding to future challenges and planning future approaches. This overview of students’ attitudes associated with moving from in-person to online coursework may also be useful for advising students considering which of these instructional paradigms to pursue.