Scientific Reports最新文献

In light of rising concerns over the psychological and academic consequences of excessive digital engagement among College of Education students, this study investigates the complex interplay between social media use, screen time, mental health, and academic outcomes. This study investigated the complex interplay between social media use, screen time, depression, mental health, assessment integrity, and internal assessment scores among tertiary students in Ghana using a cross-sectional correlational design. A total of 970 students from public Colleges of Education across Ghana were selected through stratified random sampling. Data were analyzed using Pearson correlations, multiple regression, and mediation/moderation models (Hayes PROCESS v4.0). Findings revealed that increased social media use and screen time were significantly associated with poorer mental health outcomes (R² = 0.31, p < .001), with late-night screen usage (β = - 0.29, p < .001) and emotional investment in social media (β = - 0.30, p < .001) emerging as the strongest negative predictors of mental well-being. Depression significantly mediated the relationship between social media use and academic performance (indirect effect β = - 0.14, 95% CI [- 0.21, - 0.07], p < .001), accounting for 27% of the variance in depression and 23% in academic scores. The direct path from social media use to academic performance also remained significant (β = - 0.17, p < .001), while the total effect was stronger (β = - 0.31, p < .001), indicating partial mediation. Furthermore, depression significantly predicted increased assessment dishonesty (β = 0.29, p < .001), and this relationship was amplified among students with high non-academic screen time exposure. Correlation analysis showed significant negative associations between screen time and assessment scores (r = - .28 to - 0.32, p < .01), and between depression and academic integrity (r = .25 to 0.33, p < .01). The study concludes that digital behaviors, particularly excessive and emotionally driven screen use, detrimentally affect students' mental health and ethical academic conduct. Psychological distress, especially depression, is statistically consistent with partial mediation, digital engagement to reduced academic performance and integrity. These findings highlight the need for institutional policies that promote balanced digital habits, mental health support, and integrity-centered assessment reforms in Ghanaian tertiary institutions.

Point mutations cause many genetic disorders, but modelling them in organisms is technically challenging. Creating mouse models that mimic these mutations is crucial for establishing a causal relationship between mutations and disease phenotype, thereby supporting the development of therapeutic strategies. Adenine base editors (ABEs) can correct single-nucleotide variants (SNVs) in disease modelling without double-stranded breaks (DSBs) or donor DNA, achieving higher product purity than traditional Cas9 methods. Earlier ABE techniques faced issues like limited targetability, bystander editing, and off-target effects. By combining two editor advancements, we introduced and tested ABE9-SpRY, an improved ABE variant fused with a PAM-flexible SpRY-Cas9 nickase. Our results show that ABE9-SpRY effectively generates three out of four targeted A-to-G mutations in mouse embryos, achieving desired editing efficiencies of up to 96% in individual adult founder mice. Furthermore, we observe fewer off-target events at predicted DNA sites in mouse embryos and in an orthogonal R-loop assay compared with ABE8e-SpRY. ABE9-SpRY also enhances product purity in mouse embryos under pooled sgRNA injections and, as a proof-of-concept, at a single endogenous locus in human induced pluripotent stem cells (hiPSCs), relative to ABE8e-SpRY. Our findings support ABE9-SpRY's precision at the loci tested and PAM-flexible versatility. Although performance remains sequence-dependent, these data support ABE9-SpRY as a PAM-flexible tool for generating precise point-mutation models where bystander editing is a concern.

The natural history of pulmonary Langerhans cell histiocytosis (PLCH) is unpredictable. Therefore, the identification of prognostic biomarkers for PLCH represents a major goal for better management of patients. The aim of this study was to evaluate the levels of various blood mediators in PLCH patients at diagnosis and explore their relationships with forced expiratory volume in one second (FEV₁) outcomes. We used multiplex immunoassays to measure at diagnosis the serum concentrations of thirty mediators in patients with stable vs. declining FEV₁. Multivariable-adjusted logistic regression models, accounting for matched variables (age, sex, and daily tobacco consumption), were used to compare concentrations between patients stratified on the basis of their FEV₁ values. Nine patients with declining FEV₁ profiles over time who had an available blood sample at the time of PLCH diagnosis were paired with 16 patients whose FEV₁ profiles remained stable over a median follow-up of 3.6 years [IQR 2.2-5.1]. The levels of two biomarkers, TNF-α and MMP-7, were significantly greater in patients with a decreased FEV₁ than in those with a stable FEV₁ in the univariable analysis after adjustment for matching variables. The median serum levels of TNF-α were 137 [75-358] pg/mL in the declining FEV₁ group and 60 [45-92] pg/mL in the stable FEV₁ group (p = 0.032). Similarly, the median MMP-7 levels were 16344  [13318-18000] pg/mL and 11555 [9796-12495] pg/mL, respectively (p = 0.047). There was a negative correlation between FEV₁ values and MMP-7 levels (rho = -0.65, p = 0.001) but not with TNF-α levels (rho = -0.33, p = 0.14). TNF-α and MMP-7 levels are potential prognostic blood biomarkers in PLCH.

To address the limitations of weak interpretability and poor generalization in existing taxi-out time prediction models, this study proposes a novel prediction model for departing flights based on Stacking ensemble learning and Shapley additive explanations. Firstly, decomposing taxi-out time into unimpeded taxi-out time and dynamic taxi-out time, followed by separate correlation analysis with influencing factors. Then, constructing a Stacking-based prediction model with comparative evaluation between holistic and phased prediction approaches. Finally, implementing SHAP analysis to quantify feature importance, and validate the rationality of the model using actual operating data from Shenzhen Bao'an international airport of China. The results indicate that: (1) Unimpeded taxi-out time is mainly influenced by the configuration of the airport, while the dynamic taxi-out time is mainly influenced by surface traffic flow; (2) Phased prediction shows enhanced interpretability despite marginally inferior performance (MAPE:12.0%, MAE:113.6s, RMSE:156.7s) compared to holistic prediction; (3) The Stacking model achieves superior accuracy (± 60s/±180s/±300s prediction rates: 41.0%/86.3%/96.5%) and generalization capability over existing methods; (4) The dual feature selection mechanism based on Shapley analysis and correlation analysis can ensure high prediction accuracy of the model while effectively reducing feature dimensions. (5) SHAP analysis was employed to quantify feature impacts on taxi-out time and decode feature interactions, thereby demystifying the model's black-box nature and offering actionable insights for air traffic controllers' decision-making.

Castration is a routine management practice in livestock production, used to control temperament, reproduction, and carcass traits. Beyond these intended outcomes, castration induces substantial physiological changes, particularly in energy metabolism and thermoregulation. Recent studies have revealed close interactions between host circadian physiology and the gut microbiome, suggesting that disturbances in this relationship may influence animal performance and welfare. However, the effects of castration-induced alterations in circadian body temperature (CBT) rhythms on intestinal microbial composition remain poorly understood in ruminants. This study investigated the association between castration, CBT rhythmicity, and gut microbiota composition in goats. Castration significantly modified CBT rhythms in both early- and late-castrated animals. Linear mixed-effects cosinor analysis demonstrated distinct changes in the amplitude and acrophase of CBT oscillations, with more pronounced effects observed in late-castrated goats. These physiological changes were accompanied by shifts in intestinal microbial β-diversity, as shown by distance-based redundancy analysis, suggesting that castration-induced alterations in host circadian regulation were associated with changes in microbial community structure. Given the small sample size, this study should be considered exploratory and hypothesis-generating. These findings provide new insights into the interplay between host circadian biology and the gut microbiome in ruminants. The present study may inform future research and management approaches aimed at improving animal health and productivity.

This work uses three different modalities, namely SEM, BSE and scanning white light interference (SWLI) to image fatigue fracture surfaces of Ti-6Al-4V. Convolutional neural networks (CNNs) that were pre-trained on images of the natural world were used to predict values such as distance from load line and crack growth rate. SEM images are routinely used to study the topography of fracture surfaces because the shallower interaction volume resolves surface features while BSE images and SWLI data add information about composition and surface height. Combining the three imaging modalities via the use of color channels facilitates overlaying them on the same grid for transferability of models pre-trained on colored images. This work shows that the imaging modalities under the guise of color channels have different levels of importance depending on the model being trained. It also documents how the combination of information from these modalities improves the classification and regression results by 20 and 60 %, respectively, relative to the secondary electron images alone. (162/200).