Search Results (33,190)

The Yangtze Block provides a natural window into the tectonic evolution of Precambrian continental crusts. The Julin Group is a dominant Precambrian stratigraphic unit in the southwestern block, the depositional age of which is still poorly constrained. The lowest sequence of this group, the Pudeng Formation, is primarily composed of mica-quartz schists and quartzites intruded by a biotite monzogranite. LA–ICP–MS zircon U–Pb ages of biotite monzogranite and detrital zircons constrain the deposition of the Julin Group to between 1099 and 1052 Ma. Geochemical compositions of the mica-quartz schists and quartzites display high δCe, ΣREE, Th/Sc, and Th/U, along with low δEu, La/Sc, Ce/Th, and Al₂O₃/(Al₂O₃ + Fe₂O₃) ratios, indicating their derivation from felsic volcanic protoliths in a passive continental margin setting. The detrital zircons show distinct age peaks at 2.5, 1.85, and 1.6 Ga, with their source regions primarily located along the western and northern Yangtze Block. Integrating the magmatic records within the Yangtze Block with the ages and ε_Hf(t) values of detrital zircons indicates that the tectonic setting of the western Yangtze Block evolved from a subduction-related arc at ~2.5 Ga to an orogenic belt at ~1.86 Ga and subsequently to intracontinental extensional (rift) environments at ~1.6 Ga and ~1.2 Ga. This evolution reflects the geodynamic transition from the Arrowsmith orogeny to the assembly and development of the Columbia and Rodinia supercontinents. Full article

Background: Germline genetic variants impacting splicing are a frequent cause of disease. The clinical interpretation of such variants is challenging for many reasons including the immense complexity of splicing mechanisms. While recent advances in splicing algorithms have improved the accuracy of splice prediction, predicting the nature and abundance of aberrant splicing remains challenging. As RNA testing becomes more mainstream in the clinical diagnostic setting, the complexities of interpretation are coming to light. Methods: Data from patients undergoing concurrent DNA and RNA testing were retrospectively reviewed for unusual splicing impacts to underscore some of these complexities and serve as exemplars in how to avoid pitfalls in the interpretation of sequence variants. Results: Seven rare variants with unusual splicing impacts are presented: a variant at a consensus donor nucleotide position lacking a splice impact (NF1 c.888+2T>C); a mid-exonic missense variant creating a novel donor site and a cryptic acceptor site resulting in pseudo-intronization (BRIP1 c.727A<G p.Ile243Val); one variant creating a spliceosome switch from U12 to U2 (LZTR1 c.2232G>A p.Ala744Ala); two variants that would be expected to result in nonsense-mediated-mRNA-decay triggering splicing impacts that obviated nonsense-mediated-decay (APC c.1042C>T p.Arg348Ter and BRCA2 c.6762del; c.6816_6841+1534del); and two variants causing splicing impacts through pyrimidine tract optimization (NF1 c.5750-184_5750-178dup and ATM c.3480G>T p.Val1160Val). Conclusions: Paired DNA and RNA testing revealed unexpected splice events altering variant interpretation, expanding our knowledge of clinically important splicing mechanisms and highlighting the benefit of RNA testing. Full article

Background and Objectives: Perforated gastro-duodenal ulcers (PGDUs) are life-threatening surgical emergencies with high morbidity and mortality. This study aimed to evaluate surgical strategies, outcomes, and prognostic factors in patients treated for PGDUs in a tertiary Eastern European center. Materials and Methods: We conducted a retrospective cross-sectional analysis of 156 patients admitted with PGDUs between 2020 and 2024. Data on demographics, risk factors, ulcer location, type of surgical approach, operative details, hospital stay, and mortality were collected. Statistical analysis included chi-square, Mann–Whitney U, and multivariate logistic regression. Results: The mean age was 57.6 ± 15.9 years (range 18–91), with men accounting for 64.7% of cases. Alcohol use was significantly associated with male sex (p = 0.012), while NSAID use was equally distributed. Open surgery was the mainstay of treatment (85.9%), with laparoscopy performed in 12.8% and conversion in 1.9%. Median hospital stay was shorter after laparoscopic repair (7.5 vs. 9 days, p = 0.039. On multivariate analysis, both age and comorbidity burden were independent predictors of mortality (p < 0.01). Conclusions: PGDU management in Eastern Europe remains dominated by open surgery. Laparoscopy, though underutilized, is associated with shorter recovery. Age is the strongest determinant of mortality, highlighting the need for early risk stratification, wider adoption of minimally invasive techniques, and preventive measures targeting modifiable risk factors. Full article

Amid intensified global technological competition and increasing restrictions on cross-border knowledge transfer, enhancing the ability to identify, integrate, and recombine diverse technological knowledge has become a critical strategy for strengthening the innovation capabilities of multinational enterprises (MNEs). Based on multidimensional proximity theory and dynamic capability theory, this study takes R&D units within Huawei’s global R&D network as the research object. It constructs a cross-border collaboration framework under the dual boundaries of organization-geography to explore the differences in the role of technological distance on the innovation performance of R&D units in different cooperation scenarios. This study also introduces technological diversification as a mediating variable to reveal the conversion path from heterogeneous knowledge input to innovation output. The findings indicate: (1) A nonlinear relationship exists between technological distance and innovation performance. In local-internal and international-internal collaborations, this relationship follows an inverted U-shaped pattern, whereas in local-external collaborations, it shows a significant positive effect. (2) In international-external collaboration, due to the dual absence of geographical and organizational proximity, the positive effect of technological distance on innovation performance is not significant. (3) The technological diversification capability of R&D units is a crucial mediating factor in the process by which technological distance affects innovation performance, thereby fostering the efficiency of heterogeneous knowledge absorption and recombination. The study examines the micro-mechanisms underlying cross-border collaborations and capability building in MNEs’ R&D units from dual perspectives of contextual fit and capability development, providing theoretical support and practical guidance for MNEs to optimize international technological collaboration mechanisms and improve innovation performance. Full article

Post-weaning diarrhea caused by Enterotoxigenic Escherichia coli (ETEC) is a major disease in piglets and leads to substantial economic losses in the swine industry. Compared to conventional lysozyme, yak stomach lysozyme (YSL) demonstrates distinctive resistance to pepsin, trypsin, high temperature, and acidic conditions. This study investigated the effects of dietary YSL supplementation on intestinal health in weaned piglets challenged with ETEC, utilizing metabolomics and proteomics. A total of 18 weaned piglets were randomly divided into three groups: control (C), diarrhea (D), and YSL treatment (YLT). Groups C and D were fed a basal diet, while the YLT group received the basal diet supplemented with YSL at a dosage of 100,000 U/kg following ETEC challenge. Following an acclimation period, piglets in groups D and YLT were orally challenged with ETEC, while group C received the same volume of sterile LB broth. The feeding trial lasted for 21 days before sample collection. The results demonstrated that dietary supplementation with YSL significantly reduced the diarrhea rate (p < 0.05). Compared with the D group, the YLT group exhibited significantly increased serum albumin levels (p < 0.05), along with a tendency toward greater villus height (p = 0.085) and higher serum glucose levels (p = 0.052), indicating an improvement in nutritional and metabolic status Metabolomic analysis identified 260 differentially abundant metabolites between the YLT and D groups (81 upregulated, 179 downregulated), which were predominantly enriched in pathways related to amino acid biosynthesis and metabolism, purine metabolism, and nucleic acid metabolism. Proteomic profiling revealed 571 differentially expressed proteins (237 upregulated, 334 downregulated). Upregulated proteins were mainly involved in arginine biosynthesis and base excision repair, while downregulated proteins were associated with the PPAR signaling pathway and Salmonella infection. In summary, dietary YSL supplementation alters the metabolic and proteomic profiles in the intestines of diarrheic piglets, potentially improving gut barrier function and nutrient utilization. This study offers novel insights into the potential of YSL as a promising feed additive for prevention of post-weaning diarrhea in pigs. Full article

To address the issue of material spillage and equipment wear caused by conveyor belt deviation in complex industrial scenarios, this study proposes a detection method based on an improved U-Net. The approach adopts U-Net as the backbone network, with a ResNet34 encoder to enhance feature extraction capability. At the skip connections, a Multi-scale Adaptive Guidance Attention (MASAG) module is embedded to strengthen the fusion of semantic and detailed features. In the loss function design, a boundary loss is incorporated to improve edge segmentation accuracy. Furthermore, the segmentation results are refined via edge detection and RANSAC regression, and a reference line is constructed based on the physical stability of rollers in the image to enable quantitative measurement of deviation. Experiments on a self-constructed dataset demonstrate that the proposed method achieves higher accuracy (99.77%) compared with the baseline U-Net (99.65%) and also surpasses other categories of approaches, including detection-based (YOLOv5s), anchor-point-based (UFLD), and segmentation-based approaches represented by SEU-Net and DeepLabV3+, thereby exhibiting strong robustness and real-time performance across diverse complex operating conditions. The results validate the effectiveness of this method in practical applications and provide a reliable technical pathway for the development of intelligent monitoring systems for mining conveyor belts. Full article

Amidrapid advances in intelligent medicine, decoding brain activity from electroencephalogram (EEG) signals has emerged as a critical technical frontier for brain–computer interfaces and medical AI systems. Given the inherent spatial resolution limitations of an EEG, researchers frequently integrate functional magnetic resonance imaging (fMRI) to enhance neural activity representation. However, fMRI acquisition is inherently complex. Consequently, efforts increasingly focus on cross-modal transformation methods that map EEG signals to fMRI data, thereby extending EEG applications in neural mechanism studies. The central challenge remains generating high-fidelity fMRI images from EEG signals. To address this, we propose a diffusion model-based framework for cross-modal EEG-to-fMRI generation. To address pronounced noise contamination in electroencephalographic (EEG) signals acquired via simultaneous recording systems and temporal misalignments between EEGs and functional magnetic resonance imaging (fMRI), we first apply Fourier transforms to EEG signals and perform dimensionality expansion. This constructs a spatiotemporally aligned EEG–fMRI paired dataset. Building on this foundation, we design an EEG encoder integrating a multi-layer recursive spectral attention mechanism with a residual architecture.In response to the limited dynamic mapping capabilities and suboptimal image quality prevalent in existing cross-modal generation research, we propose a diffusion-model-driven EEG-to-fMRI generation algorithm. This framework unifies the EEG feature encoder and a cross-modal interaction module within an end-to-end denoising U-Net architecture. By leveraging the diffusion process, EEG-derived features serve as conditional priors to guide fMRI reconstruction, enabling high-fidelity cross-modal image generation. Empirical evaluations on the resting-state NODDI dataset and the task-based XP-2 dataset demonstrate that our EEG encoder significantly enhances cross-modal representational congruence, providing robust semantic features for fMRI synthesis. Furthermore, the proposed cross-modal generative model achieves marked improvements in structural similarity, the root mean square error, and the peak signal-to-noise ratio in generated fMRI images, effectively resolving the nonlinear mapping challenge inherent in EEG–fMRI data. Full article

Public perception of traditional villages’ streetscape is a crucial link for unlocking their benefits in promoting physical and mental health and realizing environmental value transformation. Current studies on the influence mechanisms of rural streetscape characteristics on perception largely rely on subjective ratings and mostly depend on linear models. To address this, this study takes a traditional village in eastern China, which is rich in natural and cultural conditions, as an example and constructs an evaluation framework comprising 29 streetscape feature indicators. Based on multimodal data including electroencephalography (EEG), image segmentation, color, and spatial depth computation, XGBoost-SHAP was employed to reveal the nonlinear influence mechanisms of streetscape features on neurophysiological indicators (alpha-band power spectral density, α PSD) in the traditional rural context, which differs from the blue–green spaces and residential, campus, and urban environments in previous studies. The results indicate that (1) the dominant factors affecting α PSD in traditional villages are tree, color consistency, architectural aesthetics, spatial enclosure index, P_EBG, and road, in descending order. (2) Threshold effects and interaction effects that differ from previous studies on campuses, window views, and other contexts were identified. The positive effect of tree view index on α activity peaks at the threshold of 0.09, beyond which diminishing returns occur. Color complexity, including high color difference from the primary village scheme (i.e., low color consistency, color diversity, and visual entropy), inhibits α activity. The effect of spatial enclosure index (SEI) on α activity exhibits an inverted U-shape, peaking at 0.35. Tree–VE_nats, road–SEI, and building–SEI show antagonistic effects. Road–sky and SEI–P_FG display conditional interaction effects. (3) Based on k-means clustering analysis, the “key factor identification—threshold effect management—multi-factor synergy optimization” design can directionally regulate α PSD, promoting relaxed and calm streetscape schemes. This approach can be applied to urban and rural environment assessment and design, providing theoretical and technical support for scientific decision-making. Full article

The contemporary healthcare environment is characterized by high stress and emotional burden, contributing to increasing rates of professional burnout among clinicians. Exposure to patient death represents one of the most emotionally taxing experiences in medicine, particularly in obstetrics and gynecology (OB/GYN), where loss of life stands in stark contrast to the life-giving nature of the field. Despite extensive research on burnout in oncology and intensive care, the impact of patient death and death perception on OB/GYN clinicians remains underexplored. Objective: This study aimed to examine the relationships between exposure to patient death, perceptions of death, professional burnout, and professional fulfillment among OB/GYN clinicians. A secondary aim was to explore whether participation in emotional regulation training was associated with these variables. Methods: A cross-sectional study was conducted among 138 OB/GYN clinicians. An author-developed questionnaire was used, comprising scales measuring professional burnout, positive and negative death perception, professional fulfillment, professional development, and a global death-impact index. Statistical analyses included Pearson’s correlation and the Mann–Whitney U test to compare clinicians who had attended emotional regulation training with those who had not. Results: Significant positive correlations were observed between burnout and the death-impact index (r = 0.90, p < 0.001) and between burnout and negative death perception (r = 0.23, p = 0.007). Professional fulfillment strongly correlated with professional development (r = 0.94, p < 0.001) and positively with positive death perception (r = 0.30, p < 0.001). No significant group differences were found regarding emotional regulation training participation. Conclusions: Exposure to patient death in OB/GYN is strongly associated with professional burnout and negative perceptions of death. Conversely, professional fulfillment and development function as factors promoting resilience and meaning. Further research should validate the applied measurement tools and examine the effectiveness of emotional regulation interventions in reducing occupational distress. Full article

Sleep spindles, together with K-complexes, are the distinctive patterns of neuronal activity in EEG recordings during stage 2 sleep. When the mechanisms of sleep spindle generation are impaired, e.g., in epilepsy, their quantitative parameters change. The analysis of these changes can provide valuable insights into the formation of epileptiform activity patterns and help to develop an additional tool for more accurate medical diagnosis. Despite the central role of EEG in the diagnosis of epilepsy, disorders of consciousness, and neurological research, resources specifically dedicated to large-scale EEG data analysis are under-represented. In our study, we collect a specialized database of clinical EEG recordings from epilepsy patients and controls during N2 sleep, characterized by rhythmic spindle activity in frontocentral and vertex regions, and manually annotate them. We then quantify four key sleep spindle characteristics using a comparison of manual annotation by a clinician and artificial intelligence technologies. A thorough evaluation of state-of-the-art deep learning architectures for detecting and characterizing sleep spindles in EEG recordings from epilepsy patients is conducted. The results show that the 1D U-Net and SEED architectures achieve competitive overall performance, but their precision-to-recall ratios differ markedly in clinical settings. This suggests that different approaches may be appropriate for each clinical situation. Furthermore, our results demonstrate that epilepsy is associated with significant and quantifiable changes in sleep spindle morphology and frequency. Automated analysis of these characteristics using artificial intelligence provides a reliable biomarker that provides a detailed picture of thalamocortical dysfunction in epilepsy. This approach has great potential for accelerated diagnosis and the development of targeted therapeutic strategies for epilepsy. Full article

The accurate detection of high-impedance faults (HIFs) in distribution systems is fundamentally dependent on the extraction of weak fault signatures. However, these features are often obscured by complex and high-level noise present in current transformer (CT) measurement data. To address this challenge, an energy-proportion-guided channel-wise attention stacked denoising autoencoder (EPGCA-SDAE) model is proposed. In this model, wavelet decomposition is employed to transform the signal into informative frequency band components. A channel attention mechanism is utilized to adaptively assign weights to each component, thereby enhancing model interpretability. Furthermore, a physics-informed prior, based on energy distribution, is introduced to guide the loss function and regulate the attention learning process. Extensive simulations using both synthetic and real-world $10\mathrm{kV}$ distribution network data are conducted. The superiority of the EPGCA-SDAE over traditional wavelet-based methods, stacked denoising autoencoders (SDAE), denoising convolutional neural network (DnCNN), and Transformer-based networks across various noise conditions is demonstrated. The lowest average mean squared error (MSE) is achieved by the proposed model (simulated: $50.60\times {10}^{-5}\mathrm{p}.\mathrm{u}.$; real: $76.45\times {10}^{-5}\mathrm{p}.\mathrm{u}.$), along with enhanced noise robustness, generalization capability, and physical interpretability. These results verify the method’s feasibility within the tested 10 kV distribution system, providing a reliable data recovery framework for fault diagnosis in noise-contaminated distribution network environments. Full article

Background/Objectives: Glioblastoma, the most common primary tumor of the central nervous system, is characterized by high malignancy and poor prognosis. One of the main challenges in neurological disorders is to develop an effective treatment modality that can cross the blood–brain barrier. Nanoparticles are revolutionary for neurodegenerative diseases due to their targeted delivery and ability to overcome biological barriers. Cerium oxide (Ce₂O₃) nanoparticles are suitable for use as drug delivery systems. Methods: In our study, we investigated the anticancer mechanism using SN-38, lithium, and Ce₂O₃, a powerful agent used in GBM treatment. We evaluated their anticancer activities separately and in combination with U373 cell lines. GBM cell line U373 cells were cultured. Then, all groups except the control group were treated with different doses of SN-38 and lithium combination therapy with SN-38, lithium, and Ce₂O₃ combination therapy. The results were evaluated using MTT and ELISA tests. Results: When the results were examined, anticancer activity was detected at PTEN, AKT, mTOR, and BAX/Bcl-2 levels in the SN-38 + NPs 25 µg/mL + Lithium 50 µg/mL and SN-38 + NPs 50 µg/mL + Lithium 50 µg/mL dose groups. In addition, findings that inflammation markers were correlated with the apoptosis mechanism were obtained. Conclusion: This study is the first to report that combining lithium with SN-38 and NPs increased oxidative stress more than lithium with SN-38, leading glioblastoma cells to apoptosis and its potential anticancer activity. These results provide a basis for further investigation of its clinical application in cancer treatment. Full article

Lung cancer remains the leading cause of cancer-related deaths worldwide, with over 2.4 million new diagnoses in 2022. Early diagnosis remains challenging due to the non-specificity of symptoms, often resulting in late-stage detection. Although 2-D and 3-D medical imaging, particularly computed tomography (CT), is widely used for detecting lung cancer, it is associated with manual segmentation, which remains time-consuming and user-dependent. This study proposes LuCa as an innovative 2.5-D deep learning model for lung cancer delineation, which combines the benefits of 2-D segmentation with 3-D volume delineation. The main novelty of LuCa is focused on its pipeline, specifically designed to be of clinical use, in order to guarantee the usability of the method. LuCa employs a U-Net architecture for segmentation, followed by a post-image-processing step for 3-D tumor volume delineation and false-positive correction. The method was trained and evaluated using the “NSCLC-Radiomics” database, comprising CT images of 422 non-small cell lung cancer patients, with clinical manual tumor annotations as ground truth. The model achieved strong performance, with high dice coefficients (87 ± 12%), intersection over union (81 ± 17%), sensitivity (84 ± 16%), and positive predictive value (94 ± 10%) on the test set. Performance was particularly high for larger tumors, reflecting the ability of the model to delineate more visible lesions accurately. Statistical analysis confirmed the high correlation and minimal error between predicted and ground truth tumor volumes. The results highlight the potential of the 2.5-D approach to improve clinical efficiency by enabling accurate tumor segmentation with reduced computational cost, compared to traditional 3-D methods. Future research will focus on assessing the use of LuCa as real-time clinical decision support, particularly for assessing tumors during treatment. Full article

Background and Objective: Accurate classification of standard fetal ultrasound planes is a critical step in prenatal diagnostics, enabling reliable biometric measurements and anomaly detection. Conventional deep learning approaches, particularly convolutional neural networks (CNNs) and transformers, often face challenges such as domain variability, noise artifacts, class imbalance, and poor calibration, which limit their clinical utility. This study proposes a hybrid state–space and vision transformer framework designed to address these limitations by integrating sequential dynamics and global contextual reasoning. Methods: The proposed framework comprises five stages: (i) preprocessing for ultrasound harmonization using intensity normalization, anisotropic diffusion filtering, and affine alignment; (ii) hybrid feature encoding with a state–space model (SSM) for sequential dependency modeling and a vision transformer (ViT) for global self-attention; (iii) multi-task learning (MTL) with anatomical regularization leveraging classification, segmentation, and biometric regression objectives; (iv) gated decision fusion for balancing local sequential and global contextual features; and (v) calibration strategies using temperature scaling and entropy regularization to ensure reliable confidence estimation. The framework was comprehensively evaluated on three publicly available datasets: FETAL_PLANES_DB, HC18, and a large-scale fetal head dataset. Results: The hybrid framework consistently outperformed baseline CNN, SSM-only, and ViT-only models across all tasks. On FETAL_PLANES_DB, it achieved an accuracy of 95.8%, a macro-F1 of 94.9%, and an ECE of 1.5%. On the Fetal Head dataset, the model achieved 94.1% accuracy and a macro-F1 score of 92.8%, along with superior calibration metrics. For HC18, it achieved a Dice score of 95.7%, an IoU of 91.7%, and a mean absolute error of 2.30 mm for head circumference estimation. Cross-dataset evaluations confirmed the model’s robustness and generalization capability. Ablation studies further demonstrated the critical role of SSM, ViT, fusion gating, and anatomical regularization in achieving optimal performance. Conclusions: By combining state–space dynamics and transformer-based global reasoning, the proposed framework delivers accurate, calibrated, and clinically meaningful predictions for fetal ultrasound plane classification and biometric estimation. The results highlight its potential for deployment in real-time prenatal screening and diagnostic systems. Full article

Theory of planned behavior (TPB) constructs and shopping routines are strong predictors of food waste behavior, while socio-demographic factors show mixed and weaker associations. We analyzed survey data from a nationally representative sample of 1066 U.S. households, measuring self-reported food waste frequency across meals, food types, and disposal methods. We applied k-medoid clustering on 19 TPB constructs and 25 shopping characteristics to identify three distinct consumer segments. “Structured Planners” (Cluster 1) showed the most deliberate shopping habits and strongest engagement in food waste reduction. “Flexible Planners” (Cluster 2) shared similar waste outcomes but approached shopping with greater spontaneity, while “Younger Wasters” (Cluster 3) were younger, lower-income, and less educated, with casual shopping habits, lower ratings of TPB constructs, and the highest food waste frequency overall. These distinct behavioral profiles enable policymakers to directly identify and target specific demographic segments for tailored food waste interventions. Particularly, “Younger Wasters” reported a significantly higher food waste frequency at 6.7 times per week, while “Structured Planners” and “Flexible Planners” were statistically similar at approximately 4.6 and 4.4 times per week. Dinner is the meal resulting in the most food waste across all groups, and “Younger Wasters” reported the highest frequency of waste in protein, oil, and grain. Post-clustering ANOVA analysis tested the predictive power of TPB, shopping characteristics, and cluster membership on food waste frequency. Results show that “Younger Wasters”, along with variables like attitude, store shopping frequency, and shopping behavior, are significantly positively associated with food waste frequency. This study demonstrates the potential of clustering analysis in exploring food waste determinants and suggests using clustered indices as proxies for respondents’ overall traits. Full article