Search Results (154,868)

The rational design of metal-free catalysts capable of efficiently converting CO₂ under atmospheric pressure remains a significant challenge in sustainable chemistry. Herein, we report a series of clamp-shaped dihydrogen-bonded iodide catalysts (CDBI catalysts) featuring a preorganized bifunctional framework that integrates dual hydrogen-bond donors and an intrinsic iodide nucleophile within a single molecular scaffold. Systematic structural variation revealed that catalytic activity is highly sensitive to electronic modulation, steric accessibility, and precise spatial arrangement between the hydrogen-bonding units and the iodide center. The optimal catalyst enabled solvent-free cycloaddition of CO₂ with epoxides at 1 atm CO₂, affording up to 99% conversion and >99% selectivity at 80 °C within 12 h. Substrate scope studies demonstrated efficient transformation of a wide range of terminal epoxides, while sterically demanding substrates exhibited reduced reactivity consistent with a confined activation mode. Mechanistic investigations support a cooperative pathway in which dual hydrogen-bond activation and proximal halide nucleophilicity operate synergistically within a preorganized clamp-shaped pocket. Comparative analysis with representative catalytic systems highlights the ability of this metal-free design to achieve high efficiency under atmospheric CO₂ without cocatalysts or solvents. These findings demonstrate that structural preorganization represents an effective strategy for promoting sustainable CO₂ utilization under operationally simple conditions. Full article

Background/Objectives: White–Sutton syndrome (WHSUS; OMIM 616364) is a rare neurodevelopmental disorder caused by pathogenic variants in the POGZ gene and characterized by developmental delay, intellectual disability, speech impairment, autism spectrum features, and dysmorphic traits. Although most reported cases are sporadic, inherited forms are exceptionally rare. We describe a familial case of WHSUS involving an affected mother and two children carrying a heterozygous POGZ nonsense variant, highlighting marked intra-familial phenotypic variability and expanding the clinical spectrum of the disorder. Methods: Clinical evaluation included multidisciplinary assessments. Genetic testing was performed using clinical exome sequencing (CES) with a virtual neurodevelopmental disorder (NDD) gene panel, followed by Sanger confirmation and segregation analysis in family members. The POGZ transcript reference NM_015100.3 was used for variant nomenclature and verified with the Mutalyzer tool. CNV detection from NGS data was performed using the Alissa CNV caller (Agilent) and visualized via IGV; the Xp11.22 microduplication was confirmed by chromosomal microarray (aCGH) and parental segregation analyses. Results: CES identified the heterozygous pathogenic POGZ variant c.1522C>T (p.Arg508*) in the female proband (III₆), an infant presenting with global developmental delay, hypotonia, speech impairment, gait abnormalities, and characteristic dysmorphic features. Segregation analysis demonstrated maternal inheritance and confirmed the presence of the variant in her affected brother (III₄), who also carries a de novo 1.79 kb microduplication at Xp11.22, while the maternal grandparents tested negative, indicating a de novo origin in the mother. The mother exhibited an attenuated phenotype, including mild neuropsychiatric and gastrointestinal manifestations. The variant is predicted to undergo nonsense-mediated decay (NMD), consistent with a moderate clinical presentation; however, experimental validation was not performed. Conclusions: This report documents a rare familial occurrence of WHSUS with highly variable expressivity. Our findings broaden the phenotypic and molecular characterization of POGZ-related disorders and emphasize the importance of comprehensive segregation studies and early genomic diagnosis. While experimental data link POGZ deficiency to DNA repair defects, no longitudinal clinical studies have demonstrated increased cancer risk in WHSUS; therefore, formal malignancy screening guidelines cannot be established at present, and this issue deserves future study in larger cohorts or registries. Full article

Accurate prediction of overpressured formations in deepwater is important for drilling safety and reservoir evaluation. However, conventional two-step inversion workflows are affected by cumulative errors and parameter crosstalk, which limits their ability to characterize the sharp pressure-transition interfaces at the top of overpressured zones. In this study, we propose a direct prestack nonlinear inversion method for the formation pressure coefficient (λ), a dimensionless and drilling-relevant indicator of overpressure intensity. Unlike previous exact-Zoeppritz direct inversions that target effective stress or elastic moduli, here a single formation pressure coefficient drives the pressure-sensitive rock-physics chain—linking pore pressure, effective stress, and pore-space stiffness to the seismic response—thereby reducing the number of free inversion variables. This single-parameter mapping is then coupled with the exact Zoeppritz equation to build a nonlinear prestack forward operator, helping to reduce the parameter coupling and error propagation associated with conventional multiparameter inversion workflows. To describe the typical blocky structural features of overpressured strata, a nonconvex Lp-norm (0 < p < 1) regularization is introduced as a structural prior, and a decoupled optimization strategy combining the alternating direction method of multipliers (ADMM) and iteratively reweighted least squares (IRLS) is developed for a stable solution. In a single pseudo-well synthetic test, the proposed method achieved a higher correlation coefficient and lower root mean square error (RMSE) than the indirect workflow, indicating improved agreement with the reference formation-pressure-coefficient profile. Application to field seismic data from the Yinggehai Basin, South China Sea, shows that the method produces clearer pressure-transition boundaries and pressure-coefficient profiles more consistent with the available well constraints. These results suggest that, under the tested conditions, the proposed method can provide useful geophysical support for pressure prediction and the characterization of deepwater overpressured reservoirs. Full article

Point-cloud analysis of sedimentary outcrops using Unmanned Aerial Vehicle (UAV) oblique photogrammetry is a crucial approach to sedimentary system characterization, stratigraphic correlation, and petroleum exploration analog studies. In large-scale field settings, however, outcrops are often scattered and fragmented, vegetation and soil cover is extensive, and class imbalance is pronounced. Manual interpretation is labor-intensive, while existing clustering algorithms, conventional machine learning methods, and general-purpose point-cloud segmentation networks struggle to simultaneously ensure geometric fidelity, rare-class recognition, and multi-scale feature integration. To address these challenges, we propose a method for extracting sedimentary outcrop point clouds from field surface point clouds using a UAV oblique photogrammetry acquisition strategy. The core segmentation module of the method, sedimentary cross-scale self-attention network (SedCSA-Net), is an enhanced version of PointNet++ that integrates collaborative improvements across four dimensions: data augmentation, sampling strategy, feature encoding, and loss optimization. Taking the Cretaceous Qingshuihe Formation in the Louzhuangzi area of the southern Junggar Basin as a case study, our experimental results indicate that SedCSA-Net overcomes the natural variability of UAV oblique photogrammetry point clouds—such as shadows, voids, and uneven density—achieving a mean Intersection over Union(mIoU) of 89.51% and an Overall Accuracy(OA) of 96.08%, with an outcrop-class Intersection over Union(IoU) of 86.90%. Attitude measurements derived from segmentation results deviate by less than 3° from manually annotated references, demonstrating that the proposed framework provides an end-to-end, generalizable approach for intelligent segmentation, geometric reconstruction, and attitude extraction of large-scale sedimentary outcrop point clouds. Full article

The legalization of euthanasia in Spain through Organic Law 3/2021 has intensified debates concerning the relationship between patients’ autonomy, the protection of life, and the freedom of conscience of healthcare professionals. In a context marked by increasing moral and religious pluralism, conscientious objection has emerged as a particularly sensitive issue in the practical implementation of assisted dying. This article adopts a legal and socio-religious approach to analyze the role of conscientious objection in the Spanish euthanasia framework. First, it examines the constitutional foundations of freedom of conscience and its specific regulation under Organic Law 3/2021, with particular attention to the guarantees and limits established for healthcare professionals. Second, it analyses the official positions of the main religious traditions present in Spain regarding euthanasia, assisted suicide, and end-of-life care, identifying both points of convergence and doctrinal diversity. Finally, the article assesses the practical impact of religious and moral convictions on the exercise of conscientious objection, drawing on the limited empirical evidence currently available. The analysis shows that, although most religious traditions oppose active euthanasia while accepting palliative care and the withdrawal of futile treatments, analysis of available empirical evidence suggests that objections are more often grounded in secular ethical or professional reasons than explicitly religious ones. The article concludes that conscientious objection should be understood as a structural feature of pluralist healthcare systems, requiring legal and organizational arrangements capable of safeguarding both freedom of conscience and effective access to legally recognized rights. Full article

The integration of distributed generation (DG) changes the fault current path, magnitude, direction, and transient characteristics of distribution networks, which increases the difficulty of fault type identification. In particular, weak fault features and high-frequency transient components may reduce the reliability of traditional feature-based diagnosis methods. To improve the representation and classification capability of fault signals, this paper proposes a fault type identification method based on wavelet packet transform and an improved CNN-BiGRU model with a channel attention mechanism. First, three-phase voltage, three-phase current, and zero-sequence voltage signals are decomposed by wavelet packet transform, and the corresponding time–frequency matrices are constructed. Then, these matrices are integrated and converted into time-frequency images, so that multi-source fault information can be represented in a unified form. On this basis, CNN is used to extract local spatial features from the time-frequency images, while BiGRU is employed to capture bidirectional dependency information of fault features. Furthermore, a channel attention mechanism is introduced to enhance informative feature channels and suppress redundant information, thereby improving the fault classification performance. Simulation results based on a 10 kV DG-integrated distribution network show that the proposed method achieves high recognition accuracy under different DG capacities and access configurations. Compared with CNN, BiGRU, and CNN-BiGRU models, the proposed CNN-BiGRU-Attention model shows better classification accuracy and adaptability, demonstrating its effectiveness for fault type identification in active distribution networks. Full article

Accurate detection of field insect pests remains a significant challenge for precision agriculture due to the elongated and variable morphology of the target organisms, their frequent resemblance to complex background textures, and the long-tail distribution of species in natural datasets. While deep convolutional neural networks (CNNs) have advanced the field, they are often constrained by a limited effective receptive field and the entanglement of semantic and spatial features, which can lead to elevated false-positive rates and missed detections for low-contrast or rare targets. This paper introduces a novel detection framework that integrates state space modeling with multi-stream feature disentanglement to address these limitations. First, a visual state space module is employed as the backbone feature extractor, enabling the establishment of a global receptive field with linear computational complexity and thereby improving the perception of long-range morphological structures. Second, a Topological Feature Disentanglement Pyramid Network is proposed. This architecture explicitly separates feature representations into semantic and spatial streams and recombines them through graph convolutional interactions, which serves to suppress background interference and enhance localization precision. A meta-auxiliary detection head, active only during training, is introduced to amplify supervision signals for hard, low-contrast samples via adversarial gradient modulation. Furthermore, an implicit neural radiance field augmentation pipeline is used to generate physically consistent synthetic views of underrepresented pest classes, mitigating the negative effects of long-tail data distributions. Experimental evaluations on the public BAU-Insectv2 benchmark demonstrate that the proposed method achieves a mean average precision (mAP@0.5) of 81.8%, representing a 4.4-percentage-point improvement over a comparable baseline, while maintaining a compact parameter count of 2.33 M and an inference speed of 178.6 FPS. The framework exhibits particular efficacy in detecting elongated, minute, and rare pests, suggesting a promising technical approach for real-time, field-based pest surveillance in precision agriculture. Full article

With the digital economy advancing at a fast pace, digital transformation plays a pivotal role in reinforcing firms’ innovation capability and promoting high-quality development. This study analyzes Chinese non-financial publicly listed firms on the A-share market over the period 2009–2023. Based on text mining of annual reports, this study constructs an index capturing digital transformation and empirically evaluate its impact on innovation output with firm and year fixed effects. The estimates suggest that digital transformation meaningfully increases firms’ innovation output; the inference is unchanged when applying instrumental-variable approaches and conducting extensive robustness checks. Mechanism analysis reveals two parallel channels: (1) the R&D investment mechanism, characterized by improvements in R&D intensity, capitalization rate, per capita efficiency, and investment growth; (2) the governance environment mechanism, reflected in enhanced internal control, improved information disclosure quality, and strengthened audit supervision. Once firms are stratified by characteristics, the estimated positive effect of digital transformation is most pronounced for firms with low financial constraints, large size, eastern locations, and state ownership. This study identifies both direct and indirect mechanisms linking digital transformation to innovation and highlights how firm- and region-specific features condition the magnitude of this effect, thereby offering empirical implications for corporate digitalization strategies and policy design. Full article

(1) Background: Changes in midsole hardness may affect lower-limb impact loading during forefoot strike (FFS) running in children, yet the biomechanical basis for discriminating elevated VALR-related loading remains unclear. (2) Methods: Fourteen school-aged boys performed FFS running tests in experimental shoes with four midsole hardness levels (37, 42, 47, and 52 Shore C). Lower-limb kinematics and surface electromyography (sEMG) data were collected during the dominant leg stance phase. After preprocessing, VALR was calculated from 336 valid trials, and 28 stance-phase biomechanical features were extracted, yielding a final machine-learning dataset of 324 trials after excluding incomplete feature data. VALR was used to compare loading changes and define trial-level elevated-loading labels based on the median VALR value. Classification models were evaluated under participant-level GroupKFold validation, and XGBoost was retained for exploratory SHAP analysis. (3) Results: VALR showed an upward trend with increasing hardness, but no statistically supported change point was identified. XGBoost achieved an accuracy of 75.93%, precision of 74.14%, recall of 79.63%, F1-value of 0.768, and pooled out-of-fold AUC of 0.738. SHAP analysis indicated that distal and non-sagittal kinematic features contributed most to model classification. (4) Conclusions: Elevated VALR-related loading during children’s FFS running may be characterized by a multi-feature model-based pattern rather than a fixed midsole hardness threshold. Full article

The growing worldwide prevalence of Diabetes Mellitus highlights the urgent need for effective early detection methods to enable prompt intervention. This study develops a machine learning-based decision-support prototype for predicting diabetes risk using health metrics from the DiaHealth dataset, a recently published Bangladeshi open-source dataset for Type 2 diabetes prediction. Five supervised learning algorithms were evaluated: Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Tree (DT), and Random Forest (RF). Models were assessed across three stages: before feature scaling, after standardisation, and following hyperparameter optimisation via GridSearchCV, using accuracy, precision, recall, and F1-score as evaluation metrics. LR and SVM showed marked improvements after standardisation, consistent with their sensitivity to feature magnitude, whilst tree-based approaches such as DT and RF remained largely unchanged. KNN displayed minimal sensitivity to scaling, which is discussed in relation to the feature distributions of the dataset. Following hyperparameter tuning, RF achieved the highest accuracy of 95%, outperforming all other models. RF predictions were interpreted using Local Interpretable Model-agnostic Explanations (LIME) to promote transparency in model decision-making. The best-performing model was subsequently deployed as an interactive web-based prototype application using Streamlit, providing real-time prediction outputs. These findings demonstrate how preprocessing choices and hyperparameter tuning can differentially affect algorithm performance and illustrate the potential of combining explainable AI with practical deployment for diabetes risk assessment in a research context. Full article

Extremely low overlap leads to severely scarce local geometric correspondences across frame pairs. Pure geometric descriptors—encoding merely low-level shape signatures—inherently fail to impose sufficient constraints for reliable transformation estimation when matches become critically sparse, rendering registration fundamentally fragile. While recent red-green-blue-depth (RGB-D) attempts have explored visual augmentation, they predominantly rely on low-level chromatic statistics or shallow convolutional neural network (CNN) features, underutilizing the rich hierarchical semantics inherent in RGB imagery. We present V3Reg, a robust registration framework that pioneers the integration of large-scale vision foundation models (DINOv3) with adaptive cross-modal fusion. Specifically, we extract mid-to-deep semantic features (Layer 11) from DINOv3 to transcend low-level texture limitations, and propose a Task-Aware Channel-Wise Gated Adaptive Fusion (TACGAF) module that dynamically calibrates geometric-visual contributions via registration-error-guided channel-wise gating. To rigorously evaluate ultra-low-overlap robustness, we reconstruct RGBD-ZeroMatch, a benchmark with controllable overlap ratios ranging from 1% to 20%. Extensive experiments demonstrate that V3Reg achieves 99.6% Feature Matching Recall and 96.3% Registration Recall on standard benchmarks. Notably, it maintains 50.2% Registration Recall at merely 5% overlap, outperforming prior methods by over 18 percentage points. Full article

Background: Amyloidosis is an infiltrative cardiomyopathy caused by amyloid deposition into the myocardium. In recent years, recognition of this treatable cause of heart failure has increased. There are striking sex differences in the diagnosis, clinical course and outcome of the disease. Notably, women have a worse prognosis than men with similar amounts of cardiac involvement. Methods: This review provides an overview of the current state of knowledge regarding the epidemiology, clinical features, diagnosis and treatment of amyloid heart disease. The differences observed between men and women are discussed, and recent advances in the field are highlighted. Results: Compared to men, women are generally older at diagnosis, appear to have less severe cardiac disease at the time of impairment and are more frequently diagnosed late. The less apparent disease manifestations in women may be responsible for the delay in diagnosis. Moreover, women may be underdiagnosed when sex-neutral diagnostic criteria are used. Conclusions: Addressing diagnostic disparities may require the use of sex-specific diagnostic thresholds, as well as a more expansive use of multimodality imaging. Future clinical trials should aim to enroll a greater number of female participants to inform optimal therapeutic approaches and to define the sex-specific disease phenotype for this increasingly treatable disease. Full article

Autonomous vehicle open-road testing is a crucial component in the development of intelligent and connected vehicle (ICV) industries. The classification of road complexity plays a key role in ensuring the safety and efficiency of such tests. This study, based on the practices of the High-Level Autonomous Driving Demonstration Zone in Beijing, proposes a scientific and systematic framework for classifying road complexity. The framework integrates static road features, dynamic traffic flow indicators, and safety event metrics, employing the Analytic Hierarchy Process (AHP) to quantify road complexity and categorize roads into five distinct levels. The findings provide significant guidance for the phased opening of test roads, optimization of autonomous driving algorithms, construction of accident scenario databases, and deployment of infrastructure. This paper further explores the practical applications and future development directions of road complexity classification, aiming to offer theoretical and practical support for the testing and demonstration of intelligent and connected vehicles. Full article

Mycoplasma pneumoniae pneumonia (MPP) is a leading cause of community-acquired pneumonia in children, yet little is known about the role of nasopharyngeal microbiota dysbiosis in susceptibility to infection and disease subtype. In this study, we performed 16S rRNA sequencing on nasopharyngeal samples from 102 pediatric MPP patients, 104 influenza A patients, and 103 healthy controls and compared the microbial diversity, composition, and functional profiles across groups. The MPP group exhibits an altered nasopharyngeal microbial composition, characterized by reduced microbial diversity and an increased relative abundance of genera including Mycoplasma, Pseudomonas, Acinetobacter, and Tannerella. Distinct microbiota profiles were identified for the MPP subtypes, with Mycoplasma more abundant in bronchopneumonia (BP) than in lobar pneumonia (LP). A microbial classifier based on the relative abundance of the nasopharyngeal microbiota was established to distinguish MPP patients from both influenza patients and healthy controls, with an area under the receiver operating characteristic curves of 0.978. Key microbial features associated with MPP included Mycoplasma, Mycobacterium, Aeromonas, and Acinetobacter. In addition, PICRUSt2-based functional predictions suggested alterations in amino acid metabolism and predicted functional pathways associated with bacterial infection and antimicrobial resistance in MPP patients. In conclusion, this study provides comprehensive insights into alterations in the nasopharyngeal microbiota in pediatric MPP. These findings highlight the potential role of dysbiosis in disease progression and suggest that changes in microbiota composition and functional profiles are associated with MPP infection. Full article

Background: Osteochondral lesions of the talus (OLT) remain a challenging condition due to the limited regenerative potential of articular cartilage. Conventional bone marrow stimulation (BMS) techniques often result in fibrocartilage formation with inferior biomechanical properties. This study aimed to evaluate the safety and preliminary clinical efficacy of an arthroscopically assisted, single-stage injection of a heparin-conjugated fibrin hydrogel (HCFH) for OLT treatment. Methods: Twelve patients with symptomatic OLT underwent arthroscopic debridement, microfracturing, and HCFH injection containing autologous mesenchymal stromal cells (MSCs) and growth factors. Safety was assessed through systematic monitoring of adverse events (graded according to Common Terminology Criteria for Adverse Events criteria), wound healing, and serial laboratory inflammatory markers (leukocytes, erythrocyte sedimentation rate, C-reactive protein) during early and late follow-up. Clinical outcomes were evaluated using the Visual Analog Scale (VAS) and American Orthopedic Foot and Ankle Society score (AOFAS) preoperatively and at 6 and 12 months. Morphological assessment was performed using magnetic resonance imaging (MRI) with the modified Magnetic Resonance Observation of Cartilage Repair Tissue (MOCART) scoring system, evaluated independently by two blinded musculoskeletal radiologists. Results: No serious adverse events (Grade III–IV) were observed during the 12-month follow-up. All adverse events were mild (Grade I) and self-limited. A transient postoperative elevation in inflammatory markers was observed, returning to clinically acceptable levels by day 14. Significant improvements were noted in pain (VAS decreased from 6.0 to 2.0) and ankle function (AOFAS increased from 70.0 to 90.6) (p < 0.001). MRI demonstrated progressive morphological improvement, with the MOCART score increasing from 34.16 ± 17.1 at 6 months to 75 ± 5.43 at 12 months (p < 0.001). This increase corresponded with imaging features consistent with tissue maturation over time. The favorable MOCART outcomes observed in this study may be explained by the regenerative properties of heparin-conjugated fibrin hydrogels; however, larger randomized controlled trials with longer follow-up are needed to confirm the durability of the regenerated tissue. Interobserver agreement was substantial to almost perfect for MOCART scoring (κ = 0.68–0.84), with perfect agreement observed for surface assessment, bony defect/overgrowth, and cysts. Conclusions: Within the limitations of this study, single-stage HCFH injection demonstrated an acceptable safety profile and favorable preliminary clinical and radiological outcomes at 12 months. These findings suggest potential regenerative capability; however, controlled studies with larger cohorts and longer follow-up are required to determine comparative efficacy and long-term durability. Full article