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Mangrove ecosystems are important in tropical and subtropical coastal zones, contributing to marine biodiversity and maintaining marine ecological balance. It is crucial to develop more efficient, intelligent, and accurate monitoring methods for mangroves to understand better and protect mangrove ecosystems. This study promotes a novel model, MangroveNet, for integrating multi-scale spectral and spatial information and detecting mangrove area. In addition, we also present an improved model, AttCloudNet+, to identify the distribution of mangrove species based on high-resolution multispectral drone images. These models incorporate spectral and spatial attention mechanisms and have been shown to effectively address the limitations of traditional methods, which have been prone to inaccuracy and low efficiency in mangrove species identification. In this study, we compare the results from MangroveNet with SegNet, UNet, and DeepUNet, etc. The findings demonstrate that the MangroveNet exhibits superior generalization learning capabilities and more accurate extraction outcomes than other deep learning models. The accuracy, F1_Score, mIoU, and precision of MangroveNet were 99.13%, 98.84%, 98.11%, and 99.14%, respectively. In terms of identifying mangrove species, the prediction results from AttCloudNet+ were compared with those obtained from traditional supervised and unsupervised classifications and various machine learning and deep learning methods. These include K-means clustering, ISODATA cluster analysis, Random Forest (RF), Support Vector Machines (SVM), and others. The comparison demonstrates that the mangrove species identification results obtained using AttCloudNet+ exhibit the most optimal performance in terms of the Kappa coefficient and the overall accuracy (OA) index, reaching 0.81 and 0.87, respectively. The two comparison results confirm the effectiveness of the two models developed in this study for identifying mangroves and their species. Overall, we provide an efficient solution based on deep learning with a dual attention mechanism in the acceptable real-time monitoring of mangroves and their species using high-resolution multispectral drone imagery.

Background Digital transformation in pathology education faces three bottlenecks: fragmented knowledge transfer, low morphological diagnostic accuracy, and weak clinical reasoning. While knowledge graphs (KGs) offer potential solutions, existing medical KG lack multimodal integration and competency assessment. We designed an integrated Multimodal Knowledge Graph (MKG) with O-PIRTAS pedagogy to bridge these gaps. Methods Following Design Science Research Methodology, we built a pathology-specific MKG featuring: (1) Semantic modeling of disease mechanisms (etiology-pathogenesis-morphology-clinical), (2) Cross-modal alignment of digital slides/animations/clinical cases, (3) Embedded metrics (KII/MDA/CCAE) for competency quantification. A quasi-experiment with 533 medical students (2022 cohort control: n  = 275; 2023 MKG-O-PIRTAS: n  = 258) evaluated outcomes via exam scores, validated questionnaires, and stratified interviews. Results The MKG-O-PIRTAS group achieved significantly higher adjusted exam scores (76.14 vs. 73.72, p  = 0.033) and 86% lower misdiagnosis rate in high performers ( p  = 0.015). Cognitive load diverged markedly (57.5 vs. 75.5, p  = 0.007), with high performers dynamically contextualizing MKG nodes into clinical reasoning, while novices required scaffolded pathways. Over 80% of students endorsed enhanced knowledge integration and process optimization. Conclusion The MKG-O-PIRTAS artifact transforms scattered pathology knowledge into actionable clinical reasoning scaffolds, proving effective for personalized competency development. Future work will scale adaptive scaffolding and integrate real-time EMR modules, establishing a replicable paradigm for medical education intelligence.

Two-dimensional (2D) van der Waals (vdW) magnetic materials are considered to be ideal candidates for the fabrication of spintronic devices because of their low dimensionality, allowing the quantization of electronic states and more degrees of freedom for device modulation. With the discovery of few-layer Cr2Ge2Te6 and monolayer CrI3 ferromagnets, the magnetism of 2D vdW materials is becoming a research focus in the fields of material science and physics. In theory, taking the Heisenberg model with finite-range exchange interactions as an example, low dimensionality and ferromagnetism are in competition. In other words, it is difficult for 2D materials to maintain their magnetism. However, the introduction of anisotropy in 2D magnetic materials enables the realization of long-range ferromagnetic order in atomically layered materials, which may offer new effective means for the design of 2D ferromagnets with high Curie temperature. Herein, current advances in the field of 2D vdW magnetic crystals, as well as intrinsic and induced ferromagnetism or antiferromagnetism, physical properties, device fabrication, and potential applications, are briefly summarized and discussed.

Flavor is a pivotal indicator influencing the meat quality and palatability of premium broiler chickens, shaped by multiple factors. The flavor differences among broiler chicken breeds/lines stem from the specificity of their metabolite profiles and volatile flavor compounds. This study aims to identify key metabolites and pathways that regulate flavor variations in high-quality broilers, providing data support and theoretical references for breeding superior broiler lines and developing technologies to enhance flavor quality. Breast Muscle tissue from 15-week-old roosters of the S3 and H lines (n = 6) was used as experimental material. Broad-targeted metabolomics and volatile metabolomics technologies were employed to identify key metabolites and volatile organic compounds (VOCs) influencing the flavor of breast meat in these two high-quality broiler lines. Broad-target metabolomics identified 167 differentially expressed metabolites (VIP > 1, p < 0.05) between the two strains, including 141 upregulated and 26 downregulated metabolites. These metabolites were primarily amino acids and their derivatives, and were significantly enriched in metabolic pathways such as ABC transporters (p < 0.05). Leu-Tyr, Ile-Tyr, Val-Leu, Val-Ile, and Tyr-Ala were identified as key metabolites influencing the flavor formation of breast meat from both high-quality broiler lines. Volatile metabolomics results identified 33 downregulated VOCs (VIP > 1 and p < 0.05). The flavor differences between the two strains primarily involved fatty and grassy flavor. Key flavor markers included 2-Nonanone, 2-Nonanone, 3-hydroxymethyl, 2-Methylheptanoic acid, and Hexanoic acid, butyl ester as the primary flavor markers. These significantly downregulated volatiles are formed through lipid oxidation and amino acid degradation pathways, respectively, collectively shaping the more pronounced fatty and grassy aromas in the S3 strain. Correlation analysis revealed a significant negative correlation between Met-Asn and Hexanoic acid, butyl ester, suggesting it may represent a key regulatory pathway influencing green flavor formation. In summary, this study elucidates key metabolites and pathways governing flavor differences in high-quality broiler rooster breast meat, providing a scientific foundation for poultry breeding, optimization of farming practices, and flavor regulation in meat products.

River conditions are complex and affected by human activities. Various hydraulic structures change the longitudinal slope and cross-sectional shape of the riverbed, which has a significant impact on the simulation of water-head evolution. With continuous population growth, the hydrological characteristics of the Yongding River Basin have undergone significant changes. Too little or too much water discharge may be insufficient to meet downstream ecological needs or lead to the wastage of water resources, respectively. It is necessary to consider whether the total flow in each key section can achieve the expected value under different discharge flows. Therefore, a reliable computer model is needed to simulate the evolution of the water head and changes in the water level and flow under different flow rates to achieve efficient water resource allocation. A one-dimensional hydrodynamic coupling model based on the Saint-Venant equations was established for the Yongding River Basin. Different coupling methods were employed to calibrate the coupling model parameters, using centralised water replenishment data for the autumn of 2022, and the simulation results were verified using centralised water replenishment data for the spring of 2023. The maximum error of the water-head arrival time between different river sections was 4 h, and the maximum error of the water-head arrival time from the Guanting Reservoir to each key cross-section was 6 h. The maximum flow error was less than 5 m3/s, and the changing trend of the flow over time was consistent with the measured data. The model effectively solved the problem of low accuracy of the water level and flow calculation results when using the traditional one-dimensional hydrodynamic model to simulate the flow movement of complex river channels in the Yongding River. The output results of the model include the time when the water head arrives at the key section, the change process of the water level and flow of each section, the change process of the water storage of lakes and gravel pits, and the change process of the total flow and water surface area of the key section. This paper reports data that support the development of an ecological water compensation scheme for the Yongding River.

Purpose There is a significant unmet need in treating patients with limited-stage small-cell lung cancer (LS-SCLC). The ETER701 study showed that Benmelstobart (TQB2450, an anti-PD-L1 antibody) combined with Anlotinib and chemotherapy achieved the longest progression-free survival (PFS) and overall survival (OS) as a first-line therapy in patients with extensive-stage small cell lung cancer (ES-SCLC). This suggests that TQB2450 and Anlotinib represent a promising treatment combination for LS-SCLC. This prospective study aimed to evaluate the efficacy and safety of TQB2450 combined with Anlotinib as maintenance therapy for LS-SCLC following concurrent or sequential chemoradiotherapy (CCRT or SCRT). Methods Patients who did not show disease progression after chemoradiotherapy were enrolled. They received TQB2450 and Anlotinib every 3 weeks for up to 24 months. TQB2450 was intravenously administered at a dose of 1200 mg every 3 weeks. Anlotinib was initiated at a dose of 8 mg daily for days 1–14; if well tolerated, the dose was increased to 10 mg. Adverse events (AEs) were recorded using electronic data capture system. The trial was registered at the ClinicalTrials.gov (NCT05942508, 06/07/2023). Results Fifteen patients were enrolled in the study between May 31, 2023 and October 13, 2023. As of October 31, 2024, the median follow-up time was 15.13 months. The 12-month PFS rate was 86.7% (95% CI, 71.1–100.0), and the OS rate at 12 months was 100%. The disease control rate was 100%. AEs were reported in 13 patients (86.67%), with fatigue being the most common treatment related AE (40.00%). And two SAEs were observed (elevation in cardiac troponin T and cerebral infarction), which were determined to be unlikely unrelated to the trial drugs. Radiation pneumonitis (RP) occurred in three patients, all classified as grade 2, and one patient developed grade 1 immune-related pneumonitis. No grade 5 AEs occurred, and no patients withdrew from the study due to AEs. Conclusions TQB2450 combined with Anlotinib showed promising efficacy and well tolerance in patients with LS-SCLC following first-line treatment. A randomized, double-blind, placebo-controlled Phase III clinical study (ClinicalTrials.gov Identifier: NCT06469879) is being conducted to further explore the efficacy and safety of TQB2450 combined with Anlotinib as maintenance therapy after definitive CCRT or SCRT for LS-SCLC. Trial registration ClinicalTrials.gov Identifier: NCT05942508. Date of registration: 7 June 2023.

Charlotte Sumner and colleagues report that mutations in the ankyrin repeat region of TRPV4 cause Charcot-Marie-Tooth disease type 2C. Their functional studies indicate that the mutations result in increased channel activity. Charcot-Marie-Tooth disease type 2C (CMT2C) is an autosomal dominant neuropathy characterized by limb, diaphragm and laryngeal muscle weakness. Two unrelated families with CMT2C showed significant linkage to chromosome 12q24.11. We sequenced all genes in this region and identified two heterozygous missense mutations in the TRPV4 gene, C805T and G806A, resulting in the amino acid substitutions R269C and R269H. TRPV4 is a well-known member of the TRP superfamily of cation channels. In TRPV4-transfected cells, the CMT2C mutations caused marked cellular toxicity and increased constitutive and activated channel currents. Mutations in TRPV4 were previously associated with skeletal dysplasias. Our findings indicate that TRPV4 mutations can also cause a degenerative disorder of the peripheral nerves. The CMT2C-associated mutations lie in a distinct region of the TRPV4 ankyrin repeats, suggesting that this phenotypic variability may be due to differential effects on regulatory protein-protein interactions.

Bivalve aquaculture plays a crucial role in the aquaculture industry due to the economic value of many bivalve species. Understanding the underlying genetic basis of bivalve growth regulation is essential for enhancing germplasm innovation and ensuring sustainable development of the industry. Though numerous candidate genes have been identified, their functional validation remains challenging. Fortunately, the dwarf surf clam ( Mulinia lateralis ) serves as a promising model organism for investigating genetic mechanisms underlying growth regulation in bivalves. The GWAS study in the Yesso scallop ( Patinopecten yessoensis ) has pinpointed the E2F3 gene as a key regulator of growth-related traits. However, the specific role of E2F3 in bivalve growth remains unclear. This study aimed to further confirm the regulatory function of the E2F3 gene in the dwarf surf clam through RNA interference experiments. Our results revealed several genes are associated with individual growth and development, including CTS7, HSP70B2 , and PGLYRP3 , as well as genes involved in lipid metabolism such as FABP2 and FASN . Functional enrichment analysis indicated that E2F3 primarily modulates critical processes like amino acid and lipid metabolism. These findings suggest that E2F3 likely regulates growth in the dwarf surf clam by influencing amino acid and lipid metabolism. Overall, this study advances our understanding on the function of E2F3 gene in growth regulation in bivalves, providing valuable insights for future research in this field.

The all-red A. arguta ( Actinidia arguta ) is an anthocyanin-rich and excellent hardy fruit. Many studies have focused on the green-fleshed A. arguta , and fewer studies have been conducted on the all-red A. arguta . Here we reported a regeneration and Agrobacterium-mediated transformation protocol by using leaves of all-red A. arguta as explants. Aseptic seedling leaves of A. arguta were used as callus-inducing materials. MS medium supplemented with 0.3 mg·L -1 2,4-D and 1.0 mg·L -1 BA was the optimal medium for callus induction of leaves, and medium supplemented with 3 mg·L -1 tZ and 0.5 mg·L -1 IAA was optimal for adventitious shoot regeneration. The best proliferation medium for adventitious buds was MS + 1.0 mg·L -1 BA + 0.3 mg·L -1 NAA. The best rooting medium was 1/2MS + 0.7 mg·L -1 IBA with a 100% rooting rate. For the red flesh hardy kiwi variety ‘Purpurna Saduwa’ ( A. arguta var. purpurea ), leaves are receptors for Agrobacterium (EHA105)-mediated transformation. The orthogonal experiment was used for the optimization of each genetic transformation parameter and the genetic transformation of the leaves was 21% under optimal conditions. Our study provides technical parameters for applying genetic resources and molecular breeding of kiwifruit with red flesh.

Radiographically confirmed pneumonia within 90 days of chemotherapy initiation is a frequent and clinically important complication in patients with non-Hodgkin lymphoma, yet interpretable tools for early individualized risk estimation are limited. To develop and internally validate an interpretable machine-learning model that predicts the 90-day risk of radiographically confirmed pneumonia after chemotherapy initiation in non-Hodgkin lymphoma. We retrospectively analyzed 205 chemotherapy-treated NHL patients. A two-step feature selection (LASSO followed by random-forest-based recursive feature elimination) identified four predictors: high-grade malignancy, drinking (alcohol use), estimated glomerular filtration rate (eGFR), and smoking. Five algorithms were trained and compared under a stratified 70/30 split (training  = 145; internal hold-out test set  = 60) with leakage-safe preprocessing (within-fold kNN imputation, SMOTE, and scaling). The gradient boosting machine (GBM) performed best and was interpreted using SHAP. A web-based prototype was implemented for research use only. On the internal hold-out test set (  = 60), the GBM achieved an AUC of 0.855 (95% CI 0.746-0.964), an F1 score of 0.679, and a Brier score of 0.155. SHAP identified reduced eGFR, smoking, drinking, and high-grade malignancy as influential contributors; case-level waterfall and force plots enhanced transparency. These estimates reflect internal validation only and were obtained without systematic microbiological confirmation or standardized radiologic rescoring. Accordingly, performance may be optimistic, and real-world use is not advised pending temporal and multicenter external validation (with potential recalibration) and prospective evaluation. The interpretable GBM model demonstrated promising discrimination and calibration on an internal hold-out test set; however, clinical deployment requires temporal and multicenter external validation (as well as prospective assessment with potential recalibration). The accompanying web calculator is a research-only prototype and is not intended for clinical decision-making until such validation is completed.