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Identification of SARS-CoV-2-binding lectins on a commercial lectin array
Abstract The Spike glycoprotein of SARS-CoV-2 is the major target for vaccines and therapeutics. Spike glycosylation is critical for ACE2 binding and subsequent viral fusion and entry. Here, we studied lectins for their ability to bind to SARS-CoV-2 Spike glycoprotein and SARS-CoV-2 virions by employing an array of 95 lectins, for 68 of which we predicted glycan-binding specificities using publically available glycan array data and MotifFinder software. We identified lectins with diverse glycan binding specificities that bound with high intensities to recombinant Spike and cultured SARS-CoV-2 virus – AAL, ABL, ACL, AMA, ASA, BANLEC, BC2L-A, RCA 120, CALSEPA, GAL3, GS-II, PALa, CA, HHA, PHA-L, PA-IIL, MNA-M, STL, LSL-N, GRFT, PSA, RS-FUC, PHA-E, CPA, LENTIL, RCA 60, GNA, ORYSATA, LcH A, PHA-P, PTL-2, MAA, Con A, TL, NPA, and SBA. Analyzing the glycan-binding specificities of these lectins, we predict that the Spike glycoprotein is modified with high mannose/hybrid N-glycans with terminal mannose residues, α1-6 core fucosylated N-glycans with terminal GlcNAc residues, and complex glycans with Lewis A, Lewis B, Lewis X, Lewis Y, and Blood group H structures on type-1 or type-2 extension sequences. The SARS-CoV-2-specific lectins identified in our study may be assessed for their antiviral potential in future studies.
Patients’ knowledge, attitude, and practice toward degenerative lumbar spine disease: A cross-sectional study in Shanxi, China
Endothelial protein C receptor and thrombomodulin facilitate protease-activated receptor 1 cleavage at Arginine-46 by thrombin and activated protein C
SAMF-YOLO: A self-supervised, high-precision approach for defect detection in complex industrial environments
As object detection models grow in complexity, balancing computational efficiency and feature expressiveness becomes a critical challenge. To address this, we propose SAMF-YOLO, a novel model integrating three key components: SONet, BFAM, and FASFF-Head. The UniRepLKNet backbone, enhanced by the Star Operation, expands the feature space with high efficiency. FASFF-Head performs adaptive multi-scale feature fusion with minimal overhead, and the Bi-temporal Feature Aggregation Module (BFAM) strengthens the detection of small defects. Additionally, the Focaler-IoU loss improves bounding box regression for challenging object scales, and a self-supervised contrastive learning strategy enhances feature representation and model robustness without relying on labeled data. Experimental results demonstrate that SAMF-YOLO surpasses YOLOv11s with a 6.38% improvement in mAP@0.5 and a notable reduction in computational cost, confirming its superiority in accuracy, efficiency, and robustness. The code is released at https://github.com/Missing24ff/SAMF-YOLO.git.
The molecular subtype based on cell cycle-related gene signature predicts the prognosis and chemotherapy and immunotherapy response in gastric cancer
Unveiling how mitotic spindle orientation in 3D human colon organoids affects matrix displacements through a 4D study using DVC
Evaluation of cultivated land ecosystem service value in the black soil region of Northeast China
A targeted next-generation sequencing panel for identification of clinically relevant mutation profiles in solid tumours
Brain complexity represents uncertainty in sequence learning and corroborates habituation deficit in Parkinson disease patients
Phosphorylation of GABAA receptor β3 subunit at Ser408–409 is essential for contextual learning at hippocampal CA1 synapses
Optimization of the heat recovery performance of enhanced geothermal system based on PSO-GA-BP neural networks and analytic hierarchy process
Abstract Numerical simulation is the most commonly used method to predict the power generation capacity of EGS during geothermal energy extraction. However, it is time-consuming to optimize the scheme only by comparing the numerical simulation methods, and it is difficult to determine the globally optimal operation strategy. In this study, five key parameters including well spacing, water injection rate, injection temperature, fracture permeability and fracture spacing are considered. Based on the numerical simulation data, optimized Back-Propagation Neural Network (BPNN) prediction models combining the Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA) were developed to investigate the impact of various factors on the heat recovery performance of a three-horizontal-well EGS in the Zhacang geothermal field. On the basis of these PSO-GA-BPNN models, the weights of the evaluation indexes for each geothermal development were calculated by hierarchical analysis method. In this study, an innovative combination of numerical simulation, PSO-GA-BPNN model, and Analytic Hierarchy Process was proposed to establish an EGS comprehensive optimization method, effectively improving the accuracy and computational efficiency of scheme optimization. The results reveal that predicting EGS with PSO-GA-BPNN models has a good prediction accuracy for each performance index. After a comprehensive comparison, the combination of well spacing of 600 m, water injection rate of 27 kg/s, injection temperature of 58 ℃, fracture permeability of 1 × 10–10 m2 and fracture spacing of 100 m was identified as the optimal power generation scheme. The EGS power plant is expected to have an installed capacity of 6.05–8.17 MW, with a total generating capacity of 3,163.16 GWh and a levelized cost of electricity of $0.033/kWh. The method is very effective in the development and optimal design of geothermal systems and can also provide a reference for other geothermal projects.
DDTC-Cu(I) inhibits human osteosarcoma cells growth by repressing MET/PI3K/AKT signaling pathway
Armless hairpin-like tRNAs in Romanomermis culicivorax: Evolutionary adaptation of a mitochondrial elongation factor EF-Tu
[18F]FDG PET/CT to reduce the need for sentinel lymph node biopsy in early-stage oral cancer: PETN0-study protocol
Using reliable techniques for detecting lymph node metastases (LNM) in oral squamous cell carcinoma (OSCC) is crucial for adequate neck treatment. Currently, palpation of the neck, computed tomography, magnetic resonance imaging, ultrasound-guided fine needle aspiration cytology and/or sentinel lymph node biopsy (SLNB) are used to stage the neck in early-stage OSCC. SLNB is a reliable diagnostic technique to detect occult LNM. However, management of the neck with SLNB has its limitations. First of all, SLNB is an invasive procedure with associated morbidity and approximately 20–30% of patients require a subsequent neck dissection. Moreover, performing a subsequent neck dissection is more complex than elective neck dissection, and carries a higher risk of complications. Therefore, it is important to improve patient selection for SLNB. Fluor-18-fluorodeoxyglucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) has shown promising results for LNM detection. The aim of the PETN0 study, a prospective Dutch multicenter cohort study (registration number NL83442.041.22), is to reduce the need for SLNB by developing scoring criteria for [18F]FDG PET/CT with a high positive predictive value (PPV) in patients with early-stage OSCC. Developing scoring criteria for a high PPV can reduce SLNBs and second-stage neck dissections by performing a neck dissection together with resection of the primary tumor in patients with predicted LNM. When focused on high PPV the sensitivity will probably be lower, but missed LNM will be detected by SLNB when performed after negative [18F]FDG PET/CT. Patients (n = 159) with cT1-3N0 OSCC (8th TNM edition; only when T3 is assessed based on tumor dimensions of >2 and ≤4 cm, with DOI > 10 mm), candidate for transoral excision and SLNB, are included in the study. [18F]FDG PET/CT will be conducted within a maximum of three weeks before SLNB. A cost-effectiveness analysis will also be performed, together with quality of life assessment using questionnaires.
Joint probability modeling of data from California homes suggests strong correlations between annual and past month pesticide use in NHANES 1999–2004
Corrosion prediction in medium pressure vent pipes at high sulfur field stations through numerical analysis of internal wall liquid phase distribution
Evaluation of coupling coordination degree between tourism urbanization and ecosystem services in urban agglomerations in the yellow river basin
Abstract Understanding the interaction between ecosystem services (ESs) and tourism urbanization (TU) is essential for regional development and decision-making in urban agglomerations in the Yellow River Basin (YRB). Evaluating the coupling coordination degree (CCD) between ESs and TU is vital for regional development in the YRB. This study employs multi-year statistical data and spatial datasets—including land use, digital elevation models, meteorological data, and soil property data—to assess tourism urbanization using the entropy method. Concurrently, ESs, which encompass water yield, soil conservation, habitat quality, and carbon sequestration, are evaluated through the InVEST model. A spatiotemporal analysis of the YRB from 2000 to 2020 reveals two key trends: (1) the spatial polarization of tourism urbanization has intensified, with 68% of high-intensity clusters concentrated in eastern provinces (e.g., Shandong, Henan), sharply contrasting with the underdeveloped western regions (e.g., Qinghai, Ningxia); and (2) the coupling coordination degrees between TU and ESs have declined by 22–35% in ecologically fragile zones, driven by habitat fragmentation and carbon loss. Specifically, water yield and soil conservation have increased by 18% and 24%, respectively, while habitat quality and carbon sequestration have decreased by 14% and 11% in urbanizing areas, reflecting unsustainable trade-offs. The novel contribution of this study lies in establishing a basin-scale CCD framework for urban agglomerations, providing empirical evidence to reconcile tourism-driven growth with ecosystem resilience in the YRB. These findings underscore the urgent need for spatially adaptive governance to mitigate developmental imbalances. Future research should integrate high-resolution data and cultural ESs to address micro-scale complexities.