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The accumulation of sand induced by wind poses a significant challenge to the safety and maintenance of railways in arid and desert regions. Accurate calculation and prediction of sand accumulation are crucial for ensuring continuous railway operation. This research is centered on the region significantly impacted by sand accumulation along the Ganquan Railway. Wind speed, wind direction, and sand carrying capacity data near this section were monitored. Using the collected wind speed, wind direction, and wind-sand flow density data, numerical simulations were conducted using the Computational Fluid Dynamics (CFD) method to predict the amount of sand accumulation within the sand mitigation measures of the Ganquan Railway. Monitoring results indicate that the dominant wind direction in spring and summer is due west, while in autumn and winter it is southwest, with an average wind speed of 12 m/s. A positive correlation was observed between wind-sand flow density and wind speed. The wind-sand flow density above 2 m was nearly zero, indicating that the wind-sand flow structure is concentrated within 2 m from the ground, with an average wind-sand flow density of 3.50×10−5 kg/m3. Through numerical simulation, the characteristics of the wind field and sand accumulation distribution within the calculation domain were determined. A relationship equation between sand accumulation mass and width over time was derived. Initially, the sand accumulation width increases uniformly and then stabilizes, while the sand accumulation mass rises uniformly to a plateau before in-creasing rapidly. From these findings, the optimal period for sand removal was identified as between 350 and 450 days after the sand mitigation measures are put into operation.

Cadmium, as a typical heavy metal, has the potential to induce soil pollution and threaten human health through the soil-plant-human pathway. The conventional evaluation method based on the total content in soil cannot accurately represent the content migrated from the food chain to plants and the human body. Previous studies focused on the process of plant enrichment of heavy metals in soil, and very few studies directly predicted human exposure or risk through the labile state of Cd in soil. Hence, a relatively accurate and convenient prediction model of Cd release and translocation in the soil-rice-human system was developed. This model utilizes available Cd and soil parameters to predict the bioavailability of Cd in soil, as well as the in vitro bioaccessibility of Cd in cooked rice. The bioavailability of Cd was determined by the Diffusive Gradients in Thin-films technology and BCR sequential extraction procedure, offering in-situ quantification, which presents a significant advantage over traditional monitoring methods and aligns closely with the actual uptake of heavy metals by plants. The experimental results show that the prediction model based on the concentration of heavy metal forms measured by BCR sequential extraction procedure and diffusive gradients in thin-films technique can accurately predict the Cd uptake in rice grains, gastric and gastrointestinal phase (R2=0.712, 0.600 and 0.629). This model accurately predicts Cd bioavailability and bioaccessibility across the soil-rice-human pathway, informing actual human Cd intake, offering scientific support for developing more effective risk assessment methods.

An unbalanced protocol recognition method based on the improved Residual U-Net was proposed to solve the challenge of network security posed by the increasing network attacks with the continuous development of the Internet.In the captured network traffic, a small proportion is constituted by malicious traffic, typically utilizing minority protocols.However, existing protocol recognition methods struggle to accurately identify these minority protocols when the class distribution of the protocol data is imbalanced.To address this issue, an unbalanced protocol recognition method was proposed, which utilized the improved Residual U-Net, incorporating a novel activation function and the Squeeze-and-Excitation Networks (SE-Net) to enhance the feature extraction capability.The loss function employed in the proposed model was the weighted Dice loss function.In cases where the recognition accuracies of the minority protocols were low, the loss function value would be high.Consequently, the optimization direction of the model would be dominated by the minority protocols, resulting in improved recognition accuracies for them.During the protocol recognition process, the network flow was extracted from the network traffic and preprocessed to convert it into a one-dimensional matrix.Subsequently, the protocol recognition model extracted the features of the protocol data, and the Softmax classifier predicted the protocol types.Experimental results demonstrate that the proposed protocol recognition model achieves more accurate recognition of the minority protocols compared to the comparison model, while also improving the recognition accuracies of the majority protocols.

Summary: Mutations of TRAPPC12 are associated with progressive childhood encephalopathy including abnormal white matter. However, the underlying pathogenesis is still unclear. Here, we found that Trappc12 deficiency in CG4 and oligodendrocyte progenitor cells (OPCs) affects their differentiation and maturation. In addition, TRAPPC12 interacts with Mea6/cTAGE5, and Mea6/cTAGE5 ablation in OPCs affects their proliferation and differentiation, leading to marked hypomyelination, compromised synaptic functionality, and aberrant behaviors in mice. We reveal that TRAPPC12 is associated with COPII components at ER exit site, and Mea6/cTAGE5 cKO disrupts the trafficking pathway by affecting the distribution and/or expression of TRAPPC12, SEC13, SEC31A, and SAR1. Moreover, we observed marked disturbances in the secretion of pleiotrophin (PTN) in Mea6-deficient OPCs. Notably, exogenous PTN supplementation ameliorated the differentiation deficits of these OPCs. Collectively, our findings indicate that the association between TRAPPC12 and MEA6 is important for cargo trafficking and white matter development.

BackgroundIn sepsis patients, kidney damage is among the most dangerous complications, with a high mortality rate. In addition, major adverse kidney events within 30 days (MAKE30) served as a comprehensive and unbiased clinical outcome measure for sepsis patients due to the recent shift toward targeting patient-centered renal outcomes in clinical research. However, the underlying predictive model for the prediction of MAKE30 in sepsis patients has not been reported in any study.MethodsA cohort of 2,849 sepsis patients from the Medical Information Mart for Intensive Care (MIMIC)-IV database was selected and subsequently allocated into a training set (n = 2,137, 75%) and a validation set (n = 712, 25%) through randomization. In addition, 142 sepsis patients from the Xi’An No. 3 Hospital as an external validation group. Univariate and multivariate logistic regression analyses were conducted to ascertain the independent predictors of MAKE30. Subsequently, a nomogram was developed utilizing these predictors, with an area under curve (AUC) above 0.6. The performance of nomogram was assessed through calibration curve, receiver operating characteristics (ROC) curve, and decision curve analysis (DCA). The secondary outcome was 30-day mortality, persistent renal dysfunction (PRD), and new renal replacement therapy (RRT). MAKE30 were a composite of death, PRD, new RRT.ResultsThe construction of the nomogram was based on several independent predictors (AUC above 0.6), including age, respiratory rate (RR), PaO2, lactate, and blood urea nitrogen (BUN). The predictive model demonstrated satisfactory discrimination for MAKE30, with an AUC of 0.740, 0.753, and 0.821 in the training, internal validation, and external validation cohorts, respectively. Furthermore, the simple prediction model exhibited superior predictive value compared to the SOFA model in both the training (AUC = 0.710) and validation (AUC = 0.692) cohorts. The nomogram demonstrated satisfactory calibration and clinical utility as evidenced by the calibration curve and DCA. Additionally, the predictive model exhibited excellent accuracy in forecasting 30-day mortality (AUC = 0.737), PRD (AUC = 0.639), and new RRT (AUC = 0.846) within the training dataset. Additionally, the model displayed predictive power for 30-day mortality (AUC = 0.765), PRD (AUC = 0.667), and new RRT (AUC = 0.783) in the validation set.ConclusionThe proposed nomogram holds the potential to estimate the risk of MAKE30 promptly and efficiently in sepsis patients within the initial 24 h of admission, thereby equipping healthcare professionals with valuable insights to facilitate personalized interventions.

Kelp, often referred to as a “sea vegetable”, holds substantial economic significance. Currently, the drying process for kelp in China primarily relies on outdoor sun-drying methods. Detecting kelp in the field presents challenges arising from issues such as overlapping and obstruction. To address these challenges, this study introduces a lightweight model, K-YOLOv5, specifically designed for the precise detection of sun-dried kelp. YOLOv5-n serves as the base model, with several enhancements implemented in this study: the addition of a detection head incorporating an upsampling layer and a convolution module to improve the recognition of small objects; the integration of an enhanced I-CBAM attention mechanism, focusing on key features to enhance the detection accuracy; the replacement of the CBS module in the neck network with GSConv to reduce the computational burden and accelerate the inference speed; and the optimization of the IoU algorithm to improve the identification of overlapping kelp. Utilizing drone-captured images of sun-dried kelp, a dataset comprising 2190 images is curated. Validation on this self-constructed dataset indicates that the improved K-YOLOv5 model significantly enhances the detection accuracy, achieving 88% precision and 78.4% recall. These values represent 6.8% and 8.6% improvements over the original model, respectively, meeting the requirements for the real-time recognition of sun-dried kelp.

The reclamation coral sand (CS) layer is the survival environment for island reef vegetation in the South China Sea. The root system within the CS bed draws water necessary for vegetation growth, implying that the water-retention capacity of CS plays a pivotal role in determining vegetation viability. Particle size distribution (PSD) significantly influences the water-retention capacity of geomaterials. This study examines the impact of PSD on the water-retention capacity of CS from both macro (soil–water characteristic curve, SWCC) and micro (pore water distribution) perspectives using the pressure plate test and nuclear magnetic resonance technique, and an F&X model was used to analyze the SWCC of CS. The findings indicated that the F&X model aptly describes the SWCC of CS with different PSDs. Both the air entry value and residual water content rise with an increased content of fine grains (d < 0.25 mm), suggesting that the presence of fine grains augments the water-retention capacity of CS. It is considered that a size range of d = 0.075–0.25 mm predominantly impacts the water-retention capacity of CS. The PSD primarily influences the water-retention capacity by affecting the pore size distribution of CS. The volume of small pores swells with the surge of fine-grain content, while the maximum pore size contracts with increasing fine-grain content. Limited pore connectivity in CS means macropores can retain water even under high suction, bolstering the water-retention capacity of CS. These findings offer theoretical guidance for selecting gradation parameters for the planting layer on island reefs.

Abstract The cooperative diagnosis of non‐coding RNAs (ncRNAs) can accurately reflect the state of cell differentiation and classification, laying the foundation of precision medicine. However, there are still challenges in simultaneous analyses of multiple ncRNAs and the integration of biomarker data for cell typing. In this study, DNA framework‐based programmable atom‐like nanoparticles (PANs) are designed to develop molecular classifiers for intra‐cellular imaging of multiple ncRNAs associated with cell differentiation. The PANs‐based molecular classifier facilitates signal amplification through the catalytic hairpin assembly. The interaction between PAN reporters and ncRNAs enables high‐fidelity conversion of ncRNAs expression level into binding events, and the assessment of in situ ncRNAs levels via measurement of the fluorescent signal changes of PAN reporters. Compared to non‐amplified methods, the detection limits of PANs are reduced by four orders of magnitude. Using human gastric cancer cell lines as a model system, the PANs‐based molecular classifier demonstrates its capacity to measure multiple ncRNAs in living cells and assesses the degree of cell differentiation. This approach can serve as a universal strategy for the classification of cancer cells during malignant transformation and tumor progression.

Abstract The current SARS-CoV-2 variants strikingly evade all authorized monoclonal antibodies and threaten the efficacy of serum-neutralizing activity elicited by vaccination or prior infection, urging the need to develop antivirals against SARS-CoV-2 and related sarbecoviruses. Here, we identified both potent and broadly neutralizing antibodies from a five-dose vaccinated donor who exhibited cross-reactive serum-neutralizing activity against diverse coronaviruses. Through single B-cell sorting and sequencing followed by a tailor-made computational pipeline, we successfully selected 86 antibodies with potential cross-neutralizing ability from 684 antibody sequences. Among them, PW5-570 potently neutralized all SARS-CoV-2 variants that arose prior to Omicron BA.5, and the other three could broadly neutralize all current SARS-CoV-2 variants of concern, SARS-CoV and their related sarbecoviruses (Pangolin-GD, RaTG13, WIV-1, and SHC014). Cryo-EM analysis demonstrates that these antibodies have diverse neutralization mechanisms, such as disassembling spike trimers, or binding to RBM or SD1 to affect ACE2 binding. In addition, prophylactic administration of these antibodies significantly protects nasal turbinate and lung infections against BA.1, XBB.1, and SARS-CoV viral challenge in golden Syrian hamsters, respectively. Importantly, post-exposure treatment with PW5-5 and PW5-535 also markedly protects against XBB.1 challenge in these models. This study reveals the potential utility of computational process to assist screening cross-reactive antibodies, as well as the potency of vaccine-induced broadly neutralizing antibodies against current SARS-CoV-2 variants and related sarbecoviruses, offering promising avenues for the development of broad therapeutic antibody drugs.