Serum Biochemical Reference Values for Adult and Non-adult Chinese Alligators during the Deep and Late Hibernation Periods

Background: The Chinese alligator (Alligator sinensis) is a critically endangered species. Due to the rapid growth of the captive population, the susceptibility to disease during the recovery period after winter hibernation, especially in young alligators, have detrimentally affected Chinese alligator populations. Serum biochemistry, which relates to metabolism, nutritional status and disease, is enormously helpful in evaluating physical conditions in reptile. Many studies have reported the serum biochemical reference values of various reptilian species, including several crocodilians. However, reference values for Chinese alligators have not yet been reported. For captive Chinese alligators, hibernation is a crucial period because winter management has a direct influence on the survival rate of juveniles and the reproduction rate of adults. The main object of the present study refore was to measure the serum biochemical values of captive Chinese alligators during hibernation.Materials, Methods & Results: As such, this study investigates the serum biochemistry as a factor of age and hibernation stage. During the deep and late hibernation periods blood samples were drawn from 30 healthy captive Chinese alligators (adults, sub-adults, and juveniles) at the Anhui Research Center of Chinese Alligator Reproduction (ARCCAR). Serum biochemical measurements were performed using an automated biochemical analyzer and compared based on the age group and hibernation stage via two-way ANOVA. During late hibernation, serum lactate dehydrogenase, alkaline phosphatase, and aspartate aminotransferase activity increased in all age groups in comparison to that in deep hibernation, while the concentration of calcium decreased. Meanwhile, the concentration of serum phosphorus, uric acid, total protein, and globulin in sub-adults and juveniles considerably increased in comparison to that in deep hibernation, while cholesterol and albumin declined. However, in adults only slight changes were noted. Based on comprehensive statistical analysis, our results indicate that sub-adults and juveniles are at risk of developing renal disease during artificial hibernation.Discussion: Chinese alligators, especially sub-adults and juveniles, are particularly vulnerable to disease when they wake from hibernation. They often display symptoms such as depression, anorexia, lethargy, sluggish movement, slow, incremental weight gain, progressive muscle wasting, and even death. The high rate of morbidity in non-adult Chinese alligators may be associated with the high density of UA and other changes in multiple biochemical markers that occur during late hibernation. These altered serum biochemical profiles may indicate kidney damage. One of the most common diseases among reptiles is nephropathy, the symptoms of which are non-specific and tend to agree with those observed post-hibernation. In summary, this study has reported the serum biochemical values of Chinese alligators of varying ages in the deep and late hibernation phases. Based on statistical analyses, interesting differences between the serum biochemical values of adults and non-adults during the deep and late hibernation have been found. The observed changes suggest that, under an artificial hibernation environment, the kidneys of sub-adults and juveniles may become impaired. We believe that the data reported in this study will provide clinical guidance to facilitate more appropriate artificial wintering conditions for Chinese alligators, and assist the breeding and management of these reptiles, as well as disease prevention, during hibernation and recovery

A Benchmark Comparison of Imitation Learning-based Control Policies for Autonomous Racing

Autonomous racing with scaled race cars has gained increasing attention as an effective approach for developing perception, planning and control algorithms for safe autonomous driving at the limits of the vehicle's handling. To train agile control policies for autonomous racing, learning-based approaches largely utilize reinforcement learning, albeit with mixed results. In this study, we benchmark a variety of imitation learning policies for racing vehicles that are applied directly or for bootstrapping reinforcement learning both in simulation and on scaled real-world environments. We show that interactive imitation learning techniques outperform traditional imitation learning methods and can greatly improve the performance of reinforcement learning policies by bootstrapping thanks to its better sample efficiency. Our benchmarks provide a foundation for future research on autonomous racing using Imitation Learning and Reinforcement Learning

Raptor Encoding for Low-Latency Concurrent Multi-PDU Session Transmission with Security Consideration in B5G Edge Network

In B5G edge networks, end-to-end low-latency and high-reliability transmissions between edge computing nodes and terminal devices are essential. This paper investigates the queue-aware coding scheduling transmission of randomly arriving data packets, taking into account potential eavesdroppers in edge networks. To address these concerns, we introduce SCLER, a Protocol Data Units (PDU) Raptor-encoded multi-path transmission method that overcomes the challenges of a larger attack surface in Concurrent Multipath Transfer (CMT), excessive delay due to asymmetric delay\&bandwidth, and lack of interaction among PDU session bearers. We propose a secure and reliable transmission scheme based on Raptor encoding and distribution that incorporates a queue length-aware encoding strategy. This strategy is modeled using Constrained Markov Decision Process (CMDP), and we solve the constraint optimization problem of optimal decision-making based on a threshold strategy. Numerical results indicate that SCLER effectively reduces data leakage risks while achieving the optimal balance between delay and reliability, thereby ensuring data security. Importantly, the proposed system is compatible with current mobile networks and demonstrates practical applicability

Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans

Gastric cancer is the third leading cause of cancer-related mortality worldwide, but no guideline-recommended screening test exists. Existing methods can be invasive, expensive, and lack sensitivity to identify early-stage gastric cancer. In this study, we explore the feasibility of using a deep learning approach on non-contrast CT scans for gastric cancer detection. We propose a novel cluster-induced Mask Transformer that jointly segments the tumor and classifies abnormality in a multi-task manner. Our model incorporates learnable clusters that encode the texture and shape prototypes of gastric cancer, utilizing self- and cross-attention to interact with convolutional features. In our experiments, the proposed method achieves a sensitivity of 85.0% and specificity of 92.6% for detecting gastric tumors on a hold-out test set consisting of 100 patients with cancer and 148 normal. In comparison, two radiologists have an average sensitivity of 73.5% and specificity of 84.3%. We also obtain a specificity of 97.7% on an external test set with 903 normal cases. Our approach performs comparably to established state-of-the-art gastric cancer screening tools like blood testing and endoscopy, while also being more sensitive in detecting early-stage cancer. This demonstrates the potential of our approach as a novel, non-invasive, low-cost, and accurate method for opportunistic gastric cancer screening.Comment: MICCAI 202

Efficient photocatalytic degradation of Malachite Green in seawater by the hybrid of Zinc-Oxide Nanorods Grown on Three-Dimensional (3D) reduced graphene oxide(RGO)/Ni foam

A hybrid of ZnO nanorods grown onto three-dimensional (3D) reduced graphene oxide (RGO)@Ni foam (ZnO/RGO@NF) is synthesized by a facile hydrothermal method. The as-prepared hybrid material is physically characterized by SEM, XRD, Raman, and X-ray photoelectron spectroscopy (XPS).When the as-prepared 3D hybrid is investigated as a photocatalyst, it demonstrates significant high photocatalytic activity for the degradation of methylene blue (MB), rhodamine (RhB), and mixed MB/RhB as organic dye pollutants. In addition, the practical application and the durability of the as-prepared catalyst to degradation of malachite green (MG) in seawater are firstly assessed in a continuous flow system. The catalyst shows a high degradation efficiency and stable photocatalytic activity for 5 h continuous operation, which should be a promising catalyst for the degradation of organic dyes in seawater

SIMULATION OF PULVERIZED COAL INJECTION IN A BLAST FURNACE

ABSTRACT A three-dimensional multiphase CFD model using an Eulerian approach is developed to simulate the process of pulverized coal injection into a blast furnace. The model provides the detailed fields of fluid flow velocity, temperatures, and compositions, as well as coal mass distributions during the devolatilization and combustion of the coal. This paper focuses on coal devolatilization and combustion in the space before entering the raceway of the blast furnace. Parametric studies have been conducted to investigate the effect of coal properties and injection operations