Eagle-YOLO : An Eagle-Inspired YOLO for Object Detection in Unmanned Aerial Vehicles Scenarios

Funding Information: This research was funded by National Natural Science Foundation of China OF FUNDER grant number 41471333, 61304199. This research was funded by Fujian Provincial Department of Science and Technology OF FUNDER grant number 2021Y4019, 2020D002, 2020L3014, 2019I0019. This research was funded by Fujian University of Technology OF FUNDER grant number KF-J21012. This research was funded by Shenzhen Science and Technology Innovation Program OF FUNDER grant number JCYJ20220530160408019. This research was funded by Basic and Applied Basic Research Foundation of Guangdong Province OF FUNDER grant number 2023A1515011915. This research was funded by the Key Research and Development Project of Hunan Province of China OF FUNDER grant number 2022GK2020. This research was funded by Hunan Natural Science Foundation of China OF FUNDER grant number 2022JJ30171. Publisher Copyright: © 2023 by the authors.Peer reviewedPublisher PD

Polymorphism and Balancing Selection of MHC Class II DAB Gene in 7 Selective Flounder (Paralichthys olivaceus) Families

In order to determine the genetic variation of the MHC class IIB exon2 allele in the offspring, 700 fry from seven families of Japanese flounder challenged with V. anguillarum were studied, and different mortality rates were found in those families. Five to ten surviving and dead fry from each of the seven families were selected to study the MHC class II B exon2 gene with PCR and a direct sequencing method. One hundred and sixteen different exon2 sequences were found and 116 different alleles were identified, while a minimum of four loci were revealed in the MHC class II B exon2 gene. The ratio (dN/dS) of nonsynonymous substitution (dN) to synonymous substitutions (dS) in the peptide-binding region (PBR) of the MHC class IIB gene was 6.234, which indicated that balancing selection is acting on the MHC class IIB genes. The MHC IIB alleles were thus being passed on to their progeny. Some alleles were significantly more frequent in surviving than dead individuals. All together our data suggested that the alleles Paol-DAB*4301, Paol-DAB*4601, Paol-DAB*4302, Paol-DAB*3803, and Paol-DAB*4101 were associated with resistance to V. anguillarum in flounder

Ulinastatin attenuates oxidation, inflammation and neural apoptosis in the cerebral cortex of adult rats with ventricular fibrillation after cardiopulmonary resuscitation

OBJECTIVE: The role of Ulinastatin in neuronal injury after cardiopulmonary resuscitation has not been elucidated. We aim to evaluate the effects of Ulinastatin on inflammation, oxidation, and neuronal injury in the cerebral cortex after cardiopulmonary resuscitation. METHODS: Ventricular fibrillation was induced in 76 adult male Wistar rats for 6 min, after which cardiopulmonary resuscitation was initiated. After spontaneous circulation returned, the rats were split into two groups: the Ulinastatin 100,000 unit/kg group or the PBS-treated control group. Blood and cerebral cortex samples were obtained and compared at 2, 4, and 8 h after return of spontaneous circulation. The protein levels of tumor necrosis factor alpha (TNF-α) and interleukin 6 (IL-6) were assayed using an enzyme-linked immunosorbent assay, and mRNA levels were quantified via real-time polymerase chain reaction. Myeloperoxidase and Malondialdehyde were measured by spectrophotometry. The translocation of nuclear factor-κB p65 was assayed by Western blot. The viable and apoptotic neurons were detected by Nissl and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL). RESULTS: Ulinastatin treatment decreased plasma levels of TNF-α and IL-6, expression of mRNA, and Myeloperoxidase and Malondialdehyde in the cerebral cortex. In addition, Ulinastatin attenuated the translocation of nuclear factor-κB p65 at 2, 4, and 8 hours after the return of spontaneous circulation. Ulinastatin increased the number of living neurons and decreased TUNEL-positive neuron numbers in the cortex at 72 h after the return of spontaneous circulation. CONCLUSIONS: Ulinastatin preserved neuronal survival and inhibited neuron apoptosis after the return of spontaneous circulation in Wistar rats via attenuation of the oxidative stress response and translocation of nuclear factor-κB p65 in the cortex. In addition, Ulinastatin decreased the production of TNF-α, IL-6, Myeloperoxidase, and Malondialdehyde

Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China

With the explosive growth of atmospheric data, machine learning models have achieved great success in air pollution forecasting because of their higher computational efficiency than the traditional chemical transport models. However, in previous studies, new prediction algorithms have only been tested at stations or in a small region; a large-scale air quality forecasting model remains lacking to date. Huge dimensionality also means that redundant input data may lead to increased complexity and therefore the over-fitting of machine learning models. Feature selection is a key topic in machine learning development, but it has not yet been explored in atmosphere-related applications. In this work, a regional feature selection-based machine learning (RFSML) system was developed, which is capable of predicting air quality in the short term with high accuracy at the national scale. Ensemble-Shapley additive global importance analysis is combined with the RFSML system to extract significant regional features and eliminate redundant variables at an affordable computational expense. The significance of the regional features is also explained physically. Compared with a standard machine learning system fed with relative features, the RFSML system driven by the selected key features results in superior interpretability, less training time, and more accurate predictions. This study also provides insights into the difference in interpretability among machine learning models (i.e., random forest, gradient boosting, and multi-layer perceptron models).</p