IDa-Det: An Information Discrepancy-aware Distillation for 1-bit Detectors

Knowledge distillation (KD) has been proven to be useful for training compact object detection models. However, we observe that KD is often effective when the teacher model and student counterpart share similar proposal information. This explains why existing KD methods are less effective for 1-bit detectors, caused by a significant information discrepancy between the real-valued teacher and the 1-bit student. This paper presents an Information Discrepancy-aware strategy (IDa-Det) to distill 1-bit detectors that can effectively eliminate information discrepancies and significantly reduce the performance gap between a 1-bit detector and its real-valued counterpart. We formulate the distillation process as a bi-level optimization formulation. At the inner level, we select the representative proposals with maximum information discrepancy. We then introduce a novel entropy distillation loss to reduce the disparity based on the selected proposals. Extensive experiments demonstrate IDa-Det's superiority over state-of-the-art 1-bit detectors and KD methods on both PASCAL VOC and COCO datasets. IDa-Det achieves a 76.9% mAP for a 1-bit Faster-RCNN with ResNet-18 backbone. Our code is open-sourced on https://github.com/SteveTsui/IDa-Det

Salvianolic acid B plays an anti-obesity role in high fat diet-induced obese mice by regulating the expression of mRNA, circRNA, and lncRNA

Background Adipose tissue plays a central role in obesity-related metabolic diseases such as type 2 diabetes. Salvianolic acid B (Sal B), a water-soluble ingredient derived from Salvia miltiorrhiza, has been shown to reduce obesity and obesity-related metabolic diseases by suppressing adipogenesis. However, the role of Sal B in white adipose tissue (WAT) is not yet clear. Methods Illumina Hiseq 4000 was used to study the effects of Sal B on the expression of long non-coding RNA (lncRNA) and circular RNA (circRNA) in epididymal white adipose tissue induced by a high fat diet in obese mice. Results RNA-Seq data showed that 234 lncRNAs, 19 circRNAs, and 132 mRNAs were differentially expressed in WAT under Sal B treatment. The up-regulated protein-coding genes in WAT of the Sal B-treated group were involved in the insulin resistance pathway, while the down-regulated genes mainly participated in the IL-17 signaling pathway. Other pathways may play an important role in the formation and differentiation of adipose tissue, such as B cell receptor signaling. Analysis of the lncRNA–mRNA network provides potential targets for lncRNAs in energy metabolism. We speculate that Sal B may serve as a potential therapeutic approach for obesity

Assessing Genetic Diversity and Estimating the Inbreeding Effect on Economic Traits of Inner Mongolia White Cashmere Goats Through Pedigree Analysis

Objective: The purpose of this study was to discover the population structure and genetic diversity of Inner Mongolia White Cashmere goats (IMCGs) and demonstrate the effect of inbreeding on the live body weight (LBW), cashmere yield (CY), fiber length (FL), and fiber diameter (FD) of IMCGs.Materials and Methods: All data were collected from pedigree information and production performance records of IMCGs from 1983 to 2019. The population structure and genetic diversity were analyzed by Endog 4.8 software. Inbreeding coefficients were obtained by the pedigree package in R. Then, a linear regression model was used to analyze how inbreeding influences economic traits in IMCGs. Four levels of inbreeding coefficients (Fi) were classified in this study, including Fi = 0, 0< Fi ≤ 6.25, 6.25< Fi ≤ 12.5 and Fi≥12.5. Variance analysis was performed to determine whether inbreeding levels had a significant effect on economic traits in IMCGs.Results: The proportions of rams and dams in IMCGs for breeding were relatively small, with values of 0.8 and 20.5%, respectively. The proportion of inbred animals in the entire population was high, with values up to 68.6%; however, the average inbreeding coefficient and relatedness coefficient were 4.50 and 8.48%, respectively. To date, the population has experienced 12 generations. The average generation interval obtained in the present study was 4.11 ± 0.01 years. The ram-to-son pathway was lowest (3.97 years), and the ewe-to-daughter pathway was highest (4.24 years). It was discovered that the LBW, CY, and FL increased by 3.88 kg, 208.7 g, and 1.151 cm, respectively, with every 1% increase in the inbreeding coefficient, and the FD decreased by 0.819 μm with every 1% increase in the inbreeding coefficient. Additionally, multiple comparison analysis indicated that when the inbreeding coefficient was higher than 6.25%, the LBW showed an obvious decreasing trend. The threshold value of inbreeding depression in the CY is 12.5%. However, inbreeding depression has not been observed in the FL and FD.Conclusion: Pedigree completeness needs to be further strengthened. The degree of inbreeding in this flock should be properly controlled when designing breeding programs

Prediction model for missed abortion of patients treated with IVF-ET based on XGBoost: a retrospective study

Aim In this study, we established a model based on XGBoost to predict the risk of missed abortion in patients treated with in vitro fertilization-embryo transfer (IVF-ET), evaluated its prediction ability, and compared the model with the traditional logical regression model. Methods We retrospectively collected the clinical data of 1,017 infertile women treated with IVF-ET. The independent risk factors were screened by performing a univariate analysis and binary logistic regression analysis, and then, all cases were randomly divided into the training set and the test set in a 7:3 ratio for constructing and validating the model. We then constructed the prediction models by the traditional logical regression method and the XGBoost method and tested the prediction performance of the two models by resampling. Results The results of the binary logistic regression analysis showed that several factors, including the age of men and women, abnormal ovarian structure, prolactin (PRL), anti-Müllerian hormone (AMH), activated partial thromboplastin time (APTT), anticardiolipin antibody (ACA), and thyroid peroxidase antibody (TPO-Ab), independently influenced missed abortion significantly (P < 0.05). The area under the receiver operating characteristic curve (AUC) score and the F1 score with the training set of the XGBoost model (0.877 ± 0.014 and 0.730 ± 0.019, respectively) were significantly higher than those of the logistic model (0.713 ± 0.013 and 0.568 ± 0.026, respectively). In the test set, the AUC and F1 scores of the XGBoost model (0.759 ± 0.023 and 0.566 ± 0.042, respectively) were also higher than those of the logistic model (0.695 ± 0.030 and 0.550 ± 049, respectively). Conclusions We established a prediction model based on the XGBoost algorithm, which can accurately predict the risk of missed abortion in patients with IVF-ET. This model performed better than the traditional logical regression model

Identification of sex determination locus and development of marker combination in Vitis based on genotyping by target sequencing

The grapevine is an important and economically valuable fruit crop, with flower sex being a key genetic trait that directly affects grapevine yield and quality. Despite its significance, there is a lack of studies on sex-linked molecular markers that can assist in grapevine breeding. In this study, we developed a grapevine single nucleotide polymorphism (SNP) marker array using a combination of genotyping by target sequencing (GBTS) and capture-in-solution technology and applied it to marker-assisted selection (MAS) of grapevine gender. The SNP array could detect a total of 20,597 core SNPs and 97,453 multiple SNPs (mSNPs), covering over 99% of the grapevine genome on each chromosome. A total of 131 progenies from a cross between Vitis vinifera 'Cabernet Sauvignon' and Vitis pseudoreticulata 'Huadong1058' that exhibited segregated sex phenotypes were sequenced using this array. Through locus mapping and a genome-wide association study (GWAS), a locus on chromosome 2 (54.74−58.80 cM) that explained 98.6% of the phenotypic variation was identified. To further utilize this locus, a sex prediction marker combination consisting of two SNPs was developed, which accurately predicted the sex of 34 natural grapevine varieties/accessions. This study demonstrates the application of GBTS in grapevine breeding and provides a reliable MAS marker set for early-stage sex selection

Biotransformation of Penindolone, an Influenza A Virus Inhibitor

Penindolone (PND) is a novel broad-spectrum anti-Influenza A Virus (IAV) agent blocking hemagglutinin-mediated adsorption and membrane fusion. The goal of this work was to reveal the metabolic route of PND in rats. Ultra-high-performance liquid chromatography tandem high-resolution mass spectrometry (UHPLC–HRMS) was used for metabolite identification in rat bile, feces and urine after administration of PND. A total of 25 metabolites, including 9 phase I metabolites and 16 phase II metabolites, were characterized. The metabolic pathways were proposed, and metabolites were visualized via Global Natural Product Social Molecular Networking (GNPS). It was found that 65.24–80.44% of the PND presented in the formation of glucuronide conjugate products in bile, and more than 51% of prototype was excreted through feces. In in vitro metabolism of PND by rat, mouse and human liver microsomes (LMs) system, PND was discovered to be eliminated in LMs to different extents with significant species differences. The effects of chemical inhibitors of isozymes on the metabolism of PND in vitro indicated that CYP2E1/2C9/3A4 and UGT1A1/1A6/1A9 were the metabolic enzymes responsible for PND metabolism. PND metabolism in vivo could be blocked by UGTs inhibitor (ibrutinib) to a certain extent. These findings provided a basis for further research and development of PND

Small RNA Sequencing Analysis of STZ-Injured Pancreas Reveals Novel MicroRNA and Transfer RNA-Derived RNA with Biomarker Potential for Diabetes Mellitus

MicroRNAs (miRNAs) and transfer RNA-derived small RNAs (tsRNAs) play critical roles in the regulation of different biological processes, but their underlying mechanisms in diabetes mellitus (DM) are still largely unknown. This study aimed to gain a better understanding of the functions of miRNAs and tsRNAs in the pathogenesis of DM. A high-fat diet (HFD) and streptozocin (STZ)-induced DM rat model was established. Pancreatic tissues were obtained for subsequent studies. The miRNA and tsRNA expression profiles in the DM and control groups were obtained by RNA sequencing and validated with quantitative reverse transcription-PCR (qRT-PCR). Subsequently, bioinformatics methods were used to predict target genes and the biological functions of differentially expressed miRNAs and tsRNAs. We identified 17 miRNAs and 28 tsRNAs that were significantly differentiated between the DM and control group. Subsequently, target genes were predicted for these altered miRNAs and tsRNAs, including Nalcn, Lpin2 and E2f3. These target genes were significantly enriched in localization as well as intracellular and protein binding. In addition, the results of KEGG analysis showed that the target genes were significantly enriched in the Wnt signaling pathway, insulin pathway, MAPK signaling pathway and Hippo signaling pathway. This study revealed the expression profiles of miRNAs and tsRNAs in the pancreas of a DM rat model using small RNA-Seq and predicted the target genes and associated pathways using bioinformatics analysis. Our findings provide a novel aspect in understanding the mechanisms of DM and identify potential targets for the diagnosis and treatment of DM

Moringa oleifera leaf supplementation relieves oxidative stress and regulates intestinal flora to ameliorate polycystic ovary syndrome in letrozole‐induced rats

Abstract This study investigated the effects of supplementation Moringa oleifera leaf (MOL) on relieving oxidative stress, anti‐inflammation, changed the relative abundance of multiple intestinal flora and blood biochemical indices during letrozole‐induced polycystic ovary syndrome (PCOS). Previous studies have shown that MOL has anti‐inflammatory, anti‐oxidation, insulin‐sensitizing effects. However, whether MOL has beneficial effects on PCOS remains to be elucidated. In the current study, 10‐week‐old female Sprague–Dawley rats received letrozole to induce PCOS‐like rats, and subsequently were treated with a MOL diet. Then, the body weight and estrus cycles were measured regularly in this period. Finally, the ovarian morphology, blood biochemical indices, anti‐oxidative, intestinal flora, and anti‐inflammation were observed at the end of the experiment. We found that MOL supplementation markedly decreased the body weight, significantly upregulated the expression of Sirt1, FoxO1, PGC‐1α, IGF1, and substantially modulated the sex hormone level and improved insulin resistance, which may be associated with the relieves oxidative stress. Moreover, the supplementation of MOL changed the relative abundance of multiple intestinal flora, the relative abundance of Fusobacterium, Prevotella were decreased, and Blautia and Parabacteroides were increased. These results indicate that MOL is potentially a supplementary medication for the management of PCOS