Comparative Analysis of the Ovary Transcriptome among Wanyue Black and Yorkshire Gilts Using RNA-Seq.

Pubertal genetic variations between the indigenous Chinese Wanyue Black pig breed and the imported Yorkshire breed significantly impact their reproductive capacity. In order to identify the differentially expressed genes, gene networks, and metabolic pathways in ovary transcriptome of gilts, the serum hormone levels were analyzed by ELISA, and RNA-seq was performed to analyze ovarian genes. Our results reveal higher estradiol (E2) levels in Wanyue black gilts compared to Yorkshire gilts, while Yorkshire gilts exhibit elevated progesterone (P4) and GnRH levels. We identified a total of 154 differentially expressed genes (DEGs), with 87 up-regulated and 67 down-regulated genes in the Wanyue black gilts ovaries compared to the Yorkshire gilts. GO enrichment analysis unveiled the participation of DEGs in processes such as "Reproduction", "Reproductive system development", and "Ovarian follicle development". Moreover, KEGG enrichment analysis revealed the involvement of DEGs in multiple signaling pathways associated with hormone biosynthesis and puberty, encompassing "Steroid hormone biosynthesis", "Estrogen signaling pathway", and "Prolactin signaling pathway". The subsequent bioinformatics analysis identified nine functional genes that potentially contribute to the disparity in ovaries between Wanyue black gilts and Yorkshire gilts. This study offers significant insights into the endocrine and genetic aspects of pubertal development in gilts.

Identification of microRNAs implicated in modulating resveratrol-induced apoptosis in porcine granulosa cells.

MicroRNAs (miRNAs) are small, noncoding RNAs that play a crucial role in the complex and dynamic network that regulates the apoptosis of porcine ovarian granulosa cells (POGCs). Resveratrol (RSV) is a nonflavonoid polyphenol compound that is involved in follicular development and ovulation. In previous study, we established a model of RSV treatment of POGCs, confirming the regulatory effect of RSV in POGCs. To investigate the miRNA-level effects of RSV on POGCs to reveal differentially expressed miRNAs, a control group (n = 3, 0 µM RSV group), a low RSV group (n = 3, 50 µM RSV group), and a high RSV group (n = 3, 100 µM RSV group) were created for small RNA-seq. In total, 113 differentially expressed miRNAs (DE-miRNAs) were identified, and a RT-qPCR analysis showed a correlation with the sequencing data. Functional annotation analysis revealed that DE-miRNAs in the LOW vs. CON group may be involved in cell development, proliferation, and apoptosis. In the HIGH vs. CON group, RSV functions were associated with metabolic processes and responses to stimuli, while the pathways were related to PI3K24, Akt, Wnt, and apoptosis. In addition, we constructed miRNA-mRNA networks related to Apoptosis and Metabolism. Then, ssc-miR-34a and ssc-miR-143-5p were selected as key miRNAs. In conclusion, this study provided an improved understanding of effects of RSV on POGCs apoptosis through the miRNA modulations. The results suggest that RSV may promote POGCs apoptosis by stimulating the miRNA expressions and provided a better understanding of the role of miRNAs combined with RSV in ovarian granulosa cell development in pigs.

2,5-Hexanedione Affects Ovarian Granulosa Cells in Swine by Regulating the CDKN1A Gene: A Transcriptome Analysis.

N-hexane, a common industrial organic solvent, causes multiple organ damage owing to its metabolite, 2,5-hexanedione (2,5-HD). To identify and evaluate the effects of 2,5-HD on sows' reproductive performance, we used porcine ovarian granulosa cells (pGCs) as a vehicle and carried out cell morphology and transcriptome analyses. 2,5-HD has the potential to inhibit the proliferation of pGCs and induce morphological changes and apoptosis depending on the dose. RNA-seq analyses identified 4817 differentially expressed genes (DEGs), with 2394 down-regulated and 2423 up-regulated following 2,5-HD exposure treatment. The DEG, cyclin-dependent kinase inhibitor 1A (CDKN1A), according to the Kyoto Encyclopedia of Genes and Genomes enrichment analysis, was significantly enriched in the p53 signaling pathway. Thus, we evaluated its function in pGC apoptosis in vitro. Then, we knocked down the CDKN1A gene in the pGCs to identify its effects on pGCs. Its knockdown decreased pGC apoptosis, with significantly fewer cells in the G1 phase (p < 0.05) and very significantly more cells in the S phase (p < 0.01). Herein, we revealed novel candidate genes that influence pGCs apoptosis and cell cycle and provided new insights into the role of CDKN1A in pGCs during apoptosis and cell cycle arrest.

An Immersive Human-Robot Interactive Game Framework Based on Deep Learning for Children's Concentration Training.

In order to alleviate bottlenecks such as the lack of professional teachers, inattention during training processes, and low effectiveness in concentration training, we have proposed an immersive human-robot interactive (HRI) game framework based on deep learning for children's concentration training and demonstrated its use through human-robot interactive games based on gesture recognition. The HRI game framework includes four functional modules: video data acquisition, image recognition modeling, a deep learning algorithm (YOLOv5), and information feedback. First, we built a gesture recognition model containing 10,000 pictures of children's gestures, using the YOLOv5 algorithm. The average accuracy in recognition trainingwas 98.7%. Second, we recruited 120 children with attention deficits (aged from 9 to 12 years) to play the HRI games, including 60 girls and 60 boys. In the HRI game experiment, we obtained 8640 sample data, which were normalized and processed.According to the results, we found that the girls had better visual short-term memory and a shorter response time than boys. The research results showed that HRI games had a high efficacy, convenience, and full freedom, making them appropriate for children's concentration training.

Intelligent Evacuation Route Planning Algorithm Based on Maximum Flow.

This paper focuses on the problem of intelligent evacuation route planning for emergencies, including natural and human resource disasters and epidemic disasters, such as the COVID-19 pandemic. The goal of this study was to quickly generate an evacuation route for a community for victims to be evacuated to safe areas as soon as possible. The evacuation route planning problem needs to determine appropriate routes and allocate a specific number of victims to each route. This paper formulates the problem as a maximum flow problem and proposes a binary search algorithm based on a maximum flow algorithm, which is an intelligent optimization evacuation route planning algorithm for the community. Furthermore, the formulation is a nonlinear optimization problem because each route's suggested evacuation time is a convex nonlinear function of the number of victims assigned to that route. Finally, numerical examples and Matlab simulations demonstrate not only the algorithm's effectiveness, but also that the algorithm has low complexity and high precision. The study's findings offer a practical solution for nonlinear models of evacuation route planning, which will be widely used in human society and robot path planning schemes.

A Physiological-Signal-Based Thermal Sensation Model for Indoor Environment Thermal Comfort Evaluation.

Traditional heating, ventilation, and air conditioning (HVAC) control systems rely mostly on static models, such as Fanger's predicted mean vote (PMV) to predict human thermal comfort in indoor environments. Such models consider environmental parameters, such as room temperature, humidity, etc., and indirect human factors, such as metabolic rate, clothing, etc., which do not necessarily reflect the actual human thermal comfort. Therefore, as electronic sensor devices have become widely used, we propose to develop a thermal sensation (TS) model that takes in humans' physiological signals for consideration in addition to the environment parameters. We conduct climate chamber experiments to collect physiological signals and personal TS under different environments. The collected physiological signals are ECG, EEG, EMG, GSR, and body temperatures. As a preliminary study, we conducted experiments on young subjects under static behaviors by controlling the room temperature, fan speed, and humidity. The results show that our physiological-signal-based TS model performs much better than the PMV model, with average RMSEs 0.75 vs. 1.07 (lower is better) and R2 0.77 vs. 0.43 (higher is better), respectively, meaning that our model prediction has higher accuracy and better explainability. The experiments also ranked the importance of physiological signals (as EMG, body temperature, ECG, and EEG, in descending order) so they can be selectively adopted according to the feasibility of signal collection in different application scenarios. This study demonstrates the usefulness of physiological signals in TS prediction and motivates further thorough research on wider scenarios, such as ages, health condition, static/motion/sports behaviors, etc.

Integrated analysis of lncRNA and mRNA for the apoptosis of porcine ovarian granulosa cells after polyphenol resveratrol treatment.

Resveratrol (RES) is a non-flavonoid polyphenol compound that can be involved in follicular development and ovulation. However, the mechanism by which resveratrol regulates the apoptosis of porcine ovarian granulosa cells (POGCs) through long non-coding RNA (lncRNA) is poorly understood. We generated POGCs models of different doses of RES (0, 25, 50, 75, and 100 µM). It was observed that the cell viability was the highest in the 50 µM group, and the highest apoptosis rates were recorded in the 100 µM group. Therefore, a control group (n = 3, 0 µM RES group), a low RES group (n = 3, 50 µM RES group), and a high RES group (n = 3, 100 µM RES group) of POGCs were created for next RNA sequencing. Gene Ontology (GO) indicated that differentially expressed lncRNAs associated with apoptotic process were highly enriched. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of lncRNA target genes found that the Wnt signaling pathway and PI3K-Akt signaling pathway were both enriched. Furthermore, we constructed lncRNA-mRNA networks related to Metabolic and Cell Apoptosis, respectively. In the networks, five key-lncRNAs were screened, which may play a significant role in the process of POGCs metabolism and apoptosis. Furthermore, we focused on the function of a lnc-GAM (lncRNA associated with Granulosa cells Apoptosis and Metabolism) and verified that lnc-GAM could influence cell apoptosis in POGCs development by affecting the mRNA expression of apoptosis-related markers, and also affects the secretion of steroid hormones and related genes expression in POGCs cultured in vitro. Our study provides seminal data and important new insights into the regulation of reproductive mechanisms in porcine and other female mammals.

Impacts of Human Robot Proxemics on Human Concentration-Training Games with Humanoid Robots.

The use of humanoid robots within a therapeutic role, that is, helping individuals with social disorders, is an emerging field, but it remains unexplored in terms of concentration training. To seamlessly integrate humanoid robots into concentration games, an investigation into the impacts of human robot interactive proxemics on concentration-training games is particularly important. In the case of an epidemic diffusion especially-for example, during the COVID-19 pandemic-HRI games may help in the therapeutic phase, significantly reducing the risk of contagion. In this paper, concentration games were designed by action imitation involving 120 participants to verify the hypothesis. Action-imitation accuracy, the assessment of emotional expression, and a questionnaire were compared with analysis of variance (ANOVA). Experimental results showed that a 2 m distance and left-front orientation for a human and a robot are optimal for human robot interactive concentration training. In addition, females worked better than males did in HRI imitation games. This work supports some valuable suggestions for the development of HRI concentration-training technology, involving the designs of friendlier and more useful robots, and HRI game scenarios.

Structural Based Screening of Antiandrogen Targeting Activation Function-2 Binding Site.

Androgen receptor (AR) plays a critical role in the development and progression of prostate cancer (PCa). Current antiandrogen therapies induce resistant mutations at the hormone binding pocket (HBP) that convert the activity of these agents from antagonist to agonist. Thus, there is a high unmet medical need for the development of novel antiandrogens which circumvent mutation-based resistance. Herein, through the analysis of AR structures with ligands binding to the activation function-2 (AF2) site, we built a combined pharmacophore model. In silico screening and the subsequent biological evaluation lead to the discovery of the novel lead compound IMB-A6 that binds to the AF2 site, which inhibits the activity of either wild-type (WT) or resistance mutated ARs. Our work demonstrates structure-based drug design is an efficient strategy to discover new antiandrogens, and provides a new class of small molecular antiandrogens for the development of novel treatment agents against PCa.

Identification of Triptophenolide from Tripterygium wilfordii as a Pan-antagonist of Androgen Receptor.

A compound, triptophenolide, derived from Tripterygium wilfordii was identified as an antiandrogen. Triptophenolide inhibits the activity of both wild-type and F876L mutant androgen receptors. Triptophenolide exhibits its antiandrogenic activity through competitive binding with androgen in the hormone-binding pocket, decreasing the expression of androgen receptor, and reducing the nuclear translocation of androgen receptor.

[Establishment and application of a drug screening model for anti-prostate cancer agents targeting androgen receptor].

Androgen receptor(AR) plays an important role in the maintenance of prostate function and development of prostate cancer. AR is the key target in the therapy of prostate cancer. In this study, a cell-based screening assay was established by dual-luciferase reporter system to analyze the activity of AR. In the screening assay, we detected the anti-prostate cancer activities of rhodiola root extract, wild kiwifruit root extract and tripterygium wilfordii root extract, which may provide a new strategy for the treatment of prostate cancer.

[The mechanisms of drug resistance in prostate cancer].

Drug therapy is one of the efficient methods for prostate cancer treatment. However, drug resistance greatly hindered the treatment of prostate cancer patients. Herein, the mechanisms of drug resistance in prostate cancer have been exhaustively reviewed, and that can provide an alternative strategy and new targets for anti-prostate cancer therapy.

A novel joint-processing adaptive nonlinear equalizer using a modular recurrent neural network for chaotic communication systems.

To eliminate nonlinear channel distortion in chaotic communication systems, a novel joint-processing adaptive nonlinear equalizer based on a pipelined recurrent neural network (JPRNN) is proposed, using a modified real-time recurrent learning (RTRL) algorithm. Furthermore, an adaptive amplitude RTRL algorithm is adopted to overcome the deteriorating effect introduced by the nesting process. Computer simulations illustrate that the proposed equalizer outperforms the pipelined recurrent neural network (PRNN) and recurrent neural network (RNN) equalizers.