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Simple Summary: Positive natural selection increases the frequency of beneficial genetic variations, promoting an organism's adaptability and survival in specific environments. In this study, we conducted whole-genome resequencing and population genetic structure analysis to investigate the genetic diversity of Zaobei cattle, local yellow cattle from China. By comparing Zaobei cattle with Simmental cattle, we identified several genomic regions exhibiting positive selection. Gene annotation of these regions revealed key traits of Zaobei cattle, such as heat tolerance, fertility, and superior meat quality. These findings not only help understand the characteristics of Zaobei cattle but also provide valuable genetic information for future breeding programs, thereby improving their production efficiency and economic value. This investigation provides a comprehensive analysis of genomic diversity and selection signatures in Zaobei beef cattle, an indigenous breed known for its adaptation to hot and humid climates and superior meat quality. Whole-genome resequencing was conducted on 23 Zaobei cattle, compared with 46 Simmental cattle to highlight genetic distinctions. Population structure analysis confirmed the genetic uniqueness of Zaobei cattle. Using methods such as DASDC v1.01, XPEHH, and θπ ratio, we identified 230, 232, and 221 genes through DASDC, including hard sweeps, soft sweeps, and linkage sweeps, respectively. Coincidentally, 109 genes were identified when using XPEHH and θπ ratio methods. Together, these analyses revealed eight positive selection genes (ARHGAP15, ZNF618, USH2A, PDZRN4, SPATA6, ROR2, KCNIP3, and VWA3B), which are linked to critical traits such as heat stress adaptation, fertility, and meat quality. Moreover, functional enrichment analyses showed pathways related to autophagy, immune response, energy metabolism, and muscle development. The comprehensive genomic insights gained from this study provide valuable knowledge for breeding programs aimed at enhancing the beneficial traits in Zaobei cattle. [ABSTRACT FROM AUTHOR]

Aerodynamic shape optimization based on computational fluid dynamics still has a huge demand for improvement in the optimization effect and efficiency when optimizing the unstable flow of airfoils. This article presents a physics-informed hot-start method combined with modified metric-based proper orthogonal decomposition (MPOD-ML-Phys). The data-based filtering strategy is a core step in the original metric-based proper orthogonal decomposition method (MPOD), but existing filtering strategies generate a significant amount of additional computational consumption. Therefore, this article applies machine learning methods to data-based filtering strategy in MPOD and establishes a modified MPOD method (MPOD-ML). In addition, during the MPOD-ML process, a lot of hidden physical knowledge that is beneficial for optimization will also be generated. This article combines Bayesian optimization to construct an MPOD-ML-Phys method, which fully utilizes the flow physical knowledge in MPOD-ML. The efficiency and effect of MPOD-ML and MPOD-ML-Phys are validated by two typical cases: inverse and direct design for airfoils. The results indicate that both MPOD-ML and MPOD-ML-Phys methods can effectively improve the overall optimization efficiency. However, the intervention of machine learning models has significantly reduced the robustness of the MPOD-ML method, while the embedding of physical knowledge makes MPOD-ML-Phys more robust. Meanwhile, the optimized airfoil obtained by MPOD-ML-Phys has better drag divergence characteristics, a later flow separation point, and better flow stability. [ABSTRACT FROM AUTHOR]

Glaesserella parasuis (GPS) can cause severe systemic inflammation in pigs, resulting in huge economic losses to the pig industry. At present, no effective method is available for the prevention and control of GPS infection. Molecular breeding for disease resistance is imminent, but disease-resistance genes have not been identified. To study the mechanism of systemic acute inflammation caused by GPS, we established three in vitro infection models (3D4/21 cells, PK15 cells, and PAVEC cells) according to its infection path. There was no significant difference in apoptosis among the three kinds of cells after 12 h of continuous GPS stimulation, while inflammatory factors were significantly upregulated. Subsequent transcriptome analysis revealed 1969, 1207, and 3564 differentially expressed genes (DEGs) in 3D4/21 cells, PK15 cells, and PAVEC cells, respectively, after GPS infection. Many of the DEGs were predicted to be associated with inflammatory responses (C3, CD44, etc.); cell proliferation, growth and apoptosis; gene expression; and protein phosphorylation. Key signaling pathways, including S100 family signaling, bacteria and virus recognition, and pathogen-induced cytokine storm signaling, were enriched based on Ingenuity Pathway Analysis (IPA). Furthermore, a total of three putative transmembrane receptors and two putative G-protein-coupled receptors, namely F3, ICAM1, PLAUR, ACKR3, and GPRC5A, were identified by IPA among the three types of cells. ACKR3 and GPRC5A play pivotal roles in bacterial adhesion, invasion, host immune response and inflammatory response through the S100 family signaling pathway. Our findings provide new insights into the pathological mechanisms underlying systemic inflammation caused by GPS infection in pigs, and they lay a foundation for further research on disease-resistance breeding to GPS. [ABSTRACT FROM AUTHOR]

Objective To explore the role of genetic interactions of MTHFR-MMP2 in risks for abdominal aortic aneurysm (AAA). Methods A 1: 2 matched case-control study was performed on the AAA patients admitted in the vascular surgery department of Chinese PLA General Hospital between 2011 and 2014, and on the non-AAA patients (control group) with matched gender and age from both the above hospital and a community of Fangshan district during the same period. Standardized questionnaires were used to collect information such as demographic characteristics, medical history and behavioral risk factors. Physical and abdominal aortic ultrasound examinations were carried out, and 5 mL peripheral venous blood samples were collected for genotyping. The interactions between MTHFR and MMP2 genes were investigated using both traditional and machine learning methods, including conditional Logistic regression model, generalized multifactor dimension reduction (GMDR) and EPI stasis sparse factor analysis (EPISFA). Whether the genetic interactions affects the risk of AAA was analyzed and determined, on additive and multiplicative scales, respectively. In addition, relative excess risk of interaction (RERI), attributable proportion of interaction (API) and synergy index (S) were adopted to assess the direction and magnitude of the genetic interactions between MTHFR and MMP2. Results A total of 465 individuals, including 155 AAA patients and 310 non-AAA subjects at a matched ratio of 1: 2 were enrolled in this study during 2011 and 2014. After adjustment of covariates, antagonistic interactions between MTHFR gene and multiple loci of MMP2 gene (rs1132896, rs1477017, rs1992116, rs243847) were found on both additive and multiplicative scales simultaneously (P < 0.05), which could affect AAA risks. And the results were verified by the above 3 methods. GMDR and EPISFA results suggested that there might be high-dimensional interactions between MTHFR and MMP2, with high cross-validation consistency and significant difference (P < 0.05). Conclusion There may be antagonistic gene-gene interactions between MTHFR and MMP2, and the interactions may potentially influence the risks for AAA.

Objective To explore the epidemiological status and related factors for comorbidities of osteoarthritis (OA) among adult residents in Beijing. Methods Based on the medical records of all designated medical institutes in Beijing from 2015 to 2017, relevant data of adult OA patients were collected, and the prevalence of major comorbidities such as type 2 diabetes, depression, hypertension and dyslipidemia in this population was statistically described and analyzed, and the differences among populations with different sociodemographic characteristics were compared. Results A total of 3 048 304 adult OA patients were included, with a mean age of 58.2±14.5 years, and females accounting for 62.2%. 79.8% of the subjects had at least 1 comorbidity, with the males (82.6%) having a higher prevalence than the females (78.1%). The prevalence of OA comorbidities was increased with age and statistically significant among different age groups (P < 0.05). The prevalence in the patients living in the rural areas (82.7%) was higher than that in urban areas (78.8%). The top 4 most common comorbidities were hypertension (68.0%), dyslipidemia (63.8%), type 2 diabetes (36.3%), and depression (8.2%). The males were more likely to suffer from hypertension, dyslipidemia and type 2 diabetes, while the females from depression. The proportion of OA patients with 1, 2, 3 or more kinds of comorbidities was 17.7%, 30.7%, 31.4% respectively. The risk factors for OA comorbidities included: male (OR=1.15, 95%CI: 1.14~1.16), ≥55 years old (OR=1.07, 95%CI: 1.07~1.07), living in rural areas (OR=1.66, 95%CI: 1.64~1.67), and taking manual labor (OR=1.02, 95%CI: 1.01~1.02). Conclusion The adult OA patients in Beijing have relatively high prevalences of comorbidities, including dyslipidemia, hypertension, type 2 diabetes and other diseases. More attention should be paid to the middle-aged and elderly patients and those in rural areas.