Genomic Association Analysis of Growth and Backfat Traits in Large White Pigs

The pig industry is significantly influenced by complex traits such as growth rate and fat deposition, which have substantial implications for economic returns. Over the years, remarkable genetic advancements have been achieved through intense artificial selection to enhance these traits in pigs. In this study, we aimed to investigate the genetic factors that contribute to growth efficiency and lean meat percentages in Large White pigs. Specifically, we focused on analyzing two key traits: age at 100 kg live weight (AGE100) and backfat thickness at 100 kg (BF100), in three distinct Large White pig populations—500 Canadian, 295 Danish, and 1500 American Large White pigs. By employing population genomic techniques, we observed significant population stratification among these pig populations. Utilizing imputed whole-genome sequencing data, we conducted single population genome-wide association studies (GWAS) as well as a combined meta-analysis across the three populations to identify genetic markers associated with the aforementioned traits. Our analyses highlighted several candidate genes, such as CNTN1—which has been linked to weight loss in mice and is potentially influential for AGE100—and MC4R, which is associated with obesity and appetite and may impact both traits. Additionally, we identified other genes—namely, PDZRN4, LIPM, and ANKRD22—which play a partial role in fat growth. Our findings provide valuable insights into the genetic basis of these important traits in Large White pigs, which may inform breeding strategies for improved production efficiency and meat quality

Using genomic selection to improve the accuracy of genomic prediction for multi-populations in pigs

The size of the reference group is among the most critical determinants of genomic estimated breeding values (GEBVs) accuracy. However, small- and medium-sized pig farms often need help accumulating adequate reference data, posing significant challenges to breeding programs. To solve this problem, exploring the potential benefits of combining reference groups of different sizes is necessary to improve GEBV accuracy. The primary objective of this investigation was to assess a more effective statistical model for combined multi-populations and its potential to enhance the accuracy of GEBVs for small and medium populations. Three populations were simulated using the QMSim software, each consisting of different sizes (300, 600, and 1 500, respectively). To assess the impact of heritability on the accuracy of GEBVs, four different levels of heritability (0.05, 0.15, 0.35, and 0.5) were simulated. Simultaneously, to investigate the impact of kinship on multi-populations, the study created four distinct scenarios for the three sizes of populations. These scenarios included: (1) the three groups are all independent, (2) the large group and the small group with a familial connection (n = 1 800), a middle group (n = 600) acting independently with no kinship, (3) the large group with a familial connection to the middle group (n = 2 100) but no connection to the small group (n = 300), and (4) the small group with a familial connection to the middle group (n = 900), while the large group (n = 1 500) acted independently with no kinship. This study evaluates and compares the accuracy of predicting breeding values using four different methods, including genomic best linear unbiased prediction (GBLUP), single-stepGBLUP (ssGBLUP), and two Bayesian models (Bayes A and Bayes B), with varying sizes of reference groups. In each scenario, three different prediction strategies were compared: (1) Merging all three different sizes of populations for predicting, (2) predicting each independent population separately, and (3) the other two populations predict the population. Our findings reveal that combining populations enhances the Bayesian models, with Bayes B yielding the highest accuracy. In independent populations, the best linear unbiased prediction (BLUP) models demonstrated the highest accuracy. However, in cases where populations were related and the heritability was high, the Bayes B model exhibited the highest overall accuracy (slightly higher than BLUP models) in the independent population. Our results underscore the importance of considering population combinations when using genetic models to predict breeding values, particularly for pig farmers with limited resources

Degrading capability and activity of extracellular xylanase secreted by a composite microbial system XDC-2

The natural lignocellulose degrading capabilities of extracellular enzyme secreted by a composite microbial system XDC-2 were studied. Peptone cellulose solution (PCS) medium was beneficial to the degradation of lignocellulosic materials and ATCC 1053 medium promoted enzyme production of XDC-2. The exocellular xylanase activities of the crude enzymes were stable below 40°C. The crude enzyme has an effective capability of degrading natural lignocellulose, especially natural hemicellulose. The corn stalk core and rice straw lost 21.1 and 11.9% of its weight, respectively, after 48 h hydrolysis by the crude enzyme, and the weight loss of hemicellulose of corn stalk core and rice straw was 84.7 and 27.8%, respectively. Qualitative scanning electron microscopes (SEM) images indicated that after 48 h crude enzymes hydrolysis at 35°C, the material structure was modified. The production of the soluble carbohydrates was up to 2, 400 mg·L-1 for corn straw and 1, 300 mg·L-1 for rice straw. It would hold the potential of further development and application of XDC-2 with the ability to hydrolyze natural lignocelluloses and release soluble carbohydrates.Keywords: Composite microbial system, lignocellulose degradation, exocellular xylanase, hydrolysis abilit

Genomic Scan for Runs of Homozygosity and Selective Signature Analysis to Identify Candidate Genes in Large White Pigs

Large White pigs are extensively utilized in China for their remarkable characteristics of rapid growth and the high proportion of lean meat. The economic traits of pigs, comprising reproductive and meat quality traits, play a vital role in swine production. In this study, 2295 individuals, representing three different genetic backgrounds Large White pig populations were used: 500 from the Canadian line, 295 from the Danish line, and 1500 from the American line. The GeneSeek 50K GGP porcine HD array was employed to genotype the three pig populations. Firstly, genomic selective signature regions were identified using the pairwise fixation index (FST) and locus-specific branch length (LSBL). By applying a top 1% threshold for both parameters, a total of 888 candidate selective windows were identified, harbouring 1571 genes. Secondly, the investigation of regions of homozygosity (ROH) was performed utilizing the PLINK software. In total, 25 genomic regions exhibiting a high frequency of ROHs were detected, leading to the identification of 1216 genes. Finally, the identified potential functional genes from candidate genomic regions were annotated, and several important candidate genes associated with reproductive traits (ADCYAP1, U2, U6, CETN1, Thoc1, Usp14, GREB1L, FGF12) and meat quality traits (MiR-133, PLEKHO1, LPIN2, SHANK2, FLVCR1, MYL4, SFRP1, miR-486, MYH3, STYX) were identified. The findings of this study provide valuable insights into the genetic basis of economic traits in Large White pigs and may have potential use in future pig breeding programs

In situ grown rare earth lanthanum on carbon nanofibre for interfacial reinforcement in Zn implants

Carbon nanofibre (CNF) is a potential reinforcement in Zn implants. Nevertheless, its poor interfacial compatibility reduces the reinforcement efficiency drastically. In this study, rare earth lanthanum (La) was used as a compatible interface layer between CNF and Zn matrix. On the one hand, La in situ grew on acidified CNF by a chemical synthesis and achieved a firm coordination covalent bond with the oxygenated functional group derived from CNF. On the other hand, La, as an active rare earth element, could carry out the alloying reaction with the Zn matrix, thus forming strong metal bonding. Results showed that the tensile strength of composites was enhanced from 180.2 ± 12.1 to 243.4 ± 10.2 MPa since the La interface layer promoted the transfer of the interfacial shear stress from the Zn matrix to CNF and thereby consumed massive fracture energy. Encouragingly, it simultaneously improved the ductility, as La activated basal slip and improved the dislocation accommodation capacity. Moreover, the Zn implants displayed excellent anti-tumour efficiency

Hydrogeochemical Characteristics, Water Quality, and Human Health Risks of Groundwater in Wulian, North China

Groundwater shortage and pollution are critical issues of global concern. In Wulian County, a typical hilly area, groundwater is the main source of water supply. This study investigates the current situation of groundwater pollution in Wulian City through the analysis of groundwater water chemistry characteristics, water quality evaluation, and health risk evaluation. After the analysis of the controlling factors of chemical components in groundwater and the analysis of ion sources, the main ion sources in groundwater were determined. The results showed that the major cations in groundwater were Ca2+ and Na+ and the major anions were HCO3− and SO42−. Nevertheless, NO3− exceeded the standard to different degrees in pore water (PW), fissure pore water (FPW), and fissure water (FW). The minimum NO3− concentration exceeded the standard in FW. Under the influence of rock weathering and salt rock dissolution, the main hydrochemical types of groundwater were the HCO3-Ca, HCO3-Ca·Mg, and SO4·Cl-Ca·Mg types. According to the water quality evaluation and health risk assessment, the FW area in the south had the highest water quality, where Class I water appeared and potable water was more widely distributed. The PW and FPW areas in the north had lower water quality, with higher health risks. Category V water gradually appeared in the FPW area, which is not suitable as a water supply source. Factor analysis and ion ratio analysis showed that the study area is strongly affected by anthropogenic factors. These research methods have important reference value to the research of groundwater pollution status

Elevated lactate dehydrogenase predicts pneumonia in spontaneous intracerebral hemorrhage

Background: Although a variety of risk factors for pneumonia after spontaneous intracerebral hemorrhage have been established, an objective and easily obtainable predictor is still needed. Lactate dehydrogenase is a nonspecific inflammatory biomarker. In this study, we aimed to assess the association between lactate dehydrogenase and pneumonia in spontaneous intracerebral hemorrhage patients. Methods: Our study was a retrospective, multicenter cohort study, undertaken in 7562 patients diagnosed with spontaneous intracerebral hemorrhage from 3 hospitals. All serum Lactate dehydrogenase was collected within 7 days from admission and divided into four groups as quartile(Q). We conducted a multivariable logistic regression analysis to assess the association of Lactate dehydrogenase with pneumonia. Results: Among a total of 7562 patients, 2971 (39.3%) patients were diagnosed with pneumonia. All grades of elevated lactate dehydrogenase were associated with increased raw and risk-adjusted risk of pneumonia. Multiple logistic regression analysis showed odds ratios for Q2-Q4 compared with Q1 were 1.21 (95% CI, 1.04–1.42), 1.64(95% CI, 1.41–1.92), and 1.92 (95% CI, 1.63–2.25) respectively. The odds ratio after adjustment was 4.42 (95% CI, 2.94–6.64) when lactate dehydrogenase was a continuous variable after log-transformed. Conclusions: Elevated lactate dehydrogenase is significantly associated with an increase in the odds of pneumonia and has a predictive value for severe pneumonia in patients with pneumonia. Lactate dehydrogenase may be used to predict pneumonia events in spontaneous intracerebral hemorrhage patients as a laboratory marker

Video1_Flare quasi-periodic pulsation associated with recurrent jets.MP4

Quasi-periodic pulsations (QPPs), which carry time features and plasma characteristics of flare emissions, are frequently observed in light curves of solar/stellar flares. In this study, we investigated non-stationary QPPs associated with recurrent jets during an M1.2 flare on 2022 July 14. A quasi-period of ∼45±10 s, determined by the wavelet transform technique, is simultaneously identified at wavelengths of soft/hard X-ray and microwave emissions, which are recorded by the Gravitational Wave High-Energy Electromagnetic Counterpart All-sky Monitor, Fermi and the Nobeyama Radio Polarimeters, respectively. A group of recurrent jets with an intermittent cadence of about 45 ± 10 s are found in the Atmospheric Imaging Assembly (AIA) image series at 304 Å, but they are 180 s earlier than the flare QPP. All observational facts suggest that the flare QPPs could be excited by recurrent jets, and they should be associated with non-thermal electrons that are periodically accelerated by a repeated energy release process, such as repetitive magnetic reconnection. Moreover, the same quasi-period is discovered at double footpoints connected by a hot flare loop in AIA 94 Å, and the phase speed is measured to be ∼1,420 km s−1. Based on the differential emission measure, the average temperatures, number densities, and magnetic field strengths at the loop top and footpoint are estimated to be ∼7.7/6.7 MK, ∼7.5/3.6 × 1010 cm−3, and ∼143/99 G, respectively. Our measurements indicate that the 45-s QPP is probably modulated by the kink-mode wave of the flare loop.</p