Short-term dietary choline supplementation alters the gut microbiota and liver metabolism of finishing pigs

Choline is an essential nutrient for pig development and plays a role in the animal's growth performance, carcass characteristics, and reproduction aspects in weaned pigs and sows. However, the effect of choline on finishing pigs and its potential regulatory mechanism remains unclear. Here, we feed finishing pigs with 1% of the hydrochloride salt of choline, such as choline chloride (CHC), under a basic diet condition for a short period of time (14 days). A 14-day supplementation of CHC significantly increased final weight and carcass weight while having no effect on carcass length, average backfat, or eye muscle area compared with control pigs. Mechanically, CHC resulted in a significant alteration of gut microbiota composition in finishing pigs and a remarkably increased relative abundance of bacteria contributing to growth performance and health, including Prevotella, Ruminococcaceae, and Eubacterium. In addition, untargeted metabolomics analysis identified 84 differently abundant metabolites in the liver between CHC pigs and control pigs, of which most metabolites were mainly enriched in signaling pathways related to the improvement of growth, development, and health. Notably, there was no significant difference in the ability of oxidative stress resistance between the two groups, although increased bacteria and metabolites keeping balance in reactive oxygen species showed in finishing pigs after CHC supplementation. Taken together, our results suggest that a short-term supplementation of CHC contributes to increased body weight gain and carcass weight of finishing pigs, which may be involved in the regulation of gut microbiota and alterations of liver metabolism, providing new insights into the potential of choline-mediated gut microbiota/metabolites in improving growth performance, carcass characteristics, and health

Two problems in applications of PDE

This thesis contains two different parts. For part I, we develop a PDE perspective on a model problem from the machine learning literature. The problem involves ''prediction" via ''regret minimization". Our PDE approach identifies the optimal strategies and the associated outcomes in a scaling limit where the number of time steps tends to 1. While our PDE is very nonlinear, explicit solutions are available in many cases due to a surprising and convenient link to the linear heat equation. For part II, We consider H–αgradient flow of the Modica-Mortola energy. The evolution law can be viewed as a nonlocal analogue of the Cahn-Hilliard equation for α > 0; the limit as α= 0 is (formally at least) the volume preserving Allen-Cahn flow. When α > ½ , we prove a time-averaged upper bound on the typical length scale, of order t ½ the true time scale of the evolution is the original time scale (and that the case α < ½ is different). Finally, we derive the sharp interface limit of the flow using heuristic arguments

Estimation of Growth Curves and Suitable Slaughter Weight of the Liangshan Pig

The Liangshan pig is a traditional Chinese small-sized breed; it has a relatively long feeding period and low meat production ability but superior meat quality. This study utilized three non-linear growth models (Von Bertalanffy, Gompertz, and logistic) to fit the growth curve of Liangshan pigs from an unselected, random-bred pig population and estimate the pigs most suitable slaughter weight. The growth development data at 20 time points of 275 Liangshan pigs (from birth to 250 d) were collected. To analyze the relative gene expression related to development, seven slaughter weight phases (50, 58, 66, 74, 82, 90, and 98 kg) (20 pigs per phase) were examined. We found that the Liangshan pig growth curve fit the typical S-curve well and that their growth turning point was 193.4 days at a weight of 62.5 kg, according to the best fit Von Bertalanffy model based on the goodness of fit criteria. Furthermore, we estimated that the most suitable slaughter weight was 62.5 to 74.9 kg based on the growth curve and the relative expression levels of growth-related genes

Potassium Improves Drought Stress Tolerance in Plants by Affecting Root Morphology, Root Exudates, and Microbial Diversity

Potassium (K) reduces the deleterious effects of drought stress on plants. However, this mitigation has been studied mainly in the aboveground plant pathways, while the effect of K on root-soil interactions in the underground part is still underexplored. Here, we conducted the experiments to investigate how K enhances plant resistance and tolerance to drought by controlling rhizosphere processes. Three culture methods (sand, water, and soil) evaluated two rapeseed cultivars’ root morphology, root exudates, soil nutrients, and microbial community structure under different K supply levels and water conditions to construct a defensive network of the underground part. We found that K supply increased the root length and density and the organic acids secretion. The organic acids were significantly associated with the available potassium decomposition, in order of formic acid &gt; malonic acid &gt; lactic acid &gt; oxalic acid &gt; citric acid. However, the mitigation had the hormesis effect, as the appropriate range of K facilitated the morphological characteristic and physiological function of the root system with increases of supply levels, while the excessive input of K could hinder the plant growth. The positive effect of K-fertilizer on soil pH, available phosphorus and available potassium content, and microbial diversity index was more significant under the water stress. The rhizosphere nutrients and pH further promoted the microbial community development by the structural equation modeling, while the non-rhizosphere nutrients had an indirect negative effect on microbes. In short, K application could alleviate drought stress on the growth and development of plants by regulating the morphology and secretion of roots and soil ecosystems

Image1_Comprehensive landscape of the functions and prognostic value of RNA binding proteins in uterine corpus endometrial carcinoma.PNG

Background: The dysregulation of RNA binding proteins (RBPs) is involved in tumorigenesis and progression. However, information on the overall function of RNA binding proteins in Uterine Corpus Endometrial Carcinoma (UCEC) remains to be studied. This study aimed to explore Uterine Corpus Endometrial Carcinoma-associated molecular mechanisms and develop an RNA-binding protein-associated prognostic model.Methods: Differently expressed RNA binding proteins were identified between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues by R packages (DESeq2, edgeR) from The Cancer Genome Atlas (TCGA) database. Hub RBPs were subsequently identified by univariate and multivariate Cox regression analyses. The cBioPortal platform, R packages (ggplot2), Human Protein Atlas (HPA), and TIMER online database were used to explore the molecular mechanisms of Uterine Corpus Endometrial Carcinoma. Kaplan-Meier (K-M), Area Under Curve (AUC), and the consistency index (c-index) were used to test the performance of our model.Results: We identified 128 differently expressed RNA binding proteins between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues. Seven RNA binding proteins genes (NOP10, RBPMS, ATXN1, SBDS, POP5, CD3EAP, ZC3H12C) were screened as prognostic hub genes and used to construct a prognostic model. Such a model may be able to predict patient prognosis and acquire the best possible treatment. Further analysis indicated that, based on our model, the patients in the high-risk subgroup had poor overall survival (OS) compared to those in the low-risk subgroup. We also established a nomogram based on seven RNA binding proteins. This nomogram could inform individualized diagnostic and therapeutic strategies for Uterine Corpus Endometrial Carcinoma.Conclusion: Our work focused on systematically analyzing a large cohort of Uterine Corpus Endometrial Carcinoma patients in the The Cancer Genome Atlas database. We subsequently constructed a robust prognostic model based on seven RNA binding proteins that may soon inform individualized diagnosis and treatment.</p

Image7_Comprehensive landscape of the functions and prognostic value of RNA binding proteins in uterine corpus endometrial carcinoma.PNG

Background: The dysregulation of RNA binding proteins (RBPs) is involved in tumorigenesis and progression. However, information on the overall function of RNA binding proteins in Uterine Corpus Endometrial Carcinoma (UCEC) remains to be studied. This study aimed to explore Uterine Corpus Endometrial Carcinoma-associated molecular mechanisms and develop an RNA-binding protein-associated prognostic model.Methods: Differently expressed RNA binding proteins were identified between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues by R packages (DESeq2, edgeR) from The Cancer Genome Atlas (TCGA) database. Hub RBPs were subsequently identified by univariate and multivariate Cox regression analyses. The cBioPortal platform, R packages (ggplot2), Human Protein Atlas (HPA), and TIMER online database were used to explore the molecular mechanisms of Uterine Corpus Endometrial Carcinoma. Kaplan-Meier (K-M), Area Under Curve (AUC), and the consistency index (c-index) were used to test the performance of our model.Results: We identified 128 differently expressed RNA binding proteins between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues. Seven RNA binding proteins genes (NOP10, RBPMS, ATXN1, SBDS, POP5, CD3EAP, ZC3H12C) were screened as prognostic hub genes and used to construct a prognostic model. Such a model may be able to predict patient prognosis and acquire the best possible treatment. Further analysis indicated that, based on our model, the patients in the high-risk subgroup had poor overall survival (OS) compared to those in the low-risk subgroup. We also established a nomogram based on seven RNA binding proteins. This nomogram could inform individualized diagnostic and therapeutic strategies for Uterine Corpus Endometrial Carcinoma.Conclusion: Our work focused on systematically analyzing a large cohort of Uterine Corpus Endometrial Carcinoma patients in the The Cancer Genome Atlas database. We subsequently constructed a robust prognostic model based on seven RNA binding proteins that may soon inform individualized diagnosis and treatment.</p

A Comparative Study on the Growth Performance and Gut Microbial Composition of Duroc and Yorkshire Boars

The intestinal microbiota is required for maintaining the development and health of the host. However, the gut microbiota contributing to the regulation of the growth performance and health of Duroc and Yorkshire boars remains largely unknown. In this study, we first evaluated the difference in the growth performance between Duroc and Yorkshire boars when their body weight reached 100 kg. Relative to Duroc boars, Yorkshire boars weighed 100 kg at a younger age and exhibited a significantly lower backfat thickness and eye muscle area. Microbial analysis of the fecal samples revealed a marked difference in gut microbiota composition between the two pig models and remarkably increased α-diversity in Yorkshire boars compared to Duroc boars. Further analysis indicated that Bacteroidota, Prevotellaceae, and Ruminococcaceae might be associated with the growth performance and lean meat rate of Yorkshire boars. Taken together, these results highlight that Yorkshire boars exhibit a faster growth rate and higher lean meat rate compared to Duroc boars, and these differences may be attributed to the influence of the gut microbiota, thereby providing valuable insight into optimizing pig breeding systems and selecting terminal paternal sires to enhance overall productivity and quality

Image6_Comprehensive landscape of the functions and prognostic value of RNA binding proteins in uterine corpus endometrial carcinoma.PNG

Background: The dysregulation of RNA binding proteins (RBPs) is involved in tumorigenesis and progression. However, information on the overall function of RNA binding proteins in Uterine Corpus Endometrial Carcinoma (UCEC) remains to be studied. This study aimed to explore Uterine Corpus Endometrial Carcinoma-associated molecular mechanisms and develop an RNA-binding protein-associated prognostic model.Methods: Differently expressed RNA binding proteins were identified between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues by R packages (DESeq2, edgeR) from The Cancer Genome Atlas (TCGA) database. Hub RBPs were subsequently identified by univariate and multivariate Cox regression analyses. The cBioPortal platform, R packages (ggplot2), Human Protein Atlas (HPA), and TIMER online database were used to explore the molecular mechanisms of Uterine Corpus Endometrial Carcinoma. Kaplan-Meier (K-M), Area Under Curve (AUC), and the consistency index (c-index) were used to test the performance of our model.Results: We identified 128 differently expressed RNA binding proteins between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues. Seven RNA binding proteins genes (NOP10, RBPMS, ATXN1, SBDS, POP5, CD3EAP, ZC3H12C) were screened as prognostic hub genes and used to construct a prognostic model. Such a model may be able to predict patient prognosis and acquire the best possible treatment. Further analysis indicated that, based on our model, the patients in the high-risk subgroup had poor overall survival (OS) compared to those in the low-risk subgroup. We also established a nomogram based on seven RNA binding proteins. This nomogram could inform individualized diagnostic and therapeutic strategies for Uterine Corpus Endometrial Carcinoma.Conclusion: Our work focused on systematically analyzing a large cohort of Uterine Corpus Endometrial Carcinoma patients in the The Cancer Genome Atlas database. We subsequently constructed a robust prognostic model based on seven RNA binding proteins that may soon inform individualized diagnosis and treatment.</p

Image5_Comprehensive landscape of the functions and prognostic value of RNA binding proteins in uterine corpus endometrial carcinoma.PNG

Background: The dysregulation of RNA binding proteins (RBPs) is involved in tumorigenesis and progression. However, information on the overall function of RNA binding proteins in Uterine Corpus Endometrial Carcinoma (UCEC) remains to be studied. This study aimed to explore Uterine Corpus Endometrial Carcinoma-associated molecular mechanisms and develop an RNA-binding protein-associated prognostic model.Methods: Differently expressed RNA binding proteins were identified between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues by R packages (DESeq2, edgeR) from The Cancer Genome Atlas (TCGA) database. Hub RBPs were subsequently identified by univariate and multivariate Cox regression analyses. The cBioPortal platform, R packages (ggplot2), Human Protein Atlas (HPA), and TIMER online database were used to explore the molecular mechanisms of Uterine Corpus Endometrial Carcinoma. Kaplan-Meier (K-M), Area Under Curve (AUC), and the consistency index (c-index) were used to test the performance of our model.Results: We identified 128 differently expressed RNA binding proteins between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues. Seven RNA binding proteins genes (NOP10, RBPMS, ATXN1, SBDS, POP5, CD3EAP, ZC3H12C) were screened as prognostic hub genes and used to construct a prognostic model. Such a model may be able to predict patient prognosis and acquire the best possible treatment. Further analysis indicated that, based on our model, the patients in the high-risk subgroup had poor overall survival (OS) compared to those in the low-risk subgroup. We also established a nomogram based on seven RNA binding proteins. This nomogram could inform individualized diagnostic and therapeutic strategies for Uterine Corpus Endometrial Carcinoma.Conclusion: Our work focused on systematically analyzing a large cohort of Uterine Corpus Endometrial Carcinoma patients in the The Cancer Genome Atlas database. We subsequently constructed a robust prognostic model based on seven RNA binding proteins that may soon inform individualized diagnosis and treatment.</p