High starch diets have been proven to increase the risk of hindgut acidosis in high-yielding dairy animals. As an effective measurement of dietary carbohydrate for ruminants, studies on rumen degradable starch (RDS) and the effects on the gut microbiota diversity of carbohydrate-active enzymes (CAZymes), and Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology functional categories are helpful to understand the mechanisms between gut microbiota and carbohydrate metabolism in dairy goats. A total of 18 lactating goats (45.8 ± 1.54 kg) were randomly divided equally into three dietary treatments with low dietary RDS concentrations of 20.52% (LRDS), medium RDS of 22.15% (MRDS), and high RDS of 24.88% (HRDS) on a DM basis for 5 weeks. Compared with the LRDS and MRDS groups, HRDS increased acetate molar proportion in the cecum. For the HRDS group, the abundance of family Ruminococcaceae and genus Ruminococcaceae UCG-010 were significantly increased in the cecum. For the LRDS group, the butyrate molar proportion and the abundance of butyrate producer family Bacteroidale_S24-7, family Lachnospiraceae, and genus Bacteroidale_S24-7_group were significantly increased in the cecum. Based on the BugBase phenotypic prediction, the microbial oxidative stress tolerant and decreased potentially pathogenic in the LRDS group were increased in the cecum compared with the HRDS group. A metagenomic study on cecal bacteria revealed that dietary RDS level could affect carbohydrate metabolism by increasing the glycoside hydrolase 95 (GH95) family and cellulase enzyme (EC 3.2.1.4) in the HRDS group; increasing the GH13_20 family and isoamylase enzyme (EC 3.2.1.68) in the LRDS group. PROBIO probiotics database showed the relative gene abundance of cecal probiotics significantly decreased in the HRDS group. Furthermore, goats fed the HRDS diet had a lower protein expression of Muc2, and greater expression RNA of interleukin-1β and secretory immunoglobulin A in cecal mucosa than did goats fed the LRDS diet. Combined with the information from previous results from rumen, dietary RDS level altered the degradation position of carbohydrates in the gastrointestinal (GI) tract and increased the relative abundance of gene encoded enzymes degrading cellulose in the HRDS group in the cecum of dairy goats. This study revealed that the HRDS diet could bring disturbances to the microbial communities network containing taxa of the Lachnospiraceae and Ruminococcaceae and damage the mucus layer and inflammation in the cecum of dairy goats.
The purpose of sentiment classification is to solve the problem of automatic judgment of text sentiment tendency. In the sentiment classification task of online reviews, traditional deep learning sentiment classification models focus on algorithm optimization to improve the classification performance of the model, but when the sample data for manually labeling sentiment tendencies is insufficient, the classification performance of the model will be poor. The deep learning sentiment classification model based on weak tagging information, on the one hand, introduces weak tagging information into the training process of the model to reduce the use of manually tagging data. On the other hand, weak tagging information can represent the sentiment tendency of reviews to a certain extent, but it also contains noise, the model reduces the negative impact of the noise in weak tagging information in order to improve the classification performance of the sentiment classification model. The experimental results show that in the sentiment classification task of hotel online reviews, the deep learning sentiment classification model based on weak tagging information has superior classification performance than the traditional deep model without increasing labor cost.
The clinical significance of the specific anti-John Milton Hagen (JMH) alloantibody in inherited JMH-negative patients remains unclear. During clinical blood transfusion, it is often classified as an anti-JMH autoantibody in acquired JMH-negative patients, which might further lead to the occurrence of haemolysis events. In this study, we found that the proportion of inherited JMH-negative people in the Guangzhou population was 0.41%, based on the study of 243 blood samples by flow cytometry. Gene sequencing analysis revealed two novel variants located in exon 11 (c.1348G>A, p.Ala449Thr) and exon 14 (c.1989G>T, p.Leu663Phe). Specific antigen presentation showed that JMH-positive RBCs (red blood cells) could be internalized by SEMA7A −/− dendritic cells (DCs) and that SEMA7A −/− DCs activated by the semaphorin 7a (Sema7a) protein or JMH-positive erythrocytes further induced activation of CD4 + T cells to secrete interferon (IFN)-γ. Transfusion of JMH-positive RBCs could lead to the production of the specific anti-JMH alloantibody in Sema7a knock-out (KO) C57 mice. After erythrocyte sensitization, complement C3 was specifically fixed, causing the destruction of JMH-positive erythrocytes. The anti-JMH alloantibody caused immunological destruction of JMH-positive erythrocytes and promoted the clearance of JMH-positive RBCs. We should be cautious when making conclusions about the clinical significance of the anti-JMH alloantibody.
After more than three years, the Federal Reserve has once again entered the interest rate hike cycle - the Fed recently announced a 25BP increase in the target range for the federal funds rate to between 0.25% and 0.5% (this is the Fed's first rate hike since December 2018) while hinting that it will soon begin to reduce its balance sheet. The Fed's rate hike and tapering based on continued high inflation will undoubtedly have a series of profound effects on the global stock market, bond market, currency market, commodity market, and other markets, as evidenced by the possible divergence in the performance of different sectors of the US stock market, with the growth sector suffering a certain impact; interest rates on US bonds will also rise sharply, etc. For China, we have to guard against the negative impact of the Fed's interest rate hike spillover effect on the economy.
Sentiment classification aims to complete the automatic judgment task of text sentiment tendency. In the sentiment classification task of online reviews, traditional deep learning models require a large number of manually annotated samples of sentiment tendency for supervised training. Faced with massive online review data, the feasibility of manual tagging is worrisome. In addition, the traditional deep learning model ignores the imbalanced distribution of the number of classification samples, which will lead to a decline in classification performance in the practical application of the model. Considering that the online review data contains weak tagging information such as scores and labels, and the distribution is imbalanced, a weak tagging and imbalanced networks for online review sentiment classification is constructed. The experimental results show that the model significantly outperforms the traditional deep learning model in the sentiment classification task of hotel review data.
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