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Yak milk, known as the "liquid gold", is a nutritious food with extensive consumption. Compared with cow milk, yak milk contains higher levels of nutrients such as dry matter, milk fat, and milk protein, which demonstrates great potential for exploitation and utilization. Protein kinase cGMP-dependent 1 (PRKG1) is an important functional molecule in the cGMP signaling pathway, and its significant influence on milk fatty acids has been discovered. The aim of this study is to explore the correlation between single nucleotide polymorphisms (SNPs) in the PRKG1 gene and the quality traits of Gannan yak milk in order to identify candidate molecular markers for Gannan yak breeding. In this study, genotyping was performed on 172 healthy, 4–5-year-old lactating Gannan yaks with similar body types, naturally grazed, and two to three parity. Three SNPs (g.404195C>T, g.404213C>T, and g.760138T>C) were detected in the PRKG1 gene of Gannan yaks, which were uniformly distributed in the yak population. Linkage disequilibrium analysis was conducted, revealing complete linkage disequilibrium between g.404195C>T and g.404213C>T. After conducting a correlation analysis between SNPs in the PRKG1 gene and milk quality in Gannan yaks, we found that PRKG1 SNPs significantly increased the content of casein, protein, and SNFs in yak milk. Among them, the TT homozygous genotype at the PRKG1 g.404195C>T loci exhibited higher casein and protein contents compared to the CC and CT genotypes (p < 0.05). The SNP g.760138T>C locus was associated with casein, protein, SNFs, and TS traits (p < 0.05). The CC genotype had higher casein and protein contents than the TT and TA genotypes (p < 0.05). However, there were no significant differences in milk fat, lactose, and acidity among the three genotypes (p > 0.05). In summary, PRKG1 gene polymorphism can serve as a candidate molecular marker for improving milk quality in Gannan yaks. [ABSTRACT FROM AUTHOR]

Protein kinase D1 (PRKD1) functions primarily in normal mammary cells, and the potassium voltage-gated channel subfamily Q member 3 (KCNQ3) gene plays an important role in controlling membrane potential and neuronal excitability, it has been found that this particular gene is linked to the percentage of milk fat in dairy cows. The purpose of this study was to investigate the relationship between nucleotide polymorphisms (SNPs) of PRKD1 and KCNQ3 genes and the milk quality of Gannan yak and to find molecular marker sites that may be used for milk quality breeding of Gannan yak. Three new SNPs were detected in the PRKD1 (g.283,619T>C, g.283,659C>A) and KCNQ3 gene (g.133,741T>C) of 172 Gannan lactating female yaks by Illumina yak cGPS 7K liquid-phase microarray technology. Milk composition was analyzed using a MilkoScanTM milk composition analyzer. We found that the mutations of these three loci significantly improved the lactose, milk fat, casein, protein, non-fat milk solid (SNF) content and acidity of Gannan yaks. The lactose content of the TC heterozygous genotype population at g.283,619T>C locus was significantly higher than that of the TT wild-type population (p < 0.05); the milk fat content of the CA heterozygous genotype population at g.283,659C>A locus was significantly higher than that of the CC wild-type and AA mutant populations (p < 0.05); the casein, protein and acidity of the CC mutant and TC heterozygous groups at the g.133,741T>C locus were significantly higher than those of the wild type (p < 0.05), and the SNF of the TC heterozygous group was significantly higher than that of the mutant group (p < 0.05). The results showed that PRKD1 and KCNQ3 genes could be used as candidate genes affecting the milk traits of Gannan yak. [ABSTRACT FROM AUTHOR]

Simple Summary: The aim of our study was to investigate the effects of dietary supplementation with different protein levels after grazing on growth performance as well as on the proteomics of the LL muscle of Jersey-yak. The results showed that a certain amount of crude protein supplementation to Jersey-yak at the end of grazing significantly improved growth performance and increased their economic efficiency. In addition, we analyzed the proteomic differences in the longest dorsal muscle of Jersey-yak. From our analyses, we identified differences in the abundance of 434 proteins involved in pathways related to muscle growth and development and energy metabolism, among others. Several of the proteins enriched in these pathways, such as MYH8, are associated with myoblast development and differentiation as well as muscle development. In conclusion, the results of this study provide proteomic insights into the different feeding patterns of yak crossbred progeny, suggesting that a greater economic income can be achieved by improving their feeding conditions. Jersey-yak is a hybrid offspring of Jersey cattle and yak (Bos grunniens). Changing the feeding system of Jersey-yak can significantly improve its growth performance. In this study, tandem mass tag (TMT) proteomics technology was used to determine the differentially expressed proteins (DEPs) of the longissimus lumborum (LL) muscle of Jersey-yak fed different protein levels of diet. The results showed that compared with the traditional grazing feeding, the growth performance of Jersey-yaks was significantly improved by crude protein supplementation after grazing. A total of 3368 proteins were detected in these muscle samples, of which 3365 were quantified. A total of 434 DEPs were identified. Through analyses, it was found that some pathways related to muscle growth and development were significantly enriched, such as Rap1 signaling pathway, mTOR signaling pathway, and TGF-beta signaling pathway. A number of DEPs enriched in these pathways are related to muscle cell development, differentiation, and muscle development, including integrin subunit alpha 7 (ITGA7), myosin heavy chain 8 (MYH8), and collagen type XII alpha 1 chain (COL12A1). In conclusion, the results of this study provide insights into the proteomics of different feeding patterns of Jersey-yak, providing a stronger basis for further understanding the biological mechanism of hybrid varieties. [ABSTRACT FROM AUTHOR]

Coiled-coil serine-rich protein 1 (CCSER 1) gene is a regulatory protein gene. This gene has been reported to be associated with various economic traits in large mammals in recent years. The aim of this study was to investigate the association between CCSER1 gene single nucleotide polymorphisms (SNPs) and Gannan yaks and to identify potential molecular marker loci for breeding milk quality in Gannan yaks. We genotyped 172 Gannan yaks using Illumina Yak cGPS 7K liquid microarrays and analyzed the correlation between the three SNPs loci of the CCSER1 gene and the milk qualities of Gannan yaks, including milk fat, protein and casein. It was found that mutations at the g.183,843A>G, g.222,717C>G and g.388,723G>T loci all affected the fat, protein, casein and lactose traits of Gannan yak milk to varying extents, and that the milk quality of individuals with mutant phenotypes was significantly improved. Among them, the milk fat content of AG heterozygous genotype population at g.183,843A>G locus was significantly higher than that of AA and GG genotype populations (p < 0.05); the casein and protein content of mutant GG and CG genotype populations at g.222,717C>G locus was significantly higher than that of wild-type CC genotype population (p < 0.05); and the g.388,723G>T locus of the casein and protein contents of the mutant TT genotype population were significantly higher (p < 0.05) than those of the wild-type GG genotype population. These results provide potential molecular marker sites for Gannan yak breeding. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the nutritional properties of yak milk in various areas of Gannan. The milk composition analyzer, automatic amino acid analyzer, and flavor analyzer were used to detect the conventional nutrients, amino acids, and volatile flavor substances of 249 yak milks in Meiren grassland, Xiahe grassland, and Maqu grassland (hereinafter referred to as Meiren yak, Xiahe yak, and Maqu yak) in the Gannan area. The results showed that the fat content of Meiren yak milk was significantly higher than that of Maqu yak and Xiahe yak (p < 0.05). The protein content of Meiren yak milk was significantly higher than that of Xiahe yak (p < 0.05), but not significantly different from that of Maqu yak (p > 0.05). The casein content in the milk of Maqu yak was significantly higher than that of Meiren yak and Xiahe yak (p < 0.05). There was no significant difference in the lactose content of yak milk in the three regions (p > 0.05). The content of glutamic acid in the milk of Meiren yak, Xiahe yak, and Maqu yak was noticeably high, which was 1.03 g/100 g, 1.07 g/100 g, and 1.10 g/100 g, respectively. The total amino acid (TAA) content was 4.78 g/100 g, 4.87 g/100 g, and 5.0 g/100 g, respectively. The ratios of essential amino acids (EAA) and total amino acids (TAA) in the milk of Meiren yak, Xiahe yak, and Maqu yak were 42.26%, 41.27%, and 41.39%, respectively, and the ratios of essential amino acids (EAA) and nonessential amino acids (NEAA) were 73.19%, 70.28%, and 70.61%, respectively. In the yak milk samples collected from three different regions, a total of 34 volatile flavor compounds were detected, including 10 aldehydes, five esters, six ketones, four alcohols, two acids, and seven others. The main flavor substances qualitatively obtained from Meiren yak milk were ethyl acetate, n-valeraldehyde, acetic acid, heptanal, and n-hexanal. Xiahe yak milk mainly contains ethyl acetate, isoamyl alcohol, n-valeraldehyde, heptanal, and ethyl butyrate. Maqu yak milk mainly contains ethyl acetate, n-valeraldehyde, isoamyl alcohol, heptanal, ethyl butyrate, and n-hexanal. Principal component analysis showed that the flavor difference between Xiahe yak and Maqu yak was small, while the flavor difference between Xiahe yak, Maqu yak, and Meiren yak was large. The findings of this research can serve as a foundation for the future advancement and application of yak milk. [ABSTRACT FROM AUTHOR]

Career choice has a pivotal role in college students' life planning. In the past, professional career appraisers used questionnaires or diagnoses to quantify the factors potentially influencing career choices. However, due to the complexity of each person's goals and ideas, it is difficult to properly forecast their career choices. Recent evidence suggests that we could use students' behavioral data to predict their career choices. Based on the simple premise that the most remarkable characteristics of classes are reflected by the main samples of a category, we propose a model called the Approach Cluster Centers Based On XGBOOST (ACCBOX) model to predict students' career choices. The experimental results of predicting students' career choices clearly demonstrate the superiority of our method compared to the existing state-of-the-art techniques by evaluating on 13 M behavioral data of over four thousand students. [ABSTRACT FROM AUTHOR]

Traffic sign recognition is a classification problem that poses challenges for computer vision and machine learning algorithms. Although both computer vision and machine learning techniques have constantly been improved to solve this problem, the sudden rise in the number of unlabeled traffic signs has become even more challenging. Large data collation and labeling are tedious and expensive tasks that demand much time, expert knowledge, and fiscal resources to satisfy the hunger of deep neural networks. Aside from that, the problem of having unbalanced data also poses a greater challenge to computer vision and machine learning algorithms to achieve better performance. These problems raise the need to develop algorithms that can fully exploit a large amount of unlabeled data, use a small amount of labeled samples, and be robust to data imbalance to build an efficient and high-quality classifier. In this work, we propose a novel semi-supervised classification technique that is robust to small and unbalanced data. The framework integrates weakly-supervised learning and self-training with self-paced learning to generate attention maps to augment the training set and utilizes a novel pseudo-label generation and selection algorithm to generate and select pseudo-labeled samples. The method improves the performance by: (1) normalizing the class-wise confidence levels to prevent the model from ignoring hard-to-learn samples, thereby solving the imbalanced data problem; (2) jointly learning a model and optimizing pseudo-labels generated on unlabeled data; and (3) enlarging the training set to satisfy the hunger of deep learning models. Extensive evaluations on two public traffic sign recognition datasets demonstrate the effectiveness of the proposed technique and provide a potential solution for practical applications. [ABSTRACT FROM AUTHOR]

In this paper, we address an NPN Boolean matching algorithm. The proposed structural difference signature (SDS) of a Boolean function significantly reduces the search space in the Boolean matching process. The paper analyses the size of the search space from three perspectives: the total number of possible transformations, the number of candidate transformations and the number of decompositions. We test the search space and run time on a large number of randomly generated circuits and Microelectronics Center of North Carolina (MCNC) benchmark circuits with 7–22 inputs. The experimental results show that the search space of Boolean matching is greatly reduced and the matching speed is obviously accelerated. [ABSTRACT FROM AUTHOR]