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Background Vitamin A and retinoic acid (RA, a metabolite of vitamin A), are inextricably involved to the development of skeletal muscle in animals. However, the mechanisms regulating skeletal muscle development by vitamin A remain poorly reported. The current study designed to investigate the underlying mechanism of vitamin A affecting myogenic differentiation of lamb myoblasts through transcriptome sequencing (RNA-Seq) and gene function validation experiments. It provides a theoretical basis for elucidating the regulation of vitamin A on skeletal muscle development as well as for improving the economic benefits of the mutton sheep industry. Results Newborn lambs were injected with 7,500 IU vitamin A, and longissimus dorsi (LD) muscle tissue was surgically sampled for RNA-Seq analysis and primary myoblasts isolation at 3 weeks of age. The results showed that a total of 14 down-regulated and 3 up-regulated genes, were identified between control and vitamin A groups. Among them, BHLHE40 expression was upregulated in vitamin A group lambs. Furthermore, BHLHE40 expression is significantly increased after initiation of differentiation in myoblasts, and RA addition during differentiation greatly promoted BHLHE40 mRNA expression. In vitro, RA inhibited myoblasts proliferation and promoted myoblasts myogenic differentiation through BHLHE40 . Moreover, BHLHE40 was proved to inhibit the expression of the DNA binding inhibitor 3 ( ID3 ), and meanwhile, ID3 could effectively promote myoblasts proliferation and inhibit myoblasts myogenic differentiation. Conclusions Taken together, our results suggested that vitamin A inhibited myoblasts proliferation and promoted myoblasts myogenic differentiation by inhibiting ID3 expression through BHLHE40.

In actual production environments, antibiotic-resistant genes (ARGs) are abundant in pig manure, which can form transmission chains through animals, the environment, and humans, thereby threatening human health. Therefore, based on metagenomic analysis methods, ARGs and mobile genetic elements (MGEs) were annotated in pig manure samples from 6 pig farms in 3 regions of Shanxi Province, and the potential hosts of ARGs were analyzed. The results showed that a total of 14 ARG types were detected, including 182 ARG subtypes, among which tetracycline, phenol, aminoglycoside, and macrolide resistance genes were the main ones. ARG profiles, MGE composition, and microbial communities were significantly different in different regions as well as between different pig farms. In addition, Anaerobutyricum , Butyrivibrio , and Turicibacter were significantly associated with multiple ARGs, and bacteria such as Prevotella , Bacteroides , and the family Oscillospiraceae carried multiple ARGs, suggesting that these bacteria are potential ARG hosts in pig manure. Procrustes analysis showed that bacterial communities and MGEs were significantly correlated with ARG profiles. Variation partitioning analysis results indicated that the combined effect of MGEs and bacterial communities accounted for 64.08% of resistance variation and played an important role in ARG profiles. These findings contribute to our understanding of the dissemination and persistence of ARGs in actual production settings, and offer some guidance for the prevention and control of ARGs contamination.

Rice (Oryza sativa L.) yield can be quantified by measuring the number of ears per square meter, grain number per ear, percentage of filled grains (PFG), and 1000‐grain weight, of which PFG is an important yield parameter. Therefore, it is pivotal to improve the measuring efficiency of PFG. The objective of the present study was to construct an automatic method for measuring PFG using the shadow features of grains. Filled grains can be distinguished from unfilled grains by their shadow characteristics under lamplight. Image segmentation algorithms were used to extract rice grains and shadows. Learning vector quantization neural networks then classified rice grains based on their shadow characteristics. To improve the flexibility of the measurement system, 19 typical rice cultivars were used to train the networks. Sixteen cultivars from two subspecies were selected to validate the new PFG measurement method, and the results showed that the PFG measured by the new method was closely related to the PFG measured by the water‐float method. The correlation coefficient between the two methods was 0.952 (P < 0.01). Shadow characteristics showed a significant difference between filled and unfilled grains. We concluded that shadow traits of rice grains could be used to discriminate filled and unfilled grains. The new PFG measurement method is a reliable method to estimate the PFG of rice and also shows great potential to improve the efficiency of trait evaluation in rice breeding and cultivation. The percentage of filled grains of rice was measured. Characteristics of grains’ shadow were used to identify filled and unfilled grains. Grain shadow characteristics values were extracted by image processing technology. Artificial neural network model is a valuable tool for grain classification.

Web 2.0 has brought with it numerous user-produced data revealing one's thoughts, experiences, and knowledge, which are a great source for many tasks, such as information extraction, and knowledge base construction. However, the colloquial nature of the texts poses new challenges for current natural language processing techniques, which are more adapt to the formal form of the language. Ellipsis is a common linguistic phenomenon that some words are left out as they are understood from the context, especially in oral utterance, hindering the improvement of dependency parsing, which is of great importance for tasks relied on the meaning of the sentence. In order to promote research in this area, we are releasing a Chinese dependency treebank of 319 weibos, containing 572 sentences with omissions restored and contexts reserved.