Transcriptomic analysis reveals that non-forage or forage fiber source promotes rumen development through different metabolic processes in lambs

Liu, Ting; Li, Fadi; Xu, Jianfeng; La, Yongfu; Zhou, Juwang; Zheng, Chen; Weng, Xiuxiu

doi:10.1080/10495398.2021.2011738

Cited by 2 publications

(1 citation statement)

References 75 publications

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“…Since then, the theoretical research and application of convolutional neural networks have been stagnant due to the lack of hardware computing power and data samples. It was not until 2012 that AlexNet won the ImageNet challenge that convolutional neural networks became a hot topic for academic research again [ 20 ]. With the increase of GPU computing power, the network structure has developed in a deeper direction from 8-layer AlexNet to 16-layer VGGNet to 152-layer ResNet.…”

Section: Algorithm Designmentioning

confidence: 99%

Long Jump Action Recognition Based on Deep Convolutional Neural Network

Wang

2022

Computational Intelligence and Neuroscience

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Long jump is a test item of national student physical health monitoring, which can reflect the quality of students’ lower limb strength. Long jump is a highly technical activity, which includes four basic movements: running aid, jumping, vacating, and landing. Many students have problems with the technical aspects, resulting in test scores that do not objectively reflect the true physical condition of the students, which affects the accuracy of the test results. From the perspective of rapid diagnostic feedback of students’ long jump movements, we design and develop a long jump movement recognition method based on deep convolutional neural network. In this paper, we firstly summarize the traditional visual action recognition algorithm, then apply 3D convolution to extract the spatiotemporal features of long jump action from three directions of the video block, and fuse the spatiotemporal features of the three directions in different ways to achieve feature complementation; finally, using the multimodality of long jump action data, we use 3D convolutional neural network to train the RGB images and then train the depth. This joint training method can accelerate the convergence speed and improve the accuracy of the network on both depth and edge images. The experiments compared the recognition effects of the tandem fusion of features, the maximum fusion, and the multiplicative fusion in the scoring layer, and the highest accuracy of 82.3% was achieved by the tandem fusion of features with the fusion of three modalities.

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Section: Algorithm Designmentioning

confidence: 99%

Long Jump Action Recognition Based on Deep Convolutional Neural Network

Wang

2022

Computational Intelligence and Neuroscience

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Multi-omics reveals that forage fiber promotes rumen development of pre-weaning calves compared to non-forage fiber

Wang

Zhao

et al. 2023

Preprint

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Background: The rumen development of calves can result in long-term effects on growth, health, and milk yields in adult cattle. Dietary fiber supplement can stimulate rumen development in calves during pre-weaning period. Non-forage fiber can be used as starter fiber to reduce gastrointestinal fill and increase the content of rumen volatile fatty acid (VFA) because of higher biodegradable fiber. Numerous studies revealed that non-forage fiber has no negative effect on pre-weaning calves compared to forage fiber, but few evidences of rumen development were given. It is unclear whether different fiber sources impact the development of rumen in pre-weaning calves. Here, this study investigated the synergistic responses of rumen microbiota, metabolites and epithelium to different fiber source. Results: Thirty healthy Holstein calves with similar age (4.2 ± 2.3 days) and body weight (39.2 ± 3.6 kg) were allocated to two groups (15 cows per group), and were fed with one of the two diets with different fiber sources, short oat hay (SO), or short soybean hull (SS) for 8 weeks, respectively. At the end of the experiment, 6 calves of each group with similar body weight were selected to slaughter. Observations showed that SO-feeding calves has lengthened and widened rumen epithelial papillae. Metagenomic results revealed that different bacteria responded to different fiber source by different Carbohydrate-active enzymes (CAZymes) (P < 0.05). Metabolomics analysis indicated that “vitamin B6 metabolism” pathway was significantly enriched based on the 22 increased metabolites in the SO calves (P < 0.05, pathway impact > 0.05). The Pyridoxine, 4a-Carbinolamine tetrahydrobiopterin and Gentisic acid were enriched under SO feeding (P < 0.05, VIP > 1). Transcriptome analysis revealed that 129 differentially expressed genes (DEGs) were identified from comparison of the two groups. The DEGs related to transcription, translation and energy metabolism were upregulated (P < 0.05). Conclusions:The SO feeding (forage fiber) can lead to the enrichment of beneficial metabolites and metabolic pathways, promote the expression of genes related to energy metabolism, transcription and translation, and ultimately promote the development of rumen epithelium. Therefore, in pre-weaning calves production, attention should be paid to the impacts of forage fiber (short oat hay) introduction on rumen morphology and function.

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Transcriptomic analysis reveals that non-forage or forage fiber source promotes rumen development through different metabolic processes in lambs

Cited by 2 publications

References 75 publications

Long Jump Action Recognition Based on Deep Convolutional Neural Network

Long Jump Action Recognition Based on Deep Convolutional Neural Network

Multi-omics reveals that forage fiber promotes rumen development of pre-weaning calves compared to non-forage fiber

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