Unlocking the Transcriptional Control of NCAPG in Bovine Myoblasts: CREB1 and MYOD1 as Key Players.

Muscle formation directly determines meat production and quality. The non-SMC condensin I complex subunit G (NCAPG) is strongly linked to the growth features of domestic animals because it is essential in controlling muscle growth and development. This study aims to elucidate the tissue expression level of the bovine NCAPG gene, and determine the key transcription factors for regulating the bovine NCAPG gene. In this study, we observed that the bovine NCAPG gene exhibited high expression levels in longissimus dorsi and spleen tissues. Subsequently, we cloned and characterized the promoter region of the bovine NCAPG gene, consisting of a 2039 bp sequence, through constructing the deletion fragment double-luciferase reporter vector and site-directed mutation-identifying core promoter region with its key transcription factor binding site. In addition, the key transcription factors of the core promoter sequence of the bovine NCAPG gene were analyzed and predicted using online software. Furthermore, by integrating overexpression experiments and the electrophoretic mobility shift assay (EMSA), we have shown that cAMP response element binding protein 1 (CREB1) and myogenic differentiation 1 (MYOD1) bind to the core promoter region (-598/+87), activating transcription activity in the bovine NCAPG gene. In conclusion, these findings shed important light on the regulatory network mechanism that underlies the expression of the NCAPG gene throughout the development of the muscles in beef cattle.

Pseudo-Data Based Self-Supervised Federated Learning for Classification of Histopathological Images.

Computer-aided diagnosis (CAD) can help pathologists improve diagnostic accuracy together with consistency and repeatability for cancers. However, the CAD models trained with the histopathological images only from a single center (hospital) generally suffer from the generalization problem due to the straining inconsistencies among different centers. In this work, we propose a pseudo-data based self-supervised federated learning (FL) framework, named SSL-FT-BT, to improve both the diagnostic accuracy and generalization of CAD models. Specifically, the pseudo histopathological images are generated from each center, which contain both inherent and specific properties corresponding to the real images in this center, but do not include the privacy information. These pseudo images are then shared in the central server for self-supervised learning (SSL) to pre-train the backbone of global mode. A multi-task SSL is then designed to effectively learn both the center-specific information and common inherent representation according to the data characteristics. Moreover, a novel Barlow Twins based FL (FL-BT) algorithm is proposed to improve the local training for the CAD models in each center by conducting model contrastive learning, which benefits the optimization of the global model in the FL procedure. The experimental results on four public histopathological image datasets indicate the effectiveness of the proposed SSL-FL-BT on both diagnostic accuracy and generalization.

Transcriptome Analysis of mRNA and lncRNA Related to Muscle Growth and Development in Gannan Yak and Jeryak.

The production performance of Jeryak, resulting from the F1 generation of the cross between Gannan yak and Jersey cattle, exhibits a significantly superior outcome compared with that of Gannan yak. Therefore, we used an RNA-seq approach to identify differentially expressed mRNAs (DEMs) and differentially expressed lncRNAs (DELs) influencing muscle growth and development in Gannan yaks and Jeryaks. A total of 304 differentially expressed lncRNAs and 1819 differentially expressed mRNAs were identified based on the screening criteria of |log 2 FC| > 1 and FDR < 0.05. Among these, 132 lncRNAs and 1081 mRNAs were found to be down-regulated, while 172 lncRNAs and 738 mRNAs were up-regulated. GO and KEGG analyses showed that the identified DELs and DEMs were enriched in the entries of pathways associated with muscle growth and development. On this basis, we constructed an lncRNA-mRNA interaction network. Interestingly, two candidate DELs (MSTRG.16260.9 and MSTRG.22127.1) had targeting relationships with 16 (MYC, IGFBP5, IGFBP2, MYH4, FGF6, etc.) genes related to muscle growth and development. These results could provide a basis for further studies on the roles of lncRNAs and mRNAs in muscle growth in Gannan yaks and Jeryak breeds.

Comprehensive Analysis of miRNA and mRNA Expression Profiles during Muscle Development of the Longissimus Dorsi Muscle in Gannan Yaks and Jeryaks.

A hybrid offspring of Gannan yak and Jersey cattle, the Jeryak exhibits apparent hybrid advantages over the Gannan yak in terms of production performance and other factors. The small non-coding RNAs known as miRNAs post-transcriptionally exert a significant regulatory influence on gene expression. However, the regulatory mechanism of miRNA associated with muscle development in Jeryak remains elusive. To elucidate the regulatory role of miRNAs in orchestrating skeletal muscle development in Jeryak, we selected longissimus dorsi muscle tissues from Gannan yak and Jeryak for transcriptome sequencing analysis. A total of 230 (DE) miRNAs were identified in the longissimus dorsi muscle of Gannan yak and Jeryak. The functional enrichment analysis revealed a significant enrichment of target genes from differentially expressed (DE)miRNAs in signaling pathways associated with muscle growth, such as the Ras signaling pathway and the MAPK signaling pathway. The network of interactions between miRNA and mRNA suggest that some (DE)miRNAs, including miR-2478-z, miR-339-x, novel-m0036-3p, and novel-m0037-3p, played a pivotal role in facilitating muscle development. These findings help us to deepen our understanding of the hybrid dominance of Jeryaks and provide a theoretical basis for further research on the regulatory mechanisms of miRNAs associated with Jeryak muscle growth and development.

B-mode ultrasound based CAD for liver cancers via multi-view privileged information learning.

B-mode ultrasound-based computer-aided diagnosis model can help sonologists improve the diagnostic performance for liver cancers, but it generally suffers from the bottleneck due to the limited structure and internal echogenicity information in B-mode ultrasound images. Contrast-enhanced ultrasound images provide additional diagnostic information on dynamic blood perfusion of liver lesions for B-mode ultrasound images with improved diagnostic accuracy. Since transfer learning has indicated its effectiveness in promoting the performance of target computer-aided diagnosis model by transferring knowledge from related imaging modalities, a multi-view privileged information learning framework is proposed to improve the diagnostic accuracy of the single-modal B-mode ultrasound-based diagnosis for liver cancers. This framework can make full use of the shared label information between the paired B-mode ultrasound images and contrast-enhanced ultrasound images to guide knowledge transfer It consists of a novel supervised dual-view deep Boltzmann machine and a new deep multi-view SVM algorithm. The former is developed to implement knowledge transfer from the multi-phase contrast-enhanced ultrasound images to the B-mode ultrasound-based diagnosis model via a feature-level learning using privileged information paradigm, which is totally different from the existing learning using privileged information paradigm that performs knowledge transfer in the classifier. The latter further fuses and enhances feature representation learned from three pre-trained supervised dual-view deep Boltzmann machine networks for the classification task. An experiment is conducted on a bimodal ultrasound liver cancer dataset. The experimental results show that the proposed framework outperforms all the compared algorithms with the best classification accuracy of 88.91 ± 1.52%, sensitivity of 88.31 ± 2.02%, and specificity of 89.50 ± 3.12%. It suggests the effectiveness of our proposed MPIL framework for the BUS-based CAD of liver cancers.

[Retracted] miR­29b enhances the proliferation and migration of bone marrow mesenchymal stem cells in rats with castration­induced osteoporosis through the PI3K/AKT and TGF­ß/Smad signaling pathways.

[This retracts the article DOI: 10.3892/etm.2020.9045.].

Transcriptional analysis of microRNAs related to unsaturated fatty acid synthesis by interfering bovine adipocyte ACSL1 gene.

Long-chain fatty acyl-CoA synthase 1 (ACSL1) plays a vital role in the synthesis and metabolism of fatty acids. The proportion of highly unsaturated fatty acids in beef not only affects the flavor and improves the meat's nutritional value. In this study, si-ACSL1 and NC-ACSL1 were transfected in bovine preadipocytes, respectively, collected cells were isolated on the fourth day of induction, and then RNA-Seq technology was used to screen miRNAs related to unsaturated fatty acid synthesis. A total of 1,075 miRNAs were characterized as differentially expressed miRNAs (DE-miRNAs), of which the expressions of 16 miRNAs were upregulated, and that of 12 were downregulated. Gene ontology analysis indicated that the target genes of DE-miRNAs were mainly involved in biological regulation and metabolic processes. Additionally, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis identified that the target genes of DE-miRNAs were mainly enriched in metabolic pathways, fatty acid metabolism, PI3K-Akt signaling pathway, glycerophospholipid metabolism, fatty acid elongation, and glucagon signaling pathway. Combined with the previous mRNA sequencing results, several key miRNA-mRNA targeting relationship pairs, i.e., novel-m0035-5p-ACSL1, novel-m0035-5p-ELOVL4, miR-9-X-ACSL1, bta-miR-677-ACSL1, miR-129-X-ELOVL4, and bta-miR-485-FADS2 were screened via the miRNA-mRNA interaction network. Thus, the results of this study provide a theoretical basis for further research on miRNA regulation of unsaturated fatty acid synthesis in bovine adipocytes.

Self-Supervised Bi-Channel Transformer Networks for Computer-Aided Diagnosis.

Self-supervised learning (SSL) can alleviate the issue of small sample size, which has shown its effectiveness for the computer-aided diagnosis (CAD) models. However, since the conventional SSL methods share the identical backbone in both the pretext and downstream tasks, the pretext network generally cannot be well trained in the pre-training stage, if the pretext task is totally different from the downstream one. In this work, we propose a novel task-driven SSL method, namely Self-Supervised Bi-channel Transformer Networks (SSBTN), to improve the diagnostic accuracy of a CAD model by enhancing SSL flexibility. In SSBTN, we innovatively integrate two different networks for the pretext and downstream tasks, respectively, into a unified framework. Consequently, the pretext task can be flexibly designed based on the data characteristics, and the corresponding designed pretext network thus learns more effective feature representation to be transferred to the downstream network. Furthermore, a transformer-based transfer module is developed to efficiently enhance knowledge transfer by conducting feature alignment between two different networks. The proposed SSBTN is evaluated on two publicly available datasets, namely the full-field digital mammography INbreast dataset and the wireless video capsule CrohnIPI dataset. The experimental results indicate that the proposed SSBTN outperforms all the compared algorithms.

Doubly Supervised Transfer Classifier for Computer-Aided Diagnosis With Imbalanced Modalities.

Transfer learning (TL) can effectively improve diagnosis accuracy of single-modal-imaging-based computer-aided diagnosis (CAD) by transferring knowledge from other related imaging modalities, which offers a way to alleviate the small-sample-size problem. However, medical imaging data generally have the following characteristics for the TL-based CAD: 1) The source domain generally has limited data, which increases the difficulty to explore transferable information for the target domain; 2) Samples in both domains often have been labeled for training the CAD model, but the existing TL methods cannot make full use of label information to improve knowledge transfer. In this work, we propose a novel doubly supervised transfer classifier (DSTC) algorithm. In particular, DSTC integrates the support vector machine plus (SVM+) classifier and the low-rank representation (LRR) into a unified framework. The former makes full use of the shared labels to guide the knowledge transfer between the paired data, while the latter adopts the block-diagonal low-rank (BLR) to perform supervised TL between the unpaired data. Furthermore, we introduce the Schatten-p norm for BLR to obtain a tighter approximation to the rank function. The proposed DSTC algorithm is evaluated on the Alzheimer's disease neuroimaging initiative (ADNI) dataset and the bimodal breast ultrasound image (BBUI) dataset. The experimental results verify the effectiveness of the proposed DSTC algorithm.

Diagnosis of Infantile Hip Dysplasia With B-Mode Ultrasound via Two-Stage Meta-Learning Based Deep Exclusivity Regularized Machine.

The B-mode ultrasound (BUS) based computer-aided diagnosis (CAD) has shown its effectiveness for developmental dysplasia of the hip (DDH) in infants. In this work, a two-stage meta-learning based deep exclusivity regularized machine (TML-DERM) is proposed for the BUS-based CAD of DDH. TML-DERM integrates deep neural network (DNN) and exclusivity regularized machine into a unified framework to simultaneously improve the feature representation and classification performance. Moreover, the first-stage meta-learning is mainly conducted on the DNN module to alleviate the overfitting issue caused by the significantly increased parameters in DNN, and a random sampling strategy is adopted to self-generate the meta-tasks; while the second-stage meta-learning mainly learns the combination of multiple weak classifiers by a weight vector to improve the classification performance, and also optimizes the unified framework again. The experimental results on a DDH ultrasound dataset show the proposed TML-DERM algorithm achieves the superior classification performance with the mean accuracy of 85.89%, sensitivity of 86.54%, and specificity of 85.23%.

Interference With ACSL1 Gene in Bovine Adipocytes: Transcriptome Profiling of mRNA and lncRNA Related to Unsaturated Fatty Acid Synthesis.

The enzyme long-chain acyl-CoA synthetase 1 (ACSL1) is essential for lipid metabolism. The ACSL1 gene controls unsaturated fatty acid (UFA) synthesis as well as the formation of lipid droplets in bovine adipocytes. Here, we used RNA-Seq to determine lncRNA and mRNA that regulate UFA synthesis in bovine adipocytes using RNA interference and non-interference with ACSL1. The corresponding target genes of differentially expressed (DE) lncRNAs and the DE mRNAs were found to be enriched in lipid and FA metabolism-related pathways, according to GO and KEGG analyses. The differentially expressed lncRNA- differentially expressed mRNA (DEL-DEM) interaction network indicated that some DELs, such as TCONS_00069661, TCONS_00040771, TCONS_ 00035606, TCONS_00048301, TCONS_001309018, and TCONS_00122946, were critical for UFA synthesis. These findings assist our understanding of the regulation of UFA synthesis by lncRNAs and mRNAs in bovine adipocytes.

Corrigendum: Microbial Community and Its Association With Physicochemical Factors During Compost Bedding for Dairy Cows.

[This corrects the article DOI: 10.3389/fmicb.2020.00254.].

miR-29b enhances the proliferation and migration of bone marrow mesenchymal stem cells in rats with castration-induced osteoporosis through the PI3K/AKT and TGF-ß/Smad signaling pathways.

The aim of the present study was to investigate the role of microRNA (miR)-29b in the proliferation and migration of bone marrow mesenchymal stem cells (BMSCs) in rats with castration-induced osteoporosis and the relevant mechanisms. The gene expression profiling microarray technique was utilized to sequence the BMSCs with overexpressed miR-29b. The intersection of the potential targets and the genes downregulated in the sequencing were utilized for GO enrichment analysis. Gene set enrichment analysis (GSEA) was employed to analyze the effect of miR-29b on signaling pathways. Additionally, the effects of miR-29b overexpression on the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) and transforming growth factor-ß (TGF-ß)/Drosophila mothers against decapentaplegic protein (Smad) signaling pathways were detected via RT-qPCR assay and western blotting. The expression level of miR-29b was found to be significantly reduced in bone marrow tissues of postmenopausal osteoporosis patients and BMSCs of rats with castration-induced osteoporosis established via ovariectomy. Based on transcriptome sequencing and bioinformatics software prediction, 76 potential targets of miR-29b were obtained, which were distinctly enriched in such biological processes as cell proliferation, cell cycle, cell migration and cell adhesion. The results of CCK-8 and EdU assays showed that overexpression of miR-29b overtly promoted the proliferation of BMSCs in rats with castration-induced osteoporosis. Moreover, the Transwell assay results revealed that the overexpression of miR-29b significantly facilitated the migration of BMSCs in rats with castration-induced osteoporosis. According to RT-qPCR assay and western blotting, miR-29b activated the PI3K/AKT and TGF-ß/Smad signaling pathways. miR-29b exhibited a clearly lower expression level in the bone marrow tissues of the postmenopausal osteoporosis patients and BMSCs of rats with castration-induced osteoporosis established via ovariectomy. Overexpression of miR-29b was able to enhance the proliferation and migration ability of BMSCs in rats with castration-induced osteoporosis, and such an enhancement may be correlated with the activation of the PI3K/AKT and TGF-ß/Smad signaling pathways.

Microbial Community and Its Association With Physicochemical Factors During Compost Bedding for Dairy Cows.

Overproduction of livestock manure can cause significant environmental challenges. Compost bedding (CB) is considered an effective approach for recycling the agricultural byproducts and improving the welfare of dairy cattle. During the CB preparing, the composition of microbial communities is usually altered; however, the patterns and drivers of CB microbial communities remains to be investigated. The current study aimed to explore the dynamics of bacterial and fungal communities during the various padded stages, using high throughput sequencing technology and qPCR. The relationships across physicochemical parameters, microbial community composition, and abundance were also evaluated. Sequencing results revealed that Proteobacteria, Bacteroidetes, Actinobacteria, and Firmicutes of bacteria, and Ascomycota of fungi as the major phyla found in CB. qPCR results showed a significant increase in the number of bacterial genome copies from 1.20 × 107 to 3.35 × 107 copies/gram of dry soil, while the number of fungal genome copies significantly increased from 8.43 × 104 to 7.02 × 106 copies/gram of dry soil. Linear discriminant analysis effect size (LEfSe) showed that Actinobacteria was the primary indicator in raw materials while the phylum Bacteroidetes was in the other padded stages. Dothideomycetes was significantly enriched in the initial stage of fungi, whereas Sordariomycetes, including a pathogen Scedosporium prolificans, was the major indicator in CB after 9 days of padding. Mantel test showed that pH significantly influenced bacterial community composition while temperature and total organic carbon (TOC) had a significant effect on fungal community structure. Redundancy analysis indicated that TOC, temperature, and water content had a significant effect on bacterial abundance while total nitrogen, water content, and pH significantly affected fungal abundance. Our finding contributed to the understanding of microbial community succession in CB across different padded stages, and suggests CB management by changing the bedding material every 7 days.

Outer membrane vesicle-associated lipase FtlA enhances cellular invasion and virulence in Francisella tularensis LVS.

Francisella tularensis is a highly infectious intracellular pathogen that infects a wide range of host species and causes fatal pneumonic tularemia in humans. ftlA was identified as a potential virulence determinant of the F. tularensis live vaccine strain (LVS) in our previous transposon screen, but its function remained undefined. Here, we show that an unmarked deletion mutant of ftlA was avirulent in a pneumonia mouse model with a severely impaired capacity to infect host cells. Consistent with its sequence homology with GDSL lipase/esterase family proteins, the FtlA protein displayed lipolytic activity in both E. coli and F. tularensis with a preference for relatively short carbon-chain substrates. FtlA thus represents the first F. tularensis lipase to promote bacterial infection of host cells and in vivo fitness. As a cytoplasmic protein, we found that FtlA was secreted into the extracellular environment as a component of outer membrane vesicles (OMVs). Further confocal microscopy analysis revealed that the FtlA-containing OMVs isolated from F. tularensis LVS attached to the host cell membrane. Finally, the OMV-associated FtlA protein complemented the genetic deficiency of the ΔftlA mutant in terms of host cell infection when OMVs purified from the parent strain were co-incubated with the mutant bacteria. These lines of evidence strongly suggest that the FtlA lipase promotes F. tularensis adhesion and internalization by modifying bacterial and/or host molecule(s) when it is secreted as a component of OMVs.

Management of Humeral Shaft Fractures With Intramedullary Interlocking Nail Versus Locking Compression Plate.

Surgical fixation of humeral shaft fractures generally involves plating or nailing. It is unclear whether one method is more effective than the other. The aim of this study was to compare the results of the intramedullary nail and locking compression plate for the treatment of humeral shaft fractures. A total of 60 patients with humeral shaft fractures were randomized to undergo surgery with an intramedullary interlocking nail (n=30) or locking compression plate (n=30). The outcome was assessed in terms of intraoperative blood loss, operative time, hospital stay, union time, union rate, functional outcome, and incidence of complications. Functional outcome was assessed using the Constant score and the American Shoulder and Elbow Surgeons (ASES) score. Intraoperative blood loss, operative time, and hospital stay in group A (intramedullary interlocking nail) were significantly lower than those in group B (locking compression plate). No statistically significant difference was found regarding the union rate, mean Constant score, and mean ASES score between the groups. The average union time was found to be significantly lower for the intramedullary interlocking nail compared with the locking compression plate. The incidence of complications such as radial nerve palsy was found to be higher with the locking compression plate compared with the intramedullary interlocking nail. The intramedullary interlocking nail can be considered a better surgical option for the management of humeral shaft fractures because it offers decreased intraoperative blood loss; shorter operative times, hospital stays, and union times; and a lower incidence of serious complications such as radial nerve palsy.