Uncovering the genetic diversity and adaptability of Butuo Black Sheep through whole-genome re-sequencing.

The Butuo Black Sheep (BBS) is well-known for its ability to thrive at high altitudes, resist diseases, and produce premium-quality meat. Nonetheless, there is insufficient data regarding its genetic diversity and population-specific Single nucleotide polymorphisms (SNPs). This paper centers on the genetic diversity of (BBS). The investigation conducted a whole-genome resequencing of 33 BBS individuals to recognize distinct SNPs exclusive to BBS. The inquiry utilized bioinformatic analysis to identify and explain SNPs and pinpoint crucial mutation sites. The findings reveal that reproductive-related genes (GHR, FSHR, PGR, BMPR1B, FST, ESR1), lipid-related genes (PPARGC1A, STAT6, DGAT1, ACACA, LPL), and protein-related genes (CSN2, LALBA, CSN1S1, CSN1S2) were identified as hub genes. Functional enrichment analysis showed that genes associated with reproduction, immunity, inflammation, hypoxia, PI3K-Akt, and AMPK signaling pathways were present. This research suggests that the unique ability of BBS to adapt to low oxygen levels in the plateau environment may be owing to mutations in a variety of genes. This study provides valuable insights into the genetic makeup of BBS and its potential implications for breeding and conservation efforts. The genes and SPNs identified in this study could serve as molecular markers for BBS.

Bridge crack detection based on improved single shot multi-box detector.

Owing to the development of computerized vision technology, object detection based on convolutional neural networks is being widely used in the field of bridge crack detection. However, these networks have limited utility in bridge crack detection because of low precision and poor real-time performance. In this study, an improved single-shot multi-box detector (SSD) called ISSD is proposed, which seamlessly combines the depth separable deformation convolution module (DSDCM), inception module (IM), and feature recalibration module (FRM) in a tightly coupled manner to tackle the challenges of bridge crack detection. Specifically, DSDCM was utilized for extracting the characteristic information of irregularly shaped bridge cracks. IM was designed to expand the width of the network, reduce network calculations, and improve network computing speed. The FRM was employed to determine the importance of each feature channel through learning, enhance the useful features according to their importance, and suppress the features that are insignificant for bridge crack detection. The experimental results demonstrated that ISSD is effective in bridge crack detection tasks and offers competitive performance compared to state-of-the-art networks.

Semantic segmentation method of underwater images based on encoder-decoder architecture.

With the exploration and development of marine resources, deep learning is more and more widely used in underwater image processing. However, the quality of the original underwater images is so low that traditional semantic segmentation methods obtain poor segmentation results, such as blurred target edges, insufficient segmentation accuracy, and poor regional boundary segmentation effects. To solve these problems, this paper proposes a semantic segmentation method for underwater images. Firstly, the image enhancement based on multi-spatial transformation is performed to improve the quality of the original images, which is not common in other advanced semantic segmentation methods. Then, the densely connected hybrid atrous convolution effectively expands the receptive field and slows down the speed of resolution reduction. Next, the cascaded atrous convolutional spatial pyramid pooling module integrates boundary features of different scales to enrich target details. Finally, the context information aggregation decoder fuses the features of the shallow network and the deep network to extract rich contextual information, which greatly reduces information loss. The proposed method was evaluated on RUIE, HabCam UID, and UIEBD. Compared with the state-of-the-art semantic segmentation algorithms, the proposed method has advantages in segmentation integrity, location accuracy, boundary clarity, and detail in subjective perception. On the objective data, the proposed method achieves the highest MIOU of 68.3 and OA of 79.4, and it has a low resource consumption. Besides, the ablation experiment also verifies the effectiveness of our method.

Novel cationic cardiolipin analogue-based liposome for efficient DNA and small interfering RNA delivery in vitro and in vivo.

Cationic liposomes have been successfully used as an alternative approach to viral systems to deliver nucleic acids. However, high toxicity and inconsistent transfection efficiency have been associated with the currently available liposomes. Therefore, a novel cationic liposome was developed based on a synthetic cationic cardiolipin analogue (CCLA) to test the DNA transfection efficiency. This CCLA-based liposome was also used to determine the therapeutic efficacy of c-raf small interfering RNA (siRNA) in mice. In this report, we showed that the CCLA-based liposome was less toxic and effectively transfected reporter genes in vitro and in vivo. The transfection efficiency in mice was seven-fold higher than the commercially available DOTAP-based liposome. In addition, c-raf siRNA in the presence of CCLA-based liposome induced up to 62% of growth inhibition in cancer cells. Treatment of c-raf siRNA/CCLA complex in SCID mice bearing human breast xenograft tumors resulted in 73% of tumor growth suppression as compared to free c-raf siRNA group. In conclusion, a novel CCLA-based liposome showed less toxicity and broad usage both in vitro and in vivo with DNA and siRNA.