Comparative analysis of genome-wide copy number variations between Tibetan sheep and White Suffolk sheep.

The DNA copy number variations (CNVs) are widely involved in affecting various kinds of biological functions, such as environmental adaptation. Tibetan sheep and White Suffolk sheep are two representative indigenous and exotic breeds raised in Sichuan, China, and both of them have many contrasting biological characteristics. In this study, we employed high-throughput sequencing approach to investigate genome-wide CNVs between the two sheep breeds. A total of 11,135 CNV regions (CNVRs) consisting of 6,488 deletions and 4,647 duplications were detected, whose length ranged from 1,599 bp to 0.56 Mb with the mean of 4,658 bp. There were 281 CNVRs segregated between Tibetan sheep and White Suffolk sheep, and 18 of them have been fixed within both breeds. Functional analyses of candidate genes within the segregating CNVRs revealed the thyroid hormone signaling pathway and CTNNB1 gene that would be responsible for differential biological characteristics of breeds, such as energy metabolism, seasonal reproduction, and litter size. Furthermore, the segregating CNVRs identified in this study were overlapped with many known quantitative trait loci that are associated with growth, testis weight, and reproductive seasonality. In conclusion, these results help us better understanding differential biological characteristics between Tibetan sheep and White Suffolk sheep.

A Novel Approach to Automated 3D Spalling Defects Inspection in Railway Tunnel Linings Using Laser Intensity and Depth Information.

The detection of concrete spalling is critical for tunnel inspectors to assess structural risks and guarantee the daily operation of the railway tunnel. However, traditional spalling detection methods mostly rely on visual inspection or camera images taken manually, which are inefficient and unreliable. In this study, an integrated approach based on laser intensity and depth features is proposed for the automated detection and quantification of concrete spalling. The Railway Tunnel Spalling Defects (RTSD) database, containing intensity images and depth images of the tunnel linings, is established via mobile laser scanning (MLS), and the Spalling Intensity Depurator Network (SIDNet) model is proposed for automatic extraction of the concrete spalling features. The proposed model is trained, validated and tested on the established RSTD dataset with impressive results. Comparison with several other spalling detection models shows that the proposed model performs better in terms of various indicators such as MPA (0.985) and MIoU (0.925). The extra depth information obtained from MLS allows for the accurate evaluation of the volume of detected spalling defects, which is beyond the reach of traditional methods. In addition, a triangulation mesh method is implemented to reconstruct the 3D tunnel lining model and visualize the 3D inspection results. As a result, a 3D inspection report can be outputted automatically containing quantified spalling defect information along with relevant spatial coordinates. The proposed approach has been conducted on several railway tunnels in Yunnan province, China and the experimental results have proved its validity and feasibility.

Convolutional Neural Networks-Based Object Detection Algorithm by Jointing Semantic Segmentation for Images.

In recent years, increasing image data comes from various sensors, and object detection plays a vital role in image understanding. For object detection in complex scenes, more detailed information in the image should be obtained to improve the accuracy of detection task. In this paper, we propose an object detection algorithm by jointing semantic segmentation (SSOD) for images. First, we construct a feature extraction network that integrates the hourglass structure network with the attention mechanism layer to extract and fuse multi-scale features to generate high-level features with rich semantic information. Second, the semantic segmentation task is used as an auxiliary task to allow the algorithm to perform multi-task learning. Finally, multi-scale features are used to predict the location and category of the object. The experimental results show that our algorithm substantially enhances object detection performance and consistently outperforms other three comparison algorithms, and the detection speed can reach real-time, which can be used for real-time detection.

Towards Automated 3D Inspection of Water Leakages in Shield Tunnel Linings Using Mobile Laser Scanning Data.

On-site manual inspection of metro tunnel leakages has been faced with the problems of low efficiency and poor accuracy. An automated, high-precision, and robust water leakage inspection method is vital to improve the manual approach. Existing approaches cannot provide the leakage location due to the lack of spatial information. Therefore, an integrated deep learning method of water leakage inspection using tunnel lining point cloud data from mobile laser scanning is presented in this paper. It is composed of three parts as follows: (1) establishment of the water leakage dataset using the acquired point clouds of tunnel linings; (2) automated leakage detection via a mask-region-based convolutional neural network; and (3) visualization and quantitative evaluation of the water leakage in 3D space via a novel triangle mesh method. The testing result reveals that the proposed method achieves automated detection and evaluation of tunnel lining water leakages in 3D space, which provides the inspectors with an intuitive overall 3D view of the detected water leakages and the leakage information (area, location, lining segments, etc.).

Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert-Huang Transform and Deep Learning.

In this paper, we propose a novel method for fault diagnosis in micro-electromechanical system (MEMS) inertial sensors using a bidirectional long short-term memory (BLSTM)-based Hilbert-Huang transform (HHT) and a convolutional neural network (CNN). First, the method for fault diagnosis of inertial sensors is formulated into an HHT-based deep learning problem. Second, we present a new BLSTM-based empirical mode decomposition (EMD) method for converting one-dimensional inertial data into two-dimensional Hilbert spectra. Finally, a CNN is used to perform fault classification tasks that use time-frequency HHT spectrums as input. According to our experimental results, significantly improved performance can be achieved, on average, for the proposed BLSTM-based EMD algorithm in terms of EMD computational efficiency compared with state-of-the-art algorithms. In addition, the proposed fault diagnosis method achieves high accuracy in fault classification.

Safety evaluation of preoperative stent insertion and clinical analysis on comparison of outcomes between preoperative stent insertion and emergency surgery in the treatment of obstructive left-sided colorectal cancer.

OBJECTIVE: To evaluate the safety of preoperative stent insertion and compare the short- and long-term outcomes between preoperative stent insertion and emergency surgery in the treatment of obstructive left-sided colorectal cancer. METHODS: The clinical data of 302 patients who underwent surgery for obstructive left-sided colorectal cancer from January 2009 to May 2014 were retrospectively analyzed. They were divided into two groups according to whether to receive stenting for the success rate and complications of stent insertion in colonic lumen by colonoscope, and the number of cases of primary resection and anastomosis, and short-term complications such as incision infection, anastomotic leakage, spleen tear and abdominal abscess as well as mortality and survival rate during hospitalization were compared. RESULTS: The success rate of endoscopic nitinol alloy memorial stent insertion in colonic lumen was 97.62%, and the overall incidence of complications was 14.5%, of which the incidence of serious complications (perforation, stent migration) was 4.76%. The primary anastomosis rate was significantly higher in the stent insertion group (85.71%) than that in the emergency surgery group (36.24%). The overall complication rate in the stent insertion group (14 cases) was significantly lower than that in the emergency surgery group (102 cases). There was no significant difference between survival curves (P>0.05). CONCLUSION: Preoperative stent insertion in colonic lumen by colonoscope for decompression is an ideal auxiliary method in the treatment of obstructive left-sided colorectal cancer, and may increase primary anastomosis rate, avoid neostomy, reduce short-term complications, and improve the long-term survival compared to emergency surgery.

Expression of Interleukin-6 and Integrin ανß6 in Colon Cancer: Association with Clinical Outcomes and Prognostic Implications.

As important factors in the tumor microenvironment, interleukin-6 (IL-6) and integrin ανß6 play significant roles in accumulating mutations that drive the progression and metastatic capacities of cancer. The aim of this study was to investigate the expression of IL-6 and integrin ανß6, their clinical significance, as well as their correlation in the colon cancer tissues of 145 cases using immunohistochemistry. Our results showed that IL-6 and integrin ανß6 are indicators of cancer progression and poor prognosis in patients with colon cancer. Moreover, their relationship may provide clues for further studies on how the tumor microenvironment mediates the development of colon cancer, as well as strategies for the identification of novel therapeutic targets in the prevention and treatment of colon cancer.

Proteome-wide Lysine Glutarylation Profiling of the Mycobacterium tuberculosis H37Rv.

Lysine glutarylation, a new protein posttranslational modification (PTM), was recently identified and characterized in both prokaryotic and eukaryotic cells. To explore the distribution of lysine glutarylation in Mycobacterium tuberculsosis, by using a comprehensive method combining the immune affinity peptide enrichment by the glutaryl-lysine antibody with LC-MS, we finally identified 41 glutarylation sites in 24 glutarylated proteins from M. tuberculosis. These glutarylated proteins are involved in various cellular functions such as translation and metabolism and exhibit diverse subcellular localizations. Three common glutarylated proteins including 50S ribosomal protein L7/L12, elongation factor Tu, and dihydrolipoamide succinyltransferase are shared between Escherichia coli and M. tuberculosis. Moreover, comparison with other PTMs characterized in M. tuberculosis, 15 glutarylated proteins, are found to be both acetylated and succinylated. Notably, several stress-response-associated proteins including HspX are glutarylated. Our data provide the first analysis of M. tuberculosis lysine glutarylated proteins. Further studies on the role of the glutarylated proteins will unveil the molecular mechanisms of glutarylation underlying M. tuberculosis physiology and pathogenesis.

Mycobacterium tuberculosis PE13 (Rv1195) manipulates the host cell fate via p38-ERK-NF-κB axis and apoptosis.

PE/PPE family proteins are mycobacteria unique molecules, named after their N-terminal conserved PE (Pro-Glu) and PPE (Pro-Pro-Glu) domains. Mycobacterium tuberculosis (Mtb) PE family gene encoded cell surface proteins are previously reported to be involved in virulence and interaction with host. To explore the role of a novel PE member (PE13, Rv1195), M. smegmatis was used as surrogate host. The study showed that Rv1195 was a cell wall associated protein. Rv1195 can enhance the survival of recombinants under stress conditions such as H2O2, SDS, low pH. This is largely due to the upregulated transcription of Rv1195, since diverse stresses can increase the promoter activity of Rv1195 gene, consistent with enhanced survival within macrophages. Ms_Rv1195 infection also increased the production of interlukin-6 (IL-6) and IL-1ß from macrophages, while decreased the secretion of suppressor of cytokine signaling 3 (SOCS3) in comparison with the vector-only control. The cell death was also precipitated by the Ms_Rv1195 infection. Inhibitors treatment showed that the p38-ERK-NF-κB axis was involved in the Rv1195 triggered change of IL-6 and IL-1ß expression. In summary, we showed that PE13 (Rv1195) is a new PE family member actively engaged in the interaction between Mycobacterium and host, signaling through p38-ERK-NF-κB axis and apoptosis.

Rationally Designed Redox-Sensitive Protein Hydrogels with Tunable Mechanical Properties.

Protein hydrogels are an important class of materials for applications in biotechnology and medicine. The fine-tuning of their sequence, molecular weight, and stereochemistry offers unique opportunities to engineer biofunctionality, biocompatibility, and biodegradability into these materials. Here we report a new family of redox-sensitive protein hydrogels with controllable mechanical properties composed of recombinant silk-elastin-like protein polymers (SELPs). The SELPs were designed and synthesized with different ratios of silk-to-elastin blocks that incorporated periodic cysteine residues. The cysteine-containing SELPs were thermally responsive in solution and rapidly formed hydrogels at body temperature under physiologically relevant, mild oxidative conditions. Upon addition of a low concentration of hydrogen peroxide at 0.05% (w/v), gelation occurred within minutes for the SELPs with a protein concentration of approximately 4% (w/v). The gelation time and mechanical properties of the hydrogels were dependent on the ratio of silk to elastin. These polymer designs also significantly affected redox-sensitive release of a highly polar model drug from the hydrogels in vitro. Furthermore, oxidative gelation was performed at other physiologically relevant temperatures, and this resulted in hydrogels with tunable mechanical properties, thus, providing a secondary level of control over hydrogel stiffness. These newly developed injectable SELP hydrogels with redox-sensitive features and tunable mechanical properties may be potentially useful as biomaterials with broad applications in controlled drug delivery and tissue engineering.

First succinyl-proteome profiling of extensively drug-resistant Mycobacterium tuberculosis revealed involvement of succinylation in cellular physiology.

Protein lysine succinylation, an emerging protein post-translational modification widespread among eukaryotic and prokaryotic cells, represents an important regulator of cellular processes. However, the extent and function of lysine succinylation in Mycobacterium tuberculosis, especially extensively drug-resistant strain, remain elusive. Combining protein/peptide prefractionation, immunoaffinity enrichment, and LC-MS/MS analysis, a total of 686 succinylated proteins and 1739 succinylation sites of M. tuberculosis were identified, representing the first global profiling of M. tuberculosis lysine succinylation. The identified succinylated proteins are involved in a variety of cellular functions such as metabolic processes, transcription, translation, and stress responses and exhibit different subcellular localization via GO, protein interaction network, and other bioinformatic analysis. Notably, proteins involved in protein biosynthesis and carbon metabolism are preferred targets of lysine succinylation. Moreover, two prevalent sequence patterns: EK(suc) and K*****K(suc), can be found around the succinylation sites. There are 109 lysine-succinylated homologues in E. coli, suggesting highly conserved succinylated proteins. Succinylation was found to occur at the active sites predicted by Prosite signature including Rv0946c, indicating that lysine succinylation may affect their activities. There is extensive overlapping between acetylation sites and succinylation sites in M. tuberculosis. Many M. tuberculosis metabolic enzymes and antibiotic resistance proteins were succinylated. This study provides a basis for further characterization of the pathophysiological role of lysine succinylation in M. tuberculosis.

The Role of PARP-1 in Host-Pathogen Interaction and Cellular Stress Responses.

Cells are exposed to diverse stresses; poly(ADP-ribose) polymerase-1 (PARP-1), which processes diverse signals and directs cells to specific fates (survival or death), is a key player in responses to cellular stress. PARP-1 usually uses NAD+ as a donor of ADP-ribose units to regulate the synthesis of poly(ADP-ribose). Over 100 novel substrates of PARP-1 have been identified, most of which are involved in cellular processes such as ribosome biogenesis and transcription regulation. In addition, PARP-1 functions in inflammation by modulating inflammatory-relevant gene expression. PARP-1 also is involved in the tissue damage caused by ischemia/reperfusion conditions. Common inflammatory mediators (inducible nitric oxide synthase, interleukin [IL]-1ß, and tumor necrosis factor-α) are regulated by PARP-1, which helps amplify nuclear factor-κB-mediated inflammation. PARP-1 plays a role in adaptive immunity by modulating the ability of dendritic cells to stimulate T cells. The expression of several genes (such as IL-2 and IL-10) and T-cell proliferation also are controlled by the activation of PARP-1. Inhibition of PARP-1 enzymatic activity attenuates the secretion of proinflammatory cytokines and therefore alleviates autoimmune diseases. PARP inhibitors may represent a new avenue for disease treatment.

Dual Thermosensitive Hydrogels Assembled from the Conserved C-Terminal Domain of Spider Dragline Silk.

Stimuli-responsive hydrogels have great potentials in biomedical and biotechnological applications. Due to the advantages of precise control over molecular weight and being biodegradable, protein-based hydrogels and their applications have been extensively studied. However, protein hydrogels with dual thermosensitive properties are rarely reported. Here we present the first report of dual thermosensitive hydrogels assembled from the conserved C-terminal domain of spider dragline silk. First, we found that recombinant C-terminal domain of major ampullate spidroin 1 (MaSp1) of the spider Nephila clavipes formed hydrogels when cooled to approximately 2 °C or heated to 65 °C. The conformational changes and self-assembly of the recombinant protein were studied to understand the mechanism of the gelation processes using multiple methods. It was proposed that the gelation in the low-temperature regime was dominated by hydrogen bonding and hydrophobic interaction between folded protein molecules, whereas the gelation in the high-temperature regime was due to cross-linking of the exposed hydrophobic patches resulting from partial unfolding of the protein upon heating. More interestingly, genetic fusion of the C-terminal domain to a short repetitive region of N. clavipes MaSp1 resulted in a chimeric protein that formed a hydrogel with significantly improved mechanical properties at low temperatures between 2 and 10 °C. Furthermore, the formation of similar hydrogels was observed for the recombinant C-terminal domains of dragline silk of different spider species, thus demonstrating the conserved ability to form dual thermosensitive hydrogels. These findings may be useful in the design and construction of novel protein hydrogels with tunable multiple thermosensitivity for applications in the future.

Conditional expression of the dominant-negative TGF-ß receptor type II elicits lingual epithelial hyperplasia in transgenic mice.

BACKGROUND: The transforming growth factor-ß (TGF-ß) signaling pathway is generally believed to be a potent inhibitor of proliferation. However, many epithelia lacking the essential Tgfbr2 gene still maintain normal tissue homeostasis. Here, transgenic mice expressing rtTA from the human keratin 14 (K14) promoter were used to generate an inducible dominant-negative TGF-ß receptor type II (Tgfbr2) mutant model, which allowed us to distinguish between the primary and secondary effects of TGF-ß signaling disruption by Doxycycline treatment in K14+ epithelial stem cells. RESULTS: We showed that in mice lacking TGF-ß signaling in K14+ cells, invasive carcinomas developed on the ventral surface of the tip of the tongue, while filiform papillae on the dorsal surface showed different pathological changes from the tip to the posterior of the tongue. In addition, acetylation levels of histone H4 and histone H3 rapidly increased, while pMAPK activity was enhanced and Jagged2 inactivated in lingual epithelia after disruption of TGF-ß signaling. CONCLUSIONS: Our results contribute to the understanding of TGF-ß signaling in regulating homeostasis and carcinogenesis in lingual epithelia.

Image Quality Assessment: Measuring Perceptual Degradation via Distribution Measures in Deep Feature Spaces.

This study aims to develop advanced and training-free full-reference image quality assessment (FR-IQA) models based on deep neural networks. Specifically, we investigate measures that allow us to perceptually compare deep network features and reveal their underlying factors. We find that distribution measures enjoy advanced perceptual awareness and test the Wasserstein distance (WSD), Jensen-Shannon divergence (JSD), and symmetric Kullback-Leibler divergence (SKLD) measures when comparing deep features acquired from various pretrained deep networks, including the Visual Geometry Group (VGG) network, SqueezeNet, MobileNet, and EfficientNet. The proposed FR-IQA models exhibit superior alignment with subjective human evaluations across diverse image quality assessment (IQA) datasets without training, demonstrating the advanced perceptual relevance of distribution measures when comparing deep network features. Additionally, we explore the applicability of deep distribution measures in image super-resolution enhancement tasks, highlighting their potential for guiding perceptual enhancements. The code is available on website. (https://github.com/Buka-Xing/Deep-network-based-distribution-measures-for-full-reference-image-quality-assessment).

The impact of Thiopeptide antibiotics on inflammatory responses in periodontal tissues through the regulation of the MAPK pathway.

Periodontitis is a bacteria-induced inflammatory disease that damages the tissues supporting the teeth, gums, periodontal ligaments, and alveolar bone. Conventional treatments such as surgical procedures, anti-inflammatory drugs, and antibiotics, are somewhat effective; however, these may lead to discomfort and adverse events, thereby affecting patient outcomes. Therefore, this study aimed to find an effective method to prevent the onset of periodontal disease and explore the specific mechanisms of their action.The impact of thiostrepton on Porphyromonas gingivalis and periodontal ligament stem cells was evaluated in an inflammatory microenvironment. In vivo experiments were performed using a mouse periodontitis model to assess the effectiveness of locally applied thiostrepton combined with a silk fibroin hydrogel in impeding periodontitis progression. Thiostrepton exhibited significant antimicrobial effects against Porphyromonas gingivalis and anti-inflammatory properties by regulating the MAPK pathway through DUSP2. Locally applied thiostrepton effectively impeded the progression of periodontitis and reduced tissue damage. Thiostrepton treatment is a promising and tolerable preventive strategy for periodontitis, offering antimicrobial and anti-inflammatory benefits. These findings suggest the potential of thiostrepton as a valuable addition to periodontitis management, warranting further research and clinical exploration to improve patient outcomes.

Graph-Represented Distribution Similarity Index for Full-Reference Image Quality Assessment.

In this paper, we propose a graph-represented image distribution similarity (GRIDS) index for full-reference (FR) image quality assessment (IQA), which can measure the perceptual distance between distorted and reference images by assessing the disparities between their distribution patterns under a graph-based representation. First, we transform the input image into a graph-based representation, which is proven to be a versatile and effective choice for capturing visual perception features. This is achieved through the automatic generation of a vision graph from the given image content, leading to holistic perceptual associations for irregular image regions. Second, to reflect the perceived image distribution, we decompose the undirected graph into cliques and then calculate the product of the potential functions for the cliques to obtain the joint probability distribution of the undirected graph. Finally, we compare the distances between the graph feature distributions of the distorted and reference images at different stages; thus, we combine the distortion distribution measurements derived from different graph model depths to determine the perceived quality of the distorted images. The empirical results obtained from an extensive array of experiments underscore the competitive nature of our proposed method, which achieves performance on par with that of the state-of-the-art methods, demonstrating its exceptional predictive accuracy and ability to maintain consistent and monotonic behaviour in image quality prediction tasks. The source code is publicly available at the following website https://github.com/Land5cape/GRIDS.

US2Mask: Image-to-mask generation learning via a conditional GAN for cardiac ultrasound image segmentation.

Cardiac ultrasound (US) image segmentation is vital for evaluating clinical indices, but it often demands a large dataset and expert annotations, resulting in high costs for deep learning algorithms. To address this, our study presents a framework utilizing artificial intelligence generation technology to produce multi-class RGB masks for cardiac US image segmentation. The proposed approach directly performs semantic segmentation of the heart's main structures in US images from various scanning modes. Additionally, we introduce a novel learning approach based on conditional generative adversarial networks (CGAN) for cardiac US image segmentation, incorporating a conditional input and paired RGB masks. Experimental results from three cardiac US image datasets with diverse scan modes demonstrate that our approach outperforms several state-of-the-art models, showcasing improvements in five commonly used segmentation metrics, with lower noise sensitivity. Source code is available at https://github.com/energy588/US2mask.

An Injectable silk-based hydrogel as a novel biomineralization seedbed for critical-sized bone defect regeneration.

The healing process of critical-sized bone defects urges for a suitable biomineralization environment. However, the unsatisfying repair outcome usually results from a disturbed intricate milieu and the lack of in situ mineralization resources. In this work, we have developed a composite hydrogel that mimics the natural bone healing processes and serves as a seedbed for bone regeneration. The oxidized silk fibroin and fibrin are incorporated as rigid geogrids, and amorphous calcium phosphate (ACP) and platelet-rich plasma serve as the fertilizers and loam, respectively. Encouragingly, the seedbed hydrogel demonstrates excellent mechanical and biomineralization properties as a stable scaffold and promotes vascularized bone regeneration in vivo. Additionally, the seedbed serves a succinate-like function via the PI3K-Akt signaling pathway and subsequently orchestrates the mitochondrial calcium uptake, further converting the exogenous ACP into endogenous ACP. Additionally, the seedbed hydrogel realizes the succession of calcium resources and promotes the evolution of the biotemplate from fibrin to collagen. Therefore, our work has established a novel silk-based hydrogel that functions as an in-situ biomineralization seedbed, providing a new insight for critical-sized bone defect regeneration.

Shape memory and hemostatic silk-laponite scaffold for alveolar bone regeneration after tooth extraction trauma.

Persistent bleeding and the absence of alveolar bone stress following tooth loss can hinder socket healing, complicating future dental implant procedures, and potentially leading to neighboring tooth instability. Therefore, developing materials that promote alveolar bone regeneration and possess both hemostatic and osteogenic properties is crucial for preserving the extraction sites. This study introduces a silk-based laponite composite scaffold material with proficient hemostatic and osteogenic functions, and excellent shape-memory properties for efficient extraction- site filling. In vitro studies research demonstrated that the scaffold's inherent negative charge of the scaffold significantly enhanced blood coagulation and thrombin generation. Moreover, its porous structure and slightly rough inner surface promoted blood cell adhesion and, improved the hemostatic performance. Furthermore, the scaffold facilitated stem cell osteogenic differentiation by activating the TRPM7 channel through the released of magnesium ions. In vivo tests using rat models confirmed its effectiveness in promoting coagulation and mandibular regeneration. Thus, this study proposes a promising approach for post-extraction alveolar bone regenerative repair. The composite scaffold material, with its hemostatic and osteogenic capabilities and shape-memory features, can potentially enhance dental implant success and overall oral health.