MASANet: Multi-Angle Self-Attention Network for Semantic Segmentation of Remote Sensing Images

As an important research direction in the field of pattern recognition, semantic segmentation has become an important method for remote sensing image information extraction. However, due to the loss of global context information, the effect of semantic segmentation is still incomplete or misclassified. In this paper, we propose a multi-angle self-attention network (MASANet) to solve this problem. Specifically, we design a multi-angle self-attention module to enhance global context information, which uses three angles to enhance features and takes the obtained three features as the inputs of self-attention to further extract the global dependencies of features. In addition, atrous spatial pyramid pooling (ASPP) and global average pooling (GAP) further improve the overall performance. Finally, we concatenate the feature maps of different scales obtained in the feature extraction stage with the corresponding feature maps output by ASPP to further extract multi-scale features. The experimental results show that MASANet achieves good segmentation performance on high-resolution remote sensing images. In addition, the comparative experimental results show that MASANet is superior to some state-of-the-art models in terms of some widely used evaluation criteria

The GUA-Speech System Description for CNVSRC Challenge 2023

This study describes our system for Task 1 Single-speaker Visual Speech Recognition (VSR) fixed track in the Chinese Continuous Visual Speech Recognition Challenge (CNVSRC) 2023. Specifically, we use intermediate connectionist temporal classification (Inter CTC) residual modules to relax the conditional independence assumption of CTC in our model. Then we use a bi-transformer decoder to enable the model to capture both past and future contextual information. In addition, we use Chinese characters as the modeling units to improve the recognition accuracy of our model. Finally, we use a recurrent neural network language model (RNNLM) for shallow fusion in the inference stage. Experiments show that our system achieves a character error rate (CER) of 38.09% on the Eval set which reaches a relative CER reduction of 21.63% over the official baseline, and obtains a second place in the challenge.Comment: CNVSRC 2023 Challeng

The role and mechanism of bacterial outer membrane vesicles in the development of periodontitis

Outer membrane vesicles (OMVs), nanoscale structures actively secreted by Gram-negative bacteria, have emerged as critical pathogenic components in periodontitis. While periodontitis has traditionally been associated with biofilm accumulation and bacterial colonization, recent studies highlight that OMVs contribute to disease progression independently of whole-cell bacterial presence. These vesicles are enriched with bioactive cargo such as lipopolysaccharides (LPS), proteases, DNA, and toxins, enabling them to persist in the periodontal microenvironment and interact with host immune and structural cells. They are also actively involved in biofilm formation and contribute to the development of antimicrobial resistance. Despite growing recognition of their involvement in periodontal disease, the extent of OMV interactions with host tissues and polymicrobial communities remains unclear. This review outlines the mechanisms through which OMVs influence inflammation, immune evasion, biofilm stability, and antibiotic resistance in periodontitis. It also highlights current knowledge gaps and concludes with potential therapeutic strategies targeting OMVs for the treatment of periodontitis

Efficacy and safety of neoadjuvant systemic therapy in resectable hepatocellular carcinoma: a Systematic Review and meta-analysis

BackgroundNeoadjuvant systemic therapy has been shown to benefit patients with solid tumors such as breast cancer and colorectal cancer, but its application in hepatocellular carcinoma (HCC) is still in the exploratory stage, with no established effective regimen. This systematic review and meta-analysis aims to investigate the efficacy and safety of neoadjuvant systemic therapy in patients with resectable HCC.MethodsThe clinical trials of resectable HCC neoadjuvant systemic therapy in PubMed, Embase and the Cochrane Library were systematically searched. A meta-analysis was performed using STATA/MP18.0 software, and the effect size was calculated using either a fixed effects model or a random effects model, and 95% confidence intervals (CIs) were calculated. Subgroup analysis was performed according to the neoadjuvant systemic therapy regimen.ResultsThis meta-analysis included 328 patients from 15 studies. In patients with resectable HCC, the pooled pathologic complete response (pCR) rate was 15% (95%CI: 10%–21%), the major pathologic response (MPR) rate was 28% (95%CI: 21%–35%), the incidence of grade 3–4 treatment-related adverse events (TRAEs) was 11% (95% CI: 4%–20%), the objective response rate (ORR) was 27% (95% CI: 20%–35%), the surgical resection rate was 84% (95%CI: 75%–92%), and the delay rate was 0.00% (95% CI: 0%–4%). The results of subgroup analysis showed that the efficacy of targeted therapy combined with immunotherapy is superior to dual ICI (immune checkpoint inhibitor) combination therapy and ICI monotherapy, while the safety of the ICI monotherapy was the highest, superior to the dual ICIs and the targeted therapy combined with immunotherapy.ConclusionNeoadjuvant systemic therapy shows preliminarily beneficial outcomes in resectable HCC treatment. However, future large-scale and multicenter randomized controlled trials are needed to confirm this conclusion.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD4202456225

Safety and effectiveness of indocyanine green fluorescence imaging-guided laparoscopic hepatectomy for hepatic tumor: a systematic review and meta-analysis

IntroductionPrevious clinical investigations have reported inconsistent findings regarding the feasibility of utilizing indocyanine green fluorescence imaging (ICGFI) in laparoscopic liver tumor removal. This meta-analysis aims to comprehensively evaluate the safety and effectiveness of ICGFI in laparoscopic hepatectomy (LH).MethodsA systematic search of pertinent clinical studies published before January 30th, 2023 was conducted in databases including PubMed, Embase, Cochrane, and Web of Science. The search strategy encompassed key terms such as “indocyanine green fluorescence,” “ICG fluorescence,” “laparoscopic hepatectomy,” “hepatectomies,” “liver Neoplasms,” “hepatic cancer,” and “liver tumor.” Additionally, we scrutinized the reference lists of included articles to identify supplementary studies. we assessed the quality of the incorporated studies and extracted clinical data. Meta-analysis was performed using STATA v.17.0 software. Either a fixed-effects or a random-effects model was employed to compute combined effect sizes, accompanied by 95% confidence intervals (CIs), based on varying levels of heterogeneity.ResultsThis meta-analysis encompassed eleven retrospective cohort studies, involving 959 patients in total. Our findings revealed that, in comparison to conventional laparoscopic hepatectomy, patients receiving ICGFI-guided LH exhibited a higher R0 resection rate (OR: 3.96, 95% CI: 1.28, 12.25, I2 = 0.00%, P = 0.778) and a diminished incidence of intraoperative blood transfusion (OR: 0.42, 95% CI: 0.22, 0.81, I2 = 51.1%, P = 0.056). Additionally, they experienced shorter postoperative hospital stays (WMD: −1.07, 95% CI: −2.00, −0.14, I2 = 85.1%, P = 0.000). No statistically significant differences emerged between patients receiving ICGFI-guided LH vs. those undergoing conventional LH in terms of minimal margin width and postoperative complications.ConclusionICGFI-guided LH demonstrates marked superiority over conventional laparoscopic liver tumor resection in achieving R0 resection and reducing intraoperative blood transfusion rates. This technique appears to hold substantial promise. Nonetheless, further studies are needed to explore potential long-term benefits associated with patients undergoing ICGFI-guided LH.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD 42023398195

MyoPS A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore potential of solutions, as well as to provide a benchmark for future research. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. Note that MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/)

Integrating large-scale meta-GWAS and PigGTEx resources to decipher the genetic basis of 232 complex traits in pigs

Understanding the molecular and cellular mechanisms underlying complex traits in pigs is crucial for enhancing genetic gain via artificial selection and utilizing pigs as models for human disease and biology. Here, we conducted comprehensive genome-wide association studies (GWAS) followed by a cross-breed meta-analysis for 232 complex traits and a within-breed meta-analysis for 12 traits, using 28.3 million imputed sequence variants in 70 328 animals across 14 pig breeds. We identified 6878 quantitative trait loci (QTL) for 139 complex traits. Leveraging the Pig Genotype-Tissue Expression resource, we systematically investigated the biological context and regulatory mechanisms behind these trait-QTLs, ultimately prioritizing 14 829 variant-gene-tissue-trait regulatory circuits. For instance, rs344053754 regulates UGT2B31 expression in the liver and intestines, potentially by modulating enhancer activity, ultimately influencing litter weight at weaning in pigs. Furthermore, we observed conservation of certain genetic and regulatory mechanisms underlying complex traits between humans and pigs. Overall, our cross-breed meta-GWAS in pigs provides invaluable resources and novel insights into the genetic regulatory and evolutionary mechanisms of complex traits in mammals.</p

A compendium of genetic regulatory effects across pig tissues

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.</p