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Under the background of the rapid rise of open-source software and the gradual popularization of various software development tools, a large amount of development activity data has been accumulated on the Internet. In the process of using these data to construct data sets, due to their poor traceability and narrow application scope, the quality of data in development activities is not high and the accuracy of analysis results is not high. The application of the hierarchical feature similarity integration method of data sets can make the multi-version and multi-level development smoother and more orderly. In this paper, a hierarchical feature similarity integration method based on hierarchical deep learning is proposed for data sets. Firstly, the dynamic mesh partitioning method is used to divide the sparse and dense regions in the space, which reduces the scale of data detection and shortens the execution time of detection. Then, through the hierarchical deep learning process, the professional knowledge and the distribution information of data attribute value are fused to realize the detection of discrete data in the database. Experimental results show that this method can accurately complete the detection of discrete data in the database in a relatively short time, and has more application advantages than traditional methods.

Background This study investigated the efficacy of radiomics to predict survival outcome for locally advanced breast cancer (LABC) patients and the association of radiomics with tumor heterogeneity and microenvironment. Methods Patients with LABC from 2010 to 2015 were retrospectively reviewed. Radiomics features were extracted from enhanced MRI. We constructed the radiomics score using lasso and assessed its prognostic value. An external validation cohort from The Cancer Imaging Archive was used to assess phenotype reproducibility. Sequencing data from TCGA and our center were applied to reveal genomic landscape of different radiomics score groups. Tumor infiltrating lymphocytes map and bioinformatics methods were applied to evaluate the heterogeneity of tumor microenvironment. Computational histopathology was also applied. Results A total of 278 patients were divided into training cohort and validation cohort. Radiomics score was constructed and significantly associated with disease-free survival (DFS) of the patients in training cohort, validation cohort and external validation cohort ( p  < 0.001, p  = 0.014 and p  = 0.041, respectively). The radiomics-based nomogram showed better predictive performance of DFS compared with TNM model. Distinct gene expression patterns were identified. Immunophenotype and immune cell composition was different in each radiomics score group. The link between radiomics and computational histopathology was revealed. Conclusions The radiomics score could effectively predict prognosis of LABC after neoadjuvant chemotherapy and radiotherapy. Radiomics revealed heterogeneity of tumor cell and tumor microenvironment and holds great potential to facilitate individualized DFS estimation and guide personalized care.

Summary Bacterial vectors, as microscopic living ‘robotic factories’, can be reprogrammed into microscopic living ‘robotic factories’, using a top‐down bioengineering approach to produce and deliver anticancer agents. Most of the current research has focused on bacterial species such as Salmonella typhimurium or Clostridium novyi. However, Escherichia coli Nissle 1917 (EcN) is another promising candidate with probiotic properties. EcN offers increased applicability for cancer treatment with the development of new molecular biology and complete genome sequencing techniques. In this review, we discuss the genetics and physical properties of EcN. We also summarize and analyse recent studies regarding tumour therapy mediated by EcN. Many challenges remain in the development of more promising strategies for combatting cancer with EcN. Escherichia coli Nissle 1917 (EcN) is a promising candidate with probiotic properties for cancer treatment. This review discuss the genetics and physical properties of EcN, and summarize recent studies regarding tumour therapy mediated by EcN.

Under the condition of low signal-to-noise ratio, the target detection performance of radar decreases, which seriously affects the tracking and recognition for the long-range small targets. To solve it, this paper proposes a target detection algorithm using convolutional neural network to process graphically expressed range time series signals. First, the two-dimensional echo signal was processed graphically. Second, the graphical echo signal was detected by the improved convolutional neural network. The simulation results under the condition of low signal-to-noise ratio show that, compared with the multi-pulse accumulation detection method, the detection method based on convolutional neural network proposed in this paper has a higher target detection probability, which reflects the effectiveness of the method proposed in this paper.

ObjectivesTo identify a CT-based radiomics nomogram for survival prediction in patients with resected pancreatic ductal adenocarcinoma (PDAC).MethodsA total of 220 patients (training cohort n = 147; validation cohort n = 73) with PDAC were enrolled. A total of 300 radiomics features were extracted from CT images. And the least absolute shrinkage and selection operator algorithm were applied to select features and develop a radiomics score (Rad-score). The radiomics nomogram was constructed by multivariate regression analysis. Nomogram discrimination, calibration, and clinical usefulness were evaluated. The association of the Rad-score and recurrence pattern in PDAC was evaluated.ResultsThe Rad-score was significantly associated with PDAC patient’s disease-free survival (DFS) and overall survival (OS) (both p < 0.001 in two cohorts). Incorporating the Rad-score into the radiomics nomogram resulted in better performance of the survival prediction than that of the clinical model and TNM staging system. In addition, the radiomics nomogram exhibited good discrimination, calibration, and clinical usefulness in both the training and validation cohorts. There was no association between the Rad-score and recurrence pattern.ConclusionsThe radiomics nomogram integrating the Rad-score and clinical data provided better prognostic prediction in resected PDAC patients, which may hold great potential for guiding personalized care for these patients. The Rad-score was not a predictor of the recurrence pattern in resected PDAC patients.Key Points• The Rad-score developed by CT radiomics features was significantly associated with PDAC patients’ prognosis.• The radiomics nomogram integrating the Rad-score and clinical data has value to permit non-invasive, low-cost, and personalized evaluation of prognosis in PDAC patients.• The radiomics nomogram outperformed clinical model and the TNM staging system in terms of survival estimation.

A critical question in microbial ecology is how the complex microbial communities are formed in natural ecosystems with the existence of thousands different species, thereby performing essential ecosystem functions and maintaining ecosystem stability. Previous studies disentangling the community assembly mechanisms mainly focus on microbial taxa, ignoring the functional traits they carry. Microorganisms drive much of the marine nitrogen (N) cycle, which jointly controls the primary production in the global ocean. However, our understanding of the microbial communities driving the global ocean N cycle remains fragmented. Focusing on “who is doing what, where, and how?”, this study draws a clear picture describing the global biogeography of marine N-cycling microbial communities by utilizing the Tara Oceans shotgun metagenomes. The marine N-cycling communities are highly variable taxonomically but relatively even at the functional trait level, showing clear functional redundancy properties. The functional traits and taxonomic groups are shaped by the same set of geo-environmental factors, among which, depth is the major factor impacting marine N-cycling communities, differentiating mesopelagic from epipelagic communities. Latitudinal diversity gradients and distance-decay relationships are observed for taxonomic groups, but rarely or weakly for functional traits. The composition of functional traits is strongly deterministic as revealed by null model analysis, while a higher degree of stochasticity is observed for taxonomic composition. Integrating multiple lines of evidence, in addition to drawing a biogeographic picture of marine N-cycling communities, this study also demonstrated an essential microbial ecological theory—determinism governs the assembly of microbial communities performing essential biogeochemical processes; the environment selects functional traits rather than taxonomic groups; functional redundancy underlies stochastic taxonomic community assembly. IMPORTANCE A critical question in microbial ecology is how the complex microbial communities are formed in natural ecosystems with the existence of thousands different species, thereby performing essential ecosystem functions and maintaining ecosystem stability. Previous studies disentangling the community assembly mechanisms mainly focus on microbial taxa, ignoring the functional traits they carry. By anchoring microbial functional traits and their carrying taxonomic groups involved in nitrogen cycling processes, this study demonstrated an important mechanism associated with the complex microbial community assembly. Evidence shows that the environment selects functional traits rather than taxonomic groups, and functional redundancy underlies stochastic taxonomic community assembly. This study is expected to provide valuable mechanistic insights into the complex microbial community assembly in both natural and artificial ecosystems.

Identifying reliable biomarkers in peripheral blood is critical for advancing the diagnosis and management of multiple sclerosis (MS), particularly given the invasive nature of cerebrospinal fluid (CSF) sampling. This review explores the role of B cells and immunoglobulins (Igs), particularly IgG and IgM, as biomarkers for MS. B cell oligoclonal bands (OCBs) in the CSF are well-established diagnostic tools, yet peripheral biomarkers remain underdeveloped. Emerging evidence highlights structural and functional variations in immunoglobulin that may correlate with disease activity and progression. A recent novel discovery of blood IgG aggregates in MS patients that fail to bind Protein A reveals promising diagnostic potential and confirms previous findings of the unique features of immunoglobulin G in MS and the potential link between the superantigen Protein A and MS. These aggregates, enriched in IgG1 and IgG3 subclasses, exhibit unique structural properties, including mutations in the framework region 3 (FR3) of IGHV3 genes, and are associated with complement-dependent neuronal apoptosis. Data based on ELISA have demonstrated that IgG aggregates in plasma can distinguish MS patients from healthy controls and other central nervous system (CNS) disorders with high accuracy and differentiate between disease subtypes. This suggests a role for IgG aggregates as non-invasive biomarkers for MS diagnosis and monitoring.

Intrathecal immunoglobulin G (IgG) and oligoclonal bands (OCBs) are the most consistent and characteristic features of Multiple Sclerosis (MS). OCBs in MS are considered products of clonally expanded B cells in the cerebrospinal fluid (CSF), representing the sum of contributions from B cells in the brain. However, large amounts of IgG can be eluted from MS plaques in which lymphocytes are absent, and there is no correlation between levels of plaque-associated IgG and the presence of lymphocytes. It is calculated that it would take 3.2 billion lymphocytes to generate such large amounts of intrathecal IgG (30 mg in 500 ml CSF) in MS patients. Therefore, circulating lymphocytes in CSF could only account for <0.1% of the extra IgG in MS. We analyzed clinical laboratory parameters from sera and CSF of 115 patients including 91 patients with MS and 24 patients with other inflammatory central nervous system (CNS) disorders (IC). We investigated the relationship between oligoclonal bands, IgG antibodies, CSF cells, IgG Index, albumin, and total protein. MS patients have significantly elevated serum concentrations of IgG antibodies, albumin, and total protein, lower levels of lymphocytes, albumin, and total protein in the cerebrospinal fluid, but no difference in CSF IgG concentration compared to those with other inflammatory neurological disorders. Furthermore, in MS there was no linear relationship between the numbers of OCBs, CSF lymphocytes, CSF IgG, and IgG Index, and between serum IgG and serum albumin, but significant correlation between IgG in CSF and serum, and between CSF IgG and CSF albumin. There are unique differences between MS and patients with other inflammatory neurological disorders. Our data suggest that in MS patient (a) B cells and their products in the CSF may not be the sole source of intrathecal IgG; (b) oligoclonal bands may not be the products of single B cell clones in the CSF; and (c) there is a strong connection between serum components in the peripheral circulation and the central nervous system.

Background Phosphorus (P) is one of the most essential macronutrients on the planet, and microorganisms (including bacteria and archaea) play a key role in P cycling in all living things and ecosystems. However, our comprehensive understanding of key P cycling genes (PCGs) and microorganisms (PCMs) as well as their ecological functions remains elusive even with the rapid advancement of metagenome sequencing technologies. One of major challenges is a lack of a comprehensive and accurately annotated P cycling functional gene database. Results In this study, we constructed a well-curated P cycling database (PCycDB) covering 139 gene families and 10 P metabolic processes, including several previously ignored PCGs such as pafA encoding phosphate-insensitive phosphatase, ptxABCD (phosphite-related genes), and novel aepXVWPS genes for 2-aminoethylphosphonate transporters. We achieved an annotation accuracy, positive predictive value (PPV), sensitivity, specificity, and negative predictive value (NPV) of 99.8%, 96.1%, 99.9%, 99.8%, and 99.9%, respectively, for simulated gene datasets. Compared to other orthology databases, PCycDB is more accurate, more comprehensive, and faster to profile the PCGs. We used PCycDB to analyze P cycling microbial communities from representative natural and engineered environments and showed that PCycDB could apply to different environments. Conclusions We demonstrate that PCycDB is a powerful tool for advancing our understanding of microbially driven P cycling in the environment with high coverage, high accuracy, and rapid analysis of metagenome sequencing data. The PCycDB is available at https://github.com/ZengJiaxiong/Phosphorus-cycling-database . AHu1_cCWG4mk_RgzEdzu5A Video Abstract

Background Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) is a good alternative and diagnostic tool for gastrointestinal wall thickening with prior negative endoscopic biopsies. Case presentation Here we reported a case of a 60-years-old woman admitted with atrophic right kidney and hydronephrosis and intermittent postprandial bloating. Esophagogastroduodenoscopy and small bowel endoscopy revealed wall thickening and stenosis at the junction of the descending and inferior duodenum. Biopsies from endoscopy showed no specific findings. EUS-FNA of the thickened duodenal wall was performed and histopathological examinations revealed poorly differentiated carcinoma. Immunohistochemically staining was positive for pan-cytokeratin, CK7, CK20, and weakly positive for GATA-3 and P63. These results were highly suggestive of metastatic urothelial cancer. Conclusions EUS-FNA played an important role in the diagnosis of unexplained gastrointestinal wall thickening and rare metastases to the gastrointestinal wall.