scAnnoX: an R package integrating multiple public tools for single-cell annotation.

Background: Single-cell annotation plays a crucial role in the analysis of single-cell genomics data. Despite the existence of numerous single-cell annotation algorithms, a comprehensive tool for integrating and comparing these algorithms is also lacking. Methods: This study meticulously investigated a plethora of widely adopted single-cell annotation algorithms. Ten single-cell annotation algorithms were selected based on the classification of either reference dataset-dependent or marker gene-dependent approaches. These algorithms included SingleR, Seurat, sciBet, scmap, CHETAH, scSorter, sc.type, cellID, scCATCH, and SCINA. Building upon these algorithms, we developed an R package named scAnnoX for the integration and comparative analysis of single-cell annotation algorithms. Results: The development of the scAnnoX software package provides a cohesive framework for annotating cells in scRNA-seq data, enabling researchers to more efficiently perform comparative analyses among the cell type annotations contained in scRNA-seq datasets. The integrated environment of scAnnoX streamlines the testing, evaluation, and comparison processes among various algorithms. Among the ten annotation tools evaluated, SingleR, Seurat, sciBet, and scSorter emerged as top-performing algorithms in terms of prediction accuracy, with SingleR and sciBet demonstrating particularly superior performance, offering guidance for users. Interested parties can access the scAnnoX package at https://github.com/XQ-hub/scAnnoX.

Discovery of an independent poor-prognosis subtype associated with tertiary lymphoid structures in breast cancer.

Introduction: Tertiary lymphoid structures (TLSs) are ectopic lymphoid formations that arise in non-lymphoid tissues due to chronic inflammation. The pivotal function of TLSs in regulating tumor invasion and metastasis has been established across several cancers, such as lung cancer, liver cancer, and melanoma, with a positive correlation between increased TLS presence and improved prognosis. Nevertheless, the current research about the clinical significance of TLSs in breast cancer remains limited. Methods: In our investigation, we discovered TLS-critical genes that may impact the prognosis of breast cancer patients, and categorized breast cancer into three distinct subtypes based on critical gene expression profiles, each exhibiting substantial differences in prognosis (p = 0.0046, log-rank test), with Cluster 1 having the best prognosis, followed by Cluster 2, and Cluster 3 having the worst prognosis. We explored the impact of the heterogeneity of these subtypes on patient prognosis, the differences in the molecular mechanism, and their responses to drug therapy and immunotherapy. In addition, we designed a machine learning-based classification model, unveiling highly consistent prognostic distinctions in several externally independent cohorts. Results: A notable marker gene CXCL13 was identified in Cluster 3, potentially pivotal in enhancing patient prognosis. At the single-cell resolution, we delved into the adverse prognosis of Cluster 3, observing an enhanced interaction between fibroblasts, myeloid cells, and basal cells, influencing patient prognosis. Furthermore, we identified several significantly upregulated genes (CD46, JAG1, IL6, and IL6R) that may positively correlate with cancer cells' survival and invasive capabilities in this subtype. Discussion: Our study is a robust foundation for precision medicine and personalized therapy, presenting a novel perspective for the contemporary classification of breast cancer.

The Impact of New Entrepreneurial Spirit on Cultivating Entrepreneurial Values and Entrepreneurial Ability of College Students.

The objectives were to deeply study the impact of new entrepreneurial spirit on the cultivation of entrepreneurial values and entrepreneurial ability of college students. First, the influencing factors of college students' entrepreneurial values were analyzed based on new media, entrepreneurial spirit, entrepreneurial values, and other related theories. Second, the corresponding questionnaire was designed and elaborated on the four aspects of college students' entrepreneurial values, namely, entrepreneurial competence, entrepreneurial risk, and entrepreneurial ethics. Finally, the data results of the questionnaire were studied. The results show that in entrepreneurial values of college students, they believe that entrepreneurship is the most important for personal development, with an overall average of 3.86. In the point of view of entrepreneurial competence, they think that independent learning ability plays a significant role in entrepreneurship, with an average of 3.91. In the view of entrepreneurial ethics, they consider that the law is the most crucial during the entrepreneurial, with an average value of 4.35. It means that college students still have certain legal knowledge. After analyzing the results of the questionnaire on entrepreneurial factors, it is found that college students lack social experience and have a low ability to take risks. Meanwhile, entertainment is the first choice for college students on new media platforms, and the time of viewing is more than 5 h/day. Therefore, the influence of new entrepreneurial spirit is researched on the cultivation of entrepreneurial values and entrepreneurial ability of college students, which provides a new theoretical basis and optimization direction for entrepreneurial education in universities in the future.

Body shape and stable employment opportunity analysis of China's nonagricultural labor market.

Based on the integrated data of the China General Social Survey (CGSS) from 2010 to 2017, this study observes that body shape - being overweight or underweight - is important for labor market outcomes. Body shape significantly affects the employment opportunities of Chinese individuals, and this effect differs by gender and across the occupational hierarchy. Women face both slim premium and obesity penalty effects. Slim women, those with normal and lower but not excessively lower body weight, are more likely to gain long-term employment contracts in the labor market, while the opposite is observed for overweight individuals. The relationship between women's body shape and employment opportunities also varies by occupation. The obesity penalty is more pronounced in occupations with a higher International Socio-Economic Index (ISEI), while the slim premium is more evident in occupations with a low ISEI. The results suggest that the Chinese labor market is highly demanding regarding women's figures, while it is relatively tolerant of men's figures. By mechanism analysis, health capital is found to be the leading cause of the body shape effect. In addition, socialization is also a possible pathway of action. This paper has extended implications for the study of stature and employment stability, enriching the empirical research on labor market discrimination.

Influence of Entrepreneurial Psychology and Spirit on the Cultivation of Students' Entrepreneurial Values and Ability Under the New Media.

This paper studies the influence of entrepreneurial spirit on college students' entrepreneurial ability and entrepreneurial values. Firstly, the impact of entrepreneurial psychology and entrepreneurial spirit on entrepreneurial values is analyzed. Secondly, the role of entrepreneurial values in Innovation and Entrepreneurship Education (IEE) is analyzed and summarized under new media. Then, based on entrepreneurial psychology and entrepreneurial spirit, a Questionnaire Survey (QS) is designed to investigate the entrepreneurial values of students in one university in Shaanxi Province, China. The QS analysis suggests that most respondents hold a positive attitude, and their attitude is on the rise. The QS results of "Reasons for entrepreneurial failure" show that 40.37, 31.9, 25.98, and 11.75% of respondents think they lack financial support, business skills, ability, and understanding of policies and laws, respectively. The QS results of "What factors influence entrepreneurial values?" reveal that 39.43% of the respondents chose the "Models of successful entrepreneurs," successful entrepreneur models can effectively encourage students to receive IEE, followed by 28.94% who choose "Achieve their own life goals.". In terms of "Solutions against entrepreneurial risks," nearly 70% of the students have chosen the negative options. In terms of "Which is the most important entrepreneurial quality?", more students choose entrepreneurial values rather than entrepreneurial quality, proving that students generally lack entrepreneurial values. Given these problems, corresponding countermeasures are put forward to strengthen entrepreneurial psychology and IEE, in an attempt to cultivate college students' entrepreneurial values and entrepreneurial ability under the new media. The results provide some data support for the impact of college students' entrepreneurial values and entrepreneurial ability.

Body Mass and Income: Gender and Occupational Differences.

This paper aims to examine the influence of body shape on income, which varies with gender and occupational structure in China. The data were obtained from the CGSS (Chinese General Social Survey) 2010-2017 Survey. The overall finding in this paper is that women and men face different body shape-income effects. For females, the obesity penalty is significant and is reinforced with increasing occupational rank. For men, the thinness penalty (or weight premium) is enhanced as the occupational class decreases. Body shape-income gaps are mainly caused by the occupational structure. Twenty-nine percent of the income gap between overweight and average weight women can be explained by the obesity penalty, 37% of the income gap between overweight and average weight men can be interpreted by the weight premium, and 11% of the gap between underweight and normal weight men can be explained by the thinness penalty. The findings also suggest that the effect of body shape on income consists of two pathways: body shape affects health capital and socialization, and therefore income. Healthy lifestyles and scientific employment concepts should be promoted, and measures to close the gender gap should be implemented.

Berberine protects Kawasaki disease-induced human coronary artery endothelial cells dysfunction by inhibiting of oxidative and endoplasmic reticulum stress.

Kawasaki disease (KD) is an acute febrile illness characterized by systemic vasculitis especially in coronary arteries. Berberine (BBR) shows several beneficial effects on cardiovascular system. The present study is to investigate whether BBR exerts protective effect against KD-induced damage of human coronary artery endothelial cell (HCAECs) and the underlying mechanisms. HCAECs exposed to medium with 15% serum from KD patients or healthy volunteers for 24 h. Stimulated HCAECs were treated with vehicle (without BBR) and BBR (20 µM) for 24 h, the cell apoptosis, cell cycle, induction of intracellular reactive oxygen species (ROS) and protein expression were examined by flow cytometry and western blot. The KD-induced differentially expressed proteins in HCAECs were determined by quantitative proteomics. BBR inhibited HCAECs from apoptosis and arrested cell cycle at G0/G1 stage. BBR protected HCAECs from injury by inhibiting expression of THBD, vWF and EDN1. Bioinformatics analysis suggested that the oxidative and ER stress were involved in KD-induced damage in HCAECs. ROS production and the protein expression of ATF4, p-EIF2α, p-PERK, XBP1, p-IRE1, HSP90B1, HSPG2, DNAJC3, P4HB and VCP were increased by serum from KD patients and decreased by BBR treatment. BBR exerts its protective effects on KD-induced damage of HCAECs through its inhibitory effects on oxidative and ER stress indicating BBR as a therapeutic candidate for KD.

A protein identification algorithm for tandem mass spectrometry by incorporating the abundance of mRNA into a binomial probability scoring model.

Peptide-spectrum matches (PSM) scoring between the experimental and theoretical spectrum is a key step in the identification of proteins using mass spectrometry (MS)-based proteomics analyses. Efficient protein identification using MS/MS data remains a challenge. The strategy of using RNA-seq data increases the number of proteins identified by re-constructing the custom search database and integrating mRNA abundance into the false discovery rate of post-PSM. However, this process lacks an algorithm that can allow the incorporation of mRNA abundance into the key scoring model of PSM. Therefore, we developed a novel PSM scoring model, which incorporates mRNA abundance for improved peptide and protein identification. In the new algorithm, abundance information of mRNA was transformed to the prior probability of protein identification and integrated to re-score in PSM using the binomial probability distribution model. Compared with other algorithms using five MS/MS datasets, the results showed that the least improvement ratios of peptide and protein groups were 3.39%-9.79% and 0.48%-8.16% in different datasets (human, rat, zebrafish, yeast, and Arabidopsis thaliana). The new strategy offers an effective solution for MS-based identification of peptides and proteins. SIGNIFICANCE: The new algorithm identifies proteins by quantifying mRNA abundance (FPKM) and incorporating it into a scoring model for peptide-spectrum matches. It is important to improve peptide and protein identification from MS/MS datasets in proteomics research.

ISOdb: A Comprehensive Database of Full-Length Isoforms Generated by Iso-Seq.

The accurate landscape of transcript isoforms plays an important role in the understanding of gene function and gene regulation. However, building complete transcripts is very challenging for short reads generated using next-generation sequencing. Fortunately, isoform sequencing (Iso-Seq) using single-molecule sequencing technologies, such as PacBio SMRT, provides long reads spanning entire transcript isoforms which do not require assembly. Therefore, we have developed ISOdb, a comprehensive resource database for hosting and carrying out an in-depth analysis of Iso-Seq datasets and visualising the full-length transcript isoforms. The current version of ISOdb has collected 93 publicly available Iso-Seq samples from eight species and presents the samples in two levels: (1) sample level, including metainformation, long read distribution, isoform numbers, and alternative splicing (AS) events of each sample; (2) gene level, including the total isoforms, novel isoform number, novel AS number, and isoform visualisation of each gene. In addition, ISOdb provides a user interface in the website for uploading sample information to facilitate the collection and analysis of researchers' datasets. Currently, ISOdb is the first repository that offers comprehensive resources and convenient public access for hosting, analysing, and visualising Iso-Seq data, which is freely available.

New stabilization for dynamical system with two additive time-varying delays.

This paper provides a new delay-dependent stabilization criterion for systems with two additive time-varying delays. The novel functional is constructed, a tighter upper bound of the derivative of the Lyapunov functional is obtained. These results have advantages over some existing ones because the combination of the delay decomposition technique and the reciprocally convex approach. Two examples are provided to demonstrate the less conservatism and effectiveness of the results in this paper.

A novel approach for enriching cancer stem cells from the human SW-13 adrenocortical carcinoma cell line.

The present study was undertaken to develop a new method for enriching cancer stem cells (CSCs) from the human adrenal cortical carcinoma (ACC) cell line SW-13. Given that the existence of CSCs in ACC causes resistance to conventional chemotherapies, treatment with cyclophosphamide was used for in vivo selection of CSCs in a BALB/c nude mouse tumor xenograft model established using the ACC cell line SW-13. The characteristics of CSCs in three generations of tumor xenografts were assessed for single-cell colony formation, flat colony formation, and cell sphere formation in serum-free suspension culture. The formation rates of single-cell colonies, flat colonies, and cell spheres were significantly higher for tumor xenograft cells treated with cyclophosphamide than for untreated engrafted tumor cells. Flow cytometry to examine expression of the CSC markers C-X-C chemokine receptor type-4 (CXCR4; CD184) and ATP-binding cassette sub-family G member-2 (ABCG2; CDw338) revealed markedly higher levels of CXCR4 and ABCG2 in cyclophosphamide-treated xenograft tumor cells compared to untreated tumor cells. Together, these results indicate that cyclophosphamide treatment of tumor xenograft cells caused enrichment of CSCs with a strong capability for self-renewal and proliferation. In this method, the administration of cyclophosphamide selectively kills cancer cells without toxicity to CSCs and thereby provides a practical approach for achieving the enrichment of CSCs in ACC.

Dispec: a novel peptide scoring algorithm based on peptide matching discriminability.

Identifying peptides from the fragmentation spectra is a fundamental step in mass spectrometry (MS) data processing. The significance (discriminability) of every peak varies, providing additional information for potentially enhancing the identification sensitivity and the correct match rate. However this important information was not considered in previous algorithms. Here we presented a novel method based on Peptide Matching Discriminability (PMD), in which the PMD information of every peak reflects the discriminability of candidate peptides. In addition, we developed a novel peptide scoring algorithm Dispec based on PMD, by taking three aspects of discriminability into consideration: PMD, intensity discriminability and m/z error discriminability. Compared with Mascot and Sequest, Dispec identified remarkably more peptides from three experimental datasets with the same confidence at 1% PSM-level FDR. Dispec is also robust and versatile for various datasets obtained on different instruments. The concept of discriminability enhances the peptide identification and thus may contribute largely to the proteome studies. As an open-source program, Dispec is freely available at http://bioinformatics.jnu.edu.cn/software/dispec/.

Binomial probability distribution model-based protein identification algorithm for tandem mass spectrometry utilizing peak intensity information.

Mass spectrometry has become one of the most important technologies in proteomic analysis. Tandem mass spectrometry (LC-MS/MS) is a major tool for the analysis of peptide mixtures from protein samples. The key step of MS data processing is the identification of peptides from experimental spectra by searching public sequence databases. Although a number of algorithms to identify peptides from MS/MS data have been already proposed, e.g. Sequest, OMSSA, X!Tandem, Mascot, etc., they are mainly based on statistical models considering only peak-matches between experimental and theoretical spectra, but not peak intensity information. Moreover, different algorithms gave different results from the same MS data, implying their probable incompleteness and questionable reproducibility. We developed a novel peptide identification algorithm, ProVerB, based on a binomial probability distribution model of protein tandem mass spectrometry combined with a new scoring function, making full use of peak intensity information and, thus, enhancing the ability of identification. Compared with Mascot, Sequest, and SQID, ProVerB identified significantly more peptides from LC-MS/MS data sets than the current algorithms at 1% False Discovery Rate (FDR) and provided more confident peptide identifications. ProVerB is also compatible with various platforms and experimental data sets, showing its robustness and versatility. The open-source program ProVerB is available at http://bioinformatics.jnu.edu.cn/software/proverb/ .

Cardioviral RNA structure logo analysis: entropy, correlations, and prediction.

In recent years, there has been an increased number of sequenced RNAs leading to the development of new RNA databases. Thus, predicting RNA structure from multiple alignments is an important issue to understand its function. Since RNA secondary structures are often conserved in evolution, developing methods to identify covariate sites in an alignment can be essential for discovering structural elements. Structure Logo is a technique established on the basis of entropy and mutual information measured to analyze RNA sequences from an alignment. We proposed an efficient Structure Logo approach to analyze conservations and correlations in a set of Cardioviral RNA sequences. The entropy and mutual information content were measured to examine the conservations and correlations, respectively. The conserved secondary structure motifs were predicted on the basis of the conservation and correlation analyses. Our predictive motifs were similar to the ones observed in the viral RNA structure database, and the correlations between bases also corresponded to the secondary structure in the database.