Weighted Change-Point Method for Detecting Differential Gene Expression in Breast Cancer Microarray Data

In previous work, we proposed a method for detecting differential gene expression based on change-point of expression profile. This non-parametric change-point method gave promising result in both simulation study and public dataset experiment. However, the performance is still limited by the less sensitiveness to the right bound and the statistical significance of the statistics has not been fully explored. To overcome the insensitiveness to the right bound we modified the original method by adding a weight function to the Dn statistic. Simulation study showed that the weighted change-point statistics method is significantly better than the original NPCPS in terms of ROC, false positive rate, as well as change-point estimate. The mean absolute error of the estimated change-point by weighted change-point method was 0.03, reduced by more than 50% comparing with the original 0.06, and the mean FPR was reduced by more than 55%. Experiment on microarray Dataset I resulted in 3974 differentially expressed genes out of total 5293 genes; experiment on microarray Dataset II resulted in 9983 differentially expressed genes among total 12576 genes. In summary, the method proposed here is an effective modification to the previous method especially when only a small subset of cancer samples has DGE

Identification and Function Prediction of Novel MicroRNAs in Laoshan Dairy Goats

MicroRNAs are a class of endogenous small RNAs that play important roles in post-transcriptional gene regulation by directing degradation of mRNAs or facilitating repression of target gene translation. In this study, three small RNA cDNA libraries from the mammary gland tissues of Laoshan dairy goats (Capra hircus) were constructed and sequenced, individually. Through Solexa high-throughput sequencing and bioinformatics analysis, we obtained 50 presumptive novel miRNAs candidates, and 55,448 putative target genes were predicted. GO annotations and KEGG pathway analyses showed the majority of target genes were involved in various biological processes and metabolic pathways. Our results discovered more information about the regulation network between miRNAs and mRNAs and paved a foundation for the molecular genetics of mammary gland development in goats

Non-Parametric Change-Point Method for Differential Gene Expression Detection

We proposed a non-parametric method, named Non-Parametric Change Point Statistic (NPCPS for short), by using a single equation for detecting differential gene expression (DGE) in microarray data. NPCPS is based on the change point theory to provide effective DGE detecting ability.NPCPS used the data distribution of the normal samples as input, and detects DGE in the cancer samples by locating the change point of gene expression profile. An estimate of the change point position generated by NPCPS enables the identification of the samples containing DGE. Monte Carlo simulation and ROC study were applied to examine the detecting accuracy of NPCPS, and the experiment on real microarray data of breast cancer was carried out to compare NPCPS with other methods.Simulation study indicated that NPCPS was more effective for detecting DGE in cancer subset compared with five parametric methods and one non-parametric method. When there were more than 8 cancer samples containing DGE, the type I error of NPCPS was below 0.01. Experiment results showed both good accuracy and reliability of NPCPS. Out of the 30 top genes ranked by using NPCPS, 16 genes were reported as relevant to cancer. Correlations between the detecting result of NPCPS and the compared methods were less than 0.05, while between the other methods the values were from 0.20 to 0.84. This indicates that NPCPS is working on different features and thus provides DGE identification from a distinct perspective comparing with the other mean or median based methods

Stakeholders, Green Manufacturing, and Practice Performance: Empirical Evidence from Chinese Fashion Businesses

This study explores the relationship among stakeholders, green manufacturing, and practice performance in the fashion business in China and focuses on assisting companies to enhance environmental awareness and green manufacturing practices. We collect research data by developing questionnaires for various Chinese enterprises. A five-point Likert scale is adopted to enable respondents to indicate the extent to which they agree with the items. Through tests and analyses, the questionnaire is validated as reliable, the structural equation model has a good fitting degree, and hypotheses are proved true. Specifically, corporate stakeholders have a significant positive impact on green manufacturing and practice performance, and green manufacturing has a significant positive impact on practice performance in the context of Chinese fashion businesses. Moreover, corporate stakeholders can have a positive impact on practice performance through green manufacturing. We also propose some policy implications, including implementing compulsive policies and regulations and encouraging and establishing preferential policies, such as tax concessions. Moreover, enterprises should actively strive to improve green manufacturing technology and management level to ensure the smooth implementation of green manufacturing practices. To retain sustained earnings and development, green manufacturing should be the bottom line of involved firms. We also emphasize that the importance of corporate stakeholders should be promoted in consideration of enterprises’ practice performance and future development

CXCR5⁺ follicular cytotoxic T cells control viral infection in B cell follicles

During unresolved infections, some viruses escape immunological control and establish a persistant reservoir in certain cell types, such as human immunodeficiency virus (HIV), which persists in follicular helper T cells (TFH cells), and Epstein-Barr virus (EBV), which persists in B cells. Here we identified a specialized group of cytotoxic T cells (TC cells) that expressed the chemokine receptor CXCR5, selectively entered B cell follicles and eradicated infected TFH cells and B cells. The differentiation of these cells, which we have called 'follicular cytotoxic T cells' (TFC cells), required the transcription factors Bcl6, E2A and TCF-1 but was inhibited by the transcriptional regulators Blimp1, Id2 and Id3. Blimp1 and E2A directly regulated Cxcr5 expression and, together with Bcl6 and TCF-1, formed a transcriptional circuit that guided TFC cell development. The identification of TFC cells has far-reaching implications for the development of strategies to control infections that target B cells and TFH cells and to treat B cell–derived malignancies

Palladium-catalyzed borylation of aryl (pseudo)halides and its applications in biaryl synthesis

Abstract A facile and efficient palladium-catalyzed borylation of aryl (pseudo)halides at room temperature has been developed. Arylboronic esters were expeditiously assembled in good yields and with a broad substrate scope and good functional group compatibility. This approach has been successfully applied to the one-pot two-step borylation/Suzuki–Miyaura cross-coupling reaction, providing a concise access to biaryl compounds from readily available aryl halides. Furthermore, a parallel synthesis of biaryl analogs is accomplished at room temperature using the strategy, which enhances the practical usefulness of this method

Patterns of information literacy and their predictors among emergency department nurses: a latent profile analysis based on the person-context interaction theory

Abstract Background With the development of information technology, information has been an important resource in clinical medicine, particularly within the emergency department. Given its role in patient rescue, the emergency department demands a high level of information literacy from nurses to effectively collect, analyze, and apply information due to the urgency and complexity of emergency nursing work. Although prior studies have investigated the information literacy of nursing staff, little has been undertaken in examining the patterns of information literacy and their predictors among emergency department nurses. Aim To clarify the subtypes of information literacy among nurses in the emergency department and explore the factors affecting profile membership. Methods A cross-sectional study was conducted among a convenience sample of 2490 nurses in the emergency department from April to June 2023. The clinical nurses completed the online self-report questionnaires including the general demographic questionnaire, information literacy scale, self-efficacy scale and social support scale. Data analyses involved the latent profile analysis, variance analysis, Chi-square tests and multivariate logistic regression. Results Four latent profiles were identified: ‘Low information literacy (Class 1)’, ‘Moderate information knowledge (Class 2)’, ‘High information knowledge and support (Class 3)’ and ‘High information literacy (Class 4)’, accounting for 20.14%, 42.11%, 23.36% and 14.39%, respectively. Each profile displayed unique characteristics representative of different information literacy patterns. Age, years of work, place of residence, hospital grade, title, professional knowledge, using databases, reading medical literature, participating in information literacy training, self-efficacy, and social support significantly predicted information literacy profile membership. Conclusions Information literacy exhibits different classification features among emergency department nurses, and over half of the nurses surveyed were at the lower or middle level. Identifying sociodemographic and internal-external predictors of profile membership can aid in developing targeted interventions tailored to the needs of emergency department nurses. Nursing managers should actively pay attention to nurses with low information literacy and provide support to improve their information literacy level. Relevance to clinical practice Insights from the current study of the latent profile analysis are beneficial to hospital managers in understanding the different types of emergency department nurses’ information literacy. These insights serve as a reference for managers to enhance nurses’ information literacy levels

Intelligent Device for Harvesting the Vibration Energy of the Automobile Exhaust with a Piezoelectric Generator

With increasing consumption of energy and increasing environmental pollution, research on capturing the vibration energy lost during transportation and vehicle driving is growing rapidly. There is a large amount of vibration energy in the automobile exhaust system that can be recycled. This paper proposes a self-powered intelligent device (SPID) using a piezoelectric energy generator. The SPID includes a piezoelectric generator and sensor unit, and the generator is installed at the end of the automobile exhaust system. The generator adopts a parallel structure of four piezoelectric power generation units, and the sensing unit comprises light-emitting diode warning lights or low-power sensors. A simulated excitation experiment verifies the working state and peak power of the piezoelectric generator unit, which can achieve 23.4 μW peak power. The self-power supply and signal monitoring functions of the intelligent device are verified in experiments conducted for driving light-emitting diode lights and low-power sensors. The device is expected to play a crucial role in the field of intelligent driving and automobile intelligence