Information quality of the Jordan Institute for Families web site

The purpose of this study was to evaluate the information quality of the Web site for the Jordan Institute for Families (URL of the web site: http://ssw.unc.edu/jif/) -- a research, training and technical assistance arm of the School of Social Work at the University of North Carolina at Chapel Hill. A set of information quality indicators, including accuracy, timeliness, easy understanding, organization, consistent representation and easy navigation, were divided into four categories and evaluated in this study. A survey was conducted to test users' performance of some information-finding tasks and to collect users' assessment of the JIF Web site. A total of 25 subjects voluntarily participated in this survey. The results of the study revealed some significant strengths and weaknesses in the design of the JIF site regarding the information quality. With the findings of the study, a list of recommended changes and suggestions were provided to improve the design of the site, make the information more clearly organized and presented, and make it easier for users to locate information on the site

LDCSF: Local depth convolution-based Swim framework for classifying multi-label histopathology images

Histopathological images are the gold standard for diagnosing liver cancer. However, the accuracy of fully digital diagnosis in computational pathology needs to be improved. In this paper, in order to solve the problem of multi-label and low classification accuracy of histopathology images, we propose a locally deep convolutional Swim framework (LDCSF) to classify multi-label histopathology images. In order to be able to provide local field of view diagnostic results, we propose the LDCSF model, which consists of a Swin transformer module, a local depth convolution (LDC) module, a feature reconstruction (FR) module, and a ResNet module. The Swin transformer module reduces the amount of computation generated by the attention mechanism by limiting the attention to each window. The LDC then reconstructs the attention map and performs convolution operations in multiple channels, passing the resulting feature map to the next layer. The FR module uses the corresponding weight coefficient vectors obtained from the channels to dot product with the original feature map vector matrix to generate representative feature maps. Finally, the residual network undertakes the final classification task. As a result, the classification accuracy of LDCSF for interstitial area, necrosis, non-tumor and tumor reached 0.9460, 0.9960, 0.9808, 0.9847, respectively. Finally, we use the results of multi-label pathological image classification to calculate the tumor-to-stromal ratio, which lays the foundation for the analysis of the microenvironment of liver cancer histopathological images. Second, we released a multilabel histopathology image of liver cancer, our code and data are available at https://github.com/panliangrui/LSF.Comment: Submitted to BIBM202

Using Software Dependency to Bug Prediction

Software maintenance, especially bug prediction, plays an important role in evaluating software quality and balancing development costs. This study attempts to use several quantitative network metrics to explore their relationships with bug prediction in terms of software dependency. Our work consists of four main steps. First, we constructed software dependency networks regarding five dependency scenes at the class-level granularity. Second, we used a set of nine representative and commonly used metrics—namely, centrality, degree, PageRank, and HITS, as well as modularity—to quantify the importance of each class. Third, we identified how these metrics were related to the proneness and severity of fixed bugs in Tomcat and Ant and determined the extent to which they were related. Finally, the significant metrics were considered as predictors for bug proneness and severity. The result suggests that there is a statistically significant relationship between class’s importance and bug prediction. Furthermore, betweenness centrality and out-degree metric yield an impressive accuracy for bug prediction and test prioritization. The best accuracy of our prediction for bug proneness and bug severity is up to 54.7% and 66.7% (top 50, Tomcat) and 63.8% and 48.7% (top 100, Ant), respectively, within these two cases

Effect of straw retention and mineral fertilization on P speciation and P-transformation microorganisms in water extractable colloids of a Vertisol

Water extractable colloids (WECs) serve as crucial micro particulate components in soils, playing a vital role in the cycling and potential bioavailability of soil phosphorus (P). Yet, the underlying information regarding soil P species and P-transformation microorganisms at the microparticle scale under long-term straw retention and mineral fertilization is barely known. Here, a fixed field experiment (~13 years) in a Vertisol was performed to explore the impacts of straw retention and mineral fertilization on inorganic P, organic P and P-transformation microorganisms in bulk soils and WECs by sequential extraction procedure, P K-edge X-ray absorptions near-edge structure (XANES), 31P nuclear magnetic resonance (NMR), and metagenomics analysis. In bulk soil, mineral fertilization led to increases in the levels of total P, available P, acid phosphatase (ACP), high-activity inorganic P fractions (Ca2-P, Ca8-P, Al-P, and Fe-P) and organic P (orthophosphate monoesters and orthophosphate diesters), but significantly decreased the abundances of P cycling genes including P mineralization, P-starvation response regulation, P-uptake and transport by decreasing soil pH and increasing P in bulk soil. Straw retention had no significant effects on P species and P-transformation microorganisms in bulk soils but brought increases for organic carbon, total P, available P concentrations in WECs. Furthermore, straw retention caused greater change in P cycling genes between WECs and bulk soils compared with the effect of mineral fertilization. The abundances of phoD gene and phoD-harbouring Proteobacteria in WECs increased significantly under straw retention, suggesting that the P mineralizing capacity increased. Thus, straw retention could potentially accelerate the turnover, mobility and availability of P by increasing the nutrient contents and P mineralizing capacity in microscopic colloidal scale

Multi-Head Attention Mechanism Learning for Cancer New Subtypes and Treatment Based on Cancer Multi-Omics Data

Due to the high heterogeneity and clinical characteristics of cancer, there are significant differences in multi-omics data and clinical features among subtypes of different cancers. Therefore, the identification and discovery of cancer subtypes are crucial for the diagnosis, treatment, and prognosis of cancer. In this study, we proposed a generalization framework based on attention mechanisms for unsupervised contrastive learning (AMUCL) to analyze cancer multi-omics data for the identification and characterization of cancer subtypes. AMUCL framework includes a unsupervised multi-head attention mechanism, which deeply extracts multi-omics data features. Importantly, a decoupled contrastive learning model (DMACL) based on a multi-head attention mechanism is proposed to learn multi-omics data features and clusters and identify new cancer subtypes. This unsupervised contrastive learning method clusters subtypes by calculating the similarity between samples in the feature space and sample space of multi-omics data. Compared to 11 other deep learning models, the DMACL model achieved a C-index of 0.002, a Silhouette score of 0.801, and a Davies Bouldin Score of 0.38 on a single-cell multi-omics dataset. On a cancer multi-omics dataset, the DMACL model obtained a C-index of 0.016, a Silhouette score of 0.688, and a Davies Bouldin Score of 0.46, and obtained the most reliable cancer subtype clustering results for each type of cancer. Finally, we used the DMACL model in the AMUCL framework to reveal six cancer subtypes of AML. By analyzing the GO functional enrichment, subtype-specific biological functions, and GSEA of AML, we further enhanced the interpretability of cancer subtype analysis based on the generalizable AMUCL framework