Precise reconstruction of the entire mouse kidney at cellular resolution.

The kidney is an important organ for excreting metabolic waste and maintaining the stability of the body's internal environment. The renal function involves multiple complex and fine structures in the whole kidney, and any change in these structures may cause impaired nephric function. Consequently, achieving three-dimensional (3D) reconstruction of the entire kidney at a single-cell resolution is of significant importance for understanding the kidney's structural characteristics and exploring the pathogenesis of kidney diseases. In this paper, we propose a pipeline from sample preparation to optical microscopic imaging of the entire kidney, followed by data processing for 3D reconstruction of the whole mouse kidney. We employed transgenic fluorescent labeling and propidium iodide (PI) labeling to obtain detailed information about the vascular structure and cytoarchitecture of the kidney. Subsequently, the entire mouse kidney was imaged at submicron-resolution using high-definition fluorescent micro-optical sectioning tomography (HD-fMOST). Finally, we reconstructed the structures of interest through various data processing methods on the original images. This included detecting glomeruli throughout the entire kidney, as well as the segmentation and visualization of the renal arteries, veins, and three different types of nephrons. Our method provides a powerful tool for studying the renal microstructure and its spatial relationships throughout the entire kidney.

ZnO@C Coated Cellulose-Based Separators Control Lithium Deposition Direction to Stable Lithium Metal Batteries.

Li metal anodes have attracted attention due to their high specific capacity and low electrochemical potential. Nevertheless, the uncontrolled growth of Li dendrites hinders the practical application of Li metal batteries. Although the various approaches have made performance improvements, safety hazards still exist since Li dendrites are still growing along the anode to the separator during the continuous plating/stripping process. Herein, a straightforward method is proposed to achieve stable Li metal batteries with directional growth control by using a functional ZnO@C/cellulose membrane as a separator. The abundant pore structure and functional groups of biomass cellulose enhance the Li-ion transport and interface compatibility. The ZnO transforms in situ to form a Li-Zn alloy layer which is uniformly coated to the separator to direct uniform ion concentration polarization and charge distribution polarization, control the growth direction of Li, significantly improve the cycling stability, and promote the reversibility of the Li plating/exfoliation process. As a result, the symmetric cell exhibits an extreme lifetime of more than 4500 h and low polarization at 3 mA cm-2 . The cycling performance of the Li||LiFePO4 full cell reaches a capacity retention of 98% after 270 cycles at a mass loading of 10 mg cm-2 .

A diabetes prediction model based on Boruta feature selection and ensemble learning.

BACKGROUND AND OBJECTIVE: As a common chronic disease, diabetes is called the "second killer" among modern diseases. Currently, there is no medical cure for diabetes. We can only rely on medication for auxiliary treatment. However, many diabetic patients still die each year. In addition, a considerable number of people do not pay attention to their physical health or opt out of treatment due to lack of money, which eventually leads to various complications. Therefore, diagnosing diabetes at an early stage and intervening early is necessary; thus, developing an early detection method for diabetes is essential. METHODS: In this study, a diabetes prediction model based on Boruta feature selection and ensemble learning is proposed. The model contains the use of Boruta feature selection, the extraction of salient features from datasets, the use of the K-Means++ algorithm for unsupervised clustering of data and stacking of an ensemble learning method for classification. It has been validated on a diabetes dataset. RESULTS: The experiments were performed on the PIMA Indian diabetes dataset. The model was evaluated by accuracy, precision and F1 index. The obtained results show that the accuracy rate of the model reaches 98% and achieves good results. CONCLUSION: Compared with other diabetes prediction models, this model achieved better results, and the obtained results indicate that this model is superior to other models in diabetes prediction and has better performance.

AngClust: Angle Feature-Based Clustering for Short Time Series Gene Expression Profiles.

When clustering gene expression, it is expected that correlation coefficients of genes in the same clusters are high, and that gene ontology (GO) enrichment analysis of most clusters will be significant. However, existing short-term gene expression clustering algorithms have limitations. To address this problem, we proposed a novel clustering process based on angular features for short-term gene expression. Our method (named AngClust) uses angular features to indicate the change of trend in gene expression levels at two neighboring time points. The changes of angles at multiple time points reflects the change of trend of the overall expression levels. Such changes are used to measure whether the expression trends of different genes are similar. To obtain functionally significant clusters from the clustering results, we evaluated numbers of genes in clusters, average correlation coefficient, fluctuation, and their correlation with GO term enrichment. The efficacy of AngClust outperform two other measures, Euclidean distance (ED) and dynamic time warping of correlation (DTW), on a dataset of yeast gene expression. The ratios of GO and pathway term-enriched of clusters of AngClust is higher than or equal to that of STEM and TMixClust on human, mouse, and yeast time series of gene expression.

webTDat: A Web-Based, Real-Time, 3D Visualization Framework for Mesoscopic Whole-Brain Images.

The popularity of mesoscopic whole-brain imaging techniques has increased dramatically, but these techniques generate teravoxel-sized volumetric image data. Visualizing or interacting with these massive data is both necessary and essential in the bioimage analysis pipeline; however, due to their size, researchers have difficulty using typical computers to process them. The existing solutions do not consider applying web visualization and three-dimensional (3D) volume rendering methods simultaneously to reduce the number of data copy operations and provide a better way to visualize 3D structures in bioimage data. Here, we propose webTDat, an open-source, web-based, real-time 3D visualization framework for mesoscopic-scale whole-brain imaging datasets. webTDat uses an advanced rendering visualization method designed with an innovative data storage format and parallel rendering algorithms. webTDat loads the primary information in the image first and then decides whether it needs to load the secondary information in the image. By performing validation on TB-scale whole-brain datasets, webTDat achieves real-time performance during web visualization. The webTDat framework also provides a rich interface for annotation, making it a useful tool for visualizing mesoscopic whole-brain imaging data.

Genome-wide discovery of long intergenic noncoding RNAs and their epigenetic signatures in the rat.

Long intergenic noncoding RNAs (lincRNAs) play a crucial role in many biological processes. The rat is an important model organism in biomedical research. Recent studies have detected rat lincRNA genes from several samples. However, identification of rat lincRNAs using large-scale RNA-seq datasets remains unreported. Herein, using more than 100 billion RNA-seq reads from 59 publications together with RefSeq and UniGene annotated RNAs, we report 39,154 lincRNA transcripts encoded by 19,162 lincRNA genes in the rat. We reveal sequence and expression similarities in lincRNAs of rat, mouse and human. DNA methylation level of lincRNAs is higher than that of protein-coding genes across the transcription start sites (TSSs). And, three lincRNA genes overlap with differential methylation regions (DMRs) which associate with spontaneously hypertensive disease. In addition, there are similar binding trends for three transcription factors (HNF4A, CEBPA and FOXA1) between lincRNA genes and protein-coding genes, indicating that they harbour similar transcription regulatory mechanisms. To date, this is the most comprehensive assessment of lincRNAs in the rat genome. We provide valuable data that will advance lincRNA research using rat as a model.

A density-based approach for detecting complexes in weighted PPI networks by semantic similarity.

Protein complex detection in PPI networks plays an important role in analyzing biological processes. A new algorithm-DBGPWN-is proposed for predicting complexes in PPI networks. Firstly, a method based on gene ontology is used to measure semantic similarities between interacted proteins, and the similarity values are used as their weights. Then, a density-based graph partitioning algorithm is developed to find clusters in the weighted PPI networks, and the identified ones are considered to be dense and similar. Experimental results demonstrate that our approach achieves good performance as compared with such algorithms as MCL, CMC, MCODE, RNSC, CORE, ClusterOne and FGN.

A Global-Relationship Dissimilarity Measure for the k-Modes Clustering Algorithm.

The k-modes clustering algorithm has been widely used to cluster categorical data. In this paper, we firstly analyzed the k-modes algorithm and its dissimilarity measure. Based on this, we then proposed a novel dissimilarity measure, which is named as GRD. GRD considers not only the relationships between the object and all cluster modes but also the differences of different attributes. Finally the experiments were made on four real data sets from UCI. And the corresponding results show that GRD achieves better performance than two existing dissimilarity measures used in k-modes and Cao's algorithms.

A Feature Selection Approach Based on Interclass and Intraclass Relative Contributions of Terms.

Feature selection plays a critical role in text categorization. During feature selecting, high-frequency terms and the interclass and intraclass relative contributions of terms all have significant effects on classification results. So we put forward a feature selection approach, IIRCT, based on interclass and intraclass relative contributions of terms in the paper. In our proposed algorithm, three critical factors, which are term frequency and the interclass relative contribution and the intraclass relative contribution of terms, are all considered synthetically. Finally, experiments are made with the help of kNN classifier. And the corresponding results on 20 NewsGroup and SougouCS corpora show that IIRCT algorithm achieves better performance than DF, t-Test, and CMFS algorithms.

A feature selection approach based on term distributions.

Feature selection has a direct impact on text categorization. Most existing algorithms are based on document level, and they haven't considered the influence of term frequency on text categorization. Based on these, we put forward a feature selection approach, FSATD, based on term distributions in the paper. In our proposed algorithm, three critical factors which are term frequency, the inter-class distribution and the intra-class distribution of the terms are all considered synthetically. Finally, experiments are made with the help of kNN classifier. And the corresponding results on 20NewsGroup and SougouCS corpus show that FSATD algorithm achieves better performance than DF and t-Test algorithms.

Awareness of and willingness to use oral pre-exposure prophylaxis for HIV prevention among HIV-serodiscordant heterosexual couples: a cross-sectional survey in Xinjiang, China.

OBJECTIVES: We aimed to investigate the awareness of and willingness to use oral pre-exposure prophylaxis (PrEP) for HIV prevention among HIV-negative partners in HIV-serodiscordant heterosexual couples in Xinjiang, China and determine factors that predict willingness to use oral PrEP. METHODS: Between November 2009 and December 2010, a cross-sectional survey was carried out among 351 HIV-negative partners in HIV-serodiscordant heterosexual couples from three cities in Xinjiang, China. Participants completed a self-administered questionnaire to assess their awareness of and willingness to use oral PrEP. Additionally, blood samples were collected to test for HIV infection. Univariate and multivariate logistic regression analyses were performed to identify predictors of willingness to use oral PrEP. RESULTS: Only 10 participants (2.8%) reported having heard of PrEP, and only two reported ever using PrEP. However, 297 (84.6%) reported that they were willing to use oral PrEP if it was proven to be both safe and effective. Results of multivariate analysis revealed the following independent predictors of willingness to use oral PrEP: monthly household income (adjusted odds ratio = 2.78, <1000 RMB vs. ≥ 1000 RMB, 95% confidence interval: 1.36-5.69), perceived likelihood of contracting HIV from HIV-positive partner (adjusted odds ratio = 2.63, likely vs. unlikely, 95% confidence interval: 1.12-6.19), and worrying about being discriminated against by others due to oral PrEP use (adjusted odds ratio = 9.43, No vs. Yes, 95% confidence interval: 3.78-23.50). CONCLUSIONS: Our results showed HIV-negative partners in HIV-serodiscordant heterosexual couples in China had low awareness of oral PrEP but high willingness to use oral PrEP for HIV prevention. Cost of oral PrEP should be taken into consideration in future PrEP prevention strategy. In addition, efforts should be made to reduce stigma attached to oral PrEP use, which may increase its acceptability among potential users.

[Acceptability of pre-exposure prophylaxis among female sex workers in Xinjiang].

OBJECTIVE: To assess the acceptability of pre-exposure prophylaxis (Pr-EP) among the female sex workers in Xinjiang. METHODS: A volunteer-based, anonymous and one-to-one questionnaire survey was conducted in 762 female sex workers (FSW) in Urumqi and Kelamayi of Xinjiang Uyghur Autonomous Region. RESULT: Among 762 FSW surveyed, 673 (88.32%) was not aware of pre-exposure prophylaxis with an awareness rate of 11.55%. The awareness rate of FSWs working in high-end entertainment venues was higher than that of FSWs working in medium-low end entertainment venues(P<0.001). Five hundred and twenty eight FSWs (69.29%) were willing to take Pr-EP, 145 (19.03%) were unwilling to take the medicine and 89 (11.68%) were possible to use the Pr-EP. There was no significant difference in willingness of using Pr-EP among FSWs working in high and medium-low end entertainment venuew (P=0.285). The subjects who were willing to take Pr-EP mainly concerned of the drug security, effectiveness and cost. The main reasons for not willing to take Pr-EP were: not having risk of infecting HIV, suspecting effectiveness of Pr-EP and worrying about side effects. CONCLUSION: The acceptability to use Pr-EP in female sex workers of Xinjiang is relatively high and the drug security, effectiveness and cost will influence the promotion and application of Pr-EP in the future.