PUEPro : A Computational Pipeline for Prediction of Urine Excretory Proteins

This work is supported by the National Natural Science Foundation of China (Grant Nos. 81320108025, 61402194, 61572227), Development Project of Jilin Province of China (20140101180JC) and China Postdoctoral Science Foundation (2014T70291).Postprin

The Yeast GSK-3 Homologue Mck1 Is a Key Controller of Quiescence Entry and Chronological Lifespan.

Upon starvation for glucose or any other core nutrient, yeast cells exit from the mitotic cell cycle and acquire a set of G0-specific characteristics to ensure long-term survival. It is not well understood whether or how cell cycle progression is coordinated with the acquisition of different G0-related features during the transition to stationary phase (SP). Here, we identify the yeast GSK-3 homologue Mck1 as a key regulator of G0 entry and reveal that Mck1 acts in parallel to Rim15 to activate starvation-induced gene expression, the acquisition of stress resistance, the accumulation of storage carbohydrates, the ability of early SP cells to exit from quiescence, and their chronological lifespan. FACS and microscopy imaging analyses indicate that Mck1 promotes mother-daughter cell separation and together with Rim15, modulates cell size. This indicates that the two kinases coordinate the transition-phase cell cycle, cell size and the acquisition of different G0-specific features. Epistasis experiments place MCK1, like RIM15, downstream of RAS2 in antagonising cell growth and activating stress resistance and glycogen accumulation. Remarkably, in the ras2∆ cells, deletion of MCK1 and RIM15 together, compared to removal of either of them alone, compromises respiratory growth and enhances heat tolerance and glycogen accumulation. Our data indicate that the nutrient sensor Ras2 may prevent the acquisition of G0-specific features via at least two pathways. One involves the negative regulation of the effectors of G0 entry such as Mck1 and Rim15, while the other likely to involve its functions in promoting respiratory growth, a phenotype also contributed by Mck1 and Rim15.This work was funded by a scholarship from Lucy Cavendish College (ZQ) and a scholarship awarded by National University of Defense Technology of China (LC). This work was also supported by the UNICELLSYS Collaborative Project (No. 201142) of the European Commission awarded to SGO.This is the published version. It first appeared at http://dx.doi.org/10.1371/journal.pgen.100528

Deep Learning Analysis and Age Prediction from Shoeprints

Human walking and gaits involve several complex body parts and are influenced by personality, mood, social and cultural traits, and aging. These factors are reflected in shoeprints, which in turn can be used to predict age, a problem not systematically addressed using any computational approach. We collected 100,000 shoeprints of subjects ranging from 7 to 80 years old and used the data to develop a deep learning end-to-end model ShoeNet to analyze age-related patterns and predict age. The model integrates various convolutional neural network models together using a skip mechanism to extract age-related features, especially in pressure and abrasion regions from pair-wise shoeprints. The results show that 40.23% of the subjects had prediction errors within 5-years of age and the prediction accuracy for gender classification reached 86.07%. Interestingly, the age-related features mostly reside in the asymmetric differences between left and right shoeprints. The analysis also reveals interesting age-related and gender-related patterns in the pressure distributions on shoeprints; in particular, the pressure forces spread from the middle of the toe toward outside regions over age with gender-specific variations on heel regions. Such statistics provide insight into new methods for forensic investigations, medical studies of gait-pattern disorders, biometrics, and sport studies.Comment: 24 pages, 20 Figure

Methods for labeling error detection in microarrays based on the effect of data perturbation on the regression model

Abstract Motivation: Mislabeled samples often appear in gene expression profile because of the similarity of different sub-type of disease and the subjective misdiagnosis. The mislabeled samples deteriorate supervised learning procedures. The LOOE-sensitivity algorithm is an approach for mislabeled sample detection for microarray based on data perturbation. However, the failure of measuring the perturbing effect makes the LOOE-sensitivity algorithm a poor performance. The purpose of this article is to design a novel detection method for mislabeled samples of microarray, which could take advantage of the measuring effect of data perturbations. Results: To measure the effect of data perturbation, we define an index named perturbing influence value (PIV), based on the support vector machine (SVM) regression model. The Column Algorithm (CAPIV), Row Algorithm (RAPIV) and progressive Row Algorithm (PRAPIV) based on the PIV value are proposed to detect the mislabeled samples. Experimental results obtained by using six artificial datasets and five microarray datasets demonstrate that all proposed methods in this article are superior to LOOE-sensitivity. Moreover, compared with the simple SVM and CL-stability, the PRAPIV algorithm shows an increase in precision and high recall. Availability: The program and source code (in JAVA) are publicly available at http://ccst.jlu.edu.cn/CSBG/PIVS/index.htm Contact: [email protected]; [email protected]

Normal Red Blood Cell Count Reference Values in Chinese Presenile Women Given by Geographical Area

Background/PurposeWe aimed to standardize the normal reference value of red blood cell (RBC) counts in Chinese presenile women using an underlying scientific basis.MethodsThis research was conducted to study the relationship between the normal reference value of 31,405 RBC samples from presenile women in eight different geographical areas in China. RBC counts were determined using a microscopic counting method.ResultsThere was a significant correlation between geographical factors and the normal reference RBC value in presenile women (F = 187.82, p = 0.000). Using stepwise regression analysis, one regression equation was obtained.ConclusionIf geographical values are obtained in a certain area, the normal RBC reference value in presenile women in this area can be obtained using the regression equation

Computational Prediction of Human Salivary Proteins from Blood Circulation and Application to Diagnostic Biomarker Identification

Proteins can move from blood circulation into salivary glands through active transportation, passive diffusion or ultrafiltration, some of which are then released into saliva and hence can potentially serve as biomarkers for diseases if accurately identified. We present a novel computational method for predicting salivary proteins that come from circulation. The basis for the prediction is a set of physiochemical and sequence features we found to be discerning between human proteins known to be movable from circulation to saliva and proteins deemed to be not in saliva. A classifier was trained based on these features using a support-vector machine to predict protein secretion into saliva. The classifier achieved 88.56% average recall and 90.76% average precision in 10-fold cross-validation on the training data, indicating that the selected features are informative. Considering the possibility that our negative training data may not be highly reliable (i.e., proteins predicted to be not in saliva), we have also trained a ranking method, aiming to rank the known salivary proteins from circulation as the highest among the proteins in the general background, based on the same features. This prediction capability can be used to predict potential biomarker proteins for specific human diseases when coupled with the information of differentially expressed proteins in diseased versus healthy control tissues and a prediction capability for blood-secretory proteins. Using such integrated information, we predicted 31 candidate biomarker proteins in saliva for breast cancer

Effects of a novel pH-sensitive liposome with cleavable esterase-catalyzed and pH-responsive double smart mPEG lipid derivative on ABC phenomenon

Daquan Chen1,2, Wanhui Liu1,2, Yan Shen3, Hongjie Mu1,2, Yanchun Zhang4 , Rongcai Liang1,2, Aiping Wang1,2, Kaoxiang Sun1,2, Fenghua Fu1,2 1School of Pharmacy, Yantai University, Yantai, People’s Republic of China; 2State Key Laboratory of Long-acting and Targeting Drug Delivery System, Yantai, People’s Republic of China; 3College of Pharmacy, China Pharmaceutical University, Nanjing, People’s Republic of China; 4College of Pharmacy, Anhui University of Traditional Chinese Medicine, Hefei, People’s Republic of China Background: The ABC phenomenon is described as a syndrome of accelerated clearance of polyethylene glycol (PEG)-modified liposomes from the bloodstream when repeatedly injected, with their increased accumulation in the liver and spleen. Methods: To clarify this immune response phenomenon, we evaluated a novel modified pH-sensitive liposome with a cleavable double smart PEG-lipid derivative (mPEG-Hz-CHEMS). Results: The ABC phenomenon in mice was brought about by repeated injection of conventional PEG-PE liposomes and was accompanied by a greatly increased uptake in the liver. However, a slight ABC phenomenon was brought about by repeated injection of mPEG-CHEMS liposomes and was accompanied by only a slightly increased uptake in the liver, and repeated injection of mPEG-Hz-CHEMS liposomes did not induce the ABC phenomenon and there was no increase in liver accumulation. This finding indicates that the cleavable mPEG-Hz-CHEMS derivative could lessen or eliminate the ABC phenomenon induced by repeated injection of PEGylated liposomes. Conclusion: This research has shed some light on a solution to the ABC phenomenon using a cleavable PEG-Hz-CHEMS derivative encapsulated in nanoparticles. Keywords: accelerated blood clearance, double smart, cleavable, mPEG-lipid derivates, pH-sensitive liposom

Effects of aerobic exercise on serum adiponectin concentrations in children and adolescents with obesity: a systematic review and meta-analysis

Serum adiponectin plays a vital role in various physiological processes, such as anti-inflammatory, anti-atherosclerotic, anti-apoptotic and pro-angiogenic activities. Any abnormalities in its concentration can lead to adverse health outcomes, particularly in children and adolescents. Therefore, it is crucial to investigate factors influencing serum adiponectin concentrations in this population. The primary objective of this study was to systematically evaluate the impact of aerobic exercise on serum adiponectin concentrations in children and adolescents with obesity. To achieve this, a comprehensive literature search was conducted up to January 2023, utilising five databases: PubMed, Web of Science, Embase, Cochrane Library and Clinicaltrial.gov. The inclusion criteria involved studies that focused solely on aerobic exercise as an intervention for children and adolescents with obesity. Only studies that reported outcome indicators related to serum adiponectin were considered for analysis. The quality of the included studies was assessed using the Cochrane Risk of Bias (ROB) assessment tool, and statistical analysis was performed using RevMan 5.4.1 analysis software. This meta-analysis incorporated data from eight trials, involving a total of 272 subjects. The results demonstrated that aerobic training significantly increased serum adiponectin concentrations [standardized mean difference (SMD) = 0.85; 95% confidence interval (CI) = 0.33 to 1.37; I2 = 0%; p = 0.001] in children and adolescents with obesity when compared to non-exercise controls. Furthermore, the magnitude of this effect appears to be influenced by the intensity of aerobic exercise, with higher-intensity aerobic exercise resulting in greater increases in serum adiponectin concentrations

The role of 245 phase in alkaline iron selenide superconductors revealed by high pressure studies

Here we show that a pressure of about 8 GPa suppresses both the vacancy order and the insulating phase, and a further increase of the pressure to about 18 GPa induces a second transition or crossover. No superconductivity has been found in compressed insulating 245 phase. The metallic phase in the intermediate pressure range has a distinct behavior in the transport property, which is also observed in the superconducting sample. We interpret this intermediate metal as an orbital selective Mott phase (OSMP). Our results suggest that the OSMP provides the physical pathway connecting the insulating and superconducting phases of these iron selenide materials.Comment: 32 pages, 4 figure