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

Warburg Effects in Cancer and Normal Proliferating Cells: Two Tales of the Same Name

It has been observed that both cancer tissue cells and normal proliferating cells (NPCs) have the Warburg effect. Our goal here is to demonstrate that they do this for different reasons. To accomplish this, we have analyzed the transcriptomic data of over 7000 cancer and control tissues of 14 cancer types in TCGA and data of five NPC types in GEO. Our analyses reveal that NPCs accumulate large quantities of ATPs produced by the respiration process before starting the Warburg effect, to raise the intracellular pH from ∼6.8 to ∼7.2 and to prepare for cell division energetically. Once cell cycle starts, the cells start to rely on glycolysis for ATP generation followed by ATP hydrolysis and lactic acid release, to maintain the elevated intracellular pH as needed by cell division since together the three processes are pH neutral. The cells go back to the normal respiration-based ATP production once the cell division phase ends. In comparison, cancer cells have reached their intracellular pH at ∼7.4 from top down as multiple acid-loading transporters are up-regulated and most acid-extruding ones except for lactic acid exporters are repressed. Cancer cells use continuous glycolysis for ATP production as way to acidify the intracellular space since the lactic acid secretion is decoupled from glycolysis-based ATP generation and is pH balanced by increased expressions of acid-loading transporters. Co-expression analyses suggest that lactic acid secretion is regulated by external, non-pH related signals. Overall, our data strongly suggest that the two cell types have the Warburg effect for very different reasons

Revealing New Technologies in Ocean Engineering Research using Machine Learning

On par with aerospace engineering, ocean engineering has caught a lot of attention re-cently. In this paper we employ machine learning and natural language processing methods to reveal new technologies and research hotspots in the ocean engineering field. Our data collection includes 14 high-impact journals, and the abstracts of almost 30,000 papers pub- lished from 2010 to 2019. We employed two topic models, Latent Dirichlet Allocation (LDA) and PhraseLDA. Used independently, the LDA model may lack interpretability and the PhraseLDA result may lose information in the final topics. We hence combined these two models and discovered the research hotspots for each year using affinity propagation cluster- ing and word-cloud-based visualization. The results reveal that several topics such as "wind power" and "ship structure", areas such as the European and Arctic seas, and some common research methods are increasing in popularity. This work consists of data collection, topic modelling, clustering, and visualization, which can help researchers understand the trends and important topics in ocean engineering as well as other fields

Ultrasound Versus Contrast-Enhanced Magnetic Resonance Imaging for Subclinical Synovitis and Tenosynovitis: A Diagnostic Performance Study

OBJECTIVES: Radiographic manifestations of synovitis (e.g., erosions) can be observed only in the late stage of rheumatoid arthritis. Ultrasound is a noninvasive, cheap, and widely available technique that enables the evaluation of inflammatory changes in the peripheral joint. In the same way, dynamic contrast-enhanced magnetic resonance imaging (MRI) enables qualitative and quantitative measurements. The objectives of the study were to compare the sensitivity and accuracy of ultrasound in detecting subclinical synovitis and tenosynovitis with those of contrast-enhanced MRI. METHODS: The ultrasonography and contrast-enhanced MRI findings of the wrist, metacarpophalangeal, and proximal interphalangeal joints (n=450) of 75 patients with a history of joint pain and morning stiffness between 6 weeks and 2 years were reviewed. The benefits score was evaluated for each modality. RESULTS: The ultrasonic findings showed inflammation in 346 (77%) joints, while contrast-enhanced MRI found signs of early rheumatoid arthritis in 372 (83%) joints. The sensitivities of ultrasound and contrast-enhanced MRI were 0.795 and 0.855, respectively, and the accuracies were 0.769 and 0.823, respectively. Contrast-enhanced MRI had a likelihood of 0–0.83 and ultrasound had a likelihood of 0–0.77 for detecting synovitis and tenosynovitis at one time. The two imaging modalities were equally competitive for detecting synovitis and tenosynovitis (p=0.055). CONCLUSION: Ultrasound could be as sensitive and specific as contrast-enhanced MRI for the diagnosis of subclinical synovitis and tenosynovitis

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]

2-[7-Chloro-1,1-dioxo-2-(2,4,5-trifluoro­benz­yl)-3,4-dihydro-2H-1,2,4-benzothia­diazin-4-yl]acetic acid

In the mol­ecule of the title compound, C16H12ClF3N2O4S, the thia­diazine ring adopts a half-chair conformation. The dihedral angle between the benzene ring of the benzothia­diazine ring system and trifluoro­phenyl group is 15.02 (7)°. In the crystal, centrosymmetrically related mol­ecules are linked into dimers via pairs of O—H⋯O hydrogen bonds, generating R 2 2(8) ring motifs. The dimers are further connected into a three-dimensional network by C—H⋯O hydrogen bonds