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The aim of this study is to investigate the relationship between plasma amylase concentration and C-peptide level in type 1 diabetic and healthy children. The cross-sectional study involved 31 Chinese type 1 diabetic children without complications and 107 healthy control subjects. Clinical examination and laboratory examinations were assessed for all participants. Significantly higher amylase concentrations were measured in type 1 diabetic children than in controls (102.8 U/l vs. 70.2 U/l, respectively; p

Consensus clustering has emerged as an important extension of the classical clustering problem. Given a set of input clusterings of a given dataset, consensus clustering aims to find a single final clustering which is a better fit in some sense than the existing clusterings. There is a significant drawback in generating a single consensus clustering since different input clusterings could differ significantly. In this paper, we develop a new framework, called Multiple Consensus Clustering (MCC), to explore multiple clustering views of a given dataset from a set of input clusterings. Instead of generating a single consensus, we propose two sets of approaches to obtain multiple consensus. One employs the meta clustering method, and the other uses a hierarchical tree structure and further applies a dynamic programming algorithm to generate a flat partition from the hierarchical tree using the modularity measure. Multiple consensuses are finally obtained by applying consensus clustering algorithms to each cluster of the partition. Extensive experimental results on 11 real-world datasets and a case study on a Protein-Protein Interaction (PPI) dataset demonstrate the effectiveness of the MCC framework.

Background A number of studies have demonstrated the critical role of long non-coding RNA gastric cancer high expressed transcript 1 (GHET1) in many cancers. This meta-analysis provides an evidence-based evaluation of the prognostic role of GHET1 in cancer. Materials and methods Literature searches were conducted in several databases including Medline, Cochrane, EMBASE, CNKI, and Wanfang. The pooled odds ratio (OR) and hazard ratio (HR) with 95% confidence interval (CI) were used to evaluate the role of GHET1 in cancer. The study protocol was registered at PROSPERO (ID: CRD42018111252). Results Sixteen studies, containing 1315 patients, were analyzed in this meta-analysis. The pooled results indicated that GHET1 overexpression was significantly associated with poor overall survival (OS) and disease-free survival (DFS) in cancer. Moreover, up-regulation of GHET1 expression predicted larger tumor size, positive lymph node metastasis, positive distant metastasis, and advanced TNM (tumor-node-metastases) stage in human cancers. Conclusion There is a significant correlation between up-regulation of GHET1 and both poor prognosis and advanced clinicopathological cancer characteristics. GHET1 may be a potential prognostic predictor for human cancers.

Finding experts in specified areas is an important task and has attracted much attention in the information retrieval community. Research on this topic has made significant progress in the past few decades and various techniques have been proposed. In this survey, we review the state-of-the-art methods in expert finding and summarize these methods into different categories based on their underlying algorithms and models. We also introduce the most widely used data collection for evaluating expert finding systems, and discuss future research directions.

A kind of composite ceramic consisted of Mg–Y co-doped ZrO2 and MgAl2O4 was prepared as a lining material of the high density graphite crucibles in nuclear industry. When the addition of MgAl2O4 was 7 wt%, the sample showed good thermal shock resistance. The obtained samples were tested in flowing molten uranium and no apparent cracks were found on the corrosion surface. Meanwhile, with the prolonging of corrosion time, the corrosion rate significantly decreased due to the formation of a protection layer consisted of UO2, Al3Zr2 and ZrC.

Text similarity measurement aims to find the commonality existing among text documents, which is fundamental to most information extraction, information retrieval, and text mining problems. Cosine similarity based on Euclidean distance is currently one of the most widely used similarity measurements. However, Euclidean distance is generally not an effective metric for dealing with probabilities, which are often used in text analytics. In this paper, we propose a new similarity measure based on sqrt-cosine similarity. We apply the proposed improved sqrt-cosine similarity to a variety of document-understanding tasks, such as text classification, clustering, and query search. Comprehensive experiments are then conducted to evaluate our new similarity measurement in comparison to existing methods. These experimental results show that our proposed method is indeed effective.

Online news articles, as a new format of press releases, have sprung up on the Internet. With its convenience and recency, more and more people prefer to read news online instead of reading the paper-format press releases. However, a gigantic amount of news events might be released at a rate of hundreds, even thousands per hour. A challenging problem is how to efficiently select specific news articles from a large corpus of newly-published press releases to recommend to individual readers, where the selected news items should match the reader's reading preference as much as possible. This issue refers to personalized news recommendation. Recently, personalized news recommendation has become a promising research direction as the Internet provides fast access to real-time information from multiple sources around the world. Existing personalized news recommendation systems strive to adapt their services to individual users by virtue of both user and news content information. A variety of techniques have been proposed to tackle personalized news recommendation, including content-based, collaborative filtering systems and hybrid versions of these two. In this paper, we provide a comprehensive investigation of existing personalized news recommenders. We discuss several essential issues underlying the problem of personalized news recommendation, and explore possible solutions for performance improvement. Further, we provide an empirical study on a collection of news articles obtained from various news websites, and evaluate the effect of different factors for personalized news recommendation. We hope our discussion and exploration would provide insights for researchers who are interested in personalized news recommendation.

Malicious executables are programs designed to infiltrate or damage a computer system without the owner’s consent, which have become a serious threat to the security of computer systems. There is an urgent need for effective techniques to detect polymorphic, metamorphic and previously unseen malicious executables of which detection fails in most of the commercial anti-virus software. In this paper, we develop interpretable string based malware detection system (SBMDS), which is based on interpretable string analysis and uses support vector machine (SVM) ensemble with Bagging to classify the file samples and predict the exact types of the malware. Interpretable strings contain both application programming interface (API) execution calls and important semantic strings reflecting an attacker’s intent and goal. Our SBMDS is carried out with four major steps: (1) first constructing the interpretable strings by developing a feature parser; (2) performing feature selection to select informative strings related to different types of malware; (3) followed by using SVM ensemble with bagging to construct the classifier; (4) and finally conducting the malware detector, which not only can detect whether a program is malicious or not, but also can predict the exact type of the malware. Our case study on the large collection of file samples collected by Kingsoft Anti-virus lab illustrate that: (1) The accuracy and efficiency of our SBMDS outperform several popular anti-virus software; (2) Based on the signatures of interpretable strings, our SBMDS outperforms data mining based detection systems which employ single SVM, Naive Bayes with bagging, Decision Trees with bagging; (3) Compared with the IMDS which utilizes the objective-oriented association (OOA) based classification on API calls, our SBMDS achieves better performance. Our SBMDS system has already been incorporated into the scanning tool of a commercial anti-virus software.

The proliferation of malware has presented a serious threat to the security of computer systems. Traditional signature-based anti-virus systems fail to detect polymorphic/metamorphic and new, previously unseen malicious executables. Data mining methods such as Naive Bayes and Decision Tree have been studied on small collections of executables. In this paper, resting on the analysis of Windows APIs called by PE files, we develop the Intelligent Malware Detection System (IMDS) using Objective-Oriented Association (OOA) mining based classification. IMDS is an integrated system consisting of three major modules: PE parser, OOA rule generator, and rule based classifier. An OOA_Fast_FP-Growth algorithm is adapted to efficiently generate OOA rules for classification. A comprehensive experimental study on a large collection of PE files obtained from the anti-virus laboratory of KingSoft Corporation is performed to compare various malware detection approaches. Promising experimental results demonstrate that the accuracy and efficiency of our IMDS system outperform popular anti-virus software such as Norton AntiVirus and McAfee VirusScan, as well as previous data mining based detection systems which employed Naive Bayes, Support Vector Machine (SVM) and Decision Tree techniques. Our system has already been incorporated into the scanning tool of KingSoft’s Anti-Virus software.