Special Libraries, December 1966

Volume 57, Issue 10https://scholarworks.sjsu.edu/sla_sl_1966/1009/thumbnail.jp

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

Data Mining

Data mining is a branch of computer science that is used to automatically extract meaningful, useful knowledge and previously unknown, hidden, interesting patterns from a large amount of data to support the decision-making process. This book presents recent theoretical and practical advances in the field of data mining. It discusses a number of data mining methods, including classification, clustering, and association rule mining. This book brings together many different successful data mining studies in various areas such as health, banking, education, software engineering, animal science, and the environment

Outlier Edge Detection Using Random Graph Generation Models and Applications

Outliers are samples that are generated by different mechanisms from other normal data samples. Graphs, in particular social network graphs, may contain nodes and edges that are made by scammers, malicious programs or mistakenly by normal users. Detecting outlier nodes and edges is important for data mining and graph analytics. However, previous research in the field has merely focused on detecting outlier nodes. In this article, we study the properties of edges and propose outlier edge detection algorithms using two random graph generation models. We found that the edge-ego-network, which can be defined as the induced graph that contains two end nodes of an edge, their neighboring nodes and the edges that link these nodes, contains critical information to detect outlier edges. We evaluated the proposed algorithms by injecting outlier edges into some real-world graph data. Experiment results show that the proposed algorithms can effectively detect outlier edges. In particular, the algorithm based on the Preferential Attachment Random Graph Generation model consistently gives good performance regardless of the test graph data. Further more, the proposed algorithms are not limited in the area of outlier edge detection. We demonstrate three different applications that benefit from the proposed algorithms: 1) a preprocessing tool that improves the performance of graph clustering algorithms; 2) an outlier node detection algorithm; and 3) a novel noisy data clustering algorithm. These applications show the great potential of the proposed outlier edge detection techniques.Comment: 14 pages, 5 figures, journal pape

Atypical parkinsonism-associated retromer mutant alters endosomal sorting of specific cargo proteins

The retromer complex acts as a scaffold for endosomal protein complexes that sort integral membrane proteins to various cellular destinations. The retromer complex is a heterotrimer of VPS29, VPS35, and VPS26. Two of these paralogues, VPS26A and VPS26B, are expressed in humans. Retromer dysfunction is associated with neurodegenerative disease, and recently, three VPS26A mutations (p.K93E, p.M112V, and p.K297X) were discovered to be associated with atypical parkinsonism. Here, we apply quantitative proteomics to provide a detailed description of the retromer interactome. By establishing a comparative proteomic methodology, we identify how this interactome is perturbed in atypical parkinsonism-associated VPS26A mutants. In particular, we describe a selective defect in the association of VPS26A (p.K297X) with the SNX27 cargo adaptor. By showing how a retromer mutant leads to altered endosomal sorting of specific PDZ ligand–containing cargo proteins, we reveal a new mechanism for perturbed endosomal cargo sorting in atypical parkinsonism

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Bit by Bit: Microcomputer Applications by Archivists in Four Southeastern States

As archivists in Georgia, Tennessee, North Carolina, and Kentucky began to automate in the early 1980s, most found microcomputers much more to their liking than main-frame computer systems or book-oriented network systems. Few archivists in the region were using computers before microcomputers were developed in the mid-1970s. The smaller computers that were marketed during this period allowed users in the region to adapt programs easily to their individual needs at minimum cost. However, the limited capacities of the first microcomputers have pushed archivists, as they move into the 1980s, to buy larger microcomputers or small minicomputers