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This case study demonstrates the application of an unsupervised topic modeling algorithm to 7,773 English-language articles published in the Library Quarterly from 1931 to 2015. The analysis of 85 years of the journal's output follows an exploratory data analysis framework to generate novel hypotheses about the history of LIS using topic modeling, a method for identifying clusters of co-occurring words within large collections of text. The paper closely examines two topics that suggest differences in gender representation in the journal to propose and support a new hypothesis regarding the historical inclusion of gendered objects of study in LIS literature. [ABSTRACT FROM AUTHOR]

Drug-drug interactions (DDIs) are a common cause of adverse drug events (ADEs). The electronic medical record (EMR) database and the FDA's adverse event reporting system (FAERS) database are the major data sources for mining and testing the ADE associated DDI signals. Most DDI data mining methods focus on pair-wise drug interactions, and methods to detect high-dimensional DDIs in medical databases are lacking. In this paper, we propose 2 novel mixture drug-count response models for detecting high-dimensional drug combinations that induce myopathy. The "count" indicates the number of drugs in a combination. One model is called fixed probability mixture drug-count response model with a maximum risk threshold (FMDRM-MRT). The other model is called count-dependent probability mixture drug-count response model with a maximum risk threshold (CMDRM-MRT), in which the mixture probability is count dependent. Compared with the previous mixture drug-count response model (MDRM) developed by our group, these 2 new models show a better likelihood in detecting high-dimensional drug combinatory effects on myopathy. CMDRM-MRT identified and validated (54; 374; 637; 442; 131) 2-way to 6-way drug interactions, respectively, which induce myopathy in both EMR and FAERS databases. We further demonstrate FAERS data capture much higher maximum myopathy risk than EMR data do. The consistency of 2 mixture models' parameters and local false discovery rate estimates are evaluated through statistical simulation studies. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present the PolyMAX module which enhances network simulator 2 (ns-2), the most popular network simulator used in academia, to provide one of the most complete simulation tools to evaluate the performance of Mobile WiMAX networks. PolyMAX is based on the National Institute of Standards and Technology (NIST) module and our specific contributions consist on the design and implementation of the Quality of Service (QoS) classes and QoS management messages, the uplink access grant-request mechanisms, Adaptive Modulation and Coding, and a scheduler handling all five WiMAX QoS classes. We also present validation results for the different components of our module and typical WiMAX simulation scenarios illustrating its flexibility and some of its features. The PolyMAX module represents an important tool enabling researchers to easily implement their Mobile WiMAX scheduling and Adaptive Modulation and Coding (AMC) algorithms and accurately evaluate their performance for realistic scenarios. [Copyright &y& Elsevier]