CMDA: a tool for Continuous Monitoring Data Analysis

Over the last few years, with the growth of time-series collecting and storing, there has been a great demand for tools and software for temporal data engineering and modeling. This paper presents a generic workflow for time series data research, including temporal data importing, preprocessing, and feature extraction. This framework is developed and built as a robust and easy-to-use Python package, called CMDA, with a modular structure that offers tools to prepare raw data, allowing both scientists and non-experts to analyze various temporal data structures

Documenting and predicting topic changes in Computers in Biology and Medicine: A bibliometric keyword analysis from 1990 to 2017

The Computers in Biology and Medicine (CBM) journal promotes the use of com-puting machinery in the fields of bioscience and medicine. Since the first volume in 1970, the importance of computers in these fields has grown dramatically, this is evident in the diversification of topics and an increase in the publication rate. In this study, we quantify both change and diversification of topics covered in CBM. This is done by analysing the author supplied keywords, since they were electronically captured in 1990. The analysis starts by selecting 40 keywords, related to Medical (M) (7), Data (D)(10), Feature (F) (17) and Artificial Intelligence (AI) (6) methods. Automated keyword clustering shows the statistical connection between the selected keywords. We found that the three most popular topics in CBM are: Support Vector Machine (SVM), Elec-troencephalography (EEG) and IMAGE PROCESSING. In a separate analysis step, we bagged the selected keywords into sequential one year time slices and calculated the normalized appearance. The results were visualised with graphs that indicate the CBM topic changes. These graphs show that there was a transition from Artificial Neural Network (ANN) to SVM. In 2006 SVM replaced ANN as the most important AI algo-rithm. Our investigation helps the editorial board to manage and embrace topic change. Furthermore, our analysis is interesting for the general reader, as the results can help them to adjust their research directions

Spinophilin loss contributes to tumorigenesis in vivo

The scaffold protein spinophilin (SPN, PPP1R9B) is a regulatory subunit of phosphatase-1a located at 17q21.31. This region is frequently associated with microsatellite instability and LOH and contains a relatively high density of known tumor suppressor genes (such as BRCA1), putative tumor suppressor genes and several unidentified candidate tumor suppressor genes located distal to BRCA1. Spn is located distal to BRCA1 and we have previously shown that the loss of Spn contributes to human tumorigenesis in the absence of p53 function. In this work, we explored the role of Spn as putative tumor suppressor in vivo models using genetically modified mice. Spn knock-out mice had decreased lifespan with increased cellular proliferation in tissues such as the mammary ducts and early appearance of tumors such as lymphoma. Furthermore, the combined loss of Spn and mutant p53 activity led to increased mammary carcinomas, confirming the functional relationship between p53 and Spn. We suggest that Spn may be a novel tumor suppressor located at 17q21. © 2011 Landes Bioscience.he A.C. lab is funded by grants from the Spanish Ministry of Science and Innovation (SAF2009-08605), Consejeria de Ciencia e Innovacion (CTS-6844) and Consejeria de Salud (PI-0142), Junta de Andalucia. I.F. was funded by the Spanish Ministry of Science and Innovation. A.C.’s Lab is also funded by a Fellowship from Fundacion Oncologica FERO supported by Fundació Josep Botet.Peer Reviewe