Motivation: Sputum in the trachea is hard to expectorate and detect directly for the patients who are unconscious, especially those in Intensive Care Unit. Medical staff should always check the condition of sputum in the trachea. This is time-consuming and the necessary skills are difficult to acquire. Currently, there are few automatic approaches to serve as alternatives to this manual approach. Results: We develop an automatic approach to diagnose the condition of the sputum. Our approach utilizes a system involving a medical device and quantitative analytic methods. In this approach, the time-frequency distribution of respiratory sound signals, determined from the spectrum, is treated as an image. The sputum detection is performed by interpreting the patterns in the image through the procedure of preprocessing and feature extraction. In this study, 272 respiratory sound samples (145 sputum sound and 127 non-sputum sound samples) are collected from 12 patients. We apply the method of leave-one out cross-validation to the 12 patients to assess the performance of our approach. That is, out of the 12 patients, 11 are randomly selected and their sound samples are used to predict the sound samples in the remaining one patient. The results show that our automatic approach can classify the sputum condition at an accuracy rate of 83.5%. Availability and implementation: The matlab codes and examples of datasets explored in this work are available at Bioinformatics online. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Summary: The R package CGManalyzer contains functions for analyzing data from a continuous glucose monitoring (CGM) study. It covers a wide and comprehensive range of data analysis methods including reading a series of datasets, obtaining summary statistics of glucose levels, plotting data, transforming the time stamp format, fixing missing values, evaluating the mean of daily difference and continuous overlapping net glycemic action, calculating multiscale sample entropy, conducting pairwise comparison, displaying results using various plots including a new type of plot called an antenna plot, etc. This package has been developed from our work in directly analyzing data from various CGM devices such as the FreeStyle Libre, Glutalor, Dexcom and Medtronic CGM. Thus, this package should greatly facilitate the analysis of various CGM studies. Availability and implementation: The package for Windows is available from CRAN: http://cran.r-project.org/mirrors.html. The source file CGManalyzer_1.0.tar.gz is available in the Supplementary Material and at the website of Zhang's lab https://quantitativelab.fhs.umac.mo/analytic-tool/. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Bioinformatics, (2018) https://doi.org/10.1093/bioinformatics/btx826 The authors of the above paper wishes to inform readers that the link provided for the source file CGManalyzer_1.0.tar.gz is no longer correct. The source file is now available at https://quantitativelab.fhs.umac.mo/analytic-tool/. An error in the file CGManalyzer_1.0.tar.gz available as supplementary data at Bioinformatics online has also been corrected. The paper has now been corrected online.

Abstract Summary: The R package displayHTS implements recently developed methods and figures for displaying data and hit selection results in high-throughput screening (HTS) experiments. It generates not only certain useful distinctive graphics such as the plate-well series plot, plate image and dual-flashlight plot but also other commonly used figures such as volcano plot and plate correlation plot. These figures are critical for visualizing the data and displaying important features of HTS data and hit selection results. Availability and implementation: The package is freely available from CRAN: http://cran.r-project.org/mirrors.html, being distributed under the GNU General Public License. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

We consider the multi-valued discrete real training set that can not be separated by one multi-valued multi-threshold neuron. Such training set is defined as linearly nonseparable set in this paper. Our objective is to use multi-valued multi-threshold neural networks to learn nonseparable training sets. First we give the method that how to determine a training set is separable or nonseparable (i.e., the necessary and sufficient condition for linearly nonseparable is given). Then we analyze the structures within linearly nonseparable sets: not all the vectors in a linearly nonseparable set are responsible for nonseparability. So the vectors in such set can be partitioned to separable vectors and nonseparable vectors. Finally, we discuss the learning problems for a linearly nonseparable set. Such set can be learned by a three-layer feedforward neural network with one hidden layer. An example throughout the paper further clarifies the results of this paper.