Multivariate distributions of correlated binary variables generated by pair-copulas

Correlated binary data are prevalent in a wide range of scientific disciplines, including healthcare and medicine. The generalized estimating equations (GEEs) and the multivariate probit (MP) model are two of the popular methods for analyzing such data. However, both methods have some significant drawbacks. The GEEs may not have an underlying likelihood and the MP model may fail to generate a multivariate binary distribution with specified marginals and bivariate correlations. In this paper, we study multivariate binary distributions that are based on D-vine pair-copula models as a superior alternative to these methods. We elucidate the construction of these binary distributions in two and three dimensions with numerical examples. For higher dimensions, we provide a method of constructing a multidimensional binary distribution with specified marginals and equicorrelated correlation matrix. We present a real-life data analysis to illustrate the application of our results.

SCReadCounts: Estimation of cell-level SNVs from scRNA-seq data

SummarySCReadCounts is a method for a cell-level estimation of the sequencing read counts bearing a particular nucleotide at genomic positions of interest from barcoded scRNA-seq alignments. SCReadCounts generates an array of outputs, including cell-SNV matrices with the absolute variant-harboring read counts, as well as cell-SNV matrices with expressed Variant Allele Fraction (VAFRNA); we demonstrate its application to estimate cell level expression of somatic mutations and RNA-editing on cancer datasets. SCReadCounts is benchmarked against GATK and Samtools and is freely available as a 64-bit self-contained binary distribution (Linux), along with MacOS and Python installation.Availabilityhttps://github.com/HorvathLab/NGS/tree/master/SCReadCountsSupplementary InformationSCReadCounts_Supplementary_Data.zip

Edge4TSC: Binary Distribution Tree-Enabled Time Series Classification in Edge Environment

In the past decade, time series data have been generated from various fields at a rapid speed, which offers a huge opportunity for mining valuable knowledge. As a typical task of time series mining, Time Series Classification (TSC) has attracted lots of attention from both researchers and domain experts due to its broad applications ranging from human activity recognition to smart city governance. Specifically, there is an increasing requirement for performing classification tasks on diverse types of time series data in a timely manner without costly hand-crafting feature engineering. Therefore, in this paper, we propose a framework named Edge4TSC that allows time series to be processed in the edge environment, so that the classification results can be instantly returned to the end-users. Meanwhile, to get rid of the costly hand-crafting feature engineering process, deep learning techniques are applied for automatic feature extraction, which shows competitive or even superior performance compared to state-of-the-art TSC solutions. However, because time series presents complex patterns, even deep learning models are not capable of achieving satisfactory classification accuracy, which motivated us to explore new time series representation methods to help classifiers further improve the classification accuracy. In the proposed framework Edge4TSC, by building the binary distribution tree, a new time series representation method was designed for addressing the classification accuracy concern in TSC tasks. By conducting comprehensive experiments on six challenging time series datasets in the edge environment, the potential of the proposed framework for its generalization ability and classification accuracy improvement is firmly validated with a number of helpful insights.

Decline in direction in CASP experiments

BACKGROUND.CASP experiment, ''critical assessment of structure predictions'', intended to discover advances in an ability of scientific groups to predict a structure of unknown protein from its sequence. The target sequences of proteins to be folded are chosen on each round. The challenge to fold a target from CASP is complicated and the structures of CASP targets are in some way different from an overall pool of known protein structures. The purpose of the study was to detect and quantify a difference between CASP targets and typical structures from the protein databank.METHODS. An averaged local complexity of a protein fold was measured in units of entropy using several metrics which reduce a fragment of a fold to a binary distribution. A complexity was measured for targets from the previous rounds of CASP. A subset of PDB structures was prepared and an averaged complexity of PDB structures was estimated. The choice of the metrics in the measurement of complexity did simulate some of the approaches which were used to predict structures in CASP competition. A measurement of a modified complexity was performed, which was based on averaged distributions for fold fragments in common PDB structures.RESULTS. A difference of CASP targets was detected by a metrics which uses hashing of distances between closely located residues. And a modified version of this metrics which emulates wide-range distance maps was shown to be most easily adjusted to utilize the difference between CASP targets and typical PDB structures. This means that, for the case of CASP targets, the methods which were trained on templates from PDB by similar metrices will guess the template structures in a new round of CASP more successfully – with an increased gap in their ability to predict neutrally selected protein structures. This means that software, which relies on inter-residue distances and performs well in CASP, will perform poorly in general-purpose structure prediction.

Return period of cold air mass in Baghdad

The occurrence of cold air masses varies in Iraq from cold to very cold at different intervals, where these air masses are concentrated only in winter. In this study, the return period of cold air masses was calculated using a binary distribution (Binomial Distribution), It was found that most cold air masses were likely to return with the same intensity for a period of five years from the study period.

64 COULD FROZEN–THAWED BOAR-SEMEN FERTILITY BE INCREASED WITH SEMINAL PLASMA ADDITION?

Post-thawed addition of seminal plasma (SP) in equine cryopreserved semen increased the integrity of plasma and acrosomal membranes (Andrade et al. 2011 Reprod. Dom. Anim. 46, 682–686) and these possibly affect sperm lifespan, improving fertility (Garcia et al. 2010 Anim. Reprod. Sci. 119, 160–165). This study was conducted to verify the pregnancy (PR) and fertility rate (FR) of addition of homologous SP in thawed boar semen. First, SP collections were made with polled sperm rich-fraction. Semen was centrifuged (500 × g for 10 min) and supernatant was removed, centrifuged one more time (2500 × g for 30 min), vacuum filtered through membranes (0.22 μm), and stored at –80°C for future use. Samples collected to frozen were pooled and divided in 2 aliquots, control (cryopreserved with SP; CON) and cryopreserved without SP (WSP; SP was removed and discarded after centrifugation – 500 × g for 10 min). Samples were extended in freezing extender (Botupharma®) to a final concentration of 300 × 106 spermatozoa per milliliter, packaged in 0.5-mL straws, and frozen in an automatic system (TK Tecnologia em Congelação®) using a rate of –0.5°C min–1 until 5°C, –20°C min–1 until –120°C, and then immersed in LN (–196°C). Ten straws, from each treatment, were thawed in water bath (37°C/30 s) and extended in Beltsville thawing solution to obtain 1.5 × 109 sperm in 40 mL. The other 10 straws of WSP were thawed and extended in Beltsville thawing solution plus 10% (v:v) of SP to originate treatment TSP (thawed added of seminal plasma). Thirty-three (11 per treatment) gilts had synchronized ovulation with altrenogest (25 mg/5 mL, Regumate®) administration per 18 days. Following day after withdrawal the altrenogest was administered with an intramuscular injection of 600 IU of eCG (Novormon®) and 2.5 mg of pLH (Lutropin®-V) after 72 h; a single deep intrauterine insemination was made 36 h after. Five days after, females were slaughtered and embryos were collected (by oviducts flushed) and evaluated by esteromicroscopia. Fertility rate and PR were analysed by SAS program (SAS Institute Inc., 2010, Cary, NC, USA). Fertility rate was analysed by Mixed models, and treatment effects were analysed by orthogonal contrasts (C1: effect without SP = CW v. NC; C2: effect of post-thawed addition of SP = CP v. CW), and PR was evaluated by binary distribution with PROC GLIMMIX test. Fertility rate was not affected (P > 0.05) by cryopreservation of boar semen in presence or absence of SP nor by its addition in Beltsville thawing solution (10.58 ± 3.92, 9.57 ± 4.92, 21.29 ± 7.37 for CON, WSP, and TSP, respectively). Treatments did not influence (P > 0.05) PR (50.00, 36.36, 72.73 for CON, WSP, and TSP, respectively). Thus, neither SP addition in thawed boar semen nor cryopreservation with or without SP can be beneficial to PR and FR, in our experimental conditions. However, a numeric large difference was observed. Therefore, these results lead us to believe that SP have a potential to increase the fertility and pregnancy rate, and that can be verified in further studies, with more repetitions.Research was supported by Agroceres Multimix, Botupharma and FAPESP process 2013/08070-8 and 2011/23484-8.