Three-dimensional Motion Skeleton Reconstruction Algorithm for Gymnastic Dancing Movements

Aiming at the problem of inaccurate matching results in the traditional three-dimensional reconstruction algorithm of gymnastic skeleton, a three-dimensional motion skeleton reconstruction algorithm of gymnastic dance action is proposed. Taking the center of gravity of the human body as the origin, the position of other nodes in the camera coordinate system relative to the center point of the human skeleton model is calculated, and the human skeleton data collection is completed through action division and posture feature calculation. Polynomial density is introduced into the integration of convolution surface, and the human body model of convolution surface is established according to convolution surface. By using the method of binary parameter matching, the accuracy of the matching results is improved, and the three-dimensional skeleton of gymnastic dance movement is reconstructed. The experimental results show that the fitting degree between the proposed method and the actual reconstruction result is 99.8%, and the reconstruction result of this algorithm has high accuracy.

PDAUG - a Galaxy based toolset for peptide library analysis, visualization, and machine learning modeling

Computational methods based on initial screening and prediction of peptides for desired functions have been proven effective alternatives to the lengthy and expensive methods traditionally utilized in peptide research, thus saving time and effort. However, for many researchers, the lack of expertise in utilizing programming libraries and the lack of access to computational resources and flexible pipelines are big hurdles to adopting these advanced methods. To address these barriers, we have implemented the Peptide Design and Analysis Under Galaxy (PDAUG) package, a Galaxy based python powered collection of tools, workflows, and datasets for a rapid in-silico peptide library analysis. PDAUG offers tools for peptide library generation, data visualization, in-built and public database based peptide sequence retrieval, peptide feature calculation, and machine learning modeling. In contrast to the existing methods like standard programming libraries or rigid web-based tools, PDAUG offers a GUI based toolset thus providing flexibility to build and distribute reproducible pipelines and workflows without programming expertise. Additionally, this toolset facilitates researchers to combine PDAUG with hundreds of compatible existing Galaxy tools for limitless analytic strategies. Finally, we demonstrate the usability of PDAUG on predicting anticancer properties of peptides using four different feature sets and assess the suitability of various machine learning algorithms.

R package for animal behaviour classification from accelerometer data - rabc

Increasingly animal behaviour studies are enhanced through the use of accelerometry. To allow translation of raw accelerometer data to animal behaviours requires the development of classifiers. Here, we present the “rabc” package to assist researchers with the interactive development of such animal-behaviour classifiers based on datasets consisting out of accelerometer data with their corresponding animal behaviours. Using an accelerometer and a corresponding behavioural dataset collected on white stork (Ciconia ciconia), we illustrate the workflow of this package, including raw data visualization, feature calculation, feature selection, feature visualization, extreme gradient boost model training, validation, and, finally, a demonstration of the behaviour classification results.

Novel Technology Based on the Spectral Kurtosis and Wavelet Transform for Rolling Bearing Diagnosis

A novel diagnosis technology combining the benefits of spectral kurtosis and wavelet transform is proposed and validated for early defect diagnosis of rolling element bearings. A systematic procedure for feature calculation is proposed and rules for selection of technology parameters are explained. Experimental validation of the proposed method carried out for early detection of the inner race defect. A comparison between frequency band selection through wavelets and spectral kurtosis is also presented. It has been observed that the frequency band selected using spectral kurtosis provide better separation between healthy and defective bearings compared to the frequency band selection using wavelet. In terms of Fisher criterion the use of spectral kurtosis has a gain of 2.75 times compared to the wavelet.

ampir: an R package for fast genome-wide prediction of antimicrobial peptides

Summary Antimicrobial peptides (AMPs) are the key components of the innate immune system that protect against pathogens, regulate the microbiome and are promising targets for pharmaceutical research. Computational tools based on machine learning have the potential to aid discovery of genes encoding novel AMPs but existing approaches are not designed for genome-wide scans. To facilitate such genome-wide discovery of AMPs we developed a fast and accurate AMP classification framework, ampir. ampir is designed for high throughput, integrates well with existing bioinformatics pipelines, and has much higher classification accuracy than existing methods when applied to whole genome data. Availability and implementation ampir is implemented primarily in R with core feature calculation methods written in C++. Release versions are available via CRAN and work on all major operating systems. The development version is maintained at https://github.com/legana/ampir. Supplementary information Supplementary data are available at Bioinformatics online.

ampir: an R package for fast genome-wide prediction of antimicrobial peptides

SummaryAntimicrobial peptides (AMPs) are key components of the innate immune system that protect against pathogens, regulate the microbiome, and are promising targets for pharmaceutical research. Computational tools based on machine learning have the potential to aid discovery of genes encoding novel AMPs but existing approaches are not designed for genome-wide scans. To facilitate such genome-wide discovery of AMPs we developed a fast and accurate AMP classification framework, ampir. ampir is designed for high throughput, integrates well with existing bioinformatics pipelines, and has much higher classification accuracy than existing methods when applied to whole genome data.Availability and Implementationampir is implemented primarily in R with core feature calculation methods written in C++. Release versions are available via CRAN and work on all major operating systems. The development version is maintained at https://github.com/legana/[email protected]; [email protected] informationSupplementary data are available at https://github.com/legana/amp_pub

Optimizing the calculation of directional gradients for image recognition

The possibilities of the optimization of histogram of oriented gradients calculations for solving image content recognition problems described based on the 48 × 48 pixels size image example. The algorithm doesn’t change in regards to the input data and suits for the histogram of the oriented gradients calculation based on any image. The algorithm idea is taken from the work of Soojin Kim and Kyeongsoon Cho [1], which is the modification of original HOG descriptor algorithm presented by Navneet Dalal and Bill Triggs [3] aimed at optimization of the calculation speed without loosing accuracy during image content recognition using HOG descriptor to generate the set of features of the image content. The algorithm is described in detail in the next sequence of actions. 1) Original HOG feature calculation. We use it as the first step since the algorithm is the optimized version of the original HOG feature calculation. 2) Solving the aliasing problem and accuracy improvement by using the interpolation technique during the HOG feature calculation process. We use one of the normalization schemes, applying interpolation as the next calculation step. 3) Solving the redundant operations and calculation speed problems by using cell-based operations and applying from one to four described types to the cells, based on which depends the detection window cell calculation. The types are applied to cell based on the blocks intersection containing the cell. The computer program has been developed according to the selected optimized algorithm of HOG feature calculation. It was used during the image content features description and learning process and in the further computer vision research. The software implementation of the algorithm takes into account the capabilities of modern computer technology, Javascript programming language and modern needs of the image content recognition calculation speed and accuracy. The implementation of computer program logic is shown in the script examples, utilizing modularity and parallel calculation as the strong sides of Javascript, further improving HOG feature calculation speed.