Linux Platform Research Articles

VacPred: Sequence-based prediction of plant vacuole proteins using machine-learning techniques

Subcellular localization prediction of the proteome is one of major goals of large-scale genome or proteome sequencing projects to define the gene functions that could be possible with the help of computational modeling techniques. Previously, different methods have been developed for this purpose using multi-label classification system and achieved a high level of accuracy. However, during the validation of our blind dataset of plant vacuole proteins, we observed that they have poor performance with accuracy value range from ~1.3% to 48.5%. The results showed that the previously developed methods are not very accurate for the plant vacuole protein prediction and thus emphasize the need to develop a more accurate and reliable algorithm. In this study, we have developed various compositions as well as PSSM-based models and achieved a high accuracy than previously developed methods. We have shown that our best model achieved ~63% accuracy on blind dataset, which is far better than currently available tools. Furthermore, we have implemented our best models in the form of GUI-based free software called 'VacPred' which is compatible with both Linux and Window platform. This software is freely available for download at www.deepaklab.com/vacpred.

PacketUsher: Exploiting DPDK to accelerate compute-intensive packet processing

Many compute-intensive network applications such as application-layer traffic generator, Deep Packet Inspection(DPI) and web servers are widely deployed on commodity PC for the reason of flexibility and cheap price. However, how to improve their performance on general purpose OS is challenging due to the high packet I/O related overheads. This paper presents PacketUsher, a high-performance packets processing framework to remove these performance bottlenecks. In building PacketUsher, we constructed a DPDK wrapper as the underlying packet I/O engine to accelerate packet transmission, and utilized the strategies of zero copy, batch processing and parallelism to improve packet processing. Through RFC2544 benchmark, we demonstrate that DPDK wrapper has excellent packets transmission capability. As a case study of PacketUsher, we design and implement a commercial application-layer traffic generator. The experiment results show that the FPS (Flow Per Second) value of our traffic generator over PacketUsher is more than 4 times of that over standard Linux platform. By comparison, the FPS value over PacketUsher is about 3 times of that over existing methods (Netmap and PF_RING).

AdmixPipe: population analyses in Admixture for non-model organisms

BackgroundResearch on the molecular ecology of non-model organisms, while previously constrained, has now been greatly facilitated by the advent of reduced-representation sequencing protocols. However, tools that allow these large datasets to be efficiently parsed are often lacking, or if indeed available, then limited by the necessity of a comparable reference genome as an adjunct. This, of course, can be difficult when working with non-model organisms. Fortunately, pipelines are currently available that avoid this prerequisite, thus allowing data to be a priori parsed. An oft-used molecular ecology program (i.e., Structure), for example, is facilitated by such pipelines, yet they are surprisingly absent for a second program that is similarly popular and computationally more efficient (i.e., Admixture). The two programs differ in that Admixture employs a maximum-likelihood framework whereas Structure uses a Bayesian approach, yet both produce similar results. Given these issues, there is an overriding (and recognized) need among researchers in molecular ecology for bioinformatic software that will not only condense output from replicated Admixture runs, but also infer from these data the optimal number of population clusters (K).ResultsHere we provide such a program (i.e., AdmixPipe) that (a) filters SNPs to allow the delineation of population structure in Admixture, then (b) parses the output for summarization and graphical representation via Clumpak. Our benchmarks effectively demonstrate how efficient the pipeline is for processing large, non-model datasets generated via double digest restriction-site associated DNA sequencing (ddRAD). Outputs not only parallel those from Structure, but also visualize the variation among individual Admixture runs, so as to facilitate selection of the most appropriate K-value.ConclusionsAdmixPipe successfully integrates Admixture analysis with popular variant call format (VCF) filtering software to yield file types readily analyzed by Clumpak. Large population genomic datasets derived from non-model organisms are efficiently analyzed via the parallel-processing capabilities of Admixture. AdmixPipe is distributed under the GNU Public License and freely available for Mac OSX and Linux platforms at: https://github.com/stevemussmann/admixturePipeline.

EXFI: Exon and splice graph prediction without a reference genome.

For population genetic studies in nonmodel organisms, it is important to use every single source of genomic information. This paper presents EXFI, a Python pipeline that predicts the splice graph and exon sequences using an assembled transcriptome and raw whole‐genome sequencing reads. The main algorithm uses Bloom filters to remove reads that are not part of the transcriptome, to predict the intron–exon boundaries, to then proceed to call exons from the assembly, and to generate the underlying splice graph. The results are returned in GFA1 format, which encodes both the predicted exon sequences and how they are connected to form transcripts. EXFI is written in Python, tested on Linux platforms, and the source code is available under the MIT License at https://github.com/jlanga/exfi.

Automatic Detection System for Abnormal Storage of Sensitive Data in Coastal Port Network Communication

Wang, Z., 2020. Automatic detection system for abnormal storage of sensitive data in coastal port network communication. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 868–872. Coconut Creek (Florida), ISSN 0749-0208.In order to effectively prevent the security threat of data storage in network communication and improve the accuracy of anomaly detection, an automatic anomaly detection system for sensitive data storage in coastal port network communication based on Linux platform is proposed and designed. The system is divided into data acquisition, protocol analysis, rule loading, exception detection, console and other functional modules. Among them, the data acquisition module mainly discusses the optional method of capturing data packets and BPF capture mechanism. The acquired network packets are processed by protocol analysis module and converted into data that can be easily analyzed; the rule description language is designed in the rule loading module to effectively discover and warn the abnormal behaviors by matching with the established rules; the information entropy is used to extract the behavioral characteristic parameters of network traffic in the abnormal detection part, and the detection process and detection method are discussed. The console module is designed as a web page based on WebSocket communication protocol, which is convenient to control the detection system across platforms. Experimental results show that the system has strong detection performance, high accuracy and reliability.

Moving analytical ultracentrifugation software to a good manufacturing practices (GMP) environment

Recent advances in instrumentation have moved analytical ultracentrifugation (AUC) closer to a possible validation in a Good Manufacturing Practices (GMP) environment. In order for AUC to be validated for a GMP environment, stringent requirements need to be satisfied; analysis procedures must be evaluated for consistency and reproducibility, and GMP capable data acquisition software needs to be developed and validated. These requirements extend to multiple regulatory aspects, covering documentation of instrument hardware functionality, data handling and software for data acquisition and data analysis, process control, audit trails and automation. Here we review the requirements for GMP validation of data acquisition software and illustrate software solutions based on UltraScan that address these requirements as far as they relate to the operation and data handling in conjunction with the latest analytical ultracentrifuge, the Optima AUC by Beckman Coulter. The software targets the needs of regulatory agencies, where AUC plays a critical role in the solution-based characterization of biopolymers and macromolecular assemblies. Biopharmaceutical and regulatory agencies rely heavily on this technique for characterizations of pharmaceutical formulations, biosimilars, injectables, nanoparticles, and other soluble therapeutics. Because of its resolving power, AUC is a favorite application, despite the current lack of GMP validation. We believe that recent advances in standards, hardware, and software presented in this work manage to bridge this gap and allow AUC to be routinely used in a GMP environment. AUC has great potential to provide more detailed information, at higher resolution, and with greater confidence than other analytical techniques, and our software satisfies an urgent need for AUC operation in the GMP environment. The software, including documentation, are publicly available for free download from Github. The multi-platform software is licensed by the LGPL v.3 open source license and supports Windows, Mac and Linux platforms. Installation instructions and a mailing list are available from ultrascan.aucsolutions.com.

GPU-Accelerated Vision for Robots: Improving System Throughput Using OpenCV and CUDA

OpenCV is an open source computer vision and machine learning library for C/C++/Python available for Windows, Linux, macOS, and Android platforms. It contains low-level image processing functions as well as high-level algorithms such as object identification, face recognition, and action classification in videos. OpenCV has become very popular, with more than 47,000 people in its user community and 18 million downloads (see https://opencv.org/about/). Under a Berkeley Software Distribution (BSD) license, it can be used for both academic and commercial applications.

The research of performance of the “Cypress” lightweight block cipher on different platforms

The Cypress block cipher is a lightweight algorithm based on the Feistel network with ARX-transformation as a round function. The Cypress block cipher supports 256-bit and 512-bit block and key length. The paper presents results of researches on the performance of lightweight block ciphers Cypress-256 and Cypress-512 and it gives comparison of performance of other well-known block ciphers such as AES-256, SPECK-64/128, SPECK-128/128, SPARX-128/128, DSTU GOST 28147: 2009. Performance was evaluated on Windows, Linux and Android platforms by measuring the encryption speed in the Electronic Code Book mode, in Mbps. The Cypress block cipher has demonstrated high performance on all selected platforms. Cypress-256 showed the best result (almost 3.5 Gbps) on the Windows 10 platform with 32-bit architecture. Cypress-512 also showed the best result (almost 5 Gbps) on the Windows 10 platform with 64-bit architecture. On the Linux platform with a 64-bit architecture, Cypress-256 showed a very high speed result (more than 8 Gbps). Cypress-256 and Cypress-512 block ciphers were also the best (1.3 Gbps and 1 Gbps, respectively) on the Android platform. In terms of performance and simplicity of implementation on different software and hardware platforms, Cypress algorithm has several advantages. Two variants of cipher (Cypress-256 and Cypress-512) are oriented on 32-bit and 64-bit architectures, respectively; high speed and compact implementation of transformations regardless of the platform used (server, workstation or mobile device). Minimum amount of memory is required for high-speed implementation, there is no need in pre-computed tables; there is an ability to organize efficient secure high-speed communication channels between mobile systems and servers, including those using hardware accelerators.

A Method for Human Facial Image Annotation on Low Power Consumption Autonomous Devices.

This paper proposes a classifier designed for human facial feature annotation, which is capable of running on relatively cheap, low power consumption autonomous microcomputer systems. An autonomous system is one that depends only on locally available hardware and software—for example, it does not use remote services available through the Internet. The proposed solution, which consists of a Histogram of Oriented Gradients (HOG) face detector and a set of neural networks, has comparable average accuracy and average true positive and true negative ratio to state-of-the-art deep neural network (DNN) architectures. However, contrary to DNNs, it is possible to easily implement the proposed method in a microcomputer with very limited RAM memory and without the use of additional coprocessors. The proposed method was trained and evaluated on a large 200,000 image face data set and compared with results obtained by other researchers. Further evaluation proves that it is possible to perform facial image attribute classification using the proposed algorithm on incoming video data captured by an RGB camera sensor of the microcomputer. The obtained results can be easily reproduced, as both the data set and source code can be downloaded. Developing and evaluating the proposed facial image annotation algorithm and its implementation, which is easily portable between various hardware and operating systems (virtually the same code works both on high-end PCs and microcomputers using the Windows and Linux platforms) and which is dedicated for low power consumption devices without coprocessors, is the main and novel contribution of this research.

Wireless network signal monitoring based on LAN packet capture and protocol analysis on grid programming

As a kind of network technology with super wide coverage and obvious transmission rate advantage, wireless network has become the most widely used network technology in people’s daily life. However, when information is transmitted and exchanged in wireless networks, information blocking often occurs, which restrains the transmission rate. Therefore, the signal monitoring technology in wireless network is of great practical significance. This paper will focus on the breakthrough of wireless WLAN packet capture and protocol analysis technology in computer communication technology, and use the grid programming method to analyze the implementation process of the whole system function module. Based on this technology, a wireless network detection system is developed in VC + + 7 environment, and the algorithm flow chart is given. Finally, the system designed in this paper will be tested on Linux platform. The experimental results show that the wireless network signal monitoring technology proposed in this paper can monitor the operation of wireless network in real time. The corresponding detection accuracy error is controlled at about 1%, and the corresponding detection accuracy can reach about 60% in different signal background.

VOFTools 5: An extension to non-convex geometries of calculation tools for volume of fluid methods

VOFTools 5: An extension to non-convex geometries of calculation tools for volume of fluid methods

Big Data Analytics and Processing Platform in Czech Republic Healthcare

Big data analytics (BDA) in healthcare has made a positive difference in the integration of Artificial Intelligence (AI) in advancements of analytical capabilities, while lowering the costs of medical care. The aim of this study is to improve the existing healthcare eSystem by implementing a Big Data Analytics (BDA) platform and to meet the requirements of the Czech Republic National Health Service (Tender-Id. VZ0036628, No. Z2017-035520). In addition to providing analytical capabilities on Linux platforms supporting current and near-future AI with machine-learning and data-mining algorithms, there is the need for ethical considerations mandating new ways to preserve privacy, all of which are preconditioned by the growing body of regulations and expectations. The presented BDA platform, has met all requirements (N > 100), including the healthcare industry-standard Transaction Processing Performance Council (TPC-H) decision support benchmark in compliance with the European Union (EU) and the Czech Republic legislations. Currently, the presented Proof of Concept (PoC) that has been upgraded to a production environment has unified isolated parts of Czech Republic healthcare over the past seven months. The reported PoC BDA platform, artefacts, and concepts are transferrable to healthcare systems in other countries interested in developing or upgrading their own national healthcare infrastructure in a cost-effective, secure, scalable and high-performance manner.

JOINUS: A User-Friendly Open-Source Software to Simulate Digital Superconductor Circuits

Single flux quantum (SFQ) based circuits are the subject of renewed attention due to their high speed and their very high energy efficiency. However, the need of cryogenic temperature, the complex physics of Josephson junctions and the lack of proper EDA tools causes slow progress in the field of superconducting electronics. In this work we introduce a new open source program named JOsephson INterface Utility Software (JOINUS [1]) that incorporates SPICE-based simulator engines, improved physical models and several powerful built-in routines. JOINUS is based on a user-friendly environment available on Linux, MacOS and Windows platforms to simplify the design and analysis of superconducting digital circuits.

Computational screening and in silico docking analysis of non-selective beta-blockers over beta-3 adrenergic receptors

Background: Beta-blockers with additional beneficial activity such as beta-2 agonist and alpha-1 blocking activity are developed and available as the third-generation beta-blockers for effective management of hypertension with comorbid conditions. Hypertension associated with obesity is one such common condition seen among majority of population. Aims and Objectives: This study attempts to screen the existing beta-blockers currently in use for hypertension for additional beta-3 agonist activity by in silico prediction methods. Materials and Methods: The approved non-selective beta-blockers, carvedilol, celiprolol, nebivolol, nadolol, carteolol, pindolol, propranolol, timolol, oxprenolol, sotalol, penbutolol, and labetalol, are selected to screen by three-dimensional (3D) quantitative structure–activity relationship analysis, molecular docking and dynamics were carried out in CentOS Linux platform version 5.0 installed in HPZ 800 workstation using Schrodinger LLC, New York. Results: Among screened 12 non-selective beta-blockers, carvedilol has high free binding scores of −63.186 followed by celiprolol with −53.225 and nebivolol with −53.054. Conclusion: The current research by in silico screening of 12 3D chemical structures of non-selective beta-blockers that effectively docked over beta 3 receptors are Carvedilol, celiprolol and nebivilol. Further studies can be carried on beta 3 cell lines followed by obesity animal models to validate our study finding.

Bioinformatics Analysis of Plant Cell Wall Evolution.

In the past hundreds of millions of years, from green algae to land plants, cell walls have developed into a highly complex structure that is essential for plant growth and survival. Plant cell wall diversity and evolution can be directly investigated by chemically profiling polysaccharides and lignins in the cell walls of diverse plants and algae. With the increasingly low cost and high throughput of DNA sequencing technologies, cell wall evolution can also be studied by bioinformatics analysis of the occurrence of cell wall synthesis-related enzymes in the genomes and transcriptomes of different species. This chapter presents a bioinformatics workflow running on a Linux platform to process genomic data for such gene occurrence analysis. As a case study, cellulose synthase (CesA) and CesA-like (Csl) protein families are mined for in two newly sequenced organisms: the charophyte green alga Klebsormidium flaccidum (renamed as Klebsormidium nitens) and the fern Lygodium japonicum.

Design and Implementation of Aircraft Measurement Data Monitoring and Analysis Software under Linux

Due to the platform change, the measurement data monitoring software under the Windows platform has no running environment. In order to solve the data monitoring blind zone between the data distribution server and the router link, this paper proposes to use port mirroring technology to solve this problem. And research and development of a set of Linux platform measurement data monitoring and analysis software.

"Transitivity": A Code for Computing Kinetic and Related Parameters in Chemical Transformations and Transport Phenomena.

The Transitivity function, defined in terms of the reciprocal of the apparent activation energy, measures the propensity for a reaction to proceed and can provide a tool for implementing phenomenological kinetic models. Applications to systems which deviate from the Arrhenius law at low temperature encouraged the development of a user-friendly graphical interface for estimating the kinetic and thermodynamic parameters of physical and chemical processes. Here, we document the Transitivity code, written in Python, a free open-source code compatible with Windows, Linux and macOS platforms. Procedures are made available to evaluate the phenomenology of the temperature dependence of rate constants for processes from the Arrhenius and Transitivity plots. Reaction rate constants can be calculated by the traditional Transition-State Theory using a set of one-dimensional tunneling corrections (Bell (1935), Bell (1958), Skodje and Truhlar and, in particular, the deformed (-TST) approach). To account for the solvent effect on reaction rate constant, implementation is given of the Kramers and of Collins–Kimball formulations. An input file generator is provided to run various molecular dynamics approaches in CPMD code. Examples are worked out and made available for testing. The novelty of this code is its general scope and particular exploit of -formulations to cope with non-Arrhenius behavior at low temperatures, a topic which is the focus of recent intense investigations. We expect that this code serves as a quick and practical tool for data documentation from electronic structure calculations: It presents a very intuitive graphical interface which we believe to provide an excellent working tool for researchers and as courseware to teach statistical thermodynamics, thermochemistry, kinetics, and related areas.

VAPPER: High-throughput variant antigen profiling in African trypanosomes of livestock.

BackgroundAnalysing variant antigen gene families on a population scale is a difficult challenge for conventional methods of read mapping and variant calling due to the great variability in sequence, copy number, and genomic loci. In African trypanosomes, hemoparasites of humans and animals, this is complicated by variant antigen repertoires containing hundreds of genes subject to various degrees of sequence recombination.FindingsWe introduce Variant Antigen Profiler (VAPPER), a tool that allows automated analysis of the variant surface glycoprotein repertoires of the most prevalent livestock African trypanosomes. VAPPER produces variant antigen profiles for any isolate of the veterinary pathogens Trypanosoma congolense and Trypanosoma vivax from genomic and transcriptomic sequencing data and delivers publication-ready figures that show how the queried isolate compares with a database of existing strains. VAPPER is implemented in Python. It can be installed to a local Galaxy instance from the ToolShed (https://toolshed.g2.bx.psu.edu/) or locally on a Linux platform via the command line (https://github.com/PGB-LIV/VAPPER). The documentation, requirements, examples, and test data are provided in the Github repository.ConclusionBy establishing two different, yet comparable methodologies, our approach is the first to allow large-scale analysis of African trypanosome variant antigens, large multi-copy gene families that are otherwise refractory to high-throughput analysis.

A Theoretical Study of Hardware Performance Counters-Based Malware Detection

Malware can range from simple adware to stealthy kernel control-flow modifying rootkits. Although anti-virus software is popular, an ongoing cat-and-mouse cycle of anti-virus development and malware that thwarts the anti-virus has ensued. More recently, trusted hardware-based malware detection techniques are being developed on the premise that it is easier to bypass software-based defenses than hardware-based counterparts. One such approach is the use of hardware performance counters (HPCs) to detect malware for Linux and Android platforms. This paper, for the first time, presents an analytical framework to investigate the security provided by HPC-based malware detection techniques. The HPC readings are periodically monitored over the duration of the program execution for comparison with a golden HPC reading. We develop a mathematical framework to investigate the probability of malware detection, when HPCs are monitored at a pre-determined sampling interval. In other words, given a program, a set of HPCs, and a sampling rate, the framework can be employed to analyze the probability of malware detection.

Simulation of three-dimensional dendritic growth using H-adaptive finite element method

Based on the AFEPack adaptive software package, the H-adaptive finite element method was used to solve the three-dimensional phase field model on the Linux platform. The growth of the three-dimensional dendrites in the undercooling melt was simulated numerically, and the effects of different undercooling on three-dimensional dendritic growth were studied. The real growth process of three-dimensional dendritic was reproduced. The dendritic growth pattern that we obtained was consistent with the crystallization theory. In order to verify the reliability of the simulation results, the three-dimensional dendrite tip growth rate and the tip radius were compared with the classical theory and the same conditions under the literature value. The results are compared with those obtained by Jeong et al. and obtained by finite difference method under the same conditions. It is found that the results obtained by H-adaptive finite element method are in agreement with the calculated results obtained by these two methods. It is proved that the H-adaptive finite element method is feasible to simulate the growth of three-dimensional dendrites. The method greatly reduces the computational cost of the phase field model solution and improves the efficiency of the solution.