scholarly journals Leaf-GP: An Open and Automated Software Application for Measuring Growth Phenotypes for Arabidopsis and Wheat

2017 ◽  
Author(s):  
Ji Zhou ◽  
Christopher Applegate ◽  
Albor Dobon Alonso ◽  
Daniel Reynolds ◽  
Simon Orford ◽  
...  
Keyword(s):  
Computer Vision ◽  
High Performance ◽  
Growth Traits ◽  
Source Code ◽  
Research Community ◽  
Phenotypic Analysis ◽  
Software Application ◽  
Dynamic Growth ◽  
Scientific Libraries ◽  
Over Time

AbstractBackgroundPlants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with environmental change as well as respond to different treatments. Although the importance of measuring dynamic growth traits is widely recognised, available open software tools are limited in terms of batch processing of image datasets, multiple trait analysis, software usability and cross-referencing results between experiments, making automated phenotypic analysis problematic.ResultsHere, we present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application that can be executed on different platforms. To facilitate diverse scientific user communities, we provide three versions of the software, including a graphic user interface (GUI) for personal computer (PC) users, a command-line interface for high-performance computer (HPC) users, and an interactive Jupyter Notebook (also known as the iPython Notebook) for computational biologists and computer scientists. The software is capable of extracting multiple growth traits automatically from large image datasets. We have utilised it in Arabidopsis thaliana and wheat (Triticum aestivum) growth studies at the Norwich Research Park (NRP, UK). By quantifying growth phenotypes over time, we are able to identify diverse plant growth patterns based on a variety of key growth-related phenotypes under varied experimental conditions.As Leaf-GP has been evaluated with noisy image series acquired by different imaging devices and still produced reliable biologically relevant outputs, we believe that our automated analysis workflow and customised computer vision based feature extraction algorithms can facilitate a broader plant research community for their growth and development studies. Furthermore, because we implemented Leaf-GP based on open Python-based computer vision, image analysis and machine learning libraries, our software can not only contribute to biological research, but also exhibit how to utilise existing open numeric and scientific libraries (including Scikit-image, OpenCV, SciPy and Scikit-learn) to build sound plant phenomics analytic solutions, efficiently and effectively.ConclusionsLeaf-GP is a comprehensive software application that provides three approaches to quantify multiple growth phenotypes from large image series. We demonstrate its usefulness and high accuracy based on two biological applications: (1) the quantification of growth traits for Arabidopsis genotypes under two temperature conditions; and (2) measuring wheat growth in the glasshouse over time. The software is easy-to-use and cross-platform, which can be executed on Mac OS, Windows and high-performance computing clusters (HPC), with open Python-based scientific libraries preinstalled. We share our modulated source code and executables (.exe for Windows; .app for Mac) together with this paper to serve the plant research community. The software, source code and experimental results are freely available at https://github.com/Crop-Phenomics-Group/Leaf-GP/releases.

scholarly journals PFRED: A computational platform for siRNA and antisense oligonucleotides design

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0238753
Author(s):  
Simone Sciabola ◽  
Hualin Xi ◽  
Dario Cruz ◽  
Qing Cao ◽  
Christine Lawrence ◽  
...  
Keyword(s):  
User Interface ◽  
Antisense Oligonucleotides ◽  
Source Code ◽  
Research Community ◽  
Design Criteria ◽  
Specific Gene ◽  
Software Application ◽  
Link Type ◽  
Intuitive User Interface ◽  
Computational Platform

PFRED a software application for the design, analysis, and visualization of antisense oligonucleotides and siRNA is described. The software provides an intuitive user-interface for scientists to design a library of siRNA or antisense oligonucleotides that target a specific gene of interest. Moreover, the tool facilitates the incorporation of various design criteria that have been shown to be important for stability and potency. PFRED has been made available as an open-source project so the code can be easily modified to address the future needs of the oligonucleotide research community. A compiled version is available for downloading at https://github.com/pfred/pfred-gui/releases/tag/v1.0 as a java Jar file. The source code and the links for downloading the precompiled version can be found at https://github.com/pfred.

scholarly journals PFRED: A computational platform for siRNA and antisense oligonucleotides design

2020 ◽  
Author(s):  
Simone Sciabola ◽  
Hualin Xi ◽  
Dario Cruz ◽  
Qing Cao ◽  
Christine Lawrence ◽  
...  
Keyword(s):  
User Interface ◽  
Antisense Oligonucleotides ◽  
Source Code ◽  
Research Community ◽  
Design Criteria ◽  
Specific Gene ◽  
Software Application ◽  
Link Type ◽  
Intuitive User Interface ◽  
Computational Platform

AbstractPFRED a software application for the design, analysis, and visualization of antisense oligonucleotides and siRNA is described. The software provides an intuitive user-interface for scientists to design a library of siRNA or antisense oligonucleotides that target a specific gene of interest. Moreover, the tool facilitates the incorporation of various design criteria that have been shown to be important for stability and potency. PFRED has been made available as an open-source project so the code can be easily modified to address the future needs of the oligonucleotide research community. A compiled version is available for downloading at https://github.com/pfred/pfred-gui/releases as a java Jar file. The source code and the links for downloading the precompiled version can be found at https://github.com/pfred.

МЕТОДЫ ДОСТИЖЕНИЯ МАКСИМАЛЬНОЙ ЭФФЕКТИВНОСТИ ПЛАТФОРМЫ ПРОТОТИПИРОВАНИЯ ВЫСОКОПРОИЗВОДИТЕЛЬНЫХ СИСТЕМ НА КРИСТАЛЛЕ НА ЗАДАЧАХ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

2020 ◽  
Vol 96 (3s) ◽  
pp. 585-588
Author(s):  
С.Е. Фролова ◽  
Е.С. Янакова
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computer Vision ◽  
High Performance ◽  
Systems On Chip ◽  
High Performance Systems ◽  
On Chip ◽  
Network Technologies ◽  
Neural Network Technologies

Предлагаются методы построения платформ прототипирования высокопроизводительных систем на кристалле для задач искусственного интеллекта. Изложены требования к платформам подобного класса и принципы изменения проекта СнК для имплементации в прототип. Рассматриваются методы отладки проектов на платформе прототипирования. Приведены результаты работ алгоритмов компьютерного зрения с использованием нейросетевых технологий на FPGA-прототипе семантических ядер ELcore. Methods have been proposed for building prototyping platforms for high-performance systems-on-chip for artificial intelligence tasks. The requirements for platforms of this class and the principles for changing the design of the SoC for implementation in the prototype have been described as well as methods of debugging projects on the prototyping platform. The results of the work of computer vision algorithms using neural network technologies on the FPGA prototype of the ELcore semantic cores have been presented.

scholarly journals Rough diamonds in emerging markets: legacy, competitiveness, and sustained high performance

2019 ◽  
Vol 26 (3) ◽  
pp. 363-386
Author(s):  
Seung Ho Park ◽  
Gerardo R. Ungson
Keyword(s):  
Human Capital ◽  
Emerging Markets ◽  
High Performance ◽  
Core Competencies ◽  
Market Potential ◽  
Government Support ◽  
Screening Tests ◽  
Institutional Voids ◽  
Content Type ◽  
Over Time

Purpose The purpose of this paper is to uncover the underlying drivers of sustained high performing companies based on a field study of 127 companies in Brazilian, Russian, Indian and Chinese (BRIC) and Association of Southeast Asian Nations (ASEAN) emerging markets. Understanding these companies provides a complementary way of appraising the growth, development and transformation of emerging markets. The authors synthesize the findings in an overarching framework that covers six strategies for building and sustaining legacy that leads to the succession of intergenerational wealth over time: overcoming institutional voids, inclusive markets, deepening localization, nurturing government support, building core competencies and harnessing human capital. The authors relate these strategies to different levels of development using Prahalad and Hart’s BOP framework. Design/methodology/approach This study examines the underlying drivers of sustained high-performance companies based on field studies from an initial set of 105,260 BRIC companies and close to 500 companies in ASEAN. The methods employed four screening tests to arrive at a selection of the highest-performing firms: 70 firms in the BRIC nations and 58 firms from ASEAN. Following the selection, the authors constructed cases using primary interviews and secondary data, with the assistance of Ernst & Young and with academic colleagues in Manila. These studies were originally conducted in two separate time periods and reported accordingly. This paper synthesizes the findings of these two studies to arrive at an extended integrative framework. Findings From the cases, the authors examine six strategies for building and sustaining legacy that lead to high performance over time: overcoming institutional voids, creating inclusive markets, deepening localization, nurturing government support, building core competencies and harnessing human capital. To address the evolving state of institutional voids in these countries, the authors employ similar methods to hypothesize the placement of these strategies in the context of the world economic pyramid, initially formulated as the “bottom of the pyramid” framework. Originality/value This paper synthesizes and extends the authors’ previous works by proposing the concept of legacy to describe the emergence and succession of local exemplary firms in emerging markets. This study aims to complement extant measures of nation-growth based primarily on GDP. The paper also extends the literature on institutional voids in shifting the focus from the mix of voids to their evolving state. Altogether, the paper provides a complementary narrative on assessing the market potential of emerging markets by adopting several categories of performance.

scholarly journals Markov-Modulated Continuous-Time Markov Chains to Identify Site- and Branch-Specific Evolutionary Variation in BEAST

2020 ◽  
Vol 70 (1) ◽  
pp. 181-189
Author(s):  
Guy Baele ◽  
Mandev S Gill ◽  
Paul Bastide ◽  
Philippe Lemey ◽  
Marc A Suchard
Keyword(s):  
High Performance ◽  
Markov Models ◽  
Marginal Likelihood ◽  
Likelihood Estimation ◽  
Phylogenetic Inference ◽  
Time Variability ◽  
Substitution Process ◽  
Wide Range ◽  
Markov Modulated ◽  
Over Time

Abstract Markov models of character substitution on phylogenies form the foundation of phylogenetic inference frameworks. Early models made the simplifying assumption that the substitution process is homogeneous over time and across sites in the molecular sequence alignment. While standard practice adopts extensions that accommodate heterogeneity of substitution rates across sites, heterogeneity in the process over time in a site-specific manner remains frequently overlooked. This is problematic, as evolutionary processes that act at the molecular level are highly variable, subjecting different sites to different selective constraints over time, impacting their substitution behavior. We propose incorporating time variability through Markov-modulated models (MMMs), which extend covarion-like models and allow the substitution process (including relative character exchange rates as well as the overall substitution rate) at individual sites to vary across lineages. We implement a general MMM framework in BEAST, a popular Bayesian phylogenetic inference software package, allowing researchers to compose a wide range of MMMs through flexible XML specification. Using examples from bacterial, viral, and plastid genome evolution, we show that MMMs impact phylogenetic tree estimation and can substantially improve model fit compared to standard substitution models. Through simulations, we show that marginal likelihood estimation accurately identifies the generative model and does not systematically prefer the more parameter-rich MMMs. To mitigate the increased computational demands associated with MMMs, our implementation exploits recent developments in BEAGLE, a high-performance computational library for phylogenetic inference. [Bayesian inference; BEAGLE; BEAST; covarion, heterotachy; Markov-modulated models; phylogenetics.]

Multi Object Detection and Tracking from Video File

2014 ◽  
Vol 533 ◽  
pp. 218-225 ◽  
Author(s):  
Rapee Krerngkamjornkit ◽  
Milan Simic
Keyword(s):  
Computer Vision ◽  
Background Subtraction ◽  
Moving Objects ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Video Tracking ◽  
Partial Occlusion ◽  
Video File ◽  
Detection And Tracking ◽  
Over Time

This paper describes computer vision algorithms for detection, identification, and tracking of moving objects in a video file. The problem of multiple object tracking can be divided into two parts; detecting moving objects in each frame and associating the detections corresponding to the same object over time. The detection of moving objects uses a background subtraction algorithm based on Gaussian mixture models. The motion of each track is estimated by a Kalman filter. The video tracking algorithm was successfully tested using the BIWI walking pedestrians datasets [. The experimental results show that system can operate in real time and successfully detect, track and identify multiple targets in the presence of partial occlusion.

scholarly journals Trajectory classes of job performance

2015 ◽  
Vol 20 (4) ◽  
pp. 424-442 ◽  
Author(s):  
Mariella Miraglia ◽  
Guido Alessandri ◽  
Laura Borgogni
Keyword(s):  
Individual Differences ◽  
Job Performance ◽  
Self Efficacy ◽  
High Performance ◽  
Organizational Transformation ◽  
Growth Mixture Modeling ◽  
Content Type ◽  
Stable Class ◽  
Supervisory Ratings ◽  
Over Time

Purpose – Previous literature has recognized the variability of job performance, calling attention to the inter-individual differences in performance change. Building on Murphy’s (1989) theoretical model of performance, the purpose of this paper is to verify the existence of two distinct classes of performance, reflecting stable and increasing trends, and to investigate which personal conditions prompt the inclusion of individuals in one class rather than the other. Design/methodology/approach – Overall job performance was obtained from supervisory ratings for four consecutive years for 410 professionals of a large Italian company going through significant reorganization. Objective data were merged with employees’ organizational tenure and self-efficacy. Growth Mixture Modeling was used. Findings – Two main groups were identified: the first one started at higher levels of performance and showed a stable trajectory over time (stable class); the second group started at lower levels and reported an increasing trajectory (increasing class). Employees’ with stronger efficacy beliefs and lower tenure were more likely to belong to the stable class. Originality/value – Through a powerful longitudinal database, the nature, the structure and the inter-individual differences in job performance over time are clarified. The study extends Murphy’s (1989) model, showing how transition stages in job performance may occur also as a result of organizational transformation. Moreover, it demonstrates the essential role of self-efficacy in maintaining high performance levels over time.

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