scholarly journals Plant PhysioSpace: a robust tool to compare stress response across plant species

2020 ◽  
Author(s):  
Ali Hadizadeh Esfahani ◽  
Janina Maß ◽  
Asis Hallab ◽  
Bernhard M. Schuldt ◽  
David Nevarez ◽  
...  
Keyword(s):  
Stress Response ◽  
A Priori ◽  
Plant Stress ◽  
Marker Genes ◽  
Specific Gene ◽  
Data Sets ◽  
Data Set ◽  
Plant Stress Response ◽  
Gene Sets ◽  
Public Data

AbstractGeneralization of transcriptomics results can be achieved by comparison across experiments, which is based on integration of interrelated transcriptomics studies into a compendium. Both characterization of the fate of the organism under study as well as distinguishing between generic and specific responses can be gained in such a broader context. We have built such a compendium for plant stress response, which is based on integrating publicly available data sets for plant stress response to generalize results across studies and extract the most robust and meaningful information possible from them.There are numerous methods and tools to analyze such data sets, most focusing on gene-wise dimension reduction of data to obtain marker genes and gene sets, e.g. for pathway analysis. Relying only on isolated biological modules might lead to missing of important confounders and relevant context. Therefore, we have chosen a different approach: Our novel tool, which we called Plant PhysioSpace, provides the ability to compute experimental conditions across species and platforms without a priori reducing the reference information to specific gene-sets. It extracts physiologically relevant signatures from a reference data set, a collection of public data sets, by integrating and transforming heterogeneous reference gene expression data into a set of physiology-specific patterns, called PhysioSpace. New experimental data can be mapped to these PhysioSpaces, resulting in similarity scores, providing quantitative similarity of the new experiment to an a priori compendium.Here we report the implementation of two R packages, one software and one data package, and a shiny web application, which provides plant biologists convenient ways to access the method and a precomputed compendium of more than 900 PhysioSpace basis vectors from 4 different species (Arabidopsis thaliana, Oryza sativa, Glycine max, and Triticum aestivum).The tool reduces the dimensionality of data sample-wise (and not gene-wise), which results in a vector containing all genes. This method is very robust against noise and change of platform while still being sensitive. Plant PhysioSpace can therefore be used as an inter-species or cross-platform similarity measure. We demonstrate that Plant PhysioSpace can successfully translate stress responses between different species and platforms (including single cell technologies).

scholarly journals Identification of active modules in interaction networks using node2vec network embedding

2021 ◽  
Author(s):  
Claude Pasquier ◽  
Vincent Guerlais ◽  
Denis Pallez ◽  
Raphael Rapetti-Mauss ◽  
Olivier Soriani
Keyword(s):  
Cellular Response ◽  
Single Gene ◽  
A Priori ◽  
Direct Consequence ◽  
Specific Gene ◽  
Data Sets ◽  
Network Embedding ◽  
Signaling Mechanisms ◽  
Gene Sets ◽  
Fixed Parameter

The identification of condition-specific gene sets from transcriptomic experiments is important to reveal regulatory and signaling mechanisms associated with a given cellular response. Statistical approaches using only expression data allow the identification of genes whose expression is most altered between different conditions. However, a phenotype is rarely a direct consequence of the activity of a single gene, but rather reflects the interplay of several genes to carry out certain molecular processes. Many methods have been proposed to analyze the activity of genes in light of our knowledge of their molecular interactions. However, existing methods have many limitations that make them of limited use to biologists: they detect modules that are too large, too small, or they require the users to specify a priori the size of the modules they are looking for. We propose AMINE (Active Module Identification through Network Embedding), an efficient method for the identification of active modules. Experiments carried out on artificial data sets show that the results obtained are more reliable than many available methods. Moreover, the size of the modules to be identified is not a fixed parameter of the method and does not need to be specified; rather, it adjusts according to the size of the modules to be found. The applications carried out on real datasets show that the method enables to find important genes already highlighted by approaches solely based on gene variations, but also to identify new groups of genes of high interest. In addition, AMINE method can be used as a web service on your own data (http://amine.i3s.unice.fr).

Fast and accurate detection of surface defect based on improved YOLOv4

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiawei Lian ◽  
Junhong He ◽  
Yun Niu ◽  
Tianze Wang
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Surface Defect ◽  
Steel Ingot ◽  
Industrial Applications ◽  
Data Sets ◽  
Data Set ◽  
Processing Technologies ◽  
Content Type ◽  
Public Data

Purpose The current popular image processing technologies based on convolutional neural network have the characteristics of large computation, high storage cost and low accuracy for tiny defect detection, which is contrary to the high real-time and accuracy, limited computing resources and storage required by industrial applications. Therefore, an improved YOLOv4 named as YOLOv4-Defect is proposed aim to solve the above problems. Design/methodology/approach On the one hand, this study performs multi-dimensional compression processing on the feature extraction network of YOLOv4 to simplify the model and improve the feature extraction ability of the model through knowledge distillation. On the other hand, a prediction scale with more detailed receptive field is added to optimize the model structure, which can improve the detection performance for tiny defects. Findings The effectiveness of the method is verified by public data sets NEU-CLS and DAGM 2007, and the steel ingot data set collected in the actual industrial field. The experimental results demonstrated that the proposed YOLOv4-Defect method can greatly improve the recognition efficiency and accuracy and reduce the size and computation consumption of the model. Originality/value This paper proposed an improved YOLOv4 named as YOLOv4-Defect for the detection of surface defect, which is conducive to application in various industrial scenarios with limited storage and computing resources, and meets the requirements of high real-time and precision.

scholarly journals In vivo diagnostics of early abiotic plant stress response via Raman spectroscopy

2017 ◽  
Vol 114 (13) ◽  
pp. 3393-3396 ◽  
Author(s):  
Narangerel Altangerel ◽  
Gombojav O. Ariunbold ◽  
Connor Gorman ◽  
Masfer H. Alkahtani ◽  
Eli J. Borrego ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Stress Response ◽  
High Throughput ◽  
Plant Stress ◽  
Spectroscopic Technique ◽  
Plant Phenotyping ◽  
Plant Stress Response ◽  
Concentration Changes ◽  
Concentration Levels

Development of a phenotyping platform capable of noninvasive biochemical sensing could offer researchers, breeders, and producers a tool for precise response detection. In particular, the ability to measure plant stress in vivo responses is becoming increasingly important. In this work, a Raman spectroscopic technique is developed for high-throughput stress phenotyping of plants. We show the early (within 48 h) in vivo detection of plant stress responses. Coleus (Plectranthus scutellarioides) plants were subjected to four common abiotic stress conditions individually: high soil salinity, drought, chilling exposure, and light saturation. Plants were examined poststress induction in vivo, and changes in the concentration levels of the reactive oxygen-scavenging pigments were observed by Raman microscopic and remote spectroscopic systems. The molecular concentration changes were further validated by commonly accepted chemical extraction (destructive) methods. Raman spectroscopy also allows simultaneous interrogation of various pigments in plants. For example, we found a unique negative correlation in concentration levels of anthocyanins and carotenoids, which clearly indicates that plant stress response is fine-tuned to protect against stress-induced damages. This precision spectroscopic technique holds promise for the future development of high-throughput screening for plant phenotyping and the quantification of biologically or commercially relevant molecules, such as antioxidants and pigments.

scholarly journals A SELF-ORGANIZING MAP FOR MIXED CONTINUOUS AND CATEGORICAL DATA

2011 ◽  
pp. 24-32 ◽  
Author(s):  
Nicoleta Rogovschi ◽  
Mustapha Lebbah ◽  
Younès Bennani
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Mixed Data ◽  
Categorical Variables ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Public Data ◽  
Self Organizing

Most traditional clustering algorithms are limited to handle data sets that contain either continuous or categorical variables. However data sets with mixed types of variables are commonly used in data mining field. In this paper we introduce a weighted self-organizing map for clustering, analysis and visualization mixed data (continuous/binary). The learning of weights and prototypes is done in a simultaneous manner assuring an optimized data clustering. More variables has a high weight, more the clustering algorithm will take into account the informations transmitted by these variables. The learning of these topological maps is combined with a weighting process of different variables by computing weights which influence the quality of clustering. We illustrate the power of this method with data sets taken from a public data set repository: a handwritten digit data set, Zoo data set and other three mixed data sets. The results show a good quality of the topological ordering and homogenous clustering.

Validation of Sentinel-5P TROPOMI tropospheric NO2 with airborne imaging, ground-based stationary, and mobile DOAS measurements from the S5P-VAL-DE-Ruhr campaign

2021 ◽  
Author(s):  
Kezia Lange ◽  
Andreas C. Meier ◽  
Michel Van Roozendael ◽  
Thomas Wagner ◽  
Thomas Ruhtz ◽  
...  
Keyword(s):  
A Priori ◽  
Data Sets ◽  
Vertical Column ◽  
Data Set ◽  
Ruhr Area ◽  
Spatial And Temporal Heterogeneity ◽  
Western Germany ◽  
Airborne Imaging ◽  
Urban Character ◽  
One Year

<p>Airborne imaging DOAS and ground-based stationary and mobile DOAS measurements were conducted during the ESA funded S5P-VAL-DE-Ruhr campaign in September 2020 in the Ruhr area. The Ruhr area is located in Western Germany and is a pollution hotspot in Europe with urban character as well as large industrial emitters. The measurements are used to validate data from the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) with focus on the NO<sub>2</sub> tropospheric vertical column product.</p><p>Seven flights were performed with the airborne imaging DOAS instrument, AirMAP, providing continuous maps of NO<sub>2</sub> in the layers below the aircraft. These flights cover many S5P ground pixels within an area of about 40 km side length and were accompanied by ground-based stationary measurements and three mobile car DOAS instruments. Stationary measurements were conducted by two Pandora, two zenith-sky and two MAX-DOAS instruments distributed over three target areas, partly as long-term measurements over a one-year period.</p><p>Airborne and ground-based measurements were compared to evaluate the representativeness of the measurements in time and space. With a resolution of about 100 x 30 m<sup>2</sup>, the AirMAP data creates a link between the ground-based and the TROPOMI measurements with a resolution of 3.5 x 5.5 km<sup>2</sup> and is therefore well suited to validate TROPOMI's tropospheric NO<sub>2</sub> vertical column.</p><p>The measurements on the seven flight days show strong variability depending on the different target areas, the weekday and meteorological conditions. We found an overall low bias of the TROPOMI operational NO<sub>2</sub> data for all three target areas but with varying magnitude for different days. The campaign data set is compared to custom TROPOMI NO<sub>2</sub> products, using different auxiliary data, such as albedo or a priori vertical profiles to evaluate the influence on the TROPOMI data product. Analyzing and comparing the different data sets provides more insight into the high spatial and temporal heterogeneity in NO<sub>2</sub> and its impact on satellite observations and their validation.</p>

Marlim R3D: A realistic model for controlled-source electromagnetic simulations — Phase 2: The controlled-source electromagnetic data set

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. E293-E299
Author(s):  
Jorlivan L. Correa ◽  
Paulo T. L. Menezes
Keyword(s):  
A Priori ◽  
Synthetic Data ◽  
Realistic Model ◽  
Earth Model ◽  
Data Sets ◽  
Data Set ◽  
Geoelectric Model ◽  
Controlled Source ◽  
The North ◽  
Electromagnetic Simulations

Synthetic data provided by geoelectric earth models are a powerful tool to evaluate a priori a controlled-source electromagnetic (CSEM) workflow effectiveness. Marlim R3D (MR3D) is an open-source complex and realistic geoelectric model for CSEM simulations of the postsalt turbiditic reservoirs at the Brazilian offshore margin. We have developed a 3D CSEM finite-difference time-domain forward study to generate the full-azimuth CSEM data set for the MR3D earth model. To that end, we fabricated a full-azimuth survey with 45 towlines striking the north–south and east–west directions over a total of 500 receivers evenly spaced at 1 km intervals along the rugged seafloor of the MR3D model. To correctly represent the thin, disconnected, and complex geometries of the studied reservoirs, we have built a finely discretized mesh of [Formula: see text] cells leading to a large mesh with a total of approximately 90 million cells. We computed the six electromagnetic field components (Ex, Ey, Ez, Hx, Hy, and Hz) at six frequencies in the range of 0.125–1.25 Hz. In our efforts to mimic noise in real CSEM data, we summed to the data a multiplicative noise with a 1% standard deviation. Both CSEM data sets (noise free and noise added), with inline and broadside geometries, are distributed for research or commercial use, under the Creative Common License, at the Zenodo platform.

scholarly journals Sphingolipids: towards an integrated view of metabolism during the plant stress response

2019 ◽  
Vol 225 (2) ◽  
pp. 659-670 ◽  
Author(s):  
Eloïse Huby ◽  
Johnathan A. Napier ◽  
Fabienne Baillieul ◽  
Louise V. Michaelson ◽  
Sandrine Dhondt‐Cordelier
Keyword(s):  
Stress Response ◽  
Plant Stress ◽  
Plant Stress Response

scholarly journals CNVScope: Visually Exploring Copy Number Aberrations in Cancer Genomes

2019 ◽  
Vol 18 ◽  
pp. 117693511989029
Author(s):  
James LT Dalgleish ◽  
Yonghong Wang ◽  
Jack Zhu ◽  
Paul S Meltzer
Keyword(s):  
Copy Number ◽  
High Performance ◽  
Data Sets ◽  
Data Set ◽  
The Public ◽  
Public Data ◽  
Analysis Package ◽  
Cis And Trans ◽  
High Performance Computing Cluster ◽  
Shiny Application

Motivation: DNA copy number (CN) data are a fast-growing source of information used in basic and translational cancer research. Most CN segmentation data are presented without regard to the relationship between chromosomal regions. We offer both a toolkit to help scientists without programming experience visually explore the CN interactome and a package that constructs CN interactomes from publicly available data sets. Results: The CNVScope visualization, based on a publicly available neuroblastoma CN data set, clearly displays a distinct CN interaction in the region of the MYCN, a canonical frequent amplicon target in this cancer. Exploration of the data rapidly identified cis and trans events, including a strong anticorrelation between 11q loss and17q gain with the region of 11q loss bounded by the cell cycle regulator CCND1. Availability: The shiny application is readily available for use at http://cnvscope.nci.nih.gov/ , and the package can be downloaded from CRAN ( https://cran.r-project.org/package=CNVScope ), where help pages and vignettes are located. A newer version is available on the GitHub site ( https://github.com/jamesdalg/CNVScope/ ), which features an animated tutorial. The CNVScope package can be locally installed using instructions on the GitHub site for Windows and Macintosh systems. This CN analysis package also runs on a linux high-performance computing cluster, with options for multinode and multiprocessor analysis of CN variant data. The shiny application can be started using a single command (which will automatically install the public data package).

A PROBABILISTIC SELF-ORGANIZING MAP FOR BINARY DATA TOPOGRAPHIC CLUSTERING

2008 ◽  
Vol 07 (04) ◽  
pp. 363-383 ◽  
Author(s):  
MUSTAPHA LEBBAH ◽  
YOUNÈS BENNANI ◽  
NICOLETA ROGOVSCHI
Keyword(s):  
Binary Data ◽  
Learning Algorithm ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Binary Coding ◽  
Public Data ◽  
Multivariate Binary Data ◽  
Self Organizing

This paper introduces a probabilistic self-organizing map for topographic clustering, analysis and visualization of multivariate binary data or categorical data using binary coding. We propose a probabilistic formalism dedicated to binary data in which cells are represented by a Bernoulli distribution. Each cell is characterized by a prototype with the same binary coding as used in the data space and the probability of being different from this prototype. The learning algorithm, Bernoulli on self-organizing map, that we propose is an application of the EM standard algorithm. We illustrate the power of this method with six data sets taken from a public data set repository. The results show a good quality of the topological ordering and homogenous clustering.

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