scholarly journals Robotic Assay for Drought (RoAD): An Automated Phenotyping System for Brassinosteroid and Drought Response

2020 ◽  
Author(s):  
Lirong Xiang ◽  
Trevor M. Nolan ◽  
Yin Bao ◽  
Mitch Elmore ◽  
Taylor Tuel ◽  
...  
Keyword(s):  
Stress Responses ◽  
Imaging System ◽  
Leaf Length ◽  
Machine Learning Algorithms ◽  
Phenotypic Trait ◽  
Drought Response ◽  
3 Dimensional ◽  
The Road ◽  
Applied Machine Learning ◽  
Maize Plants

Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the BR pathway have previously been identified and characterized. However, BR phenotyping experiments are typically performed on petri plates and/or in a low-throughput manner. Additionally, the BR pathway has extensive crosstalk with drought responses, but drought experiments are time-consuming and difficult to control. Thus, we developed Robotic Assay for Drought (RoAD) to perform BR and drought response experiments in soil-grown Arabidopsis plants. RoAD is equipped with a bench scale, a precisely controlled watering system, an RGB camera, and a laser profilometer. It performs daily weighing, watering, and imaging tasks and is capable of administering BR response assays by watering plants with Propiconazole (PCZ), a BR biosynthesis inhibitor. We developed image processing algorithms for both plant segmentation and phenotypic trait extraction in order to accurately measure traits in 2-dimensional (2D) and 3-dimensional (3D) spaces including plant surface area, leaf length, and leaf width. We then applied machine learning algorithms that utilized the extracted phenotypic parameters to identify image-derived traits that can distinguish control, drought, and PCZ-treated plants. We carried out PCZ and drought experiments on a set of BR mutants and Arabidopsis accessions with altered BR responses. Finally, we extended the RoAD assays to perform BR response assays using PCZ in Zea mays (maize) plants. This study establishes an automated and non-invasive robotic imaging system as a tool to accurately measure morphological and growth-related traits of Arabidopsis and maize plants, providing insights into the BR-mediated control of plant growth and stress responses.

scholarly journals Financial Context News Sentiment Analysis for the Lithuanian Language

2021 ◽  
Vol 11 (10) ◽  
pp. 4443
Author(s):  
Rokas Štrimaitis ◽  
Pavel Stefanovič ◽  
Simona Ramanauskaitė ◽  
Asta Slotkienė
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Experimental Investigations ◽  
Support Vector ◽  
Applied Machine Learning ◽  
Bayes Algorithm ◽  
Website Content

Financial area analysis is not limited to enterprise performance analysis. It is worth analyzing as wide an area as possible to obtain the full impression of a specific enterprise. News website content is a datum source that expresses the public’s opinion on enterprise operations, status, etc. Therefore, it is worth analyzing the news portal article text. Sentiment analysis in English texts and financial area texts exist, and are accurate, the complexity of Lithuanian language is mostly concentrated on sentiment analysis of comment texts, and does not provide high accuracy. Therefore in this paper, the supervised machine learning model was implemented to assign sentiment analysis on financial context news, gathered from Lithuanian language websites. The analysis was made using three commonly used classification algorithms in the field of sentiment analysis. The hyperparameters optimization using the grid search was performed to discover the best parameters of each classifier. All experimental investigations were made using the newly collected datasets from four Lithuanian news websites. The results of the applied machine learning algorithms show that the highest accuracy is obtained using a non-balanced dataset, via the multinomial Naive Bayes algorithm (71.1%). The other algorithm accuracies were slightly lower: a long short-term memory (71%), and a support vector machine (70.4%).

scholarly journals Precise Estimation of NDVI with a Simple NIR Sensitive RGB Camera and Machine Learning Methods for Corn Plants

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3208 ◽  
Author(s):  
Liangju Wang ◽  
Yunhong Duan ◽  
Libo Zhang ◽  
Tanzeel U. Rehman ◽  
Dongdong Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Professional Training ◽  
Imaging System ◽  
Estimation Method ◽  
Machine Learning Algorithms ◽  
Plant Phenotyping ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Corn Plants ◽  
Hyperspectral Camera

The normalized difference vegetation index (NDVI) is widely used in remote sensing to monitor plant growth and chlorophyll levels. Usually, a multispectral camera (MSC) or hyperspectral camera (HSC) is required to obtain the near-infrared (NIR) and red bands for calculating NDVI. However, these cameras are expensive, heavy, difficult to geo-reference, and require professional training in imaging and data processing. On the other hand, the RGBN camera (NIR sensitive RGB camera, simply modified from standard RGB cameras by removing the NIR rejection filter) have also been explored to measure NDVI, but the results did not exactly match the NDVI from the MSC or HSC solutions. This study demonstrates an improved NDVI estimation method with an RGBN camera-based imaging system (Ncam) and machine learning algorithms. The Ncam consisted of an RGBN camera, a filter, and a microcontroller with a total cost of only $70 ~ 85. This new NDVI estimation solution was compared with a high-end hyperspectral camera in an experiment with corn plants under different nitrogen and water treatments. The results showed that the Ncam with two-band-pass filter achieved high performance (R2 = 0.96, RMSE = 0.0079) at estimating NDVI with the machine learning model. Additional tests showed that besides NDVI, this low-cost Ncam was also capable of predicting corn plant nitrogen contents precisely. Thus, Ncam is a potential option for MSC and HSC in plant phenotyping projects.

Comparison of a Fluoroscopic 3-Dimensional Imaging System and Conventional CT in Detection of Pars Fractures in the Cadaveric Lumbar Spine

2012 ◽  
Vol 25 (8) ◽  
pp. 429-432 ◽  
Author(s):  
Christopher K. Kepler ◽  
Helene Pavlov ◽  
Richard J. Herzog ◽  
Bernard A. Rawlins ◽  
Yoshimi Endo ◽  
...  
Keyword(s):  
Lumbar Spine ◽  
Imaging System ◽  
3 Dimensional ◽  
Conventional Ct

scholarly journals Hyperspectral Imaging Using Laser Excitation for Fast Raman and Fluorescence Hyperspectral Imaging for Sorting and Quality Control Applications

2018 ◽  
Vol 4 (10) ◽  
pp. 110 ◽  
Author(s):  
Florian Gruber ◽  
Philipp Wollmann ◽  
Wulf Grählert ◽  
Stefan Kaskel
Keyword(s):  
Quality Control ◽  
Hyperspectral Imaging ◽  
Lead Zirconate Titanate ◽  
Imaging System ◽  
Measurement Data ◽  
Machine Learning Algorithms ◽  
Area Measurement ◽  
Large Area ◽  
Sorting Tasks

A hyperspectral measurement system for the fast and large area measurement of Raman and fluorescence signals was developed, characterized and tested. This laser hyperspectral imaging system (Laser-HSI) can be used for sorting tasks and for continuous quality monitoring. The system uses a 532 nm Nd:YAG laser and a standard pushbroom HSI camera. Depending on the lens selected, it is possible to cover large areas (e.g., field of view (FOV) = 386 mm) or to achieve high spatial resolutions (e.g., 0.02 mm). The developed Laser-HSI was used for four exemplary experiments: (a) the measurement and classification of a mixture of sulphur and naphthalene; (b) the measurement of carotenoid distribution in a carrot slice; (c) the classification of black polymer particles; and, (d) the localization of impurities on a lead zirconate titanate (PZT) piezoelectric actuator. It could be shown that the measurement data obtained were in good agreement with reference measurements taken with a high-resolution Raman microscope. Furthermore, the suitability of the measurements for classification using machine learning algorithms was also demonstrated. The developed Laser-HSI could be used in the future for complex quality control or sorting tasks where conventional HSI systems fail.

scholarly journals Analysis of Non-Structural Carbohydrates and Xylem Anatomy of Leaf Petioles Offers New Insights in the Drought Response of Two Grapevine Cultivars

2020 ◽  
Vol 21 (4) ◽  
pp. 1457 ◽  
Author(s):  
Rachele Falchi ◽  
Elisa Petrussa ◽  
Enrico Braidot ◽  
Paolo Sivilotti ◽  
Francesco Boscutti ◽  
...  
Keyword(s):  
Water Stress ◽  
Drought Stress ◽  
Stress Response ◽  
Stress Responses ◽  
Cabernet Sauvignon ◽  
Drought Response ◽  
Xylem Anatomy ◽  
Anatomical Analysis ◽  
Starch Mobilization ◽  
Xylem Conduits

In grapevine, the anatomy of xylem conduits and the non-structural carbohydrates (NSCs) content of the associated living parenchyma are expected to influence water transport under water limitation. In fact, both NSC and xylem features play a role in plant recovery from drought stress. We evaluated these traits in petioles of Cabernet Sauvignon (CS) and Syrah (SY) cultivars during water stress (WS) and recovery. In CS, the stress response was associated to NSC consumption, supporting the hypothesis that starch mobilization is related to an increased supply of maltose and sucrose, putatively involved in drought stress responses at the xylem level. In contrast, in SY, the WS-induced increase in the latter soluble NSCs was maintained even 2 days after re-watering, suggesting a different pattern of utilization of NSC resources. Interestingly, the anatomical analysis revealed that conduits are constitutively wider in SY in well-watered (WW) plants, and that water stress led to the production of narrower conduits only in this cultivar.

scholarly journals Spastic diparetic does not directly affect the capacity to ascend and descend access ramps: three-dimensional analysis

2017 ◽  
Vol 30 (3) ◽  
pp. 537-547 ◽  
Author(s):  
Tainá Ribas Mélo ◽  
Ana Tereza Bittencourt Guimarães ◽  
Vera Lúcia Israel
Keyword(s):  
Imaging System ◽  
Three Dimensional ◽  
Control Group ◽  
Initial Contact ◽  
Experimental Conditions ◽  
3 Dimensional ◽  
Flexion Extension ◽  
Posterior Trunk ◽  
Hip Flexion ◽  
Study Participants

Abstract Introduction: Diplegic children have difficulties in gait and therefore ramps are used as strategies of accessibility. Objective: The present study investigated the influence of an inclined surface (ascending and descending) on the kinematic characteristics during gait of the diplegic group (DG) when compared to typically developing children of the control group (CG). Methods: Study participants included 20 children (10 with DG and 10 CG) matched by age, which were evaluated in three experimental conditions (horizontal and inclined ascending and inclined descending surfaces of 7º) through an optoelectronic imaging system. Results: Among the linear kinematic variables, only step width differed among groups, however, without influence of the surface. The foot height differed among the groups only in the descending phase, where DG had greater difficulty in raising the foot. The 3-dimensional gait analyses could not provide more evidences of differences in kinematics variables, especially in transverse plane, between DG and CG, but provide some evidence to support that hip range of motion (ROM) during the gait cycle, hip flexion-extension in initial contact, knee ROM and the 2nd anterior-posterior trunk peak amplitude of the DG were influenced on descent by their flexor pattern. Conclusion: The DG was most affected by the inclination plane than CG especially on descent. Although a hip and knee flexor pattern is evident for DG on inclination of 7º, this angle is accessible since it allows independent gait functional activity.

scholarly journals Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Hye-Jin Kim ◽  
Sung Min Park ◽  
Byung Jin Choi ◽  
Seung-Hyun Moon ◽  
Yong-Hyuk Kim
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Quality Control ◽  
Mean Squared Error ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Weather Element ◽  
Applied Machine Learning ◽  
Squared Error ◽  
Atmospheric Data

We propose three quality control (QC) techniques using machine learning that depend on the type of input data used for training. These include QC based on time series of a single weather element, QC based on time series in conjunction with other weather elements, and QC using spatiotemporal characteristics. We performed machine learning-based QC on each weather element of atmospheric data, such as temperature, acquired from seven types of IoT sensors and applied machine learning algorithms, such as support vector regression, on data with errors to make meaningful estimates from them. By using the root mean squared error (RMSE), we evaluated the performance of the proposed techniques. As a result, the QC done in conjunction with other weather elements had 0.14% lower RMSE on average than QC conducted with only a single weather element. In the case of QC with spatiotemporal characteristic considerations, the QC done via training with AWS data showed performance with 17% lower RMSE than QC done with only raw data.

scholarly journals Endoplasmic reticulum stress response in the roadway for the effects of non-steroidal anti-inflammatory drugs

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Fernanda L.B. Mügge ◽  
Aristóbolo M. Silva
Keyword(s):  
Endoplasmic Reticulum ◽  
Stress Response ◽  
Er Stress ◽  
Stress Responses ◽  
Endoplasmic Reticulum Stress Response ◽  
The Road ◽  
Er Stress Response ◽  
Mediated Effects ◽  
Inflammatory Drugs ◽  
Anti Inflammatory

AbstractOver the past decade, a handful of evidence has been provided that nonsteroidal anti-inflammatory drugs (NSAIDs) display effects on the homeostasis of the endoplasmic reticulum (ER). Their uptake into cells will eventually lead to activation or inhibition of key molecules that mediate ER stress responses, raising not only a growing interest for a pharmacological target in ER stress responses but also important questions how the ER-stress mediated effects induced by NSAIDs could be therapeutically advantageous or not. We review here the toxicity effects and therapeutic applications of NSAIDs involving the three majors ER stress arms namely PERK, IRE1, and ATF6. First, we provide brief introduction on the well-established and characterized downstream events mediated by these ER stress players, followed by presentation of the NSAIDs compounds and mode of action, and finally their effects on ER stress response. NSAIDs present promising drug agents targeting the components of ER stress in different aspects of cancer and other diseases, but a better comprehension of the mechanisms underlying their benefits and harms will certainly pave the road for several diseases’ therapy.

scholarly journals Location Based Augmented Reality Navigation Application

2019 ◽  
pp. 241-245
Author(s):  
Aditya Anerao ◽  
Samridhi Pramanik ◽  
Hemraj Bobade ◽  
S.N Firame
Keyword(s):  
Augmented Reality ◽  
3 Dimensional ◽  
The Road ◽  
Current Location ◽  
New Perspective ◽  
Blue Line ◽  
Line Path

We will be integrating Augmented Reality to the traditional maps which will give a new perspective to the maps. We will be using Location based and Markerless Aug- mented Reality which will track the user's current location and calculate the destination by calculating distance between them and will facilitate information about the places which appears within the journey. The problem occuring in traditional mapping is not proper direction representation. A traditional map cannot provide a proper view for instance if there is a flyover and a road below it, traditional map cannot differentiate between them and will show both as a single blue line (path) which will confuse the user which way to go. This problem can be solved as we would be providing a 3 Dimensional view . Along with a generous assistant which will help the user in interactive manner. The assistant will direct the user till the actual destination which will solve our above problem whether to take flyover or the road??It will also give information about the places nearby in the route with their details on for example if we come across a restaurant, it will augment name of the cafe its ratings etc which help user to make decision if he requires anything during the journey!!In this way one of the latest technology can be used for the betterment of the user and to solve the problem of users getting confused while finding their destination.

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