scholarly journals Temporal progression of foliar plant diseases in corn hybrids

2019 ◽  
pp. 1731-1739
Author(s):  
Karoliny de Souza Almeida ◽  
Anderson Rodrigo da Silva ◽  
Natanael Marcos Lemos ◽  
Walter Baida Garcia Coutinho ◽  
Jakelinny Martins Silva ◽  
...  
Keyword(s):  
Disease Progression ◽  
Disease Severity ◽  
Control Strategies ◽  
Principal Component ◽  
Plant Diseases ◽  
Crop Season ◽  
Corn Hybrids ◽  
Foliar Disease ◽  
Dependent Variables ◽  
Disease Complexes

The environment’s impact on foliar disease growth in annual crops and the various types of differentiation must be investigated to adapt effective disease control strategies. We studied the temporal progression of foliar disease complexes in 14 commercial corn (Zea mays L.) hybrids during the 2015/2016 crop season (Ipameri, Goiás, Brazil). The experiment consisted of 10 blocks and evaluated foliar disease severity using a diagrammatic scale. The evaluations occurred at 47, 53, 59, 74, 81 and 95 days after planting. At each time point, a plant was chosen randomly from each block (10 plants total), and the diseases causing foliar damage were identified. The areas under the disease progression curves (AUDPCs) and yields were calculated. Dependent variables were evaluated using a principal component analysis to study relationships between the hybrids and the disease severity on each leaf (biplot). Heatmaps were used to determine which leaves demonstrated the greatest disease severity and temporal disease progression, and an adjusted linear correlation model was used to predict yield relative to AUDPC. The foliar disease complex consisted of helmintosporiosis, common rust, macrospora leaf spot, cercosporiosis and maize white spot. The Ns90PRO© hybrid showed limited disease progression and; therefore, was considered more resistant and consequently had a lower AUDPC value. The Dow2B610PW© hybrid showed greater disease progression. Agroceres7098PRO2© had a greater yield and consequently a lower AUDPC value, while Lg6050PRO2© had a lower yield and a higher AUDPC value. In general, the more advanced the phenological stage, the more severe the leaf disease; however, disease progression (from plant base to plant tip) was genotype- dependent.

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis

2007 ◽  
Vol 56 (6) ◽  
pp. 75-83 ◽  
Author(s):  
X. Flores ◽  
J. Comas ◽  
I.R. Roda ◽  
L. Jiménez ◽  
K.V. Gernaey
Keyword(s):  
Cluster Analysis ◽  
Control Strategies ◽  
Treatment Plant ◽  
Principal Component ◽  
Statistical Techniques ◽  
Data Sets ◽  
Data Set ◽  
Casual Relation ◽  
Evaluation Matrix ◽  
Natural Groups

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

scholarly journals Platforms for High-Throughput Screening and Force Measurements on Fungi and Oomycetes

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 639
Author(s):  
Yiling Sun ◽  
Ayelen Tayagui ◽  
Sarah Sale ◽  
Debolina Sarkar ◽  
Volker Nock ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Drug Targets ◽  
Control Strategies ◽  
Pathogenic Fungi ◽  
Antifungal Drugs ◽  
Plant Diseases ◽  
Force Measurements ◽  
Technical Developments ◽  
Alternative Drug

Pathogenic fungi and oomycetes give rise to a significant number of animal and plant diseases. While the spread of these pathogenic microorganisms is increasing globally, emerging resistance to antifungal drugs is making associated diseases more difficult to treat. High-throughput screening (HTS) and new developments in lab-on-a-chip (LOC) platforms promise to aid the discovery of urgently required new control strategies and anti-fungal/oomycete drugs. In this review, we summarize existing HTS and emergent LOC approaches in the context of infection strategies and invasive growth exhibited by these microorganisms. To aid this, we introduce key biological aspects and review existing HTS platforms based on both conventional and LOC techniques. We then provide an in-depth discussion of more specialized LOC platforms for force measurements on hyphae and to study electro- and chemotaxis in spores, approaches which have the potential to aid the discovery of alternative drug targets on future HTS platforms. Finally, we conclude with a brief discussion of the technical developments required to improve the uptake of these platforms into the general laboratory environment.

Serum glial fibrillary acidic protein correlates with multiple sclerosis disease severity

2018 ◽  
Vol 26 (2) ◽  
pp. 210-219 ◽  
Author(s):  
Heidi Högel ◽  
Eero Rissanen ◽  
Christian Barro ◽  
Markus Matilainen ◽  
Marjo Nylund ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Nervous System ◽  
Glial Fibrillary Acidic Protein ◽  
Disease Progression ◽  
Disease Severity ◽  
Single Molecule ◽  
Progressive Disease ◽  
Disease Duration ◽  
Acidic Protein ◽  
Csf Levels

Background: Cerebrospinal fluid (CSF) levels of two soluble biomarkers, glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL), have been shown to associate with multiple sclerosis (MS) disease progression. Now, both biomarkers can be detected reliably in serum, and importantly, their serum levels correlate well with their CSF levels. Objective: To evaluate the usability of serum GFAP measurement as a biomarker of progressive disease and disease severity in MS. Methods: Clinical course, Expanded Disability Status Scale (EDSS), disease duration, patient age and magnetic resonance imaging (MRI) parameters were reviewed in 79 MS patients in this cross-sectional hospital-based study. Serum samples were collected for measurement of GFAP and NfL concentrations using single molecule array (Simoa) assay. A cohort of healthy controls was evaluated for comparison. Results: Higher serum concentrations of both GFAP and NfL were associated with higher EDSS, older age, longer disease duration, progressive disease course and MRI pathology. Conclusion: Earlier studies have demonstrated that GFAP, unlike NfL, is not increased in association with acute focal inflammation-related nervous system damage. Our work suggests that GFAP serum level associates with disease progression in MS and could potentially serve as an easily measurable biomarker of central nervous system (CNS) pathology related to disease progression in MS.

scholarly journals Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot

2015 ◽  
Vol 50 (8) ◽  
pp. 649-657 ◽  
Author(s):  
Regina Maria Villas Bôas de Campos Leite ◽  
Maria Cristina Neves de Oliveira
Keyword(s):  
Principal Component Analysis ◽  
Disease Severity ◽  
Principal Components ◽  
Leaf Spot ◽  
Oil Content ◽  
Principal Component ◽  
Component Analysis ◽  
High Yield ◽  
Gge Biplot ◽  
Alternaria Leaf Spot

Abstract:The objective of this work was to evaluate the suitability of the multivariate method of principal component analysis (PCA) using the GGE biplot software for grouping sunflower genotypes for their reaction to Alternaria leaf spot disease (Alternariaster helianthi), and for their yield and oil content. Sixty-nine genotypes were evaluated for disease severity in the field, at the R3 growth stage, in seven growing seasons, in Londrina, in the state of Paraná, Brazil, using a diagrammatic scale developed for this disease. Yield and oil content were also evaluated. Data were standardized using the software Statistica, and GGE biplot was used for PCA and graphical display of data. The first two principal components explained 77.9% of the total variation. According to the polygonal biplot using the first two principal components and three response variables, the genotypes were divided into seven sectors. Genotypes located on sectors 1 and 2 showed high yield and high oil content, respectively, and those located on sector 7 showed tolerance to the disease and high yield, despite the high disease severity. The principal component analysis using GGE biplot is an efficient method for grouping sunflower genotypes based on the studied variables.

scholarly journals Not Only High Number and Specific Comorbidities but Also Age Are Closely Related to Progression and Poor Prognosis in Patients With COVID-19

2022 ◽  
Vol 8 ◽  
Author(s):  
Dafeng Liu ◽  
Yongli Zheng ◽  
Jun Kang ◽  
Dongmei Wang ◽  
Lang Bai ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Disease Progression ◽  
Disease Severity ◽  
Poor Prognosis ◽  
Chronic Obstructive ◽  
Clinical Trial Registry ◽  
Alcoholic Fatty Liver ◽  
Clinical Type ◽  
Conversion Time ◽  
Negative Conversion

Background: Some patients with comorbidities and rapid disease progression have a poor prognosis.Aim: We aimed to investigate the characteristics of comorbidities and their relationship with disease progression and outcomes of COVID-19 patients.Methods: A total of 718 COVID-19 patients were divided into five clinical type groups and eight age-interval groups. The characteristics of comorbidities were compared between the different clinical type groups and between the different age-interval groups, and their relationships with disease progression and outcomes of COVID-19 patients were assessed.Results: Approximately 91.23% (655/718) of COVID-19 patients were younger than 60 years old. Approximately 64.76% (465/718) had one or more comorbidities, and common comorbidities included non-alcoholic fatty liver disease (NAFLD), hyperlipidaemia, hypertension, diabetes mellitus (DM), chronic hepatitis B (CHB), hyperuricaemia, and gout. COVID-19 patients with comorbidities were older, especially those with chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD). Hypertension, DM, COPD, chronic kidney disease (CKD) and CVD were mainly found in severe COVID-19 patients. According to spearman correlation analysis the number of comorbidities was correlated positively with disease severity, the number of comorbidities and NAFLD were correlated positively with virus negative conversion time, hypertension, CKD and CVD were primarily associated with those who died, and the above-mentioned correlation existed independently of age. Risk factors included age, the number of comorbidities and hyperlipidaemia for disease severity, the number of comorbidities, hyperlipidaemia, NAFLD and COPD for the virus negative conversion time, and the number of comorbidities and CKD for prognosis. Number of comorbidities and age played a predictive role in disease progression and outcomes.Conclusion: Not only high number and specific comorbidities but also age are closely related to progression and poor prognosis in patients with COVID-19. These findings provide a reference for clinicians to focus on not only the number and specific comorbidities but also age in COVID-19 patients to predict disease progression and prognosis.Clinical Trial Registry: Chinese Clinical Trial Register ChiCTR2000034563.

scholarly journals 4-Ethylguaiacol modulates neuroinflammation and Th1/Th17 differentiation to ameliorate disease severity in experimental autoimmune encephalomyelitis

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Wen-Tsan Weng ◽  
Ping-Chang Kuo ◽  
Dennis A. Brown ◽  
Barbara A. Scofield ◽  
Destin Furnas ◽  
...  
Keyword(s):  
Autoimmune Disease ◽  
Experimental Autoimmune Encephalomyelitis ◽  
Immune Responses ◽  
Disease Progression ◽  
Disease Severity ◽  
Autoimmune Encephalomyelitis ◽  
Relapsing Remitting ◽  
Th17 Differentiation ◽  
Experimental Autoimmune

Abstract Background Multiple sclerosis (MS) is a progressive autoimmune disease characterized by the accumulation of pathogenic inflammatory immune cells in the central nervous system (CNS) that subsequently causes focal inflammation, demyelination, axonal injury, and neuronal damage. Experimental autoimmune encephalomyelitis (EAE) is a well-established murine model that mimics the key features of MS. Presently, the dietary consumption of foods rich in phenols has been reported to offer numerous health benefits, including anti-inflammatory activity. One such compound, 4-ethylguaiacol (4-EG), found in various foods, is known to attenuate inflammatory immune responses. However, whether 4-EG exerts anti-inflammatory effects on modulating the CNS inflammatory immune responses remains unknown. Thus, in this study, we assessed the therapeutic effect of 4-EG in EAE using both chronic and relapsing-remitting animal models and investigated the immunomodulatory effects of 4-EG on neuroinflammation and Th1/Th17 differentiation in EAE. Methods Chronic C57BL/6 EAE and relapsing-remitting SJL/J EAE were induced followed by 4-EG treatment. The effects of 4-EG on disease progression, peripheral Th1/Th17 differentiation, CNS Th1/Th17 infiltration, microglia (MG) activation, and blood-brain barrier (BBB) disruption in EAE were evaluated. In addition, the expression of MMP9, MMP3, HO-1, and Nrf2 was assessed in the CNS of C57BL/6 EAE mice. Results Our results showed that 4-EG not only ameliorated disease severity in C57BL/6 chronic EAE but also mitigated disease progression in SJL/J relapsing-remitting EAE. Further investigations of the cellular and molecular mechanisms revealed that 4-EG suppressed MG activation, mitigated BBB disruption, repressed MMP3/MMP9 production, and inhibited Th1 and Th17 infiltration in the CNS of EAE. Furthermore, 4-EG suppressed Th1 and Th17 differentiation in the periphery of EAE and in vitro Th1 and Th17 cultures. Finally, we found 4-EG induced HO-1 expression in the CNS of EAE in vivo as well as in MG, BV2 cells, and macrophages in vitro. Conclusions Our work demonstrates that 4-EG confers protection against autoimmune disease EAE through modulating neuroinflammation and inhibiting Th1 and Th17 differentiation, suggesting 4-EG, a natural compound, could be potentially developed as a therapeutic agent for the treatment of MS/EAE.

scholarly journals Recognition of Tomato Late Blight by using DWT and Component Analysis

2017 ◽  
Vol 7 (1) ◽  
pp. 194
Author(s):  
Hiteshwari Sabrol ◽  
Satish Kumar
Keyword(s):  
Principal Component Analysis ◽  
Late Blight ◽  
Distance Measure ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Component Analysis ◽  
Plant Diseases ◽  
Kernel Principal Component Analysis ◽  
Healthy Plant ◽  
Testing Dataset

Plant disease recognition concept is one of the successful and important applications of image processing and able to provide accurate and useful information to timely prediction and control of plant diseases. In the study, the wavelet based features computed from RGB images of late blight infected images and healthy images. The extracted features submitted to Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA) and Independent Component Analysis performed (ICA) for reducing dimensions in feature data processing and classification. To recognize and classify late blight from healthy plant images are classified into two classes i.e.  late blight infected or healthy. The Euclidean Distance measure is used to compute the distance by these two classes of training and testing dataset for tomato late blight recognition and classification. Finally, the three-component analysis is compared for late blight recognition accuracy. The Kernel Principal Component Analysis (KPCA) yielded overall recognition accuracy with 96.4%.

scholarly journals Unravelling Plant-Pathogen Interactions: Proximal Optical Sensing as an Effective Tool for Early Detect Plant Diseases

2021 ◽  
Vol 5 (1) ◽  
pp. 18
Author(s):  
Mafalda Reis-Pereira ◽  
Rui C. Martins ◽  
Aníbal Filipe Silva ◽  
Fernando Tavares ◽  
Filipe Santos ◽  
...  
Keyword(s):  
Optical Fibers ◽  
Optical Sensing ◽  
Principal Component ◽  
Plant Diseases ◽  
Bacterial Suspension ◽  
Control Group ◽  
Post Inoculation ◽  
Early Disease Detection ◽  
Run Off ◽  
Vis Spectroscopy

This study analyzed the potential of proximal optical sensing as an effective approach for early disease detection. A compact, modular sensing system, combining direct UV–Vis spectroscopy with optical fibers, supported by a principal component analysis (PCA), was applied to evaluate the modifications promoted by the bacteria Xanthomonas euvesicatoria in tomato leaves (cv. cherry). Plant infection was achieved by spraying a bacterial suspension (108 CFU mL−1) until run-off occurred, and a similar approach was followed for the control group, where only water was applied. A total of 270 spectral measurements were performed on leaves, on five different time instances, including pre- and post-inoculation measurements. PCA was then applied to the acquired data from both healthy and inoculated leaves, which allowed their distinction and differentiation, three days after inoculation, when unhealthy plants were still asymptomatic.

scholarly journals COMPARISON OF FUZZY SUBTRACTIVE CLUSTERING AND FUZZYC-MEANS

Kursor ◽  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Annisa Eka Haryati ◽  
Sugiyarto Sugiyarto ◽  
Rizki Desi Arindra Putri
Keyword(s):  
Principal Component Analysis ◽  
Fuzzy Clustering ◽  
Principal Component ◽  
Large Data ◽  
Component Analysis ◽  
Subtractive Clustering ◽  
Validity Index ◽  
Fuzzy Clustering Method ◽  
Dependent Variables ◽  
Better Than

Multivariate statistics have related problems with large data dimensions. One method that can be used is principal component analysis (PCA). Principal component analysis (PCA) is a technique used to reduce data dimensions consisting of several dependent variables while maintaining variance in the data. PCA can be used to stabilize measurements in statistical analysis, one of which is cluster analysis. Fuzzy clustering is a method of grouping based on membership values ​​that includes fuzzy sets as a weighting basis for grouping. In this study, the fuzzy clustering method used is Fuzzy Subtractive Clustering (FSC) and Fuzzy C-Means (FCM) with a combination of the Minkowski Chebysev distance. The purpose of this study was to compare the cluster results obtained from the FSC and FCM using the DBI validity index. The results obtained indicate that the results of clustering using FCM are better than the FSC.

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