scholarly journals GENETIC DISSIMILARITY FOR RESISTANCE TO FOLIAR DISEASES ASSOCIATED WITH THE AGRONOMIC POTENTIAL IN MAIZE

2020 ◽  
Vol 33 (4) ◽  
pp. 936-944
Author(s):  
ANTÔNIA MARIA DE CÁSSIA BATISTA DE SOUSA ◽  
ANDRÉ CAVALET CHAVAGLIA ◽  
EDERSON ANTÔNIO CIVARDI ◽  
JEFFERSON FERNANDES NAVES PINTO ◽  
EDÉSIO FIALHO DOS REIS
Keyword(s):  
Average Distance ◽  
Univariate Analysis ◽  
Group Method ◽  
Sources Of Resistance ◽  
Dissimilarity Matrix ◽  
Foliar Diseases ◽  
Disease Complex ◽  
Agronomic Potential ◽  
Genetic Dissimilarity ◽  
Half Sibling

ABSTRACT In the present study the objective was to evaluate the genetic diversity among families of maize siblings for resistance to foliar diseases associated with their agronomic potential, identifying groups of families that can be used as sources of resistance in maize crop. The experiments were conducted in the experimental area of the Federal University of Goiás at the Jataí Regional Unit, in Jataí, GO, Brazil, constituted by 182 half-sibling families of maize and two commercial hybrids as a control. The 182 half-sibling families were divided into three experiments with 60, 60 and 62 families, respectively. The experimental design used was randomized blocks, with three replicates. Eight quantitative characters and 4 foliar diseases were evaluated. The multivariate analysis technique was used to measure the genetic divergence for the four foliar diseases represented by the generalized Mahalanobis distance. Based on the genetic dissimilarity matrix, the dendrogram was constructed using the clustering method of the average distance between groups (Unweighted Pair Group Method with Arithmetic Mean - UPGMA). After defining the groups, univariate analysis of variance was performed in order to evaluate the effects of the groups on each character studied. Comparisons were made between the means of the groups, using the Tukey test (p <0.05). White spot (32.53%) was the disease that most contributed to the total divergence between families. Group 10 stood out among the others as a source of resistance to the disease complex associated with yield. The genetic variability of families for foliar disease complex reveals potential for future studies facing pyramiding genes.

scholarly journals Quantitative biogeography of Orthoptera does not support classical qualitative regionalization of the Carpathian Mountains

2019 ◽  
Vol 128 (4) ◽  
pp. 887-900
Author(s):  
Benjamín Jarčuška ◽  
Peter Kaňuch ◽  
Ladislav Naďo ◽  
Anton Krištín
Keyword(s):  
Species Richness ◽  
Environmental Variables ◽  
Quantitative Data ◽  
Expert Knowledge ◽  
Species Turnover ◽  
Group Method ◽  
Mountain Range ◽  
Carpathian Mountains ◽  
Dissimilarity Matrix ◽  
The Carpathians

Abstract The first biogeographical division of the Carpathians, the second largest mountain range in Europe, was based on qualitative observational floristic data &gt; 100 years ago and has also been applied for the regional zoogeography. In this study, the recent availability of detailed quantitative data allowed us to perform a more powerful evaluation of the classical biogeographical regions of the area. Thus, we analysed updated distribution patterns of 137 Orthoptera species native to the Carpathian Mountains and, by using published species range maps, we compiled data on species presence or absence within 2576 cells of a 10 km × 10 km universal transverse mercator grid in the area. Pattern analysis of the data was based on non-metric multidimensional scaling and clustering using six different algorithms applied to a β sim dissimilarity matrix. The unweighted pair-group method using arithmetic averages, which gave the best performance in the analysis of species turnover, delineated four regions. Environmental variables and species richness were used in logistic regression as predictors of delineated clusters, and indicator species were identified for each of the inferred regions. The pattern can be explained, in part, by environmental variables and species richness (34.2%) and was also influenced by connections with the orthopterofauna from adjacent areas. The observed discrepancy between regionalization based on expert knowledge and the pattern revealed using quantitative data provides a warning that the biogeography of the Carpathians might also have been revised in other taxa, where only classical qualitative regionalization exists.

scholarly journals NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data

2020 ◽  
Vol 14 ◽  
Author(s):  
Zitong Lu ◽  
Yixuan Ku
Keyword(s):  
Univariate Analysis ◽  
Simulated Data ◽  
Spatiotemporal Pattern ◽  
Multivariate Pattern Analysis ◽  
Dissimilarity Matrix ◽  
Neural Data ◽  
Software Packages ◽  
Pattern Similarity ◽  
Task Conditions ◽  
Multivariate Pattern

In studies of cognitive neuroscience, multivariate pattern analysis (MVPA) is widely used as it offers richer information than traditional univariate analysis. Representational similarity analysis (RSA), as one method of MVPA, has become an effective decoding method based on neural data by calculating the similarity between different representations in the brain under different conditions. Moreover, RSA is suitable for researchers to compare data from different modalities and even bridge data from different species. However, previous toolboxes have been made to fit specific datasets. Here, we develop NeuroRA, a novel and easy-to-use toolbox for representational analysis. Our toolbox aims at conducting cross-modal data analysis from multi-modal neural data (e.g., EEG, MEG, fNIRS, fMRI, and other sources of neruroelectrophysiological data), behavioral data, and computer-simulated data. Compared with previous software packages, our toolbox is more comprehensive and powerful. Using NeuroRA, users can not only calculate the representational dissimilarity matrix (RDM), which reflects the representational similarity among different task conditions and conduct a representational analysis among different RDMs to achieve a cross-modal comparison. Besides, users can calculate neural pattern similarity (NPS), spatiotemporal pattern similarity (STPS), and inter-subject correlation (ISC) with this toolbox. NeuroRA also provides users with functions performing statistical analysis, storage, and visualization of results. We introduce the structure, modules, features, and algorithms of NeuroRA in this paper, as well as examples applying the toolbox in published datasets.

scholarly journals Screening techniques and sources of resistance to foliar diseases caused by fungi and bacteria in cool season food legumes

Euphytica ◽  
1994 ◽  
Vol 73 (1-2) ◽  
pp. 11-25 ◽  
Author(s):  
A. Porta-Puglia ◽  
C. C. Bernier ◽  
G. J. Jellis ◽  
W. J. Kaiser ◽  
M. V. Reddy
Keyword(s):  
Cool Season ◽  
Sources Of Resistance ◽  
Screening Techniques ◽  
Foliar Diseases ◽  
Food Legumes

scholarly journals Morphometric and Genetic Variation in Three Populations of Indian Salmon (Polydactylus plebeius)

2013 ◽  
Vol 5 (3) ◽  
pp. 275-281
Author(s):  
Ramakrishnan THIRUMARAISELVI ◽  
Muthusamy THANGARAJ ◽  
Vellaichamy RAMANADEVI
Keyword(s):  
Univariate Analysis ◽  
Arithmetic Mean ◽  
Group Method ◽  
Morphometric Character ◽  
Clear Pattern ◽  
Arbitrary Primers ◽  
Upgma Dendrogram ◽  
Pair Group ◽  
Morphometric Analyses ◽  
The Status

Morphometric character analyses and RAPD was used to discriminate and ratify the status of three populations of Indian salmon, Polydactylus plebeius along the coromandel coast of India. Morphometric analyses showed a clear pattern of differentiation between the stocks and revealed the discreteness of two groups, southern stock (Pazhayar) and northern stock (Cuddalore). The univariate analysis of variance showed significant differences between means of the samples for most morphometric descriptors. A total of 1077 scorable bands were produced using all ten arbitrary primers in three populations. An un-weighted pair-group method with arithmetic mean (UPGMA) dendrogram was constructed based on genetic values to show the genetic relationship among the three populations. The genetic diversity (H) of P. plebeius in Cuddalore was more (0.0733 ± 0.0648) than Pazhayar (0.0609 ± 0.0416) and Vellar (0.0613 ± 0.0344) populations. All the three populations had significantly (p

scholarly journals NeuroRA: A Python Toolbox of Representational Analysis from Multi-modal Neural Data

2020 ◽  
Author(s):  
Zitong Lu ◽  
Yixuan Ku
Keyword(s):  
Univariate Analysis ◽  
Simulated Data ◽  
Spatiotemporal Pattern ◽  
Multivariate Pattern Analysis ◽  
Dissimilarity Matrix ◽  
Neural Data ◽  
Software Packages ◽  
Pattern Similarity ◽  
Multivariate Pattern ◽  
The Brain

AbstractIn studies of cognitive neuroscience, multivariate pattern analysis (MVPA) is widely used as it offers richer information than traditional univariate analysis. Representational similarity analysis (RSA), as one method of MVPA, has become an effective decoding method based on neural data by calculating the similarity between different representations in the brain under different conditions. Moreover, RSA is suitable for researchers to compare data from different modalities, and even bridge data from different species. However, previous toolboxes have been made to fit for specific datasets. Here, we develop a novel and easy-to-use toolbox based on Python named NeuroRA for representational analysis. Our toolbox aims at conducting cross-modal data analysis from multi-modal neural data (e.g. EEG, MEG, fNIRS, ECoG, sEEG, neuroelectrophysiology, fMRI), behavioral data, and computer simulated data. Compared with previous software packages, our toolbox is more comprehensive and powerful. By using NeuroRA, users can not only calculate the representational dissimilarity matrix (RDM), which reflects the representational similarity between different conditions, but also conduct a representational analysis among different RDMs to achieve a cross-modal comparison. In addition, users can calculate neural pattern similarity, spatiotemporal pattern similarity (STPS) and inter-subject correlation (ISC) with this toolbox. NeuroRA also provides users with functions performing statistical analysis, storage and visualization of results. We introduce the structure, modules, features, and algorithms of NeuroRA in this paper, as well as examples applying the toolbox in published datasets.

scholarly journals Genetic variability and homozygosity in a F4 castor bean population by microsatellite markers

Bragantia ◽  
2016 ◽  
Vol 75 (3) ◽  
pp. 307-313 ◽  
Author(s):  
Edna Lobo Machado ◽  
Simone Alves Silva ◽  
Luciel dos Santos Fernandes ◽  
Helison Santos Brasileiro
Keyword(s):  
Genetic Variability ◽  
Microsatellite Markers ◽  
Castor Bean ◽  
Polymorphic Information Content ◽  
Hierarchical Cluster ◽  
Allele Frequencies ◽  
Group Method ◽  
Pair Group ◽  
Expected Heterozygosity ◽  
Genetic Dissimilarity

ABSTRACT The objectives of this study were to identify the genetic variability and estimate the level of homozygosity in a castor bean F4 population using microsatellite markers (SSR). To this end, it was performed the genotyping of the population through 53 pairs of SSR primers. Allele frequencies were estimated by number of alleles per locus, expected heterozygosity (He), observed heterozygosity (Ho) and polymorphic information content (PIC). An array of genetic dissimilarity was generated by Nei and Li index, and hierarchical cluster analysis was performed using the Unweighted Pair-Group Method Averages (UPGMA) method. Polymorphism was detected in a total of eight loci (15.09%) of the 53 evaluated, with the presence of two alleles per locus. Allele frequencies varied between 0.71 and 0.53, and the PIC, between 0.32 and 0.37. The average observed heterozygosity Ho (0.30) was lower than the expected heterozygosity He (0.47). Five dissimilar groups were formed, showing that there is genetic variability among the evaluated genotypes. The highest genetic dissimilarity was 0.708 and the lowest, 0.00. The percentages of homozygous genotypes varied from 25 to 75%. These results show that controlled selfing in castor bean raises the level of homozygosity, important for the breeding program.

Screening techniques and sources of resistance to foliar diseases caused by major necrotrophic fungi in grain legumes

Euphytica ◽  
2006 ◽  
Vol 147 (1-2) ◽  
pp. 223-253 ◽  
Author(s):  
Bernard Tivoli ◽  
Alain Baranger ◽  
Carmen M. Avila ◽  
Sabine Banniza ◽  
Martin Barbetti ◽  
...  
Keyword(s):  
Grain Legumes ◽  
Sources Of Resistance ◽  
Screening Techniques ◽  
Foliar Diseases ◽  
Necrotrophic Fungi

scholarly journals A Strain of Clover yellow vein virus that Causes Severe Pod Necrosis Disease in Snap Bean

Plant Disease ◽  
2008 ◽  
Vol 92 (7) ◽  
pp. 1026-1032 ◽  
Author(s):  
Richard C. Larsen ◽  
Phillip N. Miklas ◽  
Kenneth C. Eastwell ◽  
Craig R. Grau
Keyword(s):  
Great Lakes ◽  
Yellow Vein ◽  
Great Lakes Region ◽  
Sources Of Resistance ◽  
Protein Amino Acid ◽  
Snap Bean ◽  
Breeding Lines ◽  
Disease Complex ◽  
Putative Coat Protein ◽  
Clover Yellow Vein Virus

Soybean aphid (Aphis glycines) outbreaks occurring since 2000 have been associated with severe virus epidemics in snap bean (Phaseolus vulgaris) production in the Great Lakes region. Our objective was to identify specific viruses associated with the disease complex observed in the region and to survey bean germplasm for sources of resistance to the causal agents. The principle causal agent of the disease complex associated with extensive pod necrosis was identified as Clover yellow vein virus (ClYVV), designated ClYVV-WI. The virus alone caused severe mosaic, apical necrosis, and stunting. Putative coat protein amino acid sequence from clones of amplicons generated by reverse-transcription polymerase chain reaction was 98% identical to ClYVV strain no. 30 identified in Japan that has not been reported to cause pod necrosis. ClYVV-WI amplicons were 96% identical to a mild strain of ClYVV from Oregon. A distinguishing feature of this new strain is that it does not react with Potyvirus broad-spectrum monoclonal antibody PTY 1. A survey of common bean lines and cultivars revealed that, in addition to UI-31 and US1140 with known resistance to ClYVV, lines with the bc-3 gene for resistance to Bean common mosaic necrosis virus also were resistant to ClYVV-WI. An evaluation of 63 snap bean cultivars and breeding lines revealed just one, Roma 442, with a moderate level of tolerance to ClYVV-WI. Introgression of the bc-3 gene and resistances from UI-31 and US1140 into snap bean may offer a high level of resistance to extensive pod necrosis disease caused by ClYVV in the Great Lakes region.

Resected pancreatic cancer (PC): Impact of adjuvant therapy (Rx) at a high-volume center (HVC) on overall survival (OS).

2016 ◽  
Vol 34 (4_suppl) ◽  
pp. 191-191 ◽  
Author(s):  
Margaret T Mandelson ◽  
Vincent J. Picozzi
Keyword(s):  
Proportional Hazards ◽  
Average Distance ◽  
Univariate Analysis ◽  
High Volume ◽  
Population Based ◽  
Cox Proportional Hazards ◽  
Resection Margins ◽  
Curative Intent ◽  
The Impact

191 Background: Surgical outcomes for resected PC are known to be superior at HVCs. However, the impact of adjuvant (Rx) performed at HVCs is less studied. We examined the impact of site of adjuvant Rx administration on our resected patients (pts). Methods: Eligible pts were diagnosed 2003-2014 and resected at HVC. Pts were excluded for neoadjuvant Rx, synchronous cancer, death/lost to follow-up within 3 months or contraindications (e.g. morbidity) to adjuvant Rx.. Pts were also excluded if they refused adjuvant treatment or if a community oncologist (CC) was not identified in the medical record or in the western Washington population-based cancer registry. Pt and tumor characteristics were compared in univariate analysis and survival was calculated from date of diagnosis to death or last follow-up. Five year OS was estimated by the Kaplan Meier method and compared using Cox proportional hazards modeling to evaluate the impact of HVC adjuvant Rx on OS while adjusting for potential confounding factors. Results: 245 pts were eligible for study: 139 (57%) treated at HVC, 106 (43%) treated at CC. HVC and CC pts were similar with respect to stage and tumor size, nodal status, resection margins and average distance travelled to HVC. They differed by age (HVC: 63.1, CC: 68.2 p < 0.01). Median and 5-yr OS was 36 mos and 33%. Median OS for HVC vs CC was 44 mos vs. 28 mos (p < 0.01), and 5yr OS was 38.6% vs. 24.8% (p < 0.01), adjustment for age did not alter our findings. Conclusions: 1) With respect to adjuvant Rx for resected PC, HVC and CC pts differed with respect to age only. 2) Both median and 5- yr OS was statistically superior at HVC vs CC. 3) Our study supports the use of HVCs for all Rx components for PC treated with curative intent. 4) Ongoing investigation of patterns of care and their impact on OS in PC is warranted.

scholarly journals Genetic divergence between half-sibling progenies of kale using different multivariate approaches

2021 ◽  
Vol 39 (2) ◽  
pp. 178-185
Author(s):  
Orlando G Brito ◽  
Valter C Andrade Júnior ◽  
Alcinei M Azevedo ◽  
Luan Mateus S Donato ◽  
Antônio Júlio M Silva ◽  
...  
Keyword(s):  
Mahalanobis Distance ◽  
Genetic Divergence ◽  
Block Design ◽  
Qualitative Traits ◽  
Canonical Variables ◽  
Randomized Block Design ◽  
Commercial Cultivars ◽  
Genetic Dissimilarity ◽  
Recombinant Population ◽  
Half Sibling

ABSTRACT The aim of this study was to evaluate the genetic dissimilarity between half-sibling progenies of kale in order to determine the most divergent progenies and, also, to select potential parents. Thirty-six kale genotypes were evaluated, being thirty-three half-sibling progenies and three commercial cultivars, in a randomized block design with four replicates and six plants per plot. Twenty-eight traits were evaluated in each plant per plot, thirteen quantitative and fifteen qualitative traits. Genetic divergence was studied using MANOVA and canonical variables for quantitative observations. In addition, dendrograms were made for quantitative, qualitative and joint analyses by UPGMA method, using Mahalanobis distance. Genetic divergence was observed between genotypes. Commercial cultivars were more divergent than half-sibling progenies. Among half-sibling progenies, the most divergent ones were P1, P21, P23, P25 and P30. We concluded that half-sibling progenies P1, P23 and P30 can be used as potential parents to compose the recombinant population.

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