scholarly journals Nutritional diversity of underutilized plant species collected from Aegean Region of Turkey

2021 ◽  
Vol 12 (2) ◽  
pp. 603-615
Author(s):  
Tansel Kaygısız Aşçıoğul ◽  
Bülent Yağmur ◽  
Mehmet Kadri Bozokalfa ◽  
Dursun Eşiyok
Keyword(s):  
Multivariate Analysis ◽  
Dry Matter ◽  
Pearson Correlation ◽  
Principal Component ◽  
Wild Plant ◽  
Three Dimension ◽  
Edible Plants ◽  
Mineral Concentration ◽  
Mineral N ◽  
Aegean Region

The objective of the present work was to evaluate variability for dry matter, protein and mineral N (nitrogen), P (phosphorus), K (potassium), Ca (calcium), Mg (magnesium) composition of nutritionally important and widely consumed wild edible plants in Aegean region of Turkey, and to assess their mineral diversity using multivariate analysis. The plant material comprises 17 edible plants collected from native found, the data were subject to analysis of variance, and a Pearson correlation test used to determine the correlations between dry matter, protein content and N, P, K, Ca, Mg composition. Principal component analysis was performed on the result of examine compositions and the factor loadings, eigenvalues and percentage of cumulative variance were calculated, the patterns of relationships among nutritive element were shown three-dimension scatter plot. Multivariate analysis revealed considerable variation for the most of concentration and explained 81.49% of total variation accounted for three PC axes. The data reveal that selected wild plant provide significant nutrition and exhibited great variability among the species. Although soil mineral concentration, availability, fertilization and environment may have influenced on nutrient accumulation in plant tissue, genetic variability is considerable influenced on mineral composition of plant.

scholarly journals Use of multivariate analysis in mineral accumulation of rocket (Eruca sativa) accessions

Genetika ◽  
2011 ◽  
Vol 43 (3) ◽  
pp. 437-448 ◽  
Author(s):  
Kadri Bozokalfa ◽  
Dursun Eşiyok ◽  
Bülent Yağmur
Keyword(s):  
Multivariate Analysis ◽  
Principal Component ◽  
Mineral Elements ◽  
Leafy Vegetables ◽  
Mineral Concentration ◽  
Health Promoting ◽  
Nutritional Compounds ◽  
High Nutrient Concentration ◽  
Mineral Accumulation ◽  
Selection Of

The leafy vegetables contain high amount of mineral elements and health promoting compound. To solve nutritional problems in diet and reduced malnutrition among human population selection of specific cultivar among species would be help increasing elemental delivery in the human diet. While rocket plant observes several nutritional compounds no significant efforts have been made for genetic diversity for mineral composition of rocket plant accessions using multivariate analyses technique. The objective of this work was to evaluate variability for mineral accumulation of rocket accessions revealed by multivariate analysis to use further breeding program for achieve improving cultivar in targeting high nutrient concentration. A total twelve mineral element and twenty-three E. sativa accessions were investigated and considerable variation were observed in the most of concentration the principal component analysis explained that 77.67% of total variation accounted for four PC axis. Rocket accessions were classifies into three groups and present outcomes of experiments revealed that the first three principal components were highly valid to classify the examined accessions and separating mineral accumulations. Significant differences exhibited in mineral concentration among examined rocket accessions and the result could allow selecting those genotypes with higher elements.

scholarly journals Bioprospecting of Sechium spp. varieties for the selection of characters with pharmacological activity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
María Isabel Iñiguez-Luna ◽  
Jorge Cadena-Iñiguez ◽  
Ramón Marcos Soto-Hernández ◽  
Francisco Javier Morales-Flores ◽  
Moisés Cortes-Cruz ◽  
...  
Keyword(s):  
Cell Lines ◽  
Proliferative Activity ◽  
Meta Analysis ◽  
Pearson Correlation ◽  
Statistical Significance ◽  
Economic Value ◽  
Principal Component ◽  
Malignant Cell ◽  
Length Polymorphisms ◽  
Relevant Variables

AbstractBioprospecting identifies new sources of compounds with actual or potential economic value that come from biodiversity. An analysis was performed regarding bioprospecting purposes in ten genotypes of Sechium spp., through a meta-analysis of 20 information sources considering different variables: five morphological, 19 biochemical, anti-proliferative activity of extracts on five malignant cell lines, and 188 polymorphic bands of amplified fragment length polymorphisms, were used in order to identify the most relevant variables for the design of genetic interbreeding. Significant relationships between morphological and biochemical characters and anti-proliferative activity in cell lines were obtained, with five principal components for principal component analysis (SAS/ETS); variables were identified with a statistical significance (< 0.7 and Pearson values ≥ 0.7), with 80.81% of the accumulation of genetic variation and 110 genetic bands. Thirty-nine (39) variables were recovered using NTSYSpc software where 30 showed a Pearson correlation (> 0.5) and nine variables (< 0.05), Finally, using a cladistics analysis approach highlighted 65 genetic bands, in addition to color of the fruit, presence of thorns, bitter flavor, piriform and oblong shape, and also content of chlorophylls a and b, presence of cucurbitacins, and the IC50 effect of chayote extracts on the four cell lines.

scholarly journals Towards Understanding Variability in Droughts in Response to Extreme Climate Conditions over the Different Agro-Ecological Zones of Pakistan

2021 ◽  
Vol 13 (12) ◽  
pp. 6910
Author(s):  
Adil Dilawar ◽  
Baozhang Chen ◽  
Arfan Arshad ◽  
Lifeng Guo ◽  
Muhammad Irfan Ehsan ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Principal Component ◽  
Mitigation Strategies ◽  
Precipitation Extremes ◽  
Climate Mitigation ◽  
Mean Annual Temperature ◽  
Climate Extreme ◽  
Ecological Zones ◽  
The Mean ◽  
Agro Ecological Zones

Here, we provided a comprehensive analysis of long-term drought and climate extreme patterns in the agro ecological zones (AEZs) of Pakistan during 1980–2019. Drought trends were investigated using the standardized precipitation evapotranspiration index (SPEI) at various timescales (SPEI-1, SPEI-3, SPEI-6, and SPEI-12). The results showed that droughts (seasonal and annual) were more persistent and severe in the southern, southwestern, southeastern, and central parts of the region. Drought exacerbated with slopes of −0.02, −0.07, −0.08, −0.01, and −0.02 per year. Drought prevailed in all AEZs in the spring season. The majority of AEZs in Pakistan’s southern, middle, and southwestern regions had experienced substantial warming. The mean annual temperature minimum (Tmin) increased faster than the mean annual temperature maximum (Tmax) in all zones. Precipitation decreased in the southern, northern, central, and southwestern parts of the region. Principal component analysis (PCA) revealed a robust increase in temperature extremes with a variance of 76% and a decrease in precipitation extremes with a variance of 91% in the region. Temperature and precipitation extremes indices had a strong Pearson correlation with drought events. Higher temperatures resulted in extreme drought (dry conditions), while higher precipitation levels resulted in wetting conditions (no drought) in different AEZs. In most AEZs, drought occurrences were more responsive to precipitation. The current findings are helpful for climate mitigation strategies and specific zonal efforts are needed to alleviate the environmental and societal impacts of drought.

scholarly journals Deciphering Substrate-Specific Methane Yields of Perennial Herbaceous Wild Plant Species

Agronomy ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 451
Author(s):  
Moritz von Cossel ◽  
Lorena Agra Pereira ◽  
Iris Lewandowski
Keyword(s):  
Plant Species ◽  
Dry Matter ◽  
Plant Biomass ◽  
Cropping Systems ◽  
Wild Plant ◽  
Small Scale ◽  
Social Ecological ◽  
Wild Plant Species ◽  
Global Demand ◽  
Bioenergy Cropping Systems

The global demand for plant biomass to provide bioenergy and heat is continuously increasing because of a growing interest among many industrialized and developing countries towards climate sound and renewable energy supply. The exacerbation of land-use conflicts proliferates social-ecological demands on future bioenergy cropping systems. Perennial herbaceous wild plant mixtures (WPMs) represent an approach to providing social-ecologically more sustainably produced biogas substrate that has gained increasing public and political interest only in recent years. The focus of this study lies on three perennial wild plant species (WPS) that usually dominate the biomass yield performance of WPM cultivation. These WPS were compared with established biogas crops in terms of their substrate-specific methane yield (SMY) and lignocellulosic composition. The plant samples were investigated in a small-scale mesophilic discontinuous biogas batch test for determining the SMY. All WPS were found to have significantly lower SMY (241.5–248.5 lN kgVS−1) than maize (337.5 lN kgVS−1). This was attributed to higher contents of lignin (9.7–12.8% of dry matter) as well as lower contents of hemicellulose (9.9–11.5% of dry matter) in the WPS. Only minor, non-significant differences to cup plant and Virginia mallow were observed. Thus, when planning WPS as a diversification measure in biogas cropping systems, their lower SMY should be considered.

scholarly journals The Prospective Use of Brazilian Marine Macroalgae in Schistosomiasis Control

Marine Drugs ◽  
2021 ◽  
Vol 19 (5) ◽  
pp. 234
Author(s):  
Erika M. Stein ◽  
Sara G. Tajú ◽  
Patrícia A. Miyasato ◽  
Rafaela P. de Freitas ◽  
Lenita de F. Tallarico ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Marine Algae ◽  
Pearson Correlation ◽  
Marine Macroalgae ◽  
Parasitic Disease ◽  
Isolation And Identification ◽  
Promising Alternative ◽  
Schistosomiasis Control ◽  
Lead Compounds ◽  
Correlation Tests

Schistosomiasis is a parasitic disease that affects more than 250 million people. The treatment is limited to praziquantel and the control of the intermediate host with the highly toxic molluscicidal niclosamide. Marine algae are a poorly explored and promising alternative that can provide lead compounds, and the use of multivariate analysis could contribute to quicker discovery. As part of our search for new natural compounds with which to control schistosomiasis, we screened 45 crude extracts obtained from 37 Brazilian seaweed species for their molluscicidal activity against Biomphalaria glabrata embryos and schistosomicidal activities against Schistosoma mansoni. Two sets of extracts were taxonomically grouped for metabolomic analysis. The extracts were analyzed by GC–MS, and the data were subjected to Pattern Hunter and Pearson correlation tests. Overall, 22 species (60%) showed activity in at least one of the two models. Multivariate analysis pointed towards 3 hits against B. glabrata veliger embryos in the Laurencia/Laurenciella set, 5 hits against B. glabrata blastula embryos, and 31 against S. mansoni in the Ochrophyta set. Preliminary annotations suggested some compounds such as triquinane alcohols, prenylated guaianes, dichotomanes, and xenianes. Despite the putative identification, this work presents potential candidates and can guide future isolation and identification.

scholarly journals SPPD: A Novel Reassembly Method for 3D Terracotta Warrior Fragments Based on Fracture Surface Information

2021 ◽  
Vol 10 (8) ◽  
pp. 525
Author(s):  
Wenmin Yao ◽  
Tong Chu ◽  
Wenlong Tang ◽  
Jingyu Wang ◽  
Xin Cao ◽  
...  
Keyword(s):  
Fracture Surface ◽  
Principal Component ◽  
Point Clouds ◽  
Three Dimension ◽  
Registration Method ◽  
Series Of Experiments ◽  
Coarse To Fine ◽  
Fine Registration ◽  
Common Situation ◽  
Better Than

As one of China′s most precious cultural relics, the excavation and protection of the Terracotta Warriors pose significant challenges to archaeologists. A fairly common situation in the excavation is that the Terracotta Warriors are mostly found in the form of fragments, and manual reassembly among numerous fragments is laborious and time-consuming. This work presents a fracture-surface-based reassembling method, which is composed of SiamesePointNet, principal component analysis (PCA), and deep closest point (DCP), and is named SPPD. Firstly, SiamesePointNet is proposed to determine whether a pair of point clouds of 3D Terracotta Warrior fragments can be reassembled. Then, a coarse-to-fine registration method based on PCA and DCP is proposed to register the two fragments into a reassembled one. The above two steps iterate until the termination condition is met. A series of experiments on real-world examples are conducted, and the results demonstrate that the proposed method performs better than the conventional reassembling methods. We hope this work can provide a valuable tool for the virtual restoration of three-dimension cultural heritage artifacts.

scholarly journals Convolutional Neural Networks to Estimate Dry Matter Yield in a Guineagrass Breeding Program Using UAV Remote Sensing

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3971
Author(s):  
Gabriel Silva de Oliveira ◽  
José Marcato Junior ◽  
Caio Polidoro ◽  
Lucas Prado Osco ◽  
Henrique Siqueira ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Dry Matter ◽  
Pearson Correlation ◽  
Genetic Correlations ◽  
Remote Sensing Data ◽  
Breeding Program ◽  
Yield Traits ◽  
Dry Matter Yield ◽  
Breeding Programs ◽  
Ground Truth Data

Forage dry matter is the main source of nutrients in the diet of ruminant animals. Thus, this trait is evaluated in most forage breeding programs with the objective of increasing the yield. Novel solutions combining unmanned aerial vehicles (UAVs) and computer vision are crucial to increase the efficiency of forage breeding programs, to support high-throughput phenotyping (HTP), aiming to estimate parameters correlated to important traits. The main goal of this study was to propose a convolutional neural network (CNN) approach using UAV-RGB imagery to estimate dry matter yield traits in a guineagrass breeding program. For this, an experiment composed of 330 plots of full-sib families and checks conducted at Embrapa Beef Cattle, Brazil, was used. The image dataset was composed of images obtained with an RGB sensor embedded in a Phantom 4 PRO. The traits leaf dry matter yield (LDMY) and total dry matter yield (TDMY) were obtained by conventional agronomic methodology and considered as the ground-truth data. Different CNN architectures were analyzed, such as AlexNet, ResNeXt50, DarkNet53, and two networks proposed recently for related tasks named MaCNN and LF-CNN. Pretrained AlexNet and ResNeXt50 architectures were also studied. Ten-fold cross-validation was used for training and testing the model. Estimates of DMY traits by each CNN architecture were considered as new HTP traits to compare with real traits. Pearson correlation coefficient r between real and HTP traits ranged from 0.62 to 0.79 for LDMY and from 0.60 to 0.76 for TDMY; root square mean error (RSME) ranged from 286.24 to 366.93 kg·ha−1 for LDMY and from 413.07 to 506.56 kg·ha−1 for TDMY. All the CNNs generated heritable HTP traits, except LF-CNN for LDMY and AlexNet for TDMY. Genetic correlations between real and HTP traits were high but varied according to the CNN architecture. HTP trait from ResNeXt50 pretrained achieved the best results for indirect selection regardless of the dry matter trait. This demonstrates that CNNs with remote sensing data are highly promising for HTP for dry matter yield traits in forage breeding programs.

scholarly journals Key soil parameters affecting the survival of Panax notoginseng under continuous cropping

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wumei Xu ◽  
Fengyun Wu ◽  
Haoji Wang ◽  
Linyan Zhao ◽  
Xue Liu ◽  
...  
Keyword(s):  
Soil Ph ◽  
Pearson Correlation ◽  
Principal Component ◽  
Panax Notoginseng ◽  
Soil Parameters ◽  
Continuous Cropping ◽  
Poor Growth ◽  
Available N ◽  
The Poor ◽  
Replant Problem

AbstractNegative plant-soil feedbacks lead to the poor growth of Panax notoginseng (Sanqi), a well-known herb in Asia and has been used worldwide, under continuous cropping. However, the key soil parameters causing the replant problem are still unclear. Here we conducted a field experiment after 5-year continuous cropping. Sanqi seedlings were cultivated in 7 plots (1.5 m × 2 m), which were randomly assigned along a survival gradient. In total, 13 important soil parameters were measured to understand their relationship with Sanqi’s survival. Pearson correlation analysis showed that 6 soil parameters, including phosphatase, urease, cellulase, bacteria/fungi ratio, available N, and pH, were all correlated with Sanqi’s survival rate (P < 0.05). Principal component analysis (PCA) indicated that they explained 61% of the variances based on the first component, with soil pH being closely correlated with other parameters affecting Sanqi’s survival. The optimum pH for Sanqi growth is about 6.5, but the mean soil pH in the study area is 5.27 (4.86–5.68), therefore it is possible to ameliorate the poor growth of Sanqi by increasing soil pH. This study may also help to reduce the replant problem of other crops under continuous cropping since it is widespread in agricultural production.

scholarly journals The Development of Athlete Performance Capabilities Index (APCI) Model for Male U12 Player Selection using Multivariate Analysis

2018 ◽  
Vol 7 (4.34) ◽  
pp. 97
Author(s):  
Mohamad Razali Abdullah ◽  
Hafizan Juahir ◽  
N. Mohamad Shukri ◽  
N. A. Fuat ◽  
N. A. Mohd Ros ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Factor Score ◽  
Principal Component ◽  
Factor Loading ◽  
Group Classification ◽  
Performance Group ◽  
Two Factors ◽  
Low Performance ◽  
High Factor Loading ◽  
Athlete Performance

This study develops an Athlete Performance Capabilities Index (APCI) model using multivariate analysis for selecting the best player of under twelve (U12).  Measurement of anthropometrics and physical fitness were evaluated among 178 male players aged 12±0.52 years. Factor score derived by Principal Component Analysis were used to obtain a model for APCI and Discriminant Analysis (DA) were conducted to validate the correctness of group classification by APCI. Result was found two factors with eigenvalues greater than 1 were extracted which accounted for 62.00% of the variations present in the original variables. The two factors were used to obtain the factor score coefficients explained by 35.72% and 26.67% of the variations in athlete performance respectively. Factor 1 revealed high factor loading on fitness compared to Factor 2 as it was significantly related to anthropometrics. A model was obtained using standardized coefficient of factor 1. Three clusters of performance were shaped in view by categorizing APCI ≥ 75%, 25% ≤ APCI < 75% and APCI < 25% as high, moderate and low performance group respectively. Three discriminated variables out of thirteen variables were obtained using Forward and Backward stepwise mode of DA, which were weight, standing broad jump, and 40 meters’ speed. Such variables were established as essential indicator for selecting the best player among male U12.   

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