Tucker3-PCovR: The Tucker3 principal covariates regression model

In behavioral research, it is very common to have manage multiple datasets containing information about the same set of individuals, in such a way that one dataset attempts to explain the others. To address this need, in this paper the Tucker3-PCovR model is proposed. This model is a particular case of PCovR models which focuses on the analysis of a three-way data array and a two-way data matrix where the latter plays the explanatory role. The Tucker3-PCovR model reduces the predictors to a few components and predicts the criterion by using these components and, at the same time, the three-way data is fitted by the Tucker3 model. Both the reduction of the predictors and the prediction of the criterion are done simultaneously. An alternating least squares algorithm is proposed to estimate the Tucker3-PCovR model. A biplot representation is presented to facilitate the interpretation of the results. Some applications are made to empirical datasets from the field of psychology.

Multivariate analysis of morpho-physiological traits in Amaranthus tricolor as affected by nitric oxide and cadmium stress

Edible amaranth (Amaranthus tricolor L.) is used as a food-medicine or ornamental plant, and despite its importance, there are few reports associated with cadmium (Cd) stress. This study aimed to appraise the crosstalk between sodium nitroprusside (SNP), as a source of nitric oxide (NO), and cadmium toxicity on growth and physiological traits in edible amaranth by using different multivariate statistical methods. The results showed that growth-related traits of A. tricolor were significantly reduced under Cd stress. Contrarily, Cd treatments increased lipid peroxidation and reduced total protein content. Delving on the results of SNP application showed the suitability of its medium level (100 µM) on increasing the growth-related traits and also plant tolerance to Cd stress via lowering the lipid peroxidation and radical molecules production due to the higher activities of superoxide dismutase and catalase. Increasing the amount of Cd in roots and shoots, as the result of Cd treatment, reduced the growth and production of A. tricolor plants by high rates (over 50% in 60 mg kg^-1 Cd level), indicating its susceptibility to high Cd toxicity. Contrarily, treating plants with SNP showed no effect on shoot Cd content, while it significantly increased Cd allocation in the root, which might be attributable to the protective effect of NO on Cd toxicity by trapping Cd in the root. Subsequently, the application of a medium level of SNP (around 100 µM) is recommendable for A. tricolor plant to overcome the negative impacts of Cd toxicity. Moreover, according to the results of heatmap and biplot, under no application of Cd, the application of 100 µM SNP showed a great association with growth-related traits indicating the effectiveness of SNP on the productivity of this species even under no stress situations.

Strawberry crinkle (SCV) and Strawberry mottle (SMoV) viruses affect yield and physio-biochemical responses of three strawberry cultivars under different seasonal conditions

BACKGROUND

The current study was performed to evaluate the impact of Strawberry crinkle virus (SCV) and Strawberry mottle virus (SMoV) viruses, in single and mixed infections, on the yield and physio-biochemical traits of strawberry plants with different genetic backgrounds. Accordingly, the virus-free plantlets were produced via meristem culture and were then transplanted in a research field in different seasons.

METHODS AND RESULTS

The results indicated that genetic background clearly affects the responses of the strawberry plants to the viral infection. In addition, considering the effects of changing environmental factors, different seasons had significant influences on the strawberry responses to the virus infection. The results indicated that the components indicating destructive effects were significantly higher during the summer season. Reverse transcription-polymerase chain reaction using viral specific primers revealed 345 and 461 bp fragments for SCV and SMoV viruses, respectively, where all plants were positive for studied viruses. The comparison between the influence of SCV and SMoV viruses on the host plants indicated that SCV caused more damaging effects on the strawberry fruit yield and physio-biochemical traits. Simultaneous infection with SCV and SMoV severely impacted the strawberry plants compared with the effect of each virus individually.

CONCLUSION

It is recommended to keep the temperature of the greenhouses producing strawberry at the cooler levels to prevent the spreading of the aphid-transmitted viruses.

Genotype by environment interaction and yield stability of cowpea (Vigna unguiculata (L.) Walp.) genotypes in moisture limited areas of Southern Ethiopia

Genotype by environment interaction (GEI) markedly influences the success of breeding strategies in a versatile crop such as cowpea (Vigna unguiculata (L.) Walp.). Twenty cowpea genotypes were tested in a randomized complete block design with three replications at Gofa, Kucha, and Humbo in Meher seasons of 2016 and 2017 (E1 to E6) and Belg seasons of 2017 and 2018 (E7 to E12) to quantify and evaluate the effects of genotypes, environments and their interactions for grain yield of cowpea genotypes and to identify stable and/or high-yielding genotypes. The environment, genotype, and GEI effects were highly significant (p < 0.001), with the contribution of 42.3%, 23.0%, and 34.7%, respectively to the TSS. Additive main effect and multiplicative interaction (AMMI), genotype main effects plus genotype-environment interaction (GGE), ASV (AMMI stability value), and Genotype stability index (GSI) were used to identify stable genotypes. The GGE-biplot model showed that the twelve environments used for the study clustered under three mega-environments. Our results showed that IT96D-604(G12), IT-89KD (G16), IT93K-293-2-2 (G14), 93K-619-1(G13), IT97K-569-9(G20), and IT99K-1060(G15) scored the highest grain yield (1.67, 1.62, 1.55, 1.51, 1.51, and 1.45 t ha⁻¹), respectively, over environments. AMMI and GGE biplots analyses identified G16 (IT-89KD) and G14 (IT93K-293-2-2) as stable and high-yielding genotypes across environments and can be further tested in variety verification and later on released as varieties and can also be used for different breeding purposes in all cowpea growing areas in southern Ethiopia. The four high-yielding genotypes IT96D-604, 93K-619-1, IT97K-569-9, and IT99K-1060 could be recommended to be included in breeding or variety verification trials for release. Moreover, our results denoted the effectiveness of AMMI and GGE biplot techniques for selecting stable genotypes, high yielding, and responsive.

Genetic basis of some physiological traits and yield in early and late sowing conditions in bread wheat (Triticum aestivum L.)

The rise in human population necessitates the use of all available tools to enhance wheat productivity. In this regard, pre-breeding has mobilized novel underutilized genetic variation into breeding programs. However, this germplasm needs to be characterized for its efficient utilization. This investigation was initiated to evaluate the early and late sown wheat pre-breeding germplasm for physiology- and yield-related traits and to associate the mapped SNPs using association mapping approach. Our results indicate that the germplasm performed better in early sowing in comparison to late planting where grain yield (Yd) was found positively correlated with water use efficiency (WUE), heading time, and chlorophyll contents (Chl). We discovered a total of 210 associations involving 155 SNPs. Taking into consideration either early or late sowing and the mean values, only 12 marker traits were associated with trait germination, plant height, stomatal conductance, transpiration rate, Chl, carotenoids, and Yd. Our correlations and mapping results indicate that higher WUE along with Chl can be targeted as indirect physiological markers to enhance wheat yield.

AMMI and GGE biplot analysis of yield of different elite wheat line under terminal heat stress and irrigated environments

Wheat crop contributes to a major portion of the agriculture economy of Nepal. It is ranked as the third major cereal crop of the country even though, it faces terminal heat stress which speeds up the grain filling rate and shortens the filling period, causing reduction in grain weight, size, number and quality losses. We can minimize this loss through a genotypic selection of high-yielding lines by understanding the genotype-environment interaction. The objective of this research is to obtain a high yielding line with a stable performance across the environments. In order to do so, we conducted an experiment using eighteen elite wheat lines and two check varieties in alpha-lattice design with two replications in different environments viz. irrigated and terminal heat stress environment from November 2019 to April 2020. The analysis of variance revealed that genotype, environment and their interaction had a highly significant effect on the yield. Furthermore, the which-won–where model indicated specific adaptation of elite lines NL 1179, NL 1420, BL 4407, NL 1368 to the irrigated environment and Bhirkuti to the terminal heat-stressed environment. Similarly, the mean-versus-stability study indicated that elite lines BL 4407, NL 1368, BL 4919, NL 1350, and NL 1420 had above-average yield and higher stability whereas elite lines Gautam, NL 1412, NL 1376, NL 1387, NL 1404, and NL 1381 had below-average yield and lower stability. The ranking of elite lines biplot, PC1 explaining 73.6% and PC2 explaining 26.4% of the interaction effect, showed the rank of elite line, NL 1420 > NL 1368> NL 1350 > other lines, close to the ideal line. On the basis of the obtained results, we recommend NL 1420 with both the high yield and stability is suited across both the environments, while NL 1179 and Bhirkuti is adapted specifically for irrigated and terminal heat stress environment, respectively.

Antifungal potential of zinc against leaf spot disease in chili pepper caused by Alternaria alternata

The fungal pathogen, Alternaria alternata is responsible for causing leaf spot disease in many plants, including chili pepper. Zinc (Zn) an essential micronutrient for plant growth, also increases resistance in plants against diseases, and also acts as an antifungal agent. Here, in vitro effects of ZnSO4 on the propagation of A. alternata were investigated, and also in vivo, the effect of foliar application of ZnSO4 was investigated in chili pepper plants under disease stress. In vitro, ZnSO4 inhibited fungal growth in a dose-dependent manner, with complete inhibition being observed at the concentration of 8.50 mM. Hyphae and conidial damage were observed along with abnormal activity of antioxidant enzymes, Fourier-transform infrared spectroscopy confirmed the major changes in the protein structure of the fungal biomass after Zn accumulation. In vivo, pathogen infection caused the highest leaf spot disease incidence, and cumulative disease index, which resulted in a significant reduction in the plant's growth (length and biomass), and physiochemical traits (photosynthetic pigment, activity of catalase, peroxidase, polyphenol oxidase, and phenylalanine ammonia lyase). The heat map and principal component analysis based on disease, growth and, physico-chemical variables generated useful information regarding the best treatment useful for disease management. Foliar Zn (0.036 mM) acted as a resistance inducer in chili pepper plants that improved activities of antioxidants (CAT and POX), and defense compounds (PPO and PAL), while managing 77% of disease. The study indicated foliar ZnSO4 as an effective and sustainable agriculture practice to manage Alternaria leaf spot disease in chili pepper plants.

Pre evaluation of cassava (Manihot esculenta Crantz) germplasm for genotypic variation in the identification of K efficient genotypes through different statistical tools

Among the tropical tuber crops, cassava (Manihot esculenta Crantz) deserves special attention as regards to its higher biological efficiency in terms of dry matter production which incidentally implies to the higher amount of nutrient extraction from the soil resulting in better response to the application of manures and fertilizers. Among the major nutrients, Potassium (K) is considered as the key nutrient for cassava owing to its influence both in tuber yield and tuber quality. The above facts as well as the availability of sufficient cassava genotypes in the germplasm collection of ICAR-CTCRI made us to initiate research work to screen cassava germplasm including the pre breeding lines. The objective being to identify K efficient genotypes which can yield well under limited availability of K so that the external application of K can be reduced. This paper describes the wide variation noticed during the pre evaluation of 83 elite genotypes which was done as a prelude in the screening and identification of K efficient genotypes. The characters studied were tuber yield, tuber characters, plant dry matter percentage, plant K content, tuber quality (starch, cyanogenic glucosides) attributes, physiological efficiency and plant biometric characters. The variation among the genotypes for the above traits was assessed by making some yardstick for classification which in turn helped in determining the percent distribution of genotypes in each category. The variation among the genotypes were further affirmed through principal component analysis, wherein the first five components explained more than 77% of variability and the cluster analysis performed grouped these genotypes into five clusters. The biplot showed the traits which are closely linked to the genotypes. The dendrogram constructed indicated similar genotypes to that of the clusters to the extent of more than 50% revealing the association of members with similar traits in clusters and dendrograms. The study helped in establishing the drastic variation among the genotypes along with identification of six genotypes viz., Aniyoor, 7 Sahya (2), 7 III E3-5, W-19, CR 43-8, 6-6 for further detailed experimentation to identify K efficient genotypes.

Extension of biplot methodology to multivariate regression analysis

At the core of multivariate statistics is the investigation of relationships between different sets of variables. More precisely, the inter-variable relationships and the causal relationships. The latter is a regression problem, where one set of variables is referred to as the response variables and the other set of variables as the predictor variables. In this situation, the effect of the predictors on the response variables is revealed through the regression coefficients. Results from the resulting regression analysis can be viewed graphically using the biplot. The consequential biplot provides a single graphical representation of the samples together with the predictor variables and response variables. In addition, their effect in terms of the regression coefficients can be visualized, although sub-optimally, in the said biplot.

Combining self-organizing maps and biplot analysis to preselect maize phenotypic components based on UAV high-throughput phenotyping platform

BACKGROUND

With environmental deterioration, natural resource scarcity, and rapid population growth, mankind is facing severe global food security problems. To meet future needs, it is necessary to accelerate progress in breeding for new varieties with high yield and strong resistance. However, the traditional phenotypic screening methods have some disadvantages, such as destructive, inefficient, low-dimensional, labor-intensive and cumbersome, which seriously hinder the development of field breeding. Breeders urgently need a high-throughput technique for acquiring and evaluating phenotypic data that can efficiently screen out excellent phenotypic traits from large-scale genotype populations.

RESULTS

In the present study, we used an unmanned aerial vehicle (UAV) high-throughput phenotyping (HTP) platform to collect RGB and multispectral images for a breeding program and acquired multiple phenotypic components (or traits), such as plant height, normalized difference vegetation index, biomass accumulation, plant-height growth rate, lodging, and leaf color. By implementing self-organizing maps and principal components analysis biplots to establish phenotypic map and similarity, we proposed an UAV-assisted HTP framework for preselecting maize (Zee mays L.) phenotypic components (or traits).

CONCLUSIONS

This framework gives breeders additional information to allow them to quickly identify and preselect plants that have genotypes conferring desirable phenotypic components out of thousands of field plots. The present study also demonstrates that remote sensing is a powerful tool with which to acquire abundant phenotypic components. By using these rich phenotypic components, breeders should be able to more effectively identify and select superior genotypes.

Carcinogenic profile, toxicity and source apportionment of polycyclic aromatic hydrocarbons accumulated from urban road dust in Tokyo, Japan

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous in urban environments. Urban road dust (URD) generated by traffic is an important PAH accumulator. Twelve priority PAHs in < 2000 µm fraction of ten URD samples from Tokyo, Japan were characterized based on profile distributions, carcinogenicity, toxicity, and source apportionment by cluster analysis, biplot and diagnostic ratios. PAH concentrations (mg/kg dry weight) in arterial roads, highways, highway parking, highway drainage pit and residential area URD samples were 2.06-4.24, 0.25-3.37, 3.44, 4.94, and 5.26 respectively, dominated by the ∑4 rings (average 46%) and ∑5 + 6 rings (average 41%) PAHs. Biplot analysis revealed that the antecedent dry weather period, vehicle frequency and organic matter content were the dominant environmental factors governing PAH profiles of different road types. The total amount of carcinogenic PAHs in the residential URD (2.12 mg/kg) was higher than those in the arterial road (0.60-2.00 mg/kg) and highway (0.10-1.84 mg/kg) URD. Toxic equivalent concentrations (TECs) of residential, arterial road and highway URD were 0.54, (0.12-0.57), and (0.02-0.51) mg/kg, respectively. The dominant PAH sources were found to be petrogenic combustion in arterial road and highway URD, and pyrogenic combustion consisting of a mix of biomass, petroleum and traffic-related sources in the residential and highway drainage pit samples. This is also the first study to find that TEC-based toxicity should not be taken as a measure of URD toxicity.

Exploration of geochemical data with compositional canonical biplots

The study of the relationships between two compositions is of paramount importance in geochemical data analysis. This paper develops a compositional version of canonical correlation analysis, called CoDA-CCO, for this purpose. We consider two approaches, using the centred log-ratio transformation and the calculation of all possible pairwise log-ratios within sets. The relationships between both approaches are pointed out, and their merits are discussed. The related covariance matrices are structurally singular, and this is efficiently dealt with by using generalized inverses. We develop compositional canonical biplots and detail their properties. The canonical biplots are shown to be powerful tools for discovering the most salient relationships between two compositions. Some guidelines for compositional canonical biplot construction are discussed. A geochemical data set with X-ray fluorescence spectrometry measurements on major oxides and trace elements of European floodplains is used to illustrate the proposed method. The relationships between an analysis based on centred log-ratios and on isometric log-ratios are also shown.

Microarray meta-analysis to explore abiotic stress-specific gene expression patterns in Arabidopsis

BACKGROUND

Abiotic stresses are the major limiting factors that affect plant growth, development, yield and final quality. Deciphering the underlying mechanisms of plants' adaptations to stresses using few datasets might overlook the different aspects of stress tolerance in plants, which might be simultaneously and consequently operated in the system. Fortunately, the accumulated microarray expression data offer an opportunity to infer abiotic stress-specific gene expression patterns through meta-analysis. In this study, we propose to combine microarray gene expression data under control, cold, drought, heat, and salt conditions and determined modules (gene sets) of genes highly associated with each other according to the observed expression data.

RESULTS

By analyzing the expression variations of the Eigen genes from different conditions, we had identified two, three, and five gene modules as cold-, heat-, and salt-specific modules, respectively. Most of the cold- or heat-specific modules were differentially expressed to a particular degree in shoot samples, while most of the salt-specific modules were differentially expressed to a particular degree in root samples. A gene ontology (GO) analysis on the stress-specific modules suggested that the gene modules exclusively enriched stress-related GO terms and that different genes under the same GO terms may be alternatively disturbed in different conditions. The gene regulatory events for two genes, DREB1A and DEAR1, in the cold-specific gene module had also been validated, as evidenced through the literature search.

CONCLUSIONS

Our protocols study the specificity of the gene modules that were specifically activated under a particular type of abiotic stress. The biplot can also assist to visualize the stress-specific gene modules. In conclusion, our approach has the potential to further elucidate mechanisms in plants and beneficial for future experiments design under different abiotic stresses.

THE DUAL ROLE OF SELECTION AND EVOLUTIONARY HISTORY AS REFLECTED IN GENETIC CORRELATIONS

The patterns of genetic correlations between a series of eye and antenna characters were compared among two sets of spring-dwelling and cave-dwelling populations of Gammarus minus. The two sets of populations originate from different drainages and represent two separate invasions of cave habitats from surface-dwelling populations. Matrix correlations, using permutation tests, indicated significant correlations both between populations in the same basin and from the same habitat. The technique of biplot, which allows for the simultaneous consideration of relationships between different genetic correlations and different populations, was used to further analyze the correlation structure. A rank-3 biplot indicated that spring and cave populations were largely differentiated by eye-antennal correlations, whereas basins were differentiated by both eye-antennal and antennal-antennal correlations. Eye-antennal correlations, which are likely to be subject to selection, were most similar within habitats, which are likely to have similar selective regimes.

Development of class model based on blood biochemical parameters as a diagnostic tool of PSE meat

A fast, sensitive and effective method based on the blood biochemical parameters for the detection of PSE meat was developed in this study. A total of 200 pigs were slaughtered in the same slaughterhouse. Meat quality was evaluated by measuring pH, electrical conductivity and color at 45min, 2h and 24h after slaughtering in M. longissimus thoracis et lumborum (LD). Blood biochemical parameters were determined in blood samples collected during carcass bleeding. Principal component analysis (PCA) biplot showed that high levels of exsanguination Creatine Kinase, Lactate Dehydrogenase, Aspertate aminotransferase, blood glucose and lactate were associated with the PSE meat, and the five biochemical parameters were found to be good indicators of PSE meat Discriminant function analysis (DFA) was able to clearly identify PSE meat using the five biochemical parameters as input data, and the class model is an effective diagnostic tool in pigs which can be used to detect the PSE meat and reduce economic loss for the company.

Relationship between anaerobic digestion of biodegradable solid waste and spectral characteristics of the derived liquid digestate

The evolution of spectral properties during anaerobic digestion (AD) of 29 types of biodegradable solid waste was investigated to determine if spectral characteristics could be used for assessment of biological stabilization during AD. Biochemical methane potential tests were conducted and spectral indicators (including the ratio of ultraviolet-visible absorbance at 254nm to dissolved organic carbon concentration (SUVA254), the ratio of ultraviolet-visible absorbance measured at 465nm and 665nm (E4/E6), and the abundance of fluorescence peaks) were measured at different AD phases. Inter-relationship between organic degradation and spectral indicators were analyzed by principle component analysis. The results shows that from methane production phase to the end of methane production phase, SUVA254 increased by 0.16-10.93 times, the abundance of fulvic acid-like compounds fluorescence peak increased by 0.01-0.54 times, the abundance of tyrosine fluorescence peak decreased by 0.03-0.64 times. Therefore, these indicators were useful to judge the course of mixed waste digestion.