STUDY OF MORPHOLOGICAL AND PHYSIOLOGICAL PROPERTIES AND POLLINATORS OF THE INVASIVE CALLERY PEAR

Callery pear is an invasive tree in 15 states of United States and is currently present in 37 states. Its management has challenged landowners and land managers. Despite being listed as a noxious weed in four states, its study is limited. The accurate estimation of Callery pear biomass will give a clearer picture of the level of invasion and help land managers develop different strategies to control its population. Similarly, identifying possible pollinators of Callery pear is essential to have some insights on pollinators associated with this tree. Hence, the objectives of this study are 1) to calculate total above-ground biomass and prepare allometric equation, and 2) identify the pollinator taxa and calculate diversity metrics and community composition of pollinators using Callery pear. Forty trees from six different sites were destructively harvested. We measured diameter at breast height (DBH), total tree height, fresh weight, oven-dry weight, C:N ratio and moisture content of different tree components (i.e., leaves, fruits, stem, and branches) and examine their relationship with each other. Biomass of each tissue component was significantly related to DBH. Total wet biomass ranged from 326.75 g (1.27 cm DBH) to 160 kg (17.52 cm DBH), and total dry biomass ranged from 193.25 g to 97 kg for the same DBH. The average moisture content ranged from 39% to 42 % and the average wood density was found to be 0.88 gm/cm3. The model/equation determined for the biomass is Log linear model with 91% variation explained by the model. Five sites in South Carolina were surveyed using yellow, blue, white colored plastic cups and sweep net methods. A total of 756 insects were collected, belonging to 15 families in three orders; Diptera (611), Hymenoptera (135), and Coleoptera (10). The most common families of insects visiting Callery pear were Syrphidae, Sarcophagidae, Anthomyiidae, and Andrenidae with common species being Toxomerus, Andrena, Apis mellifera, Osmia, and Lasioglossum. Because of being present in highest number in all sites, we propose that Toxomerus spp. from Syrphidae family are the major visitors of Callery pear’s flower whereas sweep net and yellow bowl trap are most suitable methods of collecting them

ADVERSE IMPACT OF LOCKDOWN ON INDIAN SOCIAL AND PSYCHOLOGICAL BEHAVIOUR-A REVIEW

Due to high infectivity and death rates, the novel coronavirus 2019 (COVID-19) disease has caused worldwide social and psychologicalimpact by causing mass hysteria, economic burden, and feelings of aloneness during illness and financial losses. Studies have identified“coronaphobia” as a byproduct of the pandemic, where people have an extreme terror of contracting the virus. Mass fear of COVID-19 hascreated an overabundance of psychiatric manifestations across almost all strata of society. In this review, the psycho-social impacts ofCOVID-19 have been studied. As a data source Pubmed and Google Scholar are searched with the following key terms- “COVID-19 andsocial impact,” “SARS-CoV2 and social effects,” “social impact of current Pandemic,” “Psychological impact of COVID-19,” “Psycho-socialeffects and Coronavirus.” Many current published data and news were extracted that provide significant data. Our study revealed thatnationwide lockdowns and forced quarantine to fight against COVID-19 had produced acute panic, anxiety, obsessive behaviors, domesticabuse, hoarding, depression, post-traumatic stress disorder (PTSD) and food insecurity, including marked impairment in daily lifefunctioning. The psycho-social aspects of older people, their caregivers, psychiatric patients and marginalized communities are badlyaffected in different ways and need special attention. From the present work, it can be concluded that there is an urgent need to find outeffective ways to treat people and bring them out of fear and stress. As these symptoms are seen in large population sizes, we also need tostudy the long-term effects of these adverse effects on the mass level

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Not AvailableIn the present study in wheat, GWAS was conducted for identification of marker trait associations (MTAs) for the following six grain morphology traits: (1) grain cross-sectional area (GCSA), (2) grain perimeter (GP), (3) grain length (GL), (4) grain width (GWid), (5) grain length-width ratio (GLWR) and (6) grain form-density (GFD). The data were recorded on a subset of spring wheat reference set (SWRS) comprising 225 diverse genotypes, which were genotyped using 10,904 SNPs and phenotyped for two consecutive years (2017-2018, 2018-2019). GWAS was conducted using five different models including two single-locus models (CMLM, SUPER), one multi-locus model (FarmCPU), one multi-trait model (mvLMM) and a model for Q x Q epistatic interactions. False discovery rate (FDR) [P value -log10(p) ≥ 5] and Bonferroni correction [P value -log10(p) ≥ 6] (corrected p value < 0.05) were applied to eliminate false positives due to multiple testing. This exercise gave 88 main effect and 29 epistatic MTAs after FDR and 13 main effect and 6 epistatic MTAs after Bonferroni corrections. MTAs obtained after Bonferroni corrections were further utilized for identification of 55 candidate genes (CGs). In silico expression analysis of CGs in different tissues at different parts of the seed at different developmental stages was also carried out. MTAs and CGs identified during the present study are useful addition to available resources for MAS to supplement wheat breeding programmes after due validation and also for future strategic basic research.Not Availabl

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Not AvailableIn the present study in wheat, GWAS was conducted for identification of marker trait associations (MTAs) for the following six grain morphology traits: (1) grain cross-sectional area (GCSA), (2) grain perimeter (GP), (3) grain length (GL), (4) grain width (GWid), (5) grain length–width ratio (GLWR) and (6) grain form-density (GFD). The data were recorded on a subset of spring wheat reference set (SWRS) comprising 225 diverse genotypes, which were genotyped using 10,904 SNPs and phenotyped for two consecutive years (2017–2018, 2018–2019). GWAS was conducted using five different models including two single-locus models (CMLM, SUPER), one multi-locus model (FarmCPU), one multi-trait model (mvLMM) and a model for Q x Q epistatic interactions. False discovery rate (FDR) [P value -log10(p) ≥ 5] and Bonferroni correction [P value -log10(p) ≥ 6] (corrected p value < 0.05) were applied to …Not Availabl

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Not AvailableCircadian rhythms regulate several physiological and developmental processes of plants. Hence, the identification of genes with the underlying circadian rhythmic features is pivotal. Though computational methods have been developed for the identification of circadian genes, all these methods are based on gene expression datasets. In other words, we failed to search any sequence-based model, and that motivated us to deploy the present computational method to identify the proteins encoded by the circadian genes. Support vector machine (SVM) with seven kernels, i.e., linear, polynomial, radial, sigmoid, hyperbolic, Bessel and Laplace was utilized for prediction by employing compositional, transitional and physico-chemical features. Higher accuracy of 62.48% was achieved with the Laplace kernel, following the fivefold cross- validation approach. The developed model further secured 62.96% accuracy with an independent dataset. The SVM also outperformed other state-of-art machine learning algorithms, i.e., Random Forest, Bagging, AdaBoost, XGBoost and LASSO. We also performed proteome-wide identification of circadian proteins in two cereal crops namely, Oryza sativa and Sorghum bicolor, followed by the functional annotation of the predicted circadian proteins with Gene Ontology (GO) terms. To the best of our knowledge, this is the first computational method to identify the circadian genes with the sequence data. Based on the proposed method, we have developed an R-package PredCRG (https:// cran.rproject. org/ web/ packa ges/ PredC RG/ index. html) for the scientific community for proteome-wide identification of circadian genes. The present study supplements the existing computational methods as well as wet-lab experiments for the recognition of circadian genes.Not Availabl

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Not AvailableFinger millet (Eleusine coracana L.) is an important dry-land cereal in Asia and Africa because of its ability to provide assured harvest under extreme dry conditions and excellent nutritional properties. However, the genetic improvement of the crop is lacking in the absence of suitable genomic resources for reliable genotype-phenotype associations. Keeping this in view, a diverse global finger millet germplasm collection of 113 accessions was evaluated for 14 agro-morphological characters in two environments viz. ICAR-Vivekananda Institute of Hill Agriculture, Almora (E1) and Crop Research Centre (CRC), GBPUA&T, Pantnagar (E2), India. Principal component analysis and cluster analysis of phenotypic data separated the Indian and exotic accessions into two separate groups. Previously generated SNPs through genotyping by sequencing (GBS) were used for association mapping to identify reliable marker(s) linked to grain yield and its component traits. The marker trait associations were determined using single locus single trait (SLST), multi-locus mixed model (MLMM) and multi-trait mixed model (MTMM) approaches. SLST led to the identification of 20 marker-trait associations (MTAs) (p value<0.01 and <0.001) for 5 traits. While advanced models, MLMM and MTMM resulted in additional 36 and 53 MTAs, respectively. Nine MTAs were common out of total 109 associations in all the three mapping approaches (SLST, MLMM and MTMM). Among these nine SNPs, five SNP sequences showed homology to candidate genes of Oryza sativa (Rice) and Setaria italica (Foxtail millet), which play an important role in flowering, maturity and grain yield. In addition, 67 and 14 epistatic interactions were identified for 10 and 7 traits at E1 and E2 locations, respectively. Hence, the 109 novel SNPs associated with important agro-morphological traits, reported for the first time in this study could be precisely utilized in finger millet genetic improvement after validation.Not Availabl

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Not AvailableA genome-wide association study (GWAS) for 10 yield and yield component traits was conducted using an association panel comprising 225 diverse spring wheat genotypes. The panel was genotyped using 10,904 SNPs and evaluated for three years (2016–2019), which constituted three environments (E1, E2 and E3). Heritability for different traits ranged from 29.21 to 97.69%. Marker-trait associations (MTAs) were identified for each trait using data from each environment separately and also using BLUP values. Four different models were used, which included three single trait models (CMLM, FarmCPU, SUPER) and one multi-trait model (mvLMM). Hundreds of MTAs were obtained using each model, but after Bonferroni correction, only 6 MTAs for 3 traits were available using CMLM, and 21 MTAs for 4 traits were available using FarmCPU; none of the 525 MTAs obtained using SUPER could qualify after Bonferroni correction. Using BLUP, 20 MTAs were available, five of which also figured among MTAs identified for individual environments. Using mvLMM model, after Bonferroni correction, 38 multi-trait MTAs, for 15 different trait combinations were available. Epistatic interactions involving 28 pairs of MTAs were also available for seven of the 10 traits; no epistatic interactions were available for GNPS, PH, and BYPP. As many as 164 putative candidate genes (CGs) were identified using all the 50 MTAs (CMLM, 3; FarmCPU, 9; mvLMM, 6, epistasis, 21 and BLUP, 11 MTAs), which ranged from 20 (CMLM) to 66 (epistasis) CGs. In-silico expression analysis of CGs was also conducted in different tissues at different developmental stages. The information generated through the present study proved useful for developing a better understanding of the genetics of each of the 10 traits; the study also provided novel markers for marker-assisted selection (MAS) to be utilized for the development of wheat cultivars with improved agronomic traits.Not Availabl

Genome wide association mapping of agro-morphological traits among a diverse collection of finger millet (<i>Eleusine coracana</i> L.) genotypes using SNP markers

<div><p>Finger millet (<i>Eleusine coracana</i> L.) is an important dry-land cereal in Asia and Africa because of its ability to provide assured harvest under extreme dry conditions and excellent nutritional properties. However, the genetic improvement of the crop is lacking in the absence of suitable genomic resources for reliable genotype-phenotype associations. Keeping this in view, a diverse global finger millet germplasm collection of 113 accessions was evaluated for 14 agro-morphological characters in two environments viz. ICAR-Vivekananda Institute of Hill Agriculture, Almora (E1) and Crop Research Centre (CRC), GBPUA&T, Pantnagar (E2), India. Principal component analysis and cluster analysis of phenotypic data separated the Indian and exotic accessions into two separate groups. Previously generated SNPs through genotyping by sequencing (GBS) were used for association mapping to identify reliable marker(s) linked to grain yield and its component traits. The marker trait associations were determined using single locus single trait (SLST), multi-locus mixed model (MLMM) and multi-trait mixed model (MTMM) approaches. SLST led to the identification of 20 marker-trait associations (MTAs) (p value<0.01 and <0.001) for 5 traits. While advanced models, MLMM and MTMM resulted in additional 36 and 53 MTAs, respectively. Nine MTAs were common out of total 109 associations in all the three mapping approaches (SLST, MLMM and MTMM). Among these nine SNPs, five SNP sequences showed homology to candidate genes of <i>Oryza sativa</i> (Rice) and <i>Setaria italica</i> (Foxtail millet), which play an important role in flowering, maturity and grain yield. In addition, 67 and 14 epistatic interactions were identified for 10 and 7 traits at E1 and E2 locations, respectively. Hence, the 109 novel SNPs associated with important agro-morphological traits, reported for the first time in this study could be precisely utilized in finger millet genetic improvement after validation.</p></div

Significant marker-trait associations (MTAs) identified using MTMM.

<p>Significant marker-trait associations (MTAs) identified using MTMM.</p

List of agro-morphological traits consisting of 14 quantitative characters studied among finger millet accessions.

<p>List of agro-morphological traits consisting of 14 quantitative characters studied among finger millet accessions.</p