First report of Blister beetle, Mylabris pustulata Thunberg (Meloidae: Coleoptera) in maize fields from Sarson village of Almora District, Uttarakhand (India)

Orange banded blister beetle, Mylabris pustulata is an important species of Blister beetles and has been found to cause economic damage due to its polyphagous nature. In the present study, M. pustulata was found to be feeding on maize crop from Sarson village of Almora, Uttarakhand which is located on a ridge at the southern edge of the Kumaon Hills of the Himalaya range. This article brings into notice the damage by M. pustulata first time on maize from the specified area. The morphological features such as characteristic wing pattern, mouthparts, antennae etc. and feeding on sap or solid matter of floral or fruit in a similar manner as described in earlier texts revealed the similarity of test insect with M. pustulata.&nbsp

Violated Bodies and Truncated Narratives: Mapping the Changing Contours of Violence and Eco-strategies of Resistance in Contemporary South Asian Women’s Writings from Bangladesh

The article aims to portray the traumas and sufferings of female war survivors in pre and post-1971 Bangladesh in Selina Hossain’s novel Hangor, Nadi, Grenade (1976), translated into English as River of My Blood by Jackie Kabir in 2016. By using the feminist political-ecological perspectives of Wendy Harcourt and Arthuro Escobar (2002), the constructive framework of the article aims to analyze the changing contours of violence in the spheres of the body, home, environment, and social-public arenas in the lives of the female war-survivors, especially the Muktijoddhas living in the fictional places of Haldi, Bangladesh as portrayed in the novel. Considering the postcolonial ecofeminist viewpoints of Shazia Rahman (2019), this article focuses exclusively on how the bodies of female war survivors as sites of violence become sites of resilience in the face of socio-cultural, political, and ecological injustices and resistance in the face of objectification in the name of ethnocultural nationalism through an attachment to the place Bangladesh and its more-than-human-environment. Additionally, the article seeks to demonstrate how bringing private violence into the public discourse through South Asian writings works as an intervention into the dominant narratives of patriarchal nationalism, gender discrimination, and biased social structures that have been materialized through honor killing, rape, murder, and verbal abuse, and provides a tool for depicting the symbolic, cultural, and epistemic violence that affects women in South Asia

Reaping the Benefits of Next-generation Sequencing Technologies for Crop Improvement — Solanaceae

Next-generation sequencing (NGS) technologies make possible the sequencing of the whole genome of a species decoding a complete gene catalogue and transcriptome to allow the study of expression pattern of entire genes. The huge data generated through whole genome and transcriptome sequencing not only provide a basis to study variation at gene sequence (such as single-nucleotide polymorphism and InDels) and expression level but also help to understand the evolutionary relationship between different crop species. Furthermore, NGS technologies have made possible the quick correlations of phenotypes with genotypes in different crop species, thereby increasing the precision of crop improvement. The Solanaceae family represents the third most economically important family after grasses and legumes due to high nutritional components. The current advances in NGS technology and their application in Solanaceae crops made several progresses in the identification of genes responsible for economically important traits, development of molecular markers, and understanding the genome organization and evolution in Solanaceae crops. The combination of high-throughput NGS technologies with conventional crop breeding has been shown to be promising in the Solanaceae translational genomics research. As a result, NGS technologies has been seen to be adopted in a large scale to study the molecular basis of fruit and tuber development, disease resistance, and increasing quantity and quality of crop production

Advancing the STMS genomic resources for defining new locations on the intraspecific genetic linkage map of chickpea (Cicer arietinum L.)

<p>Abstract</p> <p>Background</p> <p>Chickpea (<it>Cicer arietinum </it>L.) is an economically important cool season grain legume crop that is valued for its nutritive seeds having high protein content. However, several biotic and abiotic stresses and the low genetic variability in the chickpea genome have continuously hindered the chickpea molecular breeding programs. STMS (Sequence Tagged Microsatellite Sites) markers which are preferred for the construction of saturated linkage maps in several crop species, have also emerged as the most efficient and reliable source for detecting allelic diversity in chickpea. However, the number of STMS markers reported in chickpea is still limited and moreover exhibit low rates of both inter and intraspecific polymorphism, thereby limiting the positions of the SSR markers especially on the intraspecific linkage maps of chickpea. Hence, this study was undertaken with the aim of developing additional STMS markers and utilizing them for advancing the genetic linkage map of chickpea which would have applications in QTL identification, MAS and for <it>de novo </it>assembly of high throughput whole genome sequence data.</p> <p>Results</p> <p>A microsatellite enriched library of chickpea (enriched for <b>(</b>GT/CA)_nand (GA/CT)_nrepeats) was constructed from which 387 putative microsatellite containing clones were identified. From these, 254 STMS primers were designed of which 181 were developed as functional markers. An intraspecific mapping population of chickpea, [ICCV-2 (single podded) × JG-62 (double podded)] and comprising of 126 RILs, was genotyped for mapping. Of the 522 chickpea STMS markers (including the double-podding trait, screened for parental polymorphism, 226 (43.3%) were polymorphic in the parents and were used to genotype the RILs. At a LOD score of 3.5, eight linkage groups defining the position of 138 markers were obtained that spanned 630.9 cM with an average marker density of 4.57 cM. Further, based on the common loci present between the current map and the previously published chickpea intraspecific map, integration of maps was performed which revealed improvement of marker density and saturation of the region in the vicinity of <it>sfl </it>(double-podding) gene thereby bringing about an advancement of the current map.</p> <p>Conclusion</p> <p>An arsenal of 181 new chickpea STMS markers was reported. The developed intraspecific linkage map defined map positions of 138 markers which included 101 new locations.Map integration with a previously published map was carried out which revealed an advanced map with improved density. This study is a major contribution towards providing advanced genomic resources which will facilitate chickpea geneticists and molecular breeders in developing superior genotypes with improved traits.</p

PV Output forecasting based on weather classification, SVM and ANN

211-217The expansion in solar power is expected to be dramatic soon. A number of solar parks with high capacities are being setup to harness the potential of this renewable resource. However, the variability of solar power remains an important issue for grid integration of solar PV power plants. Changing weather conditions have affected the PV output. Thus, developing methods for accurately forecasting solar PV output is essential for enabling large-scale PV deployment. This paper has proposed a model for forecasting PV output based on weather classification, using a solar PV plant in Maharashtra, India, as the sample system. The input data is first classified using RBF-SVM (Radial Basis Function Support Vector Machines) into three types based on weather conditions, namely, sunny, rainy and cloudy. Then, the neural network model corresponding to that weather type has been applied to forecast the solar PV output. The obtained results for the overall model is studied for its effectiveness and are compared with existing research

High density linkage mapping of genomic and transcriptomic SNPs for synteny analysis and anchoring the genome sequence of chickpea

This study presents genome-wide discovery of SNPs through next generation sequencing of the genome of Cicer reticulatum. Mapping of the C. reticulatum sequenced reads onto the draft genome assembly of C. arietinum (desi chickpea) resulted in identification of 842,104 genomic SNPs which were utilized along with an additional 36,446 genic SNPs identified from transcriptome sequences of the aforementioned varieties. Two new chickpea Oligo Pool All (OPAs) each having 3,072 SNPs were designed and utilized for SNP genotyping of 129 Recombinant Inbred Lines (RILs). Using Illumina GoldenGate Technology genotyping data of 5,041 SNPs were generated and combined with the 1,673 marker data from previously published studies, to generate a high resolution linkage map. The map comprised of 6698 markers distributed on eight linkage groups spanning 1083.93 cM with an average inter-marker distance of 0.16 cM. Utility of the present map was demonstrated for improving the anchoring of the earlier reported draft genome sequence of desi chickpea by ~30% and that of kabuli chickpea by 18%. The genetic map reported in this study represents the most dense linkage map of chickpea , with the potential to facilitate efficient anchoring of the draft genome sequences of desi as well as kabuli chickpea varieties

Large number of flowers and tertiary branches, and higher reproductive success increase yields under salt stress in chickpea

Salinity is a major problem worldwide and improving salt tolerance of chickpea (Cicer arietinum L.) will allow expansion of production to more marginal areas. Plant reproduction suffers under salt stress in chickpea, but it remains unclear which process is most affected and what traits discriminate tolerant from sensitive lines. Three pot experiments were carried out to compare the effects of salt application (17 g NaCl kg−1 Alfisol) at sowing (SS) and at the start of flowering (SF) on growth, canopy transpiration, plant architecture, and flower, pod and seed development (timing, numbers, mass, abortion). Six pairs of tolerant/sensitive lines with similar flowering times within each pair, but different among the pairs, were used. Shoot biomass was similar in tolerant and sensitive lines in the SS and SF treatments, whereas the seed yield decreased more under SS and SF treatments in the sensitive lines. The flower, pod and seed numbers within all pairs was higher in the tolerant than in the sensitive lines in the non-saline controls, but the differences in numbers of seeds and pods further increased in both the SS and SF treatments. By contrast, neither the duration of flowering or podding, nor the percentage of flower or pod abortion, discriminated tolerant from sensitive lines. In non-saline controls the numbers of primary branches was 100% higher across the sensitive lines, whereas the number of tertiary branches was 8-fold higher across tolerant lines. The relative transpiration of the tolerant lines in the salt treatments was above that for the sensitive lines in three pairs of tolerant/sensitive lines, but did not differ within two pairs. Our results demonstrate that constitutive traits, i.e. numbers of flowers and tertiary branches, and adaptive traits, i.e. high number of seeds under salt stress, are both critical aspects of salinity tolerance in chickpea

In Whose Voice Should a Subaltern Speak?: Reading the Problem of Agency in Amitav Ghosh's The Hungry Tide

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Photovoltaic Output Forecasting of PV Systems Based on Weather Classification, SVM and ANN

The expansion in solar power is expected to be dramatic soon. A number of solar parks with high capacities are being set up to harness the potential of this renewable resource. However, the variability of solar power remains an important issue for grid integration of solar PV power plants. Changing weather conditions affect the PV output. Thus, developing methods for accurately forecasting solar PV output is essential for enabling large-scale PV deployment. This paper proposes a model for forecasting PV output based on weather classification, using a solar PV plant in Maharashtra, India, as the sample system. The input data is first classified using RBF-SVM into three types based on weather conditions, namely, sunny, rainy and cloudy. Then, the neural network model corresponding to that weather type is applied to forecast the solar PV output. The result obtained for the overall model is studied for its effectiveness and are compared with existing research