Mechanism of action of hydrogen peroxide in wheat thermotolerance - interaction between antioxidant isoenzymes, proline and cell membrane

Terminal heat stress causes an array of physiological, biochemical and morphological changes in plants, which affect plant growth and development. It has very severe effect on the pollen viability and seed setting in wheat. In the present investigation, an altered expression of H2O2 (0.9 μg/g in C-306 and 0.75 μg/g in HD2329) was observed with the highest accumulation at seed hardening stage and against heat shock (HS) of 42°C for 2 h. With the increase in H2O2 accumulation, an increase in the number of isoenzymes of superoxide dismutase and catalase were observed with high activities under differential heat shock. A decrease in the proline accumulation was observed under differential heat shock. Exogenous application of H2O2 (10 mmole/L) leads to increase in the accumulation of intracellular H2O2 and further an increase in the number of isoenzymes of superoxide dismutase (SOD) and catalase (CAT) was observed. The tolerant cultivar was more responsive to exogenous application of H2O2 compared to susceptible cultivar. The percentage decrease in cell membrane stability under differential heat shock was low in H2O2 treated plants compared to non-treated. The results from this study suggest a potential role for H2O2 in regulating the activity of antioxidant enzymes and accumulation of proline inside cells and in turn influence the cell membrane stability under heat stress. All the defense associated genes were observed to be very responsive to intracellular H2O2, which gives inference that H2O2 has regulatory role to play in controlling the expression and activities of these proteins under abiotic stresses.Key words: Antioxidant enzymes, wheat, heat stress, H2O2, proline, catalase, superoxide dismutase, cell membrane stability, reactive oxygen species

Regulation of High-Temperature Stress Response by Small RNAs

Temperature extremes constitute one of the most common environmental stresses that adversely affect the growth and development of plants. Transcriptional regulation of temperature stress responses, particularly involving protein-coding gene networks, has been intensively studied in recent years. High-throughput sequencing technologies enabled the detection of a great number of small RNAs that have been found to change during and following temperature stress. The precise molecular action of some of these has been elucidated in detail. In the present chapter, we summarize the current understanding of small RNA-mediated modulation of high- temperature stress-regulatory pathways including basal stress responses, acclimation, and thermo-memory. We gather evidence that suggests that small RNA network changes, involving multiple upregulated and downregulated small RNAs, balance the trade-off between growth/development and stress responses, in order to ensure successful adaptation. We highlight specific characteristics of small RNA-based tem- perature stress regulation in crop plants. Finally, we explore the perspectives of the use of small RNAs in breeding to improve stress tolerance, which may be relevant for agriculture in the near future

Effect of Climate Variables on Yield of Major Crop in Samastipur District of Bihar: A Time Series Analysis

Climate change influences crop yield vis-a-vis crop production to a greater extent in Bihar. Climate change and its impacts are well recognizing today and it will affect both physical and biological system. Therefore, this study has been planned to assess the effect of climate variables on yield of major crops, adaptation measures undertaken in Samastipur district of Bihar. Secondary data on yield of maize and wheat crops were collected for the period from 1999-2019 to describe the effects of climate variable namely rainfall, maximum and minimum temperature on yield of maize and wheat. Analysis of time series data on climate variables indicated that annual rainfall was positively related to yields while maximum and minimum temperature had a negative but significant impact on maize and wheat yields. It actually revealed that other factors, such as; type of soil, soil fertility and method of farming may also be responsible for crop yield. Trend in cost as well as income of farmers indicated that income and cost of cultivation has no significant relationship with climate variable. On the basis of above observation it may be concluded that level of income of farmers changed due to change in the other factors rather than change in climatic variable over the period under study as cost of cultivation increases with increased in the price of input over the period but not due to change in climatic variabl

Web-SpikeSegNet: Deep Learning Framework for Recognition and Counting of Spikes From Visual Images of Wheat Plants

Computer vision with deep learning is emerging as a signi�cant approach for non-invasive and non-destructive plant phenotyping. Spikes are the reproductive organs of wheat plants. Detection and counting of spikes considered the grain-bearing organ have great importance in the phenomics study of large sets of germplasms. In the present study, we developed an online platform, ``Web-SpikeSegNet,'' based on a deep-learning framework for spike detection and counting from the wheat plant's visual images. The architecture of the Web-SpikeSegNet consists of 2 layers. First Layer, Client-Side Interface Layer, deals with end user's requests and corresponding responses management. In contrast, the second layer, Server Side Application Layer, consists of a spike detection and counting module. The backbone of the spike detection module comprises of deep encoder-decoder network with hourglass network for spike segmentation. The Spike counting module implements the ``Analyze Particle'' function of imageJ to count the number of spikes. For evaluating the performance of Web-SpikeSegNet, we acquired the wheat plant's visual images, and the satisfactory segmentation performances were obtained as Type I error 0.00159, Type II error 0.0586, Accuracy 99.65%, Precision 99.59% and F1 score 99.65%. As spike detection and counting in wheat phenotyping are closely related to the yield, Web-SpikeSegNet is a signi�cant step forward in the �eld of wheat phenotyping and will be very useful to the researchers and students working in the domain

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Not AvailableComputer vision with deep-learning is emerging as a major approach for non-invasive and non-destructive plant phenotyping. Spikes are the reproductive organs of wheat plants. Detection and counting of spikes considered the grain-bearing organ have great importance in the phenomics study of large sets of germplasms. In the present study, we developed an online platform “Web-SpikeSegNet” based on a deep-learning framework for spike detection and counting from the wheat plant’s visual images. The architecture of the Web-SpikeSegNet consists of 2 layers. First Layer, Client-Side Interface Layer, deals with end user’s requests and corresponding responses management. In contrast, the second layer, Server Side Application Layer, consists of a spike detection and counting module. The backbone of the spike detection module comprises of deep encoder-decoder network with hourglass for spike segmentation. The Spike counting module implements the “Analyze Particle” function of imageJ to count the number of spikes. For evaluating the performance of Web-SpikeSegNet, we acquired the wheat plant’s visual images, and the satisfactory segmentation performances were obtained as Type I error 0.00159, Type II error 0.0586, Accuracy 99.65%, Precision 99.59% and F1 score 99.65%. As spike detection and counting in wheat phenotyping are closely related to the yield, Web-SpikeSegNet is a significant step forward in the field of wheat phenotyping and will be very useful to the researchers and students working in the domain.Not Availabl

Asymptomatic Infection with Visceral Leishmaniasis in a Disease-Endemic Area in Bihar, India

A prospective study was carried out in a cohort of 355 persons in a leishmaniasis-endemic village of the Patna District in Bihar, India, to determine the prevalence of asymptomatic persons and rate of progression to symptomatic visceral leishmaniasis (VL) cases. At baseline screening, 50 persons were positive for leishmaniasis by any of the three tests (rK39 strip test, direct agglutination test, and polymerase chain reaction) used. Point prevalence of asymptomatic VL was 110 per 1,000 persons and the rate of progression to symptomatic cases was 17.85 per 1,000 person-months. The incidence rate ratio of progression to symptomatic case was 3.36 (95% confidence interval [CI] = 0.75–15.01, P = 0.09) among case-contacts of VL compared with neighbors. High prevalence of asymptomatic persons and clinical VL cases and high density of Phlebotomus argentipes sand flies can lead to transmission of VL in VL-endemic areas

Precision measurement of the structure of the CMS inner tracking system using nuclear interactions

The structure of the CMS inner tracking system has been studied using nuclear interactions of hadrons striking its material. Data from proton-proton collisions at a center-of-mass energy of 13 TeV recorded in 2015 at the LHC are used to reconstruct millions of secondary vertices from these nuclear interactions. Precise positions of the beam pipe and the inner tracking system elements, such as the pixel detector support tube, and barrel pixel detector inner shield and support rails, are determined using these vertices. These measurements are important for detector simulations, detector upgrades, and to identify any changes in the positions of inactive elements