Explore the vast range of titles available.

The wildfires over the central Indian Himalayan region have attracted the significant attention of environmental scientists. Despite their major and disastrous effects on the environment and air quality, studies on the forest fires’ impacts from a renewable energy point of view are lacking for this region. Therefore, for the first time, we examine the impact of massive forest fires on the reduction in solar energy production over the Indian subcontinent via remote sensing techniques. For this purpose, we used data from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO), the Satellite Application Facility on support to Nowcasting/Very Short-Range Forecasting Meteosat Second Generation (SAFNWC/MSG) in conjunction with radiative transfer model (RTM) simulation, in addition to 1-day aerosol forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). The energy production during the first quarter of 2021 was found to reach 650 kWh/m2 and the revenue generated was about INR (Indian rupee) 79.5 million. During the study period, the total attenuation due to aerosols and clouds was estimated to be 116 and 63 kWh/m2 for global and beam horizontal irradiance (GHI and BHI), respectively. The financial loss due to the presence of aerosols was found to be INR 8 million, with the corresponding loss due to clouds reaching INR 14 million for the total Indian solar plant’s capacity potential (40 GW). This analysis of daily energy and financial losses can help the grid operators in planning and scheduling power generation and supply during the period of fires. The findings of the present study will drastically increase the awareness among the decision makers in India about the indirect effects of forest fires on renewable energy production, and help promote the reduction in carbon emissions and greenhouse gases in the air, along with the increase in mitigation processes and policies.

Nonparametric trend detection tests like the Mann–Kendall (MK) test require independent observations, but serial autocorrelation in the datasets inflates/deflates the variance and alters the Type-I and Type-II errors. Prewhitening (PW) techniques help address this issue by removing autocorrelation prior to applying MK. We evaluate several PW schemes—von Storch (PW-S), Slope-corrected PW (PW-Cor), trend-free prewhitening (TFPW) proposed by Yue (TFPW-Y), iterative TFPW (TFPW-WS), variance-corrected TFPW (VCTFPW), and newly proposed detrended prewhitened with modified trend added (DPWMT). Through Monte Carlo simulations, we constructed a lag-1 autoregressive (AR(1)) time series and systematically assessed the performance of different PW methods relative to sample size, autocorrelation, and trend slope. Results indicate that all methods tend to overestimate weak trends in small samples (n < 40). For moderate/high trends, the slopes estimated from the VCTFPW and DPWMT series close (within a ± 20% range) to the actual trend. VCTFPW shows slightly lower RMSE than DPWMT at mid-range lag-1 autocorrelation (ρ1 = 0.3 to 0.6) but fluctuates for ρ1 ≥ 0.7. Original series and TFPW-Y fail to control Type-I error with increasing ρ1, while VCTFPW and DPWMT maintained Type-I errors below the significance level (α = 0.05) for large samples. Apart from TFPW-Y, all PW methods resulted in weak power of the test for weak trends and small samples. TFPW-WS showed high power of the test but only for strong autocorrelated data combined with strong trends. In contrast, VCTFPW failed to preserve the power of the test at high autocorrelation (≥0.7) due to slope underestimation. DPWMT restores the power of the test for 0.1 ≤ ρ1 ≤ 0.9 for moderate/strong trends. Overall, the proposed DPWMT approach demonstrates clear advantages, providing unbiased slope estimates, reasonable Type-I error control, and sufficient power in detecting linear trends in the AR(1) series.

The present study analyzes data from total suspended particulate (TSP) samples collected during 3 years (2005–2008) at Nainital, central Himalayas, India and analyzed for carbonaceous aerosols (organic carbon (OC) and elemental carbon (EC)) and inorganic species, focusing on the assessment of primary and secondary organic carbon contributions (POC, SOC, respectively) and on source apportionment by positive matrix factorization (PMF). An average TSP concentration of 69.6 ± 51.8 µg m−3 was found, exhibiting a pre-monsoon (March–May) maximum (92.9 ± 48.5 µg m−3) due to dust transport and forest fires and a monsoon (June–August) minimum due to atmospheric washout, while carbonaceous aerosols and inorganic species expressed a similar seasonality. The mean OC/EC ratio (8.0 ± 3.3) and the good correlations between OC, EC, and nss-K+ suggested that biomass burning (BB) was one of the major contributing factors to aerosols in Nainital. Using the EC tracer method, along with several approaches for the determination of the (OC/EC)pri ratio, the estimated SOC component accounted for ~25% (19.3–29.7%). Furthermore, TSP source apportionment via PMF allowed for a better understanding of the aerosol sources in the Central Himalayan region. The key aerosol sources over Nainital were BB (27%), secondary sulfate (20%), secondary nitrate (9%), mineral dust (34%), and long-range transported mixed marine aerosol (10%). The potential source contribution function (PSCF) and concentration weighted trajectory (CWT) analyses were also used to identify the probable regional source areas of resolved aerosol sources. The main source regions for aerosols in Nainital were the plains in northwest India and Pakistan, polluted cities like Delhi, the Thar Desert, and the Arabian Sea area. The outcomes of the present study are expected to elucidate the atmospheric chemistry, emission source origins, and transport pathways of aerosols over the central Himalayan region.

Drought is one of the major impediments in wheat productivity. Traditional breeding and marker assisted QTL introgression had limited success. Available wheat genomic and RNA-seq data can decipher novel drought tolerance mechanisms with putative candidate gene and marker discovery. Drought is first sensed by root tissue but limited information is available about how roots respond to drought stress. In this view, two contrasting genotypes, namely, NI5439 41 (drought tolerant) and WL711 (drought susceptible) were used to generate ~78.2 GB data for the responses of wheat roots to drought. A total of 45139 DEGs, 13820 TF, 288 miRNAs, 640 pathways and 435829 putative markers were obtained. Study reveals use of such data in QTL to QTN refinement by analysis on two model drought-responsive QTLs on chromosome 3B in wheat roots possessing 18 differentially regulated genes with 190 sequence variants (173 SNPs and 17 InDels). Gene regulatory networks showed 69 hub-genes integrating ABA dependent and independent pathways controlling sensing of drought, root growth, uptake regulation, purine metabolism, thiamine metabolism and antibiotics pathways, stomatal closure and senescence. Eleven SSR markers were validated in a panel of 18 diverse wheat varieties. For effective future use of findings, web genomic resources were developed. We report RNA-Seq approach on wheat roots describing the drought response mechanisms under field drought conditions along with genomic resources, warranted in endeavour of wheat productivity.

Tilletia indica Mitra causes Karnal bunt (KB) in wheat by pathogenic dikaryophase. The present study is the first to provide the draft genomes of the dikaryon (PSWKBGD-3) and its two monosporidial lines (PSWKBGH-1 and 2) using Illumina and PacBio reads, their annotation and the comparative analyses among the three genomes by extracting polymorphic SSR markers. The trancriptome from infected wheat grains of the susceptible wheat cultivar WL711 at 24  h , 48 h , and 7 d after inoculation of PSWKBGH-1, 2 and PSWKBGD-3 were also isolated. Further, two transcriptome analyses were performed utilizing T. indica transcriptome to extract dikaryon genes responsible for pathogenesis, and wheat transcriptome to extract wheat genes affected by dikaryon involved in plant-pathogen interaction during progression of KB in wheat. A total of 54, 529, and 87 genes at 24hai , 48 hai , and 7 dai , respectively were upregulated in dikaryon stage while 21, 35, and 134 genes of T. indica at 24hai , 48 hai , and 7 dai , respectively, were activated only in dikaryon stage. While, a total of 23, 17, and 52 wheat genes at 24 hai , 48 hai , and 7 dai , respectively were upregulated due to the presence of dikaryon stage only. The results obtained during this study have been compiled in a web resource called TiGeR ( http://backlin.cabgrid.res.in/tiger/ ), which is the first genomic resource for T. indica cataloguing genes, genomic and polymorphic SSRs of the three T. indica lines, wheat and T. indica DEGs as well as wheat genes affected by T. indica dikaryon along with the pathogenecity related proteins of T. indica dikaryon during incidence of KB at different time points. The present study would be helpful to understand the role of dikaryon in plant-pathogen interaction during progression of KB, which would be helpful to manage KB in wheat, and to develop KB-resistant wheat varieties.

Neurilemmoma is a form of nerve tumor that develops from the nerve sheaths. It is a slow-growing tumor with a rare malignant transformation. It has an incidence rate of less than 1% in lower limbs and its origin in the superficial peroneal nerve is an extremely rare occurrence. In this report, we present a case of neurilemmoma of the superficial peroneal nerve in a 67-year-old male who presented with complaints of pain and swelling at the lateral aspect of the leg. The swelling was enucleated while preserving the main nerve trunk. The patient was found to be asymptomatic after a two-year postoperative period.