Indian Agricultural Research Institute
Recent publications
An epigenome is a collective name for the biochemical alterations to nuclear DNA, changes in noncoding RNA biogenesis and histone protein modification. Without altering the underlying nucleotide sequence, these modifications frequently cause gene expression variations. The variations in chromatin structure brought on by the modifications could also alter how the genome functions or behaves. Epigenomic mechanisms regulate gene expression patterns and are influenced by various environmental factors, including nutrient availability. Nutrient use efficiency (NtUE) is the ability of an organism to acquire, assimilate, and utilize nutrients effectively, with nitrogen being one of the most essential components for the growth of plants. Efficient nitrogen uptake is crucial for optimizing crop yield, reducing fertilizer usage, and minimizing environmental impact caused by nitrogen depletion. Both endogenous and external stimuli can create epigenomic alterations and phenotypic plasticity in plants. Plant epigenomics has undergone a revolution owing to the advancement of techniques, such as next-generation sequencing methods. The initial studies concentrated on genes and DNA methylation at cell level. However, with the advent of technology, the focus is shifting towards mapping the entire epigenome of an organism. Apart from their functions in RNA degradation and translational repression, small RNAs are potent in altering chromatin and target gene expression through RNA interference (RNAi). Some epigenetic modifications are heritable and can thus determine evolutionary adaptation for stress resilience. Understanding the responsible epigenetic machinery that maybe used to improve resource use efficiency to address nutritional/food security challenges is imperative.
India is self-sufficient in food production; however, agricultural experts and the farming community still face a significant challenge in raising productivity and production to feed the country’s growing population. For agricultural productivity to rise, particularly in emerging nations, timely, affordable, and effective agricultural information is essential. Information and communication technologies (ICTs) have been a major innovation in Indian agriculture in recent years. These technologies have made it easier for farmers to obtain tailored digital information about high-yielding seed varieties, cropping patterns, fertilizer application, pest control, marketing, and entrepreneurship. Most agricultural tasks now involve the use of digital tools to get information, from the days when the Internet was only used in the agricultural sector to the present-day use of smartphones and mobile apps. The chapter presents an overview of the innovations in information dissemination in Indian agriculture and elaborates ICT-based information dissemination initiatives in Indian context. Also, this chapter captures new initiatives of the Indian government with respect to information dissemination along with ICT-based smart agriculture and G20 Policy decisions.
Climate change is increasingly impacting agriculture with rising temperatures, variable rainfall, and more frequent extreme weather events. This is particularly concerning for rice production in South Asia. Frontier technologies such as big data analytics (BDA), machine learning (ML), remote sensing (RS), and Internet of Things (IoT) are critical in developing smart innovations to mitigate these effects. Advancements in agriculture produce vast amounts of data, including soil health, geospatial maps, crop production, weather data, and survey results. BDA processes these data in cluster mode to extrapolate real-time situations and provide customized services to various stakeholders. Artificial intelligence (AI) and IoT enable real-time programming to make precise farm decisions. Through the lens of BDA along with the information and communication technology (ICT), climate data can be analyzed to generate insights such as patterns and trends and implement climate forecasting models. Predictive advisory services based on climate forecasts, early warning systems, and climate-informed crop calendars aid in crop monitoring and risk area identification, facilitating better farming decisions and risk reduction. A recent innovation in precision farming is the data-driven agronomic intelligence system, which offers location-specific tailored crop and soil management recommendations using ML techniques. This location intelligence assists fertilizer manufacturers in producing custom blends to address specific regional soil fertility issues. Similarly, the seed, pesticide, and market sectors can leverage this intelligence to reduce costs and increase resource-use efficiency. The International Rice Research Institute (IRRI) has developed various ICT tools and IoT solutions such as AutoMonPH, Rice Crop Manager, Rice Doctor, SeedCast, and EasyHarvest, supporting both scientific research and practical management solutions in rice farming. Climate-smart agriculture (CSA), with the deployment of BDA and ICT tools, is poised to significantly enhance smallholder farmers’ productivity and profitability by effectively monitoring and responding to climate change. However, achieving widespread adoption of CSA technologies requires transdisciplinary and collaborative efforts within an innovative system framework.
Crop plant architecture dictates plant performance under different ecological conditions and is responsible for its establishment, development, and morphology. It plays an important role in plant breeding for yield optimization, regulating photosynthetic rate and efficiency, utilization of resources, occurrence of pests and diseases, effective mechanical harvesting, space optimization, and improving the quality of plant produce. The subtle changes in the plant’s architecture could help the plant adapt to different ecological niches and are very important, keeping in view the challenges posed due to climate change. A set of 280 diverse genotypes, which included the core collection of chickpea was evaluated during the rabi season of 2021 and 2022. A total of 10 plant architecture related traits, including plant height, first pod height, canopy width, inter-nodal length, and days to flowering were studied and significant variability was observed as per the analysis of variance (ANOVA) and phenotypic descriptors. Diversity based on π and θ estimates suggests the presence of substantial diversity, while Tajima’s D reflects balancing selection due to the abundance of shared alleles. Significant marker trait associations (MTAs) for traits like plant height (PH), first pod height (FDPH) and days to flowering were observed using trait based or BLUP estimates. In total, 97 and 51 MTAs were identified using trait based and BLUP based on multi model GWAS analysis, respectively. Among these 17 were consistent MTAs being present either across the year or were identified using more than one GWAS model. Likewise, 9 consistent MTAs were identified using the BLUP estimates. Interestingly, two genomic regions present on chromosome 5 and 7 were found to harbor multiple MTAs for PH and FPDH. The linkage disequilibrium (LD) block analysis reflects the prevalence of multiple LD blocks in these regions. The allelic effects of the MTAs reflect their additive nature in determining the phenotype. Overall, the MTAs identified in the current study are highly useful for the chickpea breeder in modulating the plant architecture, mainly PH and FPDH.
Expression of concern for ‘Protein complexations and amyloid fibrilization as novel approaches to improve techno-functionality of plant-based proteins’ by Zakir Showkat Khan et al., Sustainable Food Technol., 2024, Accepted Manuscript, https://doi.org/10.1039/D4FB00193A.
This study aimed to analyze the effects of different zinc (Zn) sources on the nutrient content, yield, and quality of fruits in high-density apple orchards located within a sub-humid agro-climatic zone in the mid hills of Himachal Pradesh, India. Five-year-old apple trees (‘Var. Red Velox’) under high-density plantation (2.5 m × 1 m), irrigated through a drip irrigation system, were subjected to various Zn sources (nano-Zn, chelated-Zn, and zinc sulfate) applied at two different doses (single and double spray) one month after full bloom (Walnut stage) and again after 15 days. The use of different Zn sources aims to identify the most effective form for enhancing apple yield and quality. Double doses of nano Zn emerged as the most effective source, promoting increased fruit weight (235 g), higher fruit Zn content (16 ppm), and higher fruit yield (78.3 t ha⁻¹) compared to chelated (160.4 g, 12 ppm, and 76.8 t ha⁻¹) and sulfate (166.9 g, 11 ppm, and 76.5 t ha⁻¹) forms. Zn-treated apples exhibited higher firmness, increased total soluble solids (TSS) content, and reduced titratable acidity, resulting in an elevated TSS:acid ratio. The findings suggest the potential of Zn, particularly in nano form, as a valuable tool for optimizing apple orchard management. While a double dose proved to be most effective, a single dose also showed promising results, closely following the performance of the double dose in improving yield and enhancing fruit quality.
Main conclusion We cloned two variants of lipase gene—MAGL and TAGL from pearl millet. Lipase showed negative correlation with antioxidants and total phenolics. FAA can be used as marker for rancidity. Abstract Pearl millet is considered as “Nutri-cereal” due to its rich nutrient profile. Low keeping quality of the flour due to rancidity is one of the major problems in pearl millet. Lipases are a group of enzymes that produces free fatty acids that ultimately leads to rancidity. Very few lipases have been identified and characterized from pearl millet. Here, we have identified 2039 transcripts of lipases from pooled samples (leaf, stem and developing grains) of pearl millet using de novo transcriptomic approach and predicted 05 full length lipase variants. Further, we cloned 02 lipase genes—monoacylglycerol lipase (MAGL, acc. no. MZ590564) and triacylglycerol lipase (TAGL, acc. no. MZ590565) of 1.5 kb each from pearl millet cv. Pusa-1201. Conserved domain search analysis showed the presence of catalytic triad [GXSXG] near the active site which is signature domain of lipase family of proteins. MAGL showed maximum expression in PC-701 and TAGL in Pusa-1201 during mealy-ripe growth stage of endospermic tissue. Abundance of transcripts of both the lipases was observed in the harvested grains of PC-701. We observed negative correlation between the lipase activity and accumulation of antioxidants like total phenolic content (TPC), tannin, and total antioxidant potential (TAP). Free amino acid and reducing sugar were observed as potential markers for accessing the intensity/extent of rancidity in pearl millet flour. Thus, there is need to explore and characterize the lipase variants to connect the missing dots in rancidity pathway and to use it in genome editing using the CRISPR/Cas9 approach for the development of pearl millet lines free of off-odor and flour rancidity.
Coconut palm is one of the important plantation crops grown worldwide, including Bangladesh. This study investigated the possible invasion threat of invasive and exotic rugose spiraling whitefly (RSW) by assessing its seasonal dynamics over 3 consecutive years (January 2020 to December 2022) and effective management strategies. Systematic weekly observations revealed no RSW infestation until the third week of May 2020 when first detected. Consistent presence of RSW nymphs, pupae, and adults was observed throughout the subsequent year, with peaks in April–May and September–October each year. Among various climatic factors, temperature emerged as a significant determinant influencing RSW population growth. Of the six different treatments, the chemical insecticide acetamiprid (Tundra® 50SP; Auto crop care Ltd., Dhaka, Bangladesh) proved most effective, achieving a notable reduction of over 80% in pest numbers after initial and follow-up applications. Bio-pesticides, especially D‑limonene (Bio clean® 5%SL; Ispahani Agro Ltd., Gazipur, Bangladesh), initially provided moderate control (48.08–75.71%). However, upon the second application, significant reductions were observed in the nymph (81.51–84.03%), pupa (81.82–85.40%), and adult (84.21–89.76%) populations compared to untreated palms. Alternating application of acetamiprid and D‑limonene exhibited high efficacy in controlling RSW. The proposed sustainable biorational management option involves a rotating application of acetamiprid followed by D‑limonene. This approach integrates chemical and organic methods to minimize overuse of chemical pesticides and reduce the threat of RSW invasion to coconut palms. Graphic abstract
The Nilgiris, the only horticultural district in Tamil Nadu, India, faces significant challenges due to root-knot nematodes (RKNs), Meloidogyne spp., which affect economically important crops such as carrot, beetroot, and beans. In this context, sustainable nematode management practices, including the identification of alternate weed hosts, are critical. Surveys conducted in the Nilgiri Hills revealed galled roots of Parthenium hysterophorus L., an invasive weed, along crop field borders. Microscopic and molecular analyses confirmed the presence of Meloidogyne hapla Chitwood, the northern root-knot nematode, marking its first report on P. hysterophorus in India. Molecular identification was based on COX-1 gene marker (GenBank accession number PQ069793) revealed the sequence had 99.20% similarity to M. hapla. Pathogenicity tests demonstrated significant root galling (71.20 ± 3.67 galls/plant) and a high reproduction factor (18.16 ± 2.16) 30 days post-inoculation. The study highlights the dual threat posed by the invasive nature of P. hysterophorus and its ability to harbor M. hapla, a key pest of carrot, causing ~ 36% yield losses in the region. This underscores the urgent need for systematic eradication of P. hysterophorus to prevent the spread and intensification of M. hapla infestations in the Nilgiri Hills.
Biosurfactants are one of the recently investigated biomolecules that have enormous applications in many fields including agriculture. As there is a need to develop less toxic, and environmentally friendly surfactants, therefore, amino acid-based biosurfactants that are produced from renewable raw materials are of great demand nowadays and can be used as an alternative to conventional chemical surfactants. The negative effects of chemical surfactants present in agrochemicals and modern detergents can damage human health and the environment, thus there is a crucial requirement to explore innovative, well planned, as well as cost-effective natural products for the welfare of humanity. Biodegradable surfactants created through green chemistry, specifically amino acid-based surfactants, are a favourable alternative to avoid these risks. Since amino acids (AAs) are inexhaustible compounds, therefore biosurfactants based on AAs have abundant potential as eco-friendly and environmentally friendly substances. Their higher biodegradation ability, low or even no toxicity, temperature stability, and tolerance to pH fluctuations make these biosurfactants preferable over chemical surfactants. In modern agriculture, most chemical pesticides and fertilizers used are frequently associated with numerous environmental issues. Hence, the development of green molecules as biosurfactants has a promising role in this regard to ensure agricultural sustainability. Biosurfactants can be harnessed for plant pathogen management, plant growth elevation, improving the quality of agricultural soil by soil remediation, degradation of complex hydrocarbons, increasing bioavailability of nutrients for advantageous plant-microbe interactions, and improving plant immunity, hence, they can supersede the grim synthetic surfactants which are presently being used.
The sub-optimal temperature during sowing results in delayed germination, poor crop establishment, and constrained vegetative development, which results in forced maturity, low yield, and wheat with poorer quality grains in north India. Thus, in 2020–21, a field experiment with fixed plots was carried out to analyze the impact of organic liquid manure herbal kunapajala as seed priming and foliar spray on the development and output of late-sown wheat. A total of 14 different treatments were tested in this thrice-replicated randomized block design study. Treatments consisted of T1 :No seed priming + 100% RDN, T2: Hydropriming + 100% RDN, T3: 10% HK priming + 100% RDN+ foliar application of 10% HK, T4: 10% HK priming + 75% RDN+ foliar application of 10% HK, T5: 10% HK priming + 50% RDN+ foliar application of 10% HK, T6: 10% HK priming + no Fertilizer+ foliar application of 10% HK, T7: 25% HK priming + 100% RDN+ foliar application of 10% HK, T8: 25% HK priming + 75% RDN+ foliar application of 10% HK, T9: 25% HK priming + 50% RDN+ foliar application of 10% HK, T10: 25% HK priming + no fertilizer+ foliar application of 10% HK, T11:50% HK priming + 100% RDN+ foliar application of 10% HK, T12: 50% HK priming + 75% RDN+ foliar application of 10% HK, T13: 50% HK priming + 50% RDN+ foliar application of 10% HK, T14: 50% HK priming + no fertilizer+ foliar application of 10% HK. The results revealed that T7 (128.7 cm), T2 (125.1 cm), T1 (122.6 cm), and T5 (119.9 cm) had much taller plants than the others. The highest number of grains per spike was found to in T7, T3, T2 and T8 shown at par with each other (49.90, 49.90, 48.67 and 47.67 grains/spike). Similarly, fertile spikelets per spike was shown significantly higher with T7 (23.30) which was at par with T3 and T2 (21.67 and 21.20, respectively) as compared to other treatments. The maximum grain production was seen with T7 (48.0 q/ha) and T3 (47.90 q/ha), however these two treatments were at par. Higher harvest index was observed significantly from T7, T3, T2, T12, T1 and T11 (40.6, 39.7, 39.0, 37.9, 37.8 and 37.6%, respectively) as compared with other treatments but at par with each other. The maximum α-amylase activity was observed with T7 (27.9 mg of starch hydrolyzed/g of seeds) which was at par with T3 and T2 (25.1 and 24.7 mg of starch hydrolyzed/g of seeds, respectively). From the information presented above, it can be inferred that seed priming with 10% HK along with 100% of the recommended dose of nutrients, followed by foliar applications of 10% HK at various growth stages, increased the growth, productivity and enzyme activity of late sown wheat that priming with 10% HK and its foliar application under 100% RDN increased the growth, productivity, grain quality indices, enzyme activities, and economic profitability of late-sown wheat. From the current research, it can be concluded that HK is a successful and cost-efficient method that may be utilized to increase crop production under late-sown conditions.
Maize (Zea mays L.) is an important cereal in many developing countries, ensuring staple food for millions of people. However, the presence of high-level phytic acid in maize kernels is one of the major nutritional concerns. Reduction of phytic acid has become a major challenge in breeding programmes to increase the nutritional quality of foods and feeds. In the current investigation, an attempt was made to develop low-phytate maize genotypes by incorporating lpa1 and lpa2 genes associated with lower phytic acid (PA) and inorganic phosphorous (Pi) levels in the background of well-adapted agronomically elite tropical maize inbred lines, BML6 and BML45, showing high phytate contents in kernels. The stepwise transfer of lpa1 and lpa2 genes using marker-assisted backcross breeding (MABB) technique was undertaken. The foreground selection in F1 and the segregating generations (BC1F1 and IC1F1–IC1F2) were carried out using umc2230-linked STMS marker and co-dominant SNP. The background selections in segregating generations were performed with genome-wide simple sequence repeats (SSRs). Four distinct BC1F1 lines of BML6 × LPA1 (Cross 1), BML6 × LPA2 (Cross 2), BML45 × LPA1 (Cross 3), and BML45 × LPA2 (Cross 4) having lpa1 and lpa2 genes with the highest recurrent parent genome recovery (RPGR) were intercrossed to develop the IC1F1 and IC1F2 populations. Further, the lpa1 and lpa2 pyramided lines were examined for PA and Pi contents. The IC1F2 lines, #3390 (1.75 ± 0.087 mg/g) of BML6 and #3990 (1.52 ± 0.091 mg/g) of BML45, had significantly lower levels of PA as compared to recurrent parents and on par agronomic performance, whereas the kernel Pi level was found to be the highest (#3390 of BML6: 1.91 ± 0.202 mg/g; #3990 BML45: 1.93 ± 0.066 mg/g in BML45). The improved lines of BML6 and BML45 have the potential to be developed as cultivars for target agro-climatic zones as well as useful genetic stocks for breeding low-phytate maize cultivars. The results hold great significance to alleviate malnutrition issue in India and the world through biofortification of maize.
To assess the agronomic performance and genetic stability for grain yield, a set of 36 spring wheat genotypes were grown consecutively for two crop seasons in six environments with varying sowing dates at ICAR-Indian Agricultural Research Institute, New Delhi using randomized complete block design with three replications. Significant genotypic differences were observed for all the studied morpho-physiological characters in both the crop seasons suggesting the presence of substantial genetic variability among the genotypes studied. Parametric and nonparametric stability analysis methods, along with advanced techniques like AMMI and GGE biplot analysis, were employed to understand genotype-environment interactions. Positive correlations were found between yield and stability parameters, highlighting the importance of selecting genotypes with both high yield and stability. Based on the wide-ranging evaluations, three out of 36 genotypes viz., DW1615, HD3262, and DW1631, emerged most promising ones consistently demonstrated superior performance across multiple methodologies. The study underscores the need to follow a multifaceted approach for genotype evaluation to ensure sustainable improvement in wheat production systems, enabling breeders to make informed decisions for resilience and productivity in diverse/varying environmental conditions.
Water-soluble carbohydrates (WSCs) serve as a potential buffer for grain filling in wheat, when current leaf photosynthesis is inhibited by abiotic stress. The potential of genotype to store WSCs for its remobilisation is determined by its stem-specific weight i.e. stem density. To examine the extent to which mobilisation of carbon reserve occurs under multi-environment conditions and the genomic regions associated with stem density in wheat, a field experiment was conducted at Indian Agricultural Research Institute (IARI), New Delhi, India with 220 wheat RILs developed by crossing HD3086 and HI1500 under control, drought, heat and combined stress (heat and drought) conditions. Genotyping of population (21 days seedling) was done with 35 K Wheat Breeder Array followed by QTL mapping with inclusive composite interval mapping (ICIM) software. Selection of superior lines were carried out by using combined approach of multi-trait genotype-ideotype distance index (MGIDI), factor analysis and genotype-ideotype distance (FAI-BLUP) and Smith-Hazel index (SH). In our study, total 9 quantitative traits loci (QTLs) were mapped, which were linked to stem density (peduncle, penultimate and lower internode) with LOD score > 3.5, of which 7 were mapped as major QTLs. In-silico gene expression analysis revealed various important genes like sugar transport protein MST4, PPR containing protein, trehalose phosphate synthase, NRT1/PTR FAMILY 8.3-like etc., which are probably involved in maintaining stem density in wheat. Moreover, four lines (HDHI-12, HDHI-87, HDHI-142 and HDHI-194) were identified as superior lines, which can be used as potential donors to elite wheat cultivars. Our study shed light on genetic basis of regulation of stem density in wheat, which accelerates the possible marker-assisted and genomic selection for higher stem density and stem reserve mobilisation.
Crop health assessment and early yield predictions are highly crucial under biotic stress conditions for crop management and market planning by farmers and policy planners. The objective of this study was, therefore, to assess the impact of different levels of wilt disease on the biophysical parameters of chickpea and developing machine learning (ML) models for early yield prediction. Field experiments were carried out over three years at the Indian Agricultural Research Institute research farm in New Delhi. Thermal and visible images were collected alongside the measurement of crop biophysical parameters, including leaf area index (LAI), photosynthesis, transpiration rate, stomatal conductance, relative leaf water content (RWC), membrane stability index (MSI), and NDVI, for 85 chickpea genotypes with varying levels of wilt resistance. ML models were developed for early yield prediction by combining visible and thermal image indices with biophysical parameters. The results showed that the canopy temperatures were directly correlated with increasing levels of wilt severity. Crop photosynthesis, stomatal conductance, transpiration, LAI, RWC, MSI, and NDVI dropped significantly with increasing levels of wilt severity. Yield reductions of 44-69% were observed in susceptible genotypes. Machine learning models were able to give accurate early yield predictions. The accuracy of the models increases as we move closer to the harvest. Ranking of the model’s performances indicated that XGB is the best model to predict chickpea yield under wilt conditions. NDVI was identified as most important variable for yield prediction. The findings of the study quantified the impacts of wilt on important crop biophysical parameters and highlighted the suitability of ML models in early yield prediction under different levels of disease severity.
Litchi is an evergreen fruit crop which requires low temperatures during winter for panicle initiation. Flowering starts from February–March, and fruit ripens in May–June during summer in Eastern India. The production of litchi is affected by many physiological disorders such as flower and fruit drop, omission of male flowers (M1), uneven flowering, irregular bearing, shy bearing, under-developed fruit, sunburn, fruit cracking in the field and pericarp browning during storage. Most of the flowers and fruitlets drop within 1 month of fruit set. Omission of male flower (M1) and uneven opening of female flowers, which drastically reduces fruit set and fruit retention and results in lowering litchi productivity, have recently been noticed. Sunburn and fruit cracking are very common in early litchi cultivars. Irregular bearing is a major problem in “‘China’ group cultivars” (‘China’, ‘Calcutia Late’, ‘Calcutia’, ‘CHES-II’ and ‘Surguja selection’) at any age of tree and young plants of ‘Shahi’ groups, whereas shy bearing is a major problem in “‘Bedana’ group cultivars”. These problems vary with position of the fruit in the tree, location of the tree in the field, genotypes, soil, special horticultural practices and orchard management and affect the quality and marketability of fruits. Knowledge of these problems is vital for sustainable production of good quality litchi fruit.
Resistant starch (RS) is a key component of dietary fiber that offers significant health benefits, including improved gut health, glycemic control, and reduced risk of chronic diseases. Accurate prediction of RS content in foods is crucial for the development of functional foods aimed at promoting better health outcomes. Traditional methods of measuring RS are labor-intensive, time-consuming, and often require extensive analytical procedures. Machine learning (ML) techniques offer a promising alternative by utilizing large datasets of food composition, processing parameters, and digestion properties to predict RS content efficiently. This study explores the application of machine learning (ML) models to predict RS levels. The study analyzed 20 different varieties of rice (Oryza sativa) and 14 features including nutritional and functional traits to establish a correlation with RS. Analysis revealed peak viscosity (PV), hardness (HN), and gel consistency (GC) as the top three most important features contributing to the best-performing model's predictive power and provided insights into the key factors affecting RS content. Other viscosity-related metrics such as final viscosity (FV) and setback (SB) viscosity contributed moderately to the model, alongside total amylose content (TAC). Starches with higher PV often form stronger gel structures thus increasing GC and HN upon cooling, which can reduce enzyme access and slow digestion rates, subsequently high RS %. This study underscores the potential of ML models as a powerful tool to accelerate functional food innovation through accurate RS content prediction. Thus, integrating data-driven approaches to food science opens new avenues for creating healthier, functional food products with targeted health benefits, enhancing the development of diet-based solutions for improved human health.
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1,855 members
Muraleedhar S Aski
  • Division of Genetics
Bikash Mandal
  • Division of Plant Pathology, Advanced Center for Plant Virology
Suneha Goswami
  • Division of Biochemistry
Bishwajeet Paul
  • Division of Entomology
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Dr. A K Singh