Biology subjects

Hiremath, S.

Publications and source records attributed to Hiremath, S..

8 recordsLinked to original sources

bioRxiv2026

Assessment of Microbial Consortia for Biogenic Mineralization Potential of Lead (Pb) using Microbiologically Induced Calcite Precipitation and Nitrate Reduction

Groundwater contamination by lead and nitrate exceeding the permissible limits of the World Health Organization (WHO) and the Bureau of Indian Standards (BIS) of 10 ppb lead and 45 to 50 ppm nitrate poses a severe health risk of systemic toxicity in humans and animals. Microbial nitrate reduction coupled with microbiologically induced calcite precipitation (MICP) proved to be a novel bioremediation strategy to transform soluble lead nitrate into insoluble lead biominerals. This study presents statistical optimization of operational parameters for nitrate reduction and co-precipitation of biogenic lead carbonate-calcium carbonate by nitrate-reducing bacterial consortia. The SEM, TEM with SEAD, and XRD analysis of the biomineral confirmed lead carbonate co-precipitation with CaCO3. A two-level factorial design was applied to screen significant variables and interactive factors that influence nitrate reduction and biomineral precipitation. The highest nitrate reduction, maximum ammonia production, and biogenic Pb-mineralization efficiency of > 90% were obtained at the optimum conditions (initial nitrate requirement of 600 ppm, temperature 37 oC, pH 8, and carbon content of 5.5 g/L) compared to those obtained under the common process parameters. ANOVA with a high correlation coefficient (R2 > 0.99) corroborated the linear model for the biogenic mineralization process. This study on the biogenic removal of Pb by nitrate-reducing bacterial consortia revealed possible opportunities for large-scale treatment of Pb-contaminated surface and subsurface water bodies.

microbiology↗

bioRxiv2023

Genetic diversity and evidence of recombination of Horsegram yellow mosaic virus infecting pole bean (Phaseolus vulgaris L.) from South India

The yellow mosaic disease (YMD) caused by begomoviruses is a major constraint for the production of pole bean (Phaseolus vulgaris L.) in India. Survey was carried out in the eastern dry zone of Karnataka during 2019-20 to record the incidence of yellow mosaic disease in pole bean which revealed the ubiquitous prevalence of YMD in pole bean ranging from 6.02 to 80.74 per cent. Leaf samples collected (symptomatic and asymptomatic) were subjected for begomovirus detection using specific primers. Twelve samples, representing all the 12 taluks in the surveyed region were considered for full genome amplification by RCA, cloned and sequenced. Genome length of 12 current isolates ranged from 2718 - 2744 and 2668 - 2671 nucleotides for DNA-A and DNA-B, respectively. Sequence analysis using Sequence Demarcation Tool (SDT) showed >91 per cent nucleotide identity of current isolates (DNA-A) with other horsegram yellow mosaic virus (HgYMV) isolates available in the GenBank. As per existing ICTV criteria, all the current isolates can be considered as strains of HgYMV. Further, DNA-B associated with all the 12 isolates also shared >91 per cent nucleotide identity with DNA-B of HgYMV isolates, indicating absence of component re-assortment in HgYMV. Variation in the pairwise nucleotide identity and phylogenetic analysis confirmed the existence of new strains within the current HgYMV isolates. GC plot analysis reveals potential recombination in the low GC rich regions. Further, recombination breakpoint analysis indicated intra-species recombination in both DNA-A and DNA-B, which might have driven the origin of new strains in HgYMV. This is the first comprehensive study on begomoviruses ioslates associated with the yellow mosaic disease of pole bean based on complete genome sequencing in the world.

molecular biology↗

bioRxiv2023

Virome analyses by next-generation sequencing (NGS) in chilli (Capsicum anuum L.) presented with diverse symptoms phenotype revealed the association of seven plant viruses

Chilli is an important vegetable and spice crop, is known to be infected by several viruses. Techniques used in diagnosis of plant viral diseases before Next-generation sequencing (NGS) are having limitation of identifying the only known viruses. In the present study, virome analyses in infected chilli leaf samples was carried out using NGS to know the diversity of both known and unknown viruses associated with diseased symptoms. For virome profiling, samples from 19 fields were collected from chilli plants showing leaf curling, vein banding, mosaic, mottling, shoestring/rat tail/filiform/leathery and dull coloured leaves. Viral disease incidence in the surveyed fields varied from 26.66% to 47.50%. Total RNA was extracted from the 19 chilli leaf samples collected from fields and were pooled at equimolar concentration for virome profiling. From the rRNA-depleted pooled total RNA, mRNA and sRNA libraries were prepared and sequenced using Illumina NOVASEQ 6000 platform. Raw sequence data obtained was de novo assembled using three approaches; mRNAome with Trinity, sRNAome with Velvet and whole transcriptome (WT) with SPAdes assembly. Chilli virome, pairwise sequence identity and phylogenetic analyses revealed the presence of seven different viruses; chilli leaf curl virus (ChiLCV) along with its associated alpha and betasatellites, cucumber mosaic virus (CMV), groundnut bud necrosis orthotospovirus (GBNV), pepper cryptic virus-2 (PCV-2), pepper vein yellows virus (PeVYV), bell pepper alphaendornavirus (BPEV) and tobacco vein clearing virus (TVCV). From the virus associated contigs, complete/near-complete genomes for ChiLCV, CMV, PCV-2, PeVYV and BPEV and, partial genomes for GBNV and TVCV were reconstructed from the RNAome. Recombination breakpoint analyses revealed presence of recombination breakpoints in ChiLCV (coat protein and AC4 regions), CMV RNA2 (2a protein region) and P0, P3 and P5 protein regions of PeVYV. viruses identified in the current study are known to be originated from intra and interspecific recombination. Further, all the viruses detected in the pooled RNA sample were validated by PCR and loop mediated isothermal amplification (LAMP) using specific primers designed. Among the seven viruses identified in the study in chilli, PeVYV and BPEV are the first reports from India.

molecular biology↗

bioRxiv2022

Calibration of crop phenology models: Going beyond recommendations

A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. Here we propose an original, detailed approach, a protocol, for calibration of such models. The protocol covers all the steps in the calibration work-flow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are "obligatory" parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their "usual" calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and significantly reduced prediction error.

plant biology↗

bioRxiv2022

Detection and molecular characterization of a phytoplasma associated with Cucumber (Cucumis sativus) and its first report from India

Cucumber (Cucumis sativus) plants exhibiting typical phyllody symptoms were collected from farmers field of Chintamani, Chickballapur districts of Karnataka (India). Disease incidence of phyllody was 2-3%. The etiology of the cucumber phyllody phytoplasma (CuPP) was confirmed by amplifying 16S rRNA gene from symptomatic plants using PCR followed by nested PCR using universal primers pairs P1/P7 and R16F2n/R16R2. After general detection the non-ribosomal SecY and rp (ribosomal protein) genes was amplified using specific primers. The PCR amplified products 16SrRNA (1.2 kb), SecY (1.6 kb) rp gene (1.2 kb) was cloned and sequenced. Sequence and phylogenetic analyses of 16S rRNA, secY and rp genes revealed that the detected phytoplasma is a member of the 16SrI group (Candidatus Phytoplasma asteris). Further on the basis of computer-simulated RFLP (= in silico RFLP) analysis of amplified F2n/R2 region of 16S rRNA gene indicates that, the detected phytoplasma was belongs to the subgroup X (16SrII-X). This is the first report on phytoplasma associated with phyllody disease of cucumber in India.

molecular biology↗

bioRxiv2021

Crop loss identification at field parcel scale using satellite remote sensing and machine learning

Identifying crop loss at field parcel scale using satellite images is challenging: first, crop loss is caused by many factors during the growing season; second, reliable reference data about crop loss are lacking; third, there are many ways to define crop loss. This study investigates the feasibility of using satellite images to train machine learning (ML) models to classify agricultural field parcels into those with and without crop loss. The reference data for this study was provided by Finnish Food Authority (FFA) containing crop loss information of approximately 1.4 million field parcels in Finland covering about 3.5 million ha from 2000 to 2015. This reference data was combined with Normalised Difference Vegetation Index (NDVI) derived from Landsat 7 images, in which more than 80% of the possible data are missing. Despite the hard problem with extremely noisy data, among the four ML models we tested, random forest (with mean imputation and missing value indicators) achieved the average AUC (area under the ROC curve) of 0.688 {+/-} 0.059 over all 16 years with the range [0.602, 0.795] in identifying new crop-loss fields based on reference fields of the same year. To our knowledge, this is one of the first large scale benchmark study of using machine learning for crop loss classification at field parcel scale. The classification setting and trained models have numerous potential applications, for example, allowing government agencies or insurance companies to verify crop-loss claims by farmers and realise efficient agricultural monitoring.

bioinformatics↗

bioRxiv2020

The chaos in calibrating crop models

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of system models and has an important impact on simulated values. Here we propose and illustrate a novel method of developing guidelines for calibration of system models. Our example is calibration of the phenology component of crop models. The approach is based on a multi-model study, where all teams are provided with the same data and asked to return simulations for the same conditions. All teams are asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices. HighlightsO_LIWe propose a new approach to deriving calibration recommendations for system models C_LIO_LIApproach is based on analyzing calibration in multi-model simulation exercises C_LIO_LIResulting recommendations are holistic and anchored in actual practice C_LIO_LIWe apply the approach to calibration of crop models used to simulate phenology C_LIO_LIRecommendations concern: objective function, parameters to estimate, software used C_LI

plant biology↗

bioRxiv2020

Multi-model evaluation of phenology prediction for wheat in Australia

Predicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multi-modeling group mean and median had prediction errors nearly as small as the best modeling group. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. There was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques.

plant biology↗