Gaurav Sharma

Bio: Gaurav Sharma is an academic researcher from Shenzhen University. The author has contributed to research in topics: Medicine & Photocatalysis. The author has an hindex of 82, co-authored 1244 publications receiving 31482 citations. Previous affiliations of Gaurav Sharma include Northeastern University & D. E. Shaw & Co..

Influence of vermicompost and split applied nitrogen on growth, yield, nutrient uptake and soil fertility in pole type frenchbean (Phaseolus vulgaris L.) in an Acid Alfisol

Abstract: A field experiment was conducted for two years at Himachal Pradesh Agricultural University, Palampur on the influence of vermicompost and split applied nitrogen in pole French bean. Twelve treatment combinations comprising of organic manures, nitrogen fertilization and methods of application were evaluated. The performance of yield and yield related traits increased significantly with application of vermicompost, 125% of recommended nitrogen through split application. Use of vermicompost + 125% nitrogen resulted in highest seed yield (10.43 q/ha) and NPK uptake. Seed yield and NPK uptake with application of vermicompost + 75% N was at par with that of recommended application of N indicating thereby saving of 25% of nitrogen fertilizers. Split applied nitrogen at 125% of recommended dose resulted in 50% increase in seed yield over basal application. Hence, application of vermicompost and split applied nitrogen is a better option to harness high seed productivity and to maintain soil fertility.

A joint approach to vector road map registration and vehicle tracking for wide area motion imagery

Abstract: Modern aerial imaging platforms provide wide-area motion imagery (WAMI) at high spatial and moderate temporal resolutions making feasible a range of new applications. We consider the dual tasks of registering WAMI frames to geo-referenced vector road-maps and tracking vehicles through the progression of WAMI frames. We present a novel algorithm that performs these tasks jointly and offers improvements in both by exploiting the synergy between the tasks. Tracking for the large number of vehicles seen in urban-area WAMI is improved by auxiliary information that registration to the vector road-map provides by localizing roads within the scene. Similarly, registration of the WAMI frames to the vector map is improved by formulating the registration as a chamfer minimization between the vehicular trajectories and the road network, an approach that resolves challenges for registration posed by the fundamentally different data modalities between the aerial images and the vector road maps. Results obtained over our test datasets show the effectiveness of the proposed joint methodology. For both road network alignment and vehicle tracking, the proposed method offers a very significant improvement over available alternatives: the proposed approach yields better numerical metrics for quantification of registration accuracy and fewer false identification switches for tracked vehicles.

Methods and apparatus for modifying image data based on identification of marking process

Abstract: Image information is re-calibrated based on the type of marking process and materials by which the image corresponding to the image information was marked on a substrate. The marking process is automatically detected by using, for example, at least one spatial characteristic obtained from the image information.

Hybrid Machine Learning Models for Predicting Types of Human T-cell Lymphotropic Virus

Abstract: Life threatening diseases like adult T-cell leukemia, neurodegenerative diseases, and demyelinating diseases such as HTLV-1 based myelopathy/tropical spastic paraparesis (HAM/TSP), hypocalcaemia, and bone lesions are caused by a group of human retrovirus known as Human T-cell Lymphotropic virus (HTLV). Out of the four different types of HTLVs, HTLV-1 is most prominent in scourging over 20 million people around the world and still not much effort has been made in understanding the epidemiology and controlling the prevalence of this virus. This condition further worsens when most of the infected cases remain asymptomatic throughout their lifetime due to the limited diagnostic methods; that are most of the times unavailable for timely detection of infected individuals. Moreover, at present, there is no licensed vaccination for HTLV-1 infection. Therefore, there is a need to develop the faster and efficient diagnostic method for the detection of HTLV-1. Influenced from the outcomes of the machine learning techniques in the field of bio-informatics, this is the first study in which 64 hybrid machine learning techniques have been proposed for the prediction of different type of HTLVs (HTLV-1, HTLV-2, and HTLV-3). The hybrid techniques are built by permutation and combination of four classification methods, four feature weighting, and four feature selection techniques. The proposed hybrid models when evaluated on the basis of various model evaluation parameters are found to be capable of efficiently predicting the type of HTLVs. The best hybrid model has been identified by having accuracy, an AUROC value, and F1 score of 99.85 percent, 0.99, and 0.99, respectively. This kind of the system can assist the current diagnostic system for the detection of HTLV-1 as after the molecular diagnostics of HTLV by various screening tests like enzyme-linked immunoassay or particle agglutination assays there is always a need of confirmatory tests like western blotting, immuno-fluorescence assay, or radio-immuno-precipitation assay for distinguishing HTLV-1 from HTLV-2. These confirmatory tests are indeed very complex analytical techniques involving various steps. The proposed hybrid techniques can be used to support and verify the results of confirmatory test from the protein mixture. Furthermore, better insights about the virus can be obtained by exploring the physicochemical properties of the protein sequences of HTLVs.

A Comprehensive Toxicological Assessment of Fulvic Acid.

Abstract: Fulvic acid (FA), a humic substance, has several nutraceutical properties, including anti-inflammation, antimicrobial, and immune regulation abilities. However, systematic safety assessment remains insufficient. In the present study, a battery of toxicological studies was conducted per internationally accepted standards to investigate the genotoxicity and repeated-dose oral toxicity of FA. Sprague-Dawley (SD) rats or ICR mice were used. Compared to the control group, there were no significant changes (all ) in all FA treatment groups in the bacterial reverse mutation test, in vitro mammalian chromosome aberration test, in vivo sperm shape abnormality assay, and in vivo mouse micronucleus assay. The acute toxicity test showed that no mortality or toxic effect was observed following oral administration of the maximum dose of 5,000 mg/kg BW/day to mice or rats. A 60-day subchronic study was conducted at 0 (control), 200, 1,000, and 5,000 mg/kg/day. Compared to the control group, there were no significant changes (all ) in the body weights, feed consumption, clinical signs, hematology, clinical chemistry, organ weights, or histopathology examinations. In conclusion, the no-observed-adverse-effect-level (NOAEL) of FA supplementation from the 60-day study was determined to be 5,000 mg/kg body weight/day, the highest dose tested. Our findings suggest that the oral administration of FA may have higher safety.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Abstract: The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.