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Ramesh, K. V.
- MP-TRACS Crops
Abstract Views :334 |
PDF Views:97
Authors
P. Goswami
1,
K. V. Ramesh
1
Affiliations
1 CSIR Centre for Mathematical Modelling and Computer Simulation (Repositioned as CSIR-4PI), NAL Wind Tunnel Road, Bengaluru Campus, Bengaluru 560 037, IN
1 CSIR Centre for Mathematical Modelling and Computer Simulation (Repositioned as CSIR-4PI), NAL Wind Tunnel Road, Bengaluru Campus, Bengaluru 560 037, IN
Source
Current Science, Vol 108, No 5 (2015), Pagination: 770-771Abstract
No Abstract.- A Weather-Based Forecast Model for Capsule Rot of Small Cardamom
Abstract Views :266 |
PDF Views:82
Authors
Prashant Goswami
1,
Renu Goyal
1,
E. V. S. Prakasa Rao
1,
K. V. Ramesh
1,
M. R. Sudarshan
2,
D. Ajay
2
Affiliations
1 CSIR Centre for Mathematical Modelling and Computer Simulation, Wind Tunnel Road, Bangalore 560 037, IN
2 Indian Cardamom Research Institute, Kailasanadu (P.O), Myladumpara 685 553, IN
1 CSIR Centre for Mathematical Modelling and Computer Simulation, Wind Tunnel Road, Bangalore 560 037, IN
2 Indian Cardamom Research Institute, Kailasanadu (P.O), Myladumpara 685 553, IN
Source
Current Science, Vol 107, No 6 (2014), Pagination: 1013-1019Abstract
Small cardamom is an economically important spice crop. However, cardamom is susceptible to several diseases that significantly reduce yield. Proactive prevention of these diseases based on advance warning can enhance the efficiency of disease control and reduce environmental load of pesticides. Many of these diseases are governed by weather variables (for example, through control of fungal growth). This work presents a disease (capsule rot of cardamom) forecast model based on a set of meteorological variables.While no single weather variable provides successful simulation, an optimal combination of weather variables provides sufficient skill for advance warning of the disease.Keywords
Capsule Rot Disease, forecasting, Meteorological Variables, Small Cardamom.- Computational Studies of Mycorrhizal Protein: GiHsp60 and Its Interaction With Soil Organic Matter
Abstract Views :228 |
PDF Views:81
Authors
Dipti Mothay
1,
K. V. Ramesh
1
Affiliations
1 Department of Biotechnology, Jain (deemed to be University), School of Sciences, Jayanagar 3rd Block, Bengaluru 560 011, IN
1 Department of Biotechnology, Jain (deemed to be University), School of Sciences, Jayanagar 3rd Block, Bengaluru 560 011, IN
Source
Current Science, Vol 120, No 2 (2021), Pagination: 389-397Abstract
This study uses homology modelling and molecular docking approaches to explore the binding mechanism of glomalin-related soil protein from Rhizophagus irregularis (GiHsp60) with soil organic matter (SOM) and the role played by soil protein in the sequestration of common soil pollutants. Conserved domain analysis reveals that GiHsp60 belongs to chaperonin-like super-family having binding sites for ATP/Mg2+. Three-dimensional model of GiHsp60 was reasonably good based on reports generated by different validation servers. Docking results suggest that Van der Waals force is primarily responsible for the interaction between GiHsp60 and SOM. The study also reveals the role played by GiHsp60 in the sequestration of dif-ferent soil pollutants.Keywords
Docking Studies, Homology Modelling, Heat Shock Protein, Mycorrhizal Fungi, Soil Pollutants.References
- Kemper, W. D. and Rosenau, R. C., Aggregate stability and size distribution. In Methods of Soil Analysis, Part 1. Physical and Mineralogical Methods, Soil Science Society of America, Madi-son, Wisconsin, USA, Agronomy Monograph, No. 9, 1986, 2nd edn, pp. 425–442.
- Lehmann, A., Fitschen, K. and Rillig, M. C., Abiotic and biotic factors influencing the effect of microplastic on soil aggregation. Soil Syst., 2019, 3, 21.
- Leifheit, E. F., Veresoglou, S. D., Lehmann, A., Morris, E. K. and Rillig, M. C., Multiple factors influence the role of arbuscular mycorrhizal fungi in soil aggregation – a meta-analysis. Plant Soil, 2014, 374, 523–537.
- Wright, S. F. and Upadhyaya, A., Extraction of an abundant and unusual protein from soil and comparison with hyphal protein of arbuscular mycorrhizal fungi. Soil Sci., 1996, 161, 575–586.
- Purin, S. and Rillig, M. C., The arbuscular mycorrhizal fungal pro-tein glomalin: limitations, progress, and a new hypothesis for its function. Pedobiologia, 2007, 51, 123–130.
- Gadkar, V. and Rillig, M. C., The arbuscular mycorrhizal fungal protein glomalin is a putative homolog of heat shock protein 60. FEMS Microbiol. Lett., 2006, 263, 93–101.
- Rillig, M. C., Wright, S. F., Nichols, K. A., Schmidt, W. F. and Torn, M. S., Large contribution of arbuscular mycorrhizal fungi to soil carbon pools in tropical forest soils. Plant Soil, 2001, 233, 167–177.
- Jia, X., Zhao, Y., He, Y. and Chang, Y., Glomalin‐related soil pro-tein in the rhizosphere of Robinia pseudoacacia L. seedlings under higher air temperature combined with Cd‐contaminated soil. Eur. J. Soil Sci., 2018, 69, 634–645.
- Rosier, C. L., Hoye, A. T. and Rillig, M. C., Glomalin-related soil protein: assessment of current detection and quantification tools. Soil Biol. Biochem., 2006, 38, 2205–2211.
- Mothay, D. and Ramesh, K. V., Evolutionary history and genetic diversity study of heat-shock protein 60 of Rhizophagus irregu-laris. J. Genet., 2019, 98, 48.
- Bauer, M. A. et al., CDD: NCBI’s conserved domain database. Nucleic Acids Res., 2014, 43(D1), D222–D226.
- Servant, F., Bru, C., Carrere, S., Courcelle, E., Gouzy, J., Peyruc, D. and Kahn, D., ProDom: automated clustering of homologous domains. Brief Bioinform., 2002, 3, 246–251.
- Thompson, J. D., Higgins, D. G. and Gibson, T. J., CLUSTAL W: improving the sensitivity of progressive multiple sequence align-ment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res., 1994, 22, 4673–4680.
- Bailey, T. L. et al., MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res., 2009, 20, W202–W208.
- Jonassen, I., Efficient discovery of conserved patterns using a pattern graph. Bioinformatics, 1997, 13, 509–522.
- Mizuguchi, K., Deane, C. M., Blundell, T. L., Johnson, M. S. and Overington, J. P., JOY: protein sequence–structure representation and analysis. Bioinformatics, 1998, 14, 617–623.
- Gupta, R., Jung, E. and Brunak, S., Prediction of N-glycosylation sites in human proteins. In preparation, 2004, pp. 203–206.
- Schwede, T., Kopp, J., Guex, N. and Peitsch, M. C., SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res., 2003, 31, 3381–3385.
- Johansson, M. U., Zoete, V., Michielin, O. and Guex, N., Defining and searching for structural motifs using DeepView/Swiss-PdbViewer. BMC Bioinform., 2012, 13, 173.
- Fiser, A. and Do, R. K. G., Modeling of loops in protein structures. Protein Sci., 2000, 9, 1753–1773.
- Fiser, A. and Sali, A., ModLoop: automated modeling of loops in protein structures. Bioinformatics, 2003, 19, 2500–2501.
- Eisenberg, D., Lüthy, R. and Bowie, J. U., VERIFY3D: assess-ment of protein models with three-dimensional profiles. In Methods in Enzymology, Academic Press, Massachusetts, United States, 1997, vol. 277, pp. 396–404.
- Wiederstein, M. and Sippl, M. J., ProSA-web: interactive web service for the recognition of errors in three-dimensional struc-tures of proteins. Nucleic Acids Res. (Suppl. 2), 2007, 35, W407–W410.
- Laskowski, R. A., MacArthur, M. W., Moss, D. S. and Thornton, J. M., PROCHECK: a program to check the stereochemical quality of protein structures. J. Appl. Crystallogr., 1993, 26, 283–291.
- Schulten, H. R. and Schnitzer, Chemical model structures for soil organic matter and soils. Soil Sci., 1997, 162, 115–130.
- Barak, P. and Nater, E., The Virtual Museum of Minerals and Molecules, San Fransisco, 1999; http://www.soils.wisc.edu/ virtual_museum/
- Frisch, M. J. et al., Gaussian 09, Revision D. 01, Gaussian, Inc, Wallingford, CT, USA, 2009.
- Dennington, R. D. I. I., Keith, T. and Millam, J., GaussView, Ver-sion 4.1.2, Semichem Inc, Shawnee Mission, KS, USA, 2007.
- Case, D. A. et al., Amber 10 (No. BOOK), University of Califor-nia, USA, 2008.
- Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C. and Ferrin, T. E., UCSF Chimera – a visualization system for exploratory research and analysis. J. Comput. Chem., 2004, 25, 1605–1612.
- Allen, W. J. et al., DOCK 6: impact of new features and current docking performance. J. Comput. Chem., 2015, 36, 1132–1156.
- DeLano, W. L., Pymol: an open-source molecular graphics tool. CCP4 Newsl. Protein Crystallogr., 2002, 40, 82–92.
- Dolinsky, T. J., Nielsen, J. E., McCammon, J. A. and Baker, N. A., PDB2PQR: an automated pipeline for the setup of Poisson–Boltzmann electrostatics calculations. Nucleic Acids Res. (Suppl. 2), 2004, 32, W665–W667.
- Ooi, T., Oobatake, M., Nemethy, G. and Scheraga, H. A., Acces-sible surface areas as a measure of the thermodynamic parameters of hydration of peptides. Proc. Natl. Acad. Sci. USA, 1987, 84, 3086–3090.
- Lin, Z., Schwarz, F. P. and Eisenstein, E., The hydrophobic nature of GroEL-substrate binding. J. Biol. Chem., 1995, 270, 1011–1014.
- Fernandes, F. F., Rossetti, R. A. M., Coelho-Castelo, A. A. M. and Panunto-Castelo, A., Molecular modeling of heat shock protein of 60-Kda from Paracoccidioides brasiliensis: the first in silico structural model of a fungal Hsp60. J. Comput. Sci. Syst. Biol., 2014, 8, 241–244.
- Rabe, M., Verdes, D. and Seeger, S., Understanding protein ad-sorption phenomena at solid surfaces. Adv. Colloid Interface Sci., 2011, 162, 87–106.
- Perry IV, T. D., Cygan, R. T. and Mitchell, R., Molecular models of alginic acid: Interactions with calcium ions and calcite surfaces. Geochim. Cosmochim. Acta, 2006, 70, 3508–3532.
- Wu, Z., McGrouther, K., Huang, J., Wu, P., Wu, W. and Wang, H., Decomposition and the contribution of glomalin-related soil protein (GRSP) in heavy metal sequestration: field experiment. Soil Biol. Biochem., 2014, 68, 283–290.
- Wright, S. F. and Anderson, R. L., Aggregate stability and gloma-lin in alternative crop rotations for the central Great Plains. Biol. Fertil. Soils, 2000, 31, 249–253.
- Saparpakorn, P., Kim, J. and Hannongbua, S., Investigation on the binding of polycyclic aromatic hydrocarbons with soil organic matter: a theoretical approach. Molecules, 2007, 12, 703–715.
- Gil-Cardeza, M. L., Ferri, A., Cornejo, P. and Gomez, E., Distri-bution of chromium species in a Cr-polluted soil: presence of Cr(III) in glomalin related protein fraction. Sci. Total Environ., 2014, 493, 828–833.
- Zou, Y. N., Srivastava, A. K., Wu, Q. S. and Huang, Y. M., Glomalin-related soil protein and water relations in mycorrhizal citrus (Citrus tangerina) during soil water deficit. Arch. Agron. Soil Sci., 2014, 60, 1103–1114.
- Crop production estimation using deep learning technique
Abstract Views :243 |
PDF Views:82
Authors
Affiliations
1 CSIR-Fourth Paradigm Institute, Bengaluru 560 037, India and Academy of Scientific and Innovative Research, Ghaziabad 201 002, India, IN
1 CSIR-Fourth Paradigm Institute, Bengaluru 560 037, India and Academy of Scientific and Innovative Research, Ghaziabad 201 002, India, IN
Source
Current Science, Vol 121, No 8 (2021), Pagination: 1073-1079Abstract
Reliable estimation of crop requirement and production in advance, help policy makers to adopt timely decision for trade as export–import, which is a basic building block to assure food security of a country. A powerful and robust algorithm is essential to predict the future demand and production of a particular crop for subsequent years. Deep learning methods are used successfully in solving different prediction problems of various applications. This study attempts to design an efficient AI based technique specifically using long short-term memory, a deep learning approach for estimation of crop production using crop production information of neighbouring countries, which are part of the South Asian monsoon system. Detailed sensitivity analysis is conducted to identify the optimal combination of crop production of neighbouring countries that directly and indirectly impact the crop production of India. Here, we designed and developed a predictive model for rice production of India with lead time of one year using deep learning technique. Along with that, as there are significant influences of local climate (i.e. rainfall data) on crop production, that information was also considered along with crop production of neighbouring countries. The results indicated that local and regional scale parameters jointly improve the prediction capability for future years. Capability of the proposed model was validated with export–import data on crop of India and neighbouring countries, and the validation result showed that our proposed technique was efficient and robust in natureKeywords
Artificial intelligence, crop production model, deep neural networks, long short-term memory, sensitivity analysis.References
- Horie, T., Yajima, M. and Nakagawa, H., Yield forecasting. Agric. Syst., 1992, 40, 211–236; doi:10.1016/0308-521X(92)90022-G.
- Carfagna, E. and Gallego, F. J., Using remote sensing for agricultural statistics. Int. Stat. Rev., 2005, 73(3), 389–404.
- Awad, M. M., Toward precision in crop yield estimation using remote sensing and optimization techniques. Agriculture, 2019, 9(3), 54; https://doi.org/10.3390/agriculture9030054.
- Paliwal, A. and Jain, M., The accuracy of self-reported crop yield estimates and their ability to train remote sensing algorithms. Front. Sustain. Food Syst., 2020; doi:10.3389/fsufs.2020.00025.
- You, J., Li, X., Low, M., Lobell, D. and Ermon, S., Deep Gaussian process for crop yield prediction based on remote sensing data. In Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, USA, 2017, pp. 4559–4566.
- Russello, H., Convolutional neural networks for crop yield prediction using satellite images. IBM Center for Advanced Studies, Berelux, 2018.
- http://www.fao.org/about/en
- Sarker, I. H., Machine learning: algorithms, real-world applications and research directions. SN Comput. Sci., 2021, 2, 160; https:// doi.org/10.1007/s42979-021-00592-x.
- Hochreiter, S. and Schmidhuber, J., Long short-term memory. Neural Comput., 1997, 9(8), 1735–1780; doi:10.1162/neco.1997.9.8.1735.
- Solow, A. et al., The value of improved ENSO prediction to US agriculture. Climatic Change, 1998, 39, 47–60.
- Jones, J. W., Hansem, J. W., Royce, F. S. and Messina, C. D., Potential benefits of climate forecasting in agriculture. Agric. Ecosyst. Environ., 2000, 82, 169–184.
- Hansen, J. W., Applying seasonal climate prediction to agriculture production. Agric. Syst., 2002, 74(3), 305–307.
- Prasanna, V., Impact of monsoon rainfall on the total food grain yield over India. Proc. Indian Acad. Sci. (Earth Planet. Sci.), 2014, 112, 529–558.
- Rahman, M. A. et al., Impacts of temperature and rainfall variation on rice productivity in major ecosystems of Bangladesh. Agric Food Secur., 2017, 6, 10; https://doi.org/10.1186/s40066-017-0089-5.
- Baigorria, G. A., Jones, J. W., Shin, D. W., Mishra, A. and O’Brien, J. J., Assessing uncertainties in crop model simulations using daily bias-corrected regional circulation model outputs. Clim. Res., 2007, 34, 211–222.
- Cantelaube, P. and Terres, J. M., Seasonal weather forecasts for crop yield modelling in Europe. Tellus A: Dyn. Meteorol. Oceanogr., 2005, 57A, 476–487.