Study on the forest disease forecasting based on gray model

2014 ◽  
Author(s):  
Yanrong Zhang
Keyword(s):  
Disease Forecasting ◽  
Gray Model ◽  
Forest Disease

scholarly journals Using prediction polling for infectious disease forecasting

2020 ◽  
Vol 101 ◽  
pp. 374
Author(s):  
T. Sell ◽  
L. Warmbrod ◽  
M. Trotochaud ◽  
S. Ravi ◽  
E. Martin ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Disease Forecasting

scholarly journals Low mammalian species richness is associated with Kyasanur forest disease outbreak risk in deforested landscapes in the Western Ghats, India

One Health ◽  
2021 ◽  
pp. 100299
Author(s):  
Michael G. Walsh ◽  
Rashmi Bhat ◽  
Venkatesh Nagarajan-Radha ◽  
Prakash Narayanan ◽  
Navya Vyas ◽  
...  
Keyword(s):  
Species Richness ◽  
Western Ghats ◽  
Mammalian Species ◽  
Disease Outbreak ◽  
Forest Disease ◽  
Kyasanur Forest Disease ◽  
The Western Ghats

scholarly journals In-silico design of envelope based multi-epitope vaccine candidate against Kyasanur forest disease virus

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sathishkumar Arumugam ◽  
Prasad Varamballi
Keyword(s):  
Disease Virus ◽  
In Silico ◽  
Hemorrhagic Fever ◽  
Epitope Vaccine ◽  
Epitope Region ◽  
Vaccine Candidates ◽  
Forest Disease ◽  
In Silico Design ◽  
Antigenic Properties ◽  
Kyasanur Forest Disease

AbstractKyasanur forest disease virus (KFDV) causing tick-borne hemorrhagic fever which was earlier endemic to western Ghats, southern India, it is now encroaching into new geographic regions, but there is no approved medicine or effective vaccine against this deadly disease. In this study, we did in-silico design of multi-epitope subunit vaccine for KFDV. B-cell and T-cell epitopes were predicted from conserved regions of KFDV envelope protein and two vaccine candidates (VC1 and VC2) were constructed, those were found to be non-allergic and possess good antigenic properties, also gives cross-protection against Alkhurma hemorrhagic fever virus. The 3D structures of vaccine candidates were built and validated. Docking analysis of vaccine candidates with toll-like receptor-2 (TLR-2) by Cluspro and PatchDock revealed strong affinity between VC1 and TLR2. Ligplot tool was identified the intermolecular hydrogen bonds between vaccine candidates and TLR-2, iMOD server confirmed the stability of the docking complexes. JCAT sever ensured cloning efficiency of both vaccine constructs and in-silico cloning into pET30a (+) vector by SnapGene showed successful translation of epitope region. IMMSIM server was identified increased immunological responses. Finally, multi-epitope vaccine candidates were designed and validated their efficiency, it may pave the way for up-coming vaccine and diagnostic kit development.

scholarly journals Isolation of Kyasanur Forest Disease Virus from Febrile Patient, Yunnan, China

2009 ◽  
Vol 15 (2) ◽  
pp. 326-328 ◽  
Author(s):  
Jinglin Wang ◽  
Hailin Zhang ◽  
Shihong Fu ◽  
Huanyu Wang ◽  
Daxin Ni ◽  
...  
Keyword(s):  
Disease Virus ◽  
Febrile Patient ◽  
Forest Disease ◽  
Kyasanur Forest Disease

Research on Power Transformer Fault Forecast

2011 ◽  
Vol 204-210 ◽  
pp. 1553-1558
Author(s):  
Rui Rui Zheng ◽  
Ji Yin Zhao ◽  
Min Li ◽  
Bao Chun Wu
Keyword(s):  
Time Series ◽  
Fault Location ◽  
Power Transformer ◽  
Forecast Model ◽  
Ratio Method ◽  
Gray Model ◽  
Artificial Immune ◽  
Diagnosis Method ◽  
Type Data ◽  
Transformer Fault

To forecast power transformer fault, this paper proposed a integrated algorithm. Research found that discrete time series of power transformer dissolved gases concentration have 2 main types: the s type and the monotone increasing type. The gray verhulst model was chosen for forecasting the s type series, while the gray model predicted the monotone increasing type data. The two models combined a new integrated forecast model. The fault diagnosis method combines the improved three-ratio method and gray artificial immune algorithm, so it can diagnoses both single and multi power transformer faults, and give the fault location. Experiments show that the power transformer fault forecast algorithm is effective and reliable.

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