Future perspective of school disaster education in Nepal

2007 ◽  
Vol 16 (4) ◽  
pp. 576-587 ◽  
Author(s):  
Koichi Shiwaku ◽  
Rajib Shaw ◽  
Ram Chandra Kandel ◽  
Surya Narayan Shrestha ◽  
Amod Mani Dixit
Keyword(s):  
Future Perspective ◽  
Disaster Education ◽  
School Disaster

FUTURE PERSPECTIVE OF PLANT BIOTECHNOLOGY: A REVIEW

2020 ◽  
Vol 1 (1) ◽  
pp. 36-41
Author(s):  
Gaurav Ranabhat ◽  
Ashmita Dhakal ◽  
Saurav Ranabhat ◽  
Ananta Dhakal ◽  
Rakshya Aryal
Keyword(s):  
Insect Pest ◽  
Ornamental Plants ◽  
Herbicide Tolerance ◽  
Plant Biotechnology ◽  
Future Perspective ◽  
New Development ◽  
Recent Developments ◽  
Modern Biotechnology ◽  
Stable Yield ◽  
As Stress

Modern biotechnology enables an organism to produce a totally new product which the organism does not or cannot produce normally through the incorporation of the technology of ‘Genetic engineering’. Biotechnology shows its technical merits and new development prospects in breeding of new plants varieties with high and stable yield, good quality, as well as stress tolerance and resistance. Some of the most prevailing problems faced in agricultural ecosystems could be solved with the introduction of transgenic crops incorporated with traits for insect pest resistance, herbicide tolerance and resistance to viral diseases. Plant biotechnology has gained importance in the recent past for increasing the quality and quantity of agricultural, horticultural, ornamental plants, and in manipulating the plants for improved agronomic performance. Recent developments in the genome sequencing will have far reaching implications for future agriculture. From this study, we can know that the developing world adopts these fast-changing technologies soon and harness their unprecedented potential for the future benefit of human being.

Machine Learning in Quantitative Protein–peptide Affinity Prediction: Implications for Therapeutic Peptide Design

2019 ◽  
Vol 20 (3) ◽  
pp. 170-176 ◽  
Author(s):  
Zhongyan Li ◽  
Qingqing Miao ◽  
Fugang Yan ◽  
Yang Meng ◽  
Peng Zhou
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Peptide Binding ◽  
Machine Learning Techniques ◽  
Future Perspective ◽  
Peptide Design ◽  
Human Interactome ◽  
Therapeutic Peptide ◽  
Peptide Affinity ◽  
Peptide Binding Affinity

Background:Protein–peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention.Methods:We present a systematic review on the application of machine learning techniques in the quantitative modeling and prediction of protein–peptide binding affinity, particularly focusing on its implications for therapeutic peptide design. We also briefly introduce the physical quantities used to characterize protein–peptide affinity and attempt to extend the content of generalized machine learning methods.Results:Existing issues and future perspective on the statistical modeling and regression prediction of protein– peptide binding affinity are discussed.Conclusion:There is still a long way to go before establishment of general, reliable and efficient machine leaningbased protein–peptide affinity predictors.

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