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Paramesh, R.
- Validation of Bt-gene and Study of Packaging Materials on Seed Longevity in Bt-cotton Hybrids
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1 Department of Seed Science and Technology, University of Agricultural Sciences, G.K.V.K., Bengaluru, Karnataka, IN
1 Department of Seed Science and Technology, University of Agricultural Sciences, G.K.V.K., Bengaluru, Karnataka, IN
Source
Asian Journal of Bio Science, Vol 8, No 1 (2013), Pagination: 1-5Abstract
A laboratory experiment was carried out to validate Bt gene and to study the storage potential of Bt-cotton hybrids. Results revealed that, all the six Bt-cotton hybrid seed lots selected for the study were positive for Bt-gene (Cry 1 Ac). In the storage study, the seed lot L1 recorded highest germination (71.55%) and lowest in L3 (68.11%) at the end of ten months of storage. Among the packaging materials, seeds stored in P1 and P2 recorded highest germination (71.50 and 71.50%, respectively) compared to P3 (68.11 %). Seedling vigour index was highest (2007) in L1 and lowest (1792) in L6. Irrespective of seed lots, P1 recorded highest vigour (1981) followed by P2 (1963) and lowest in P3 (1759) at the end of ten months of storage. Germination per cent in polythene bag and polylined cloth was above the minimum seed certification standards (70.00%) upto ten months of storage. However, genotypic differences were observed with respect to seed quality.Keywords
Cottton, Validation, Bt Gene, Storability, Seed Quality
References
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- A.I. Based Home Doctor .
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1 no, IN
1 no, IN
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Artificial Intelligent Systems and Machine Learning, Vol 13, No 5 (2021), Pagination: 73 - 75Abstract
Artificial Intelligence (AI) will usher in a new age in medical research. However, currently existing AI systems do not interact with patients, such as for anamnesis, and are thus solely utilized by doctors to make diagnostic or prognosis predictions. These systems, on the other hand, are frequently utilized, for example, in illness or cancer prediction. We created an AI that can engage with a patient in the current study (virtual doctor) by employing a voice recognition and speech synthesis technology, and therefore communicate with the patient autonomously, which is especially essential in rural regions where access to basic medical care is severely limited. T2DM is a noninvasive sensor and deep neural network-based system. Furthermore, the method gives a simple-to-understand likelihood estimate for T2DM for a specific patient. Aside from AI development, we looked at young people's acceptance of AI in healthcare to predict the influence of such a system in the future.Keywords
Chatbot, Natural Language Processing, Med Bot, Disease Prediction.References
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