“Adrenergic receptors and monoamines in the brain and pancreatic islets of streptozotoein diabetic rats: Role in insulin secretion as a function of age”

I) To study the changes in the content of brain rrrorroamirres in streptozotocirr-irrduced tliabetes as a lirnction of age and to lirrd the role oliadrenal lrornroncs in diabetic state. 2) To assess the adrenergic receptor function in the brain stem ofstreptozotocin-induced diabetic rats ofdillerent ages. 3) To study the changes in the basal levels of second messenger cAMP in the brain stenr ofstreptozotocin-induced diabetic rats as a function of age. 4) To study the changes occurring in the content ofmorroamines and their metabolites in whole pancreas and isolated pancreatic islets of streptozotocin-diabetic rats as a function ofage and the effect of adrenal hormones. 5) To study the adrenergic receptors and basal levels of cAMP in isolated pancreatic islets in young and old streptozotoein-diabetic rats. 6) The in virro study of CAMP content in pancreatic islets of young and old rats and its ellect on glucose induced insulin secretion. 7) 'lhe in vitro study on the involvement of dopamine and corticosteroids in glucose induced insulin secretion in pancreatic islets as a function of age.

Prediction of Key Symptoms of Learning Disabilities in School-Age Children Using Rough Sets

This paper highlights the prediction of learning disabilities (LD) in school-age children using rough set theory (RST) with an emphasis on application of data mining. In rough sets, data analysis start from a data table called an information system, which contains data about objects of interest, characterized in terms of attributes. These attributes consist of the properties of learning disabilities. By finding the relationship between these attributes, the redundant attributes can be eliminated and core attributes determined. Also, rule mining is performed in rough sets using the algorithm LEM1. The prediction of LD is accurately done by using Rosetta, the rough set tool kit for analysis of data. The result obtained from this study is compared with the output of a similar study conducted by us using Support Vector Machine (SVM) with Sequential Minimal Optimisation (SMO) algorithm. It is found that, using the concepts of reduct and global covering, we can easily predict the learning disabilities in children

Prediction of Learning Disabilities in School Age Children using SVM and Decision Tree

This paper highlights the prediction of Learning Disabilities (LD) in school-age children using two classification methods, Support Vector Machine (SVM) and Decision Tree (DT), with an emphasis on applications of data mining. About 10% of children enrolled in school have a learning disability. Learning disability prediction in school age children is a very complicated task because it tends to be identified in elementary school where there is no one sign to be identified. By using any of the two classification methods, SVM and DT, we can easily and accurately predict LD in any child. Also, we can determine the merits and demerits of these two classifiers and the best one can be selected for the use in the relevant field. In this study, Sequential Minimal Optimization (SMO) algorithm is used in performing SVM and J48 algorithm is used in constructing decision trees.

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.

Age and growth of Gerres filamentosus (Cuvier, 1829) from Kodungallur, Azhikode Estuary, Kerala

The age and growth, length – weight relationship and relative condition factor of Gerres filamentosus (Cuvier, 1829) from Kodungallur, Azhikode Estuary were studied by examination of 396 specimens collected between May 2008 to October 2008. Here, length frequency method was used to study age and growth in fishes. L∞, K and t 0 obtained from seasonal and non - seasonal growth curves. Gerres filamentosus showed a low mortality rate (Z) 3.702 y-1. G. filamentosus has moderately low K value and long life span. The relation between the total length and weight of G. filamentosus was described as Log W = 1.321+2.5868 log L for males, Log W = 1.467 + 2.7227 log L for females and Log W = 1.481 + 2.7316 log L for sexes combined. The mean relative condition factor (Kn) values ranged from 0.9 to 1.14 for males, 0.89 to 1.11 for females and 0.73 to 1.08 for sexes combined. The length weight relationship and relative condition factor showed that the wellbeing of G. filamentosus were good. The morphometric measurements of various body parts were recorded. The morphometric measurements were found to be nonlinear and there is no significant difference observed between the two sexes.

DWIVEDIYUGIN KAVYA MEN RASHTRIYATA EVAM GANDHIVAD KA SWAROOP (NATIONALISM AND GANDHISM AS REFLECTED IN THE POETRY OF DWIVEDI-AGE)

HINDI