Cholinergic receptor subtypes functional regulation in spinal cord injured monoplegic rats

The present study deals with the Cholinergic Receptor subtypes functional regulation in spinal cord injured monoplegic rats: Effect of 5-HT GABA and bone marrow cells.Spinal cord injury causes permanent and irrevocable motor deficits and neurodegeneration. Disruption of the spinal cord leads to diminished transmission of descending control from the brain to motor neurons and ascending sensory information. Behavioural studies showed deficits in motor control and coordination in SCI rats. Cholinergic system plays an important role in SCI, the evaluation of which provides valuable insight on the underlying mechanisms of motor deficit that occur during SCI. The cholinergic transmission was studied by assessing the muscarinic and nicotinic receptors; cholinergic enzymes- ChAT and AChE; second messenger enzyme PLC; transcription factor CREB and second messengers - IP3, cAMP and cGMP. We observed a decrease in the cholinergic transmission in the brain and spinal cord of SCI rats. The disrupted cholinergic system is the indicative of motor deficit and neuronal degeneration in the spinal cord and brain regions. SCI mediated oxidative stress and apoptosis leads to neuronal degeneration in SCI rats. The decreased expression of anti oxidant enzymes – SOD, GPx and neuronal cell survival factors - BDNF, GDNF, IGF-1, Akt and cyclin D2 along with increased expression of apoptotic factors – Bax, caspase-8, TNFa and NF-kB augmented the neuronal degeneration in SCI condition. BMC administration in combination with 5-HT and GABA in SCI rats showed a reversal in the impaired cholinergic neurotransmission and reduced the oxidative stress and apoptosis. It also enhanced the expression of cell survival factors in the spinal cord region. In SCI rats treated with 5-HT and GABA, the transplanted BMC expressed NeuN confirming that 5-HT and GABA induced the differentiation and proliferation of BMC to neurons in the spinal cord. Neurotrophic factors and anti-apoptotic elements in SCI rats treated with 5-HT and GABA along with BMC rendered neuroprotective effects accompanied by improvement in behavioural deficits. This resulted in a significant reversal of altered cholinergic neurotransmission in SCI. The restorative and neuro protective effects of BMC in combination with 5-HT and GABA are of immense therapeutic significance in the clinical management of SCI.

Combined toxic effects of oil and pesticides on selected marine invertebrates

In recent years, pollution in general and sea water pollution in particular, has become an important topic for national and international considerations. Because of its impact on society, marine pollution has attracted great attention from politicians, administrators, natural scientists and technologists all over the world. To save our environment from further deterioration, it is essential to have an assessment of this problem This thesis involves investigation of the lethal and sub lethal effects of four pesticides and two petroleum oil, individually and in combinations on two commercially important bivalves. Among the four pesticides used two are organophosphates and the other two are organochlorines. Synthetic Pesticides, especially organophosphates and organochlorines have become increasingly important additions to chemical wastes polluting natural aquatic Communities special attention is given in the present investigation to delineate the combined toxic effect of oil and pesticides. The results are presented under different sections to make the presentation meaningful.

COMBINED FEATURE EXTRACTION TECHNIQUES AND NAIVE BAYES CLASSIFIER FOR SPEECH RECOGNITION

Speech processing and consequent recognition are important areas of Digital Signal Processing since speech allows people to communicate more natu-rally and efficiently. In this work, a speech recognition system is developed for re-cognizing digits in Malayalam. For recognizing speech, features are to be ex-tracted from speech and hence feature extraction method plays an important role in speech recognition. Here, front end processing for extracting the features is per-formed using two wavelet based methods namely Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD). Naive Bayes classifier is used for classification purpose. After classification using Naive Bayes classifier, DWT produced a recognition accuracy of 83.5% and WPD produced an accuracy of 80.7%. This paper is intended to devise a new feature extraction method which produces improvements in the recognition accuracy. So, a new method called Dis-crete Wavelet Packet Decomposition (DWPD) is introduced which utilizes the hy-brid features of both DWT and WPD. The performance of this new approach is evaluated and it produced an improved recognition accuracy of 86.2% along with Naive Bayes classifier.