War in Purananuru

When hear the word war, we always remember the destruction caused by it, the majority of damages and it brings happiness to a few and misery to many. Thus, war has threatened the people over time. Earlier, Moovendar kings are always remembered in Tamil Nadu during war. Mankind's war with nature has spawned civilizational development and cultural maturity.  The wars fought by people among themselves are considered historic on the basis of valor. A man's basic need is to be fulfilled by himself and the society around him, by the government and the rulers. When this situation does not continue, a king steals the resources of another country and gives it to his countrymen since then.  The purpose of this study is to find out the news about the war in Purananuru

Word fiction in Kurunthokai

Sangam literature is a mirror of time that expresses the life of the ancient Tamil people. This is literature dealing with the love genre, which passionately describes the feelings of such a man, as well as external literature dealing with the country, city, king, and society, which relates to man's external life. Love is the reason a man and a woman seek each other out of love between domestic and public morality. The environment plays an important role in the lives of these lovers. When the poets respect the feelings of the natural environment around a man, such as trees, plants, vines, animals, and birds, and produce them in conjunction with the domestic life of the chieftains, he employs a variety of imaginative skills. In that sense, etymology is the technique of explaining the idea that comes to be expressed by governing the idea of a word, which is an effective vocabulary. Words are the beauty of poetry. It is in the way it is set that poetry becomes special. The creation of immortal literature depends on the way the poet manipulates words and the manner in which words govern poetry. Capital is words for the poet. The poet is identified by those words. The poet emerges through words. The evidence for this is scattered in many ways in the Sangam literature. When the creator takes and explains the subtle meaning of the words buried in literature, they give them a taste. The purpose of this study is to examine the aesthetic messages revealed through fiction in the personalities of those words

A critical appraisal on wavelet based features from brain MR images for efficient characterization of ischemic stroke injuries

Ischemic stroke is a severe neuro disorder typically characterized by a block inside a blood vessel supplying blood to the brain. It remains the third leading cause for death, after heart attack and cancer. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) were the vital major imaging techniques used for diagnosing this disorder. While the CT imaging can be used at the primary stage, MRI proves to be a standard aid for progressive diagnostic planning in the treatment of stroke injuries. Developing a fully automatic approach for lesion segmentation is a challenging issue due to the complex nature of the lesions structures. This research basically aims at examining the properties of such complex structures. It analyses the characteristics of the normal brain tissues and abnormal lesion structures using a three-level wavelet decomposition procedure. Four different wavelet functions namely daubechies, symlet, coiflet and de-meyer were applied to the different datasets and the resulting observations were examined based on their feature statistics obtained. Experiments indicate the feature statistics obtained from daubechies and de-meyer wavelets were able to clearly distinguish between the typical brain tissues and abnormal lesion structures

DESIGN AND SYNTHESIS OF SOME NEWER IMIDAZOLYL HETEROCYCLES AS POTENT BTK INHIBITORS FOR THE TREATMENT OF RHEUMATOID ARTHRITIS

Objective: The rheumatoid arthritis as a global health problem over the past few decades, Emphasizes the need for discovery of new therapeutic disease modifying anti-rheumatoid Arthritis drugs (DMARD's). Bruton's tyrosine kinase (BTK) is a cytoplasmic, non-receptor, tyrosine kinase which is expressed in most of the hematopoietic cells and plays an important role in the development, differentiation and proliferation of B-lineage cells, thus making BTK an efficient therapeutic target for the treatment of rheumatoid arthritis. This prompted us to synthesise a novel series of Imidazolyl Heterocycles as potent BTK (Bruton's Tyrosine Kinase) inhibitors with alleged Anti-Rheumatoid Arthritis properties. Methods: Newer BTK inhibitors containing one hydrogen bond acceptor (HBA), one hydrogen bond donor (HBD) and three hydrophobic features based on that pharmacophore model for BTK were designed. The designed compounds were sorted by applying ADMET properties, Lipinski rule of five, molecular docking and Novelty prediction to refine the designed ligands. Finally, different five compounds containing Imidazole as the heterocyclic nucleus have been synthesized and characterized by different analytical methods like Chromatographic data, Elemental analysis and Spectral studies by IR, 1H NMR, 13C NMR, GC-MS. Molecular docking studies were performed against BTK using GLIDE 10.2. Results: Several important hydrogen bonds with BTK were revealed, which include the gatekeeper residue Glu475 and Met477 at the hinge region. Conclusion: Overall, this study suggests that the proposed ligands are found to be more effective BTK inhibitor as Anti-Rheumatoid arthritis agents

CHARACTERIZATION OF STROKE LESION USING FRACTAL ANALYSIS

Objective: The characterization of stroke lesions is a challenging research issue due to the wide variability in the structure of lesion patterns. The objective of this research work is to characterize the stroke lesion structures using fractal analysis.Methods: To characterize the complex nature of the lesion structures, fractal box counting analysis is presented in this work. Three parameters from fractal dimension (FD) are considered to characterize the nature of the normal and abnormal brain tissues.Results: The experimental results are presented for 15 different datasets. Three different parameters namely FD average, FD deviation, and FDlacunarity are extracted to quantify the properties of the stroke lesion. The observations indicate that there is a significant proportion of separationof feature values between the normal and abnormal brain tissues.Conclusion: This work presents an efficient scheme for characterizing the stroke lesions using fractal parameters. It could be further enhanced by incorporating features extracted from other non-linear techniques.Â

Immunohistochemical Expression and Evaluation of MCM 2 and Cyclin D1 in Oral Squamous Cell Carcinoma and Verrucous Carcinoma

BACKGROUND: Oral squamous cell carcinoma (OSCC), represents over 90 % of malignancies of the oral cavity. Despite the advances in diagnosis and therapy, OSCC continues to have a shorter survival rate. Verrucous carcinoma (VC) of the oral cavity is a low grade variant of OSCC. The study of cell proliferation is important for assessing the tumor behaviour, prognosis and patient survival of both these tumours. As literature search did not reveal sufficient studies of immunohistochemical expression of Cyclin D1 and Mini Chromosome Maintainance 2 (MCM2) in OSCC and VC, the present study was done to evaluate the expression of these two cell proliferation biomarkers in Oral Squamous cell carcinoma and Verrucous carcinoma. AIM OF THE STUDY: To evaluate the immunohistochemical expression of MCM 2 and Cyclin D1 in oral squamous cell carcinoma and verrucous carcinoma. MATERIALS AND METHODS: This immunohistochemical study was conducted on the archives retrieved formalin fixed, paraffin embedded t issue sections from the Department of Oral and Maxillofacial Pathology, Adhiparasakthi Dental College and Hospital, Melmaruvathur. The study group included 20 cases of histopathologically diagnosed Oral Squamous Cell Carcinoma (10 cases of well differentiated squamous cell carcinoma, 10 cases of Moderately differentiated Squamous Cell Carcinoma) and 10 cases of histopathologically diagnosed verrucous carcinoma. Control group included 10 biopsies from the normal buccal mucosa adjacent to the site of surgery during the surgical removal of third molars in patients. All samples were evaluated for the expression of Cyclin D1 and MCM 2 using standard immunohistochemistry procedure. The present study involved both qualitative and quantitative analysis. Qualitative analysis was done by evaluation of intensity of staining and area of staining. Quantitative analysis was done by calculating the percentage of positively stained cells and assessing the Labelling Index. Data obtained was subjected to statistical analysis using SPSS statistical package (version 19 .0). RESULTS: On evaluating and comparing Cyclin D1 and MCM 2 intensity and area of staining between the groups, statistically significant values (p < 0. 05) were obtained using Kruskall Wallis’ ANOVA. Comparison of LI of Cyclin D1 and MCM 2 in normal mucosa, OSCC and VC statistically significant results (p < 0.05) were obtained using Mann Whitney U test. Mean LI of MCM2 was found to be significantly higher than mean LI of cyclin D1 in all the study groups. CONCLUSION: From the present study we conclude that MCM2 has the potential to serve as a novel cell proliferation biomarker in OSCC and VC as compared to Cyclin D1

A Novel Brain MRI Analysis System for Detection of Stroke Lesions using Discrete Wavelets

Development of computer aided detection techniques for brain disorder has been gaining significant importance in the past few years. Out of the various brain diseases, stroke stands first for the reason behind fatality and disability. Significant features extracted from brain MR images, along with machine learning techniques could identify discriminative patterns for automatic detection of ischemic stroke. This research aims at examining the wavelet based statistical features for characterizing such abnormal lesion structures. Five different wavelet functions, namely daubechies, symlet, coiflet, de-meyer and bi-orthogonal wavelets were extensively analyzed for different normal and abnormal datasets. The wavelet co-efficients were calculated for different levels and statistical parameters were extracted from it as features. These features were trained using support vector machines for automatic classification. Experiments indicate that the accuracy of the proposed system was around 98%

Coherence Analysis in the Brain Network of ASD Children using Connectivity Model and Graph Theory

Autism Spectrum Disorder (ASD) belongs with the category of neuro-developmental disorders, which can be majorly categorized under decreased social relationships, communication and thought processes. Various studies in the field of biological networks prove that one of the defining features of ASD is altered brain connectivity. Hence, the understanding of the brain networks can pave the way to delve deeper into the underlying behaviour of the Autistic brains. Moreover, many studies also reveal that human brains exhibit small-world characteristics which are usually seen in simple model neural networks that emerge spontaneously upon adaptive rewiring according to the dynamical functional connectivity. Graph theory-based approaches are finding their way into the understanding of the altered connectivity in various neurological disorders. For that matter, the study focuses on implementing a graph theory-based approach to investigate on the small-world network of Autistic as well as typically developing brains and understand the behavioural changes for an Audio and Video Stimuli. The graphically generated data is then measured for functional connectivity using a symmetrical parameter known as the coherence measure

Recurrence Quantification Analysis of EEG signals for Children with ASD

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impairment in sensory modulation, repetitive behavior etc. It would lead to difficulties in adaptive behavior and intellectual functioning. Subjective scales as childhood autism rating scale, 3Di, etc. are available to assess the symptoms of Autism. Currently there are no reliable objective diagnostic methods available for assessment of Autism. Also, Early diagnosis of will help in designing customized training and putting those kids in regular stream. The purpose of this research is to observe the response of the brain for auditory/visual stimuli in typically Developing (TD) and children with autism through electroencephalography (EEG). Application of nonlinear methods for EEG signal analysis may help in characterization of brain activity to describe the neurophysiological commonalities and differences between typically developing and autism children. Among the various non-linear methods, the underlying dynamics can be analyzed well with Recurrent Quantification Analysis (RQA). But, the performance of RQA based classification depends on the choice of parameters like embedding dimension, time delay, neighborhood selection and distance metric. Different experiments were conducted by varying methods for neighborhood selection and distance metric. In this research, for better information retrieval cosine distance metric is additionally considered for analysis and compared      with other distance metrics in RQA. Each computational combination of RQA measures and the responding channels were analyzed and discussed. It was observed that FAN neighborhood with cosine distance parameters were able to discriminate between ASD and TD