A Study on Pattern Classification of Bioinformatics Datasets

Pattern Classification is a supervised technique in which the patterns are organized into groups of pattern sharing the same set of properties. Classification involves the use of techniques including applied mathematics, informatics, statistics, computer science and artificial intelligence to solve the classification problem at the attribute level and return to an output space of two or more than two classes. Probabilistic Neural Networks(PNN) is an effective neural network in the field of pattern classification. It uses training and testing data samples to build a model. However, the network becomes very complex and difficult to handle when there are large numbers of training data samples. Many other approaches like K-Nearest Neighbour (KNN) algorithms have been implemented so far to improve the performance accuracy and the convergence rate. K-Nearest Neighbour is a supervised classification scheme in which we select a subset from our whole dataset and that is used to classify the samples. Then we select a classified dataset subset and that is used to classify the training dataset. The Computation cost becomes too expensive when we have a larger dataset. Then we use genetic algorithm to design a classifier. Here we use genetic algorithm to divide the samples into different class boundaries by the help of different lines. After each generation we get the accuracy of our algorithm then we continue till we get our desired accuracy or our desired number of generation. In this project, a comparative study of Probabilistic Neural Network, K-Nearest Neighbour and Genetic Algorithm as a Classifier is done. We have tested these different algorithms using instances from lung cancer dataset, Libra Movement dataset, Parkinson dataset and Iris dataset (taken from the UCI repository and then normalized). The efficiency of the three techniques are compared on the basis of the performance accuracy on the test data, convergence time and on the implementation complexity

Foraging behavior of major insect pollinators on pumpkin, Cucurbita moschata (Duch.ex Lam)

Foraging activity period of different honey bee species on C. moschata (C-1076) flowers at different day hours during August-September (2013) revealed that A. dorsata, A. mellifera, A. cerana and A. florea initiated their activity early in the morning at 0530, 0615, 0625 and 0630 h, respectively and stopped their activity at 1030, 1020, 1025 and 1030 h of the day, respectively while on C. moschata (C-1106, A. dorsata, A. mellifera, A. cerana and A. florea initiated their activity early in the morning at 0535, 0615, 0620 and 0625 h, respectively and ceased their activity at 1045, 1025, 1015 and 1040 h of the day, respectively. The mean foraging speed (time spent per flower) in seconds on flowers of pumpkin (C-1106) was maximum of A. florea (181.72), followed by A. mellifera (7.15), A. cerana (6.05) and A. dorsata spent least time (5.83) and in pumpkin (C-1076), foraging speed was maximum in case of A. florea (178.71), followed by A. mellifera (7.63), A. cerana (6.24) and A. dorsata spent least time (6.06). The mean foraging rate (flowers visited per minute) on flowers of pumpkin (C-1106) was maximum in case of A. dorsata (5.13), followed by A. cerana (4.30), A. mellifera (4.16) and A. florea visited least flower (0.32) and in pumpkin (C-1076), foraging rate was maximum in case of A. dorsata (4.96), followed by A. cerana (4.19), A. mellifera (4.02) and A. florea visited least flower (0.33). Present study advises the farmers that they should not apply the pesticide when the activityof honey bee is on the peak period because pesticides application at the time of bee activity in the field crop causes mortality of bees

Stem Cell Antigen CD34 In Native And Engineered Form Alter Its Binding Ability To Stromal Cells And Ligands: A Classical Example Of Clinical Benefits Of Therapeutic Genetic Engineering Of Stem Cells In Transplantation

CD34 is a highly glycosylated surface-expressed sialomucin and, because it is present on hematopoietic stem cells (HSCs), has demonstrated immense clinical utility in their enumeration in aphaeresis products, immunoaffinity purification for transplantation, and disease monitoring. The success of CD34 based reagents in identifying hematopoietic progenitors led to the assumption that CD34 is expressed on cells with regenerative potential and is sufficient for hematopoietic reconstitution in marrow-ablated recipients. However, its role has not been identified in substantial detail. 

With the advent of the fact that CD34 binds adapter protein like CRK-L in cytosol and CD34 knock out studies identified a a signaling role, CD34 antigen has been proposed to play a signaling function. Since it is a sialomucin, a member of the group adhesion molecules, we attempted to identify a role by over-expreesing its gene in cell lines. We report here that CD34 and engineered forms (Ser306 & Tyr318) significantly regulates adhesion to stromal cells, like mesenchymal stem cells and bone marrow ligands. These enhance binding of cells overexpressing CD34 by upregulating integrins and we therefore propose that such cells may effectively potentiate the success of transplantation through greater homing if they are used for transfusion

Lattice Induced Transparency in Metasurfaces

Lattice modes are intrinsic to the periodic structures and their occurrence can be easily tuned and controlled by changing the lattice constant of the structural array. Previous studies have revealed excitation of sharp absorption resonances due to lattice mode coupling with the plasmonic resonances. Here, we report the first experimental observation of a lattice induced transparency (LIT) by coupling the first order lattice mode (FOLM) to the structural resonance of a metamaterial resonator at terahertz frequencies. The observed sharp transparency is a result of the destructive interference between the bright mode and the FOLM mediated dark mode. As the FOLM is swept across the metamaterial resonance, the transparency band undergoes large change in its bandwidth and resonance position. Besides controlling the transparency behaviour, LIT also shows a huge enhancement in the Q-factor and record high group delay of 28 ps, which could be pivotal in ultrasensitive sensing and slow light device applications.Comment: 5 figure