Efficacy of Phytochemicals of Cassia Angustifolia in Chronic Myeloid Leukaemia – An In-silico Analysis

Objective: To discover the compounds of Cassia having activity against the BCR-ABL fusion protein involved in the pathogenesis of CML and to compare it with previously developed inhibitor, nilotinib using in-silico molecular docking. Methodology: The 3D structure of Human BCR-ABL fusion protein was obtained from PDB (RSCB). The SMILES and Chemical Structures of the ligands were obtained from PubChem. They were prepared in Mol SDF format by the Chem Bio Draw and then converted to PDBQT format using PyRx tool for generating the atomic coordinates for molecular docking.  Molecular docking of Nilotinib, Quercimeritin, and Scutellarein with Human ABL Kinase was performed using Autodock4. The ADMET properties were described using Swiss ADME, a web-based tool. Results: All the three compounds under study bind and make stable complexes with wild-type BCR ABL with the global energies of -12.46, -16.17kCal/mol and -15.41kCal/mol for Nilotinib Scutellarein and Quercimeritin respectively which means that these compounds can act as selective inhibitors of BCR-ABL fusion protein. Quercimeritin, also form Hydrogen bonds with GLU 286 and Asp 381, Conclusion: The binding energies of the phytochemicals of Cassia are higher in comparison with Nilotinib which has a binding energy of -12.46kCal/mol which suggests a better inhibitory potential of these compounds. Quercimeritin also forms Hydrogen bonds with Glutamine 286 and Aspartate 381, hence its potential to be a potent inhibitor of the BCR- ABL fusion protein is more promising Nilotinib. Further in vitro and in vivo studies are suggested to elaborate the anti-neoplastic potential of Quercimeritin in CML

Declarative vs Rule-based Control for Flocking Dynamics

The popularity of rule-based flocking models, such as Reynolds' classic flocking model, raises the question of whether more declarative flocking models are possible. This question is motivated by the observation that declarative models are generally simpler and easier to design, understand, and analyze than operational models. We introduce a very simple control law for flocking based on a cost function capturing cohesion (agents want to stay together) and separation (agents do not want to get too close). We refer to it as {\textit declarative flocking} (DF). We use model-predictive control (MPC) to define controllers for DF in centralized and distributed settings. A thorough performance comparison of our declarative flocking with Reynolds' model, and with more recent flocking models that use MPC with a cost function based on lattice structures, demonstrate that DF-MPC yields the best cohesion and least fragmentation, and maintains a surprisingly good level of geometric regularity while still producing natural flock shapes similar to those produced by Reynolds' model. We also show that DF-MPC has high resilience to sensor noise.Comment: 7 Page

Comparative Analysis of Machine Learning Algorithms for Classification of Environmental Sounds and Fall Detection

In recent years, number of elderly people in population has been increased because of the rapid advancements in the medical field, which make it necessary to take care of old people. Accidental fall incidents are life-threatening and can lead to the death of a person if first aid is not given to the injured person. Immediate response and medical assistance are necessary in case of accidental fall incidents to elderly people. The research community explored various fall detection systems to early detect fall incidents, however, still there exist numerous limitations of the systems such as using expensive sensors, wearable sensors that are hard to wear all the time, camera violates the privacy of person, and computational complexity. In order to address the above-mentioned limitations of the existing systems, we proposed a novel set of integrated features that consist of melcepstral coefficients, gammatone cepstral coefficients, and spectral skewness. We employed a decision tree for the classification performance of both binary problems and multi-class problems. We obtained an accuracy of 91.39%, precision of 96.19%, recall of 91.81%, and F1-score of 93.95%. Moreover, we compared our method with existing state-of-the-art methods and the results of our method are higher than other methods. Experimental results demonstrate that our method is reliable for use in medical centers, nursing houses, old houses, and health care provisions. Full Tex

Comparative Analysis of Machine Learning Algorithms for Classification of Environmental Sounds and Fall Detection

In recent years, number of elderly people in population has been increased because of the rapid advancements in the medical field, which make it necessary to take care of old people. Accidental fall incidents are life-threatening and can lead to the death of a person if first aid is not given to the injured person. Immediate response and medical assistance are necessary in case of accidental fall incidents to elderly people. The research community explored various fall detection systems to early detect fall incidents, however, still there exist numerous limitations of the systems such as using expensive sensors, wearable sensors that are hard to wear all the time, camera violates the privacy of person, and computational complexity. In order to address the above-mentioned limitations of the existing systems, we proposed a novel set of integrated features that consist of melcepstral coefficients, gammatone cepstral coefficients, and spectral skewness. We employed a decision tree for the classification performance of both binary problems and multi-class problems. We obtained an accuracy of 91.39%, precision of 96.19%, recall of 91.81%, and F1-score of 93.95%. Moreover, we compared our method with existing state-of-the-art methods and the results of our method are higher than other methods. Experimental results demonstrate that our method is reliable for use in medical centers, nursing houses, old houses, and health care provisions. Full Tex

Prediction of Heart Disease using Artificial Neural Network

Heart disease is increasing rapidly due to number of reasons. If we predict cardiac arrest (dangerous conditions of heart) in the early stages, it will be very helpful to cured this disease. Although doctors and health centres collect data daily, but mostly are not using machine learning and pattern matching techniques to extract the knowledge that can be very useful in prediction. Bioinformatics is the real world application of machine learning to extract patterns from the datasets using several data mining techniques. In this research paper, data and attributes are taken from the UCI repository. Attribute extraction is very effective in mining information for the prediction. By utilizing this, various patterns can be derived to predict the heart disease earlier. In this paper, we enlighten the number of techniques in Artificial Neural Network (ANN). The accuracy is calculated and visualized such as ANN gives 94.7% but with Principle Component Analysis (PCA) accuracy rate improve to 97.7%