Hybrid Approach for Video Compression Using Block Matching Motion Estimation

To discard the redundancy present in video some video compression technique are involved .Basically video is a collection sequential frames in a sequence. video compression means reducing the size of video . In video sequence there are two types of technique are present that are temporal redundancy and spatial redundancy. In this paper we discuss about hybrid technique .Hybrid means combination of any two or more than two technique like efficient three step search algorithm(E3SS) and cross hexagonal search algorithm (CHS) .In today’s date block matching algorithm for motion estimation is powerful technique for high compression ratio and to reduce computational complexity .The motion estimation calculate the position of pixel and It is a custom to calculate the pixel from current frame to reference frame .The main function of motion estimation is reducing the search point and redundancy present in video .The experiment result shows that the proposal algorithm performs better than previous proposed block matching algorithms and required less computation than other technique

Genetic analysis for fruit yield and yield attributes in okra (Abelmoschus esculentus L. Moench)

The components of gene effects for yield and its components in okra were studied using generation mean analysis from six generations (P1, P2, F1, F2, BC1, and BC2) derived from six different genotypes. Joint scaling test was applied to detect the presence of epistasis. Epistasis was detected in both the cases. Prevalence of duplicate type of epistasis was observed in all the cases except number of fruits/ plant, fruit length in C1 (IC 3307 x IC 433645), fruit diameter, inter node length in C2 (IC- 43736 x Parbhani Kranti), fruit length, fruit diameter and fruit weight in C3 (IC-342075 X IC-332453). Additive, dominance and epistatic gene effects contribute significantly to the inheritance of various yield characters studied. The joint scaling test confirmed the inadequacy of the additive dominance model in most of the crosses for majority of the characters and indicated the presence of epistasis. Low narrow sense heritability values for most of the characters indicating that these characters are more influenced by the environment and they cannot be improved by simple selection

Antidepressant Effect of Alpha Lipoic Acid in Rats Exposed to Chronic Unpredictable Mild Stress: Putative Role of Neurotransmitters and 5HT3 Receptor

Depression is a neuropsychiatric disorder characterized by altered emotion and cognition. Alpha lipoic acid (ALA) is a potent natural antioxidant and exhibits neuroprotective effects. However, its antidepressant activity and its mechanism of action in rats exposed to chronic unpredictable mild stress (CUMS) need to be evaluated. The rats were divided into six groups. Group, I vehicle control (without stress), II- CUMS, III- fluoxetine (FLX) (50 mg/kg p.o.), IV, V, and VI were treated with ALA (50, 100, 200 mg/kg, p.o.), respectively. All the groups, except I, were subjected to CUMS + treatments from day 1 to day 42. Body weight and behavioral parameters like sucrose preference test (SPT), Morris water maze (MWM), resident intruder test (RIT), and marble-burying test (MBT) were performed on day 0, day 21, and day 42, and forced swim test (FST) on last day 42 and 43 only. The rats were further sacrificed for biochemical and histopathological evaluation. ALA significantly improved behavioral function, increased antioxidant strength, reduced lipid peroxidation, restored monoamines, and protected CA3 neurons. Further, docking studies revealed strong binding of ALA on the 5HT3 receptor. The study demonstrates that ALA might be exhibiting antidepressant effects in part by restoring monoamines and modulating the 5HT3 receptor

Machine learning-based virtual screening and molecular modelling studies for identification of butyrylcholinesterase inhibitors as anti-Alzheimer’s agent

Butyrylcholinesterase (BChE) is a hydrolase involved in the metabolism and detoxification of specific esters in the blood. It is also implicated in the progression of Alzheimer’s disease, a type of dementia. As the disease progresses, the level of BChE tends to increase, opting for a major role as an acetylcholine-degrading enzyme and surpassing the role of acetylcholinesterase. Hence, the development of BChE inhibitors could be beneficial for the latter stages of the disease. In the present study, machine learning (ML) models were developed and employed to identify new BChE inhibitors. Further, the identified molecules were subjected to molecular property filters. The filtered ligands were studied through molecular modelling techniques, viz. molecular docking and molecular dynamics (MD). Support vector machine-based ML models resulted in the identification of 3291 compounds that would have predicted IC50 values less than 200 nM. The docking study showed that compounds ART13069594, ART17350769 and LEG19710163 have mean binding energies of −9.62, −9.26 and −8.93 kcal/mol, respectively. The MD study displayed that all the selected ligands showed stable complexes with BChE. The trajectories of all the ligands were stable similar to the standard BChE inhibitors. Communicated by Ramaswamy H. Sarma</p