ABNORMAL PSYCHOLOGY AND MALADAPTIVE BEHAVIOUR EXISTS EVERYWHERE, DOES IT INFLUENCE SOCIETY?

ABSTRAC

V. Ashok Prabu.pmd

Abstract Seasonal variations of physico-chemical parameters such as rainfall, temperature, salinity, pH, dissolved oxygen and nutrients like nitrate, nitrite, inorganic phosphate and reactive silicate were studied from two different stations in Uppanar estuary, Cuddalore, southeast coast of India from April 2000 to March 2002. Atmospheric and surface water temperatures (°C) varied from 28 to 40.5 and 26 to 38 respectively. The salinity (‰), pH and dissolved oxygen (ml l -1 ) ranges were: 6.0-38.0; 7.1-8.2 and 2.4 to 4.5 respectively. Nutrient concentrations also varied considerably; nitrates: 8.15-25.66 µM, nitrites: 1.05-4.15 µM, phosphates: 0.17-2.96µM and reactive silicates: 27-168 µM. Nutrient concentrations were higher during monsoon season and low during summer

Community structure of microzooplankton in a tropical estuary (Uppanar) and a mangrove (Pichavaram) from the southeast coast of India

In the marine and estuarine waters of Cuddalore, the southeast coast of India microzooplankton have previously been sparingly investigated. Micro-zooplankton community structure (species composition, abundance, diversity, richness, evenness) of an estuary and mangroves of the Cuddalore and Pichavaram areas, southeast India were investigated in detail. Monthly samples were taken from April 2000 to March 2002, from four fixed stations. Micro-zooplankton taxon composition and abundance showed seasonal variations being highest in summer (45 to 50 ind./l in April to August 2000; 60 to 67.5 ind./l in April to June 2001) and lowest during the monsoon (6 to 16 ind./l in September to December 2000; 7 to 19 ind./l in October to December 2001). The total abundance of microzooplankton was in the range of 10.3-65.0 ind./l in Cuddalore areas (Stations 1 and 2) and 5.2 - 67.5 ind./l in Pichavaram mangroves (Stations 3 and 4). Over the study period, tintinnids dominated the microzooplankton community in terms of both abundance and species diversity. The remaining taxa included Radiolaria, Foraminifera, Rotifera, ciliates other than tintinnids, and metazoans. A total of 62 and 74 species of microzooplankton were recorded from Cuddalore and Pichavaram mangroves respectively. Canonical Correspondence Analysis (CCA) was applied to discriminate environmental factors associated with the microzooplankton community at the species level. The results of the study provide a basis for rational sustainable exploitation of Cuddalore waters and future research on its living resources. Furthermore, a comparison of results with studies from around the world showed a very strong, significant relationship between abundance and sampling methods underlining the need for standardized protocols

Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique

In recent decades, automatic vehicle classification plays a vital role in intelligent transportation systems and visual traffic surveillance systems. Especially in countries that imposed a lockdown (mobility restrictions help reduce the spread of COVID-19), it becomes important to curtail the movement of vehicles as much as possible. For an effective visual traffic surveillance system, it is essential to detect vehicles from the images and classify the vehicles into different types (e.g., bus, car, and pickup truck). Most of the existing research studies focused only on maximizing the percentage of predictions, which have poor real-time performance and consume more computing resources. To highlight the problems of classifying imbalanced data, a new technique is proposed in this research article for vehicle type classification. Initially, the data are collected from the Beijing Institute of Technology Vehicle Dataset and the MIOvision Traffic Camera Dataset. In addition, adaptive histogram equalization and the Gaussian mixture model are implemented for enhancing the quality of collected vehicle images and to detect vehicles from the denoised images. Then, the Steerable Pyramid Transform and the Weber Local Descriptor are employed to extract the feature vectors from the detected vehicles. Finally, the extracted features are given as the input to an ensemble deep learning technique for vehicle classification. In the simulation phase, the proposed ensemble deep learning technique obtained 99.13% and 99.28% of classification accuracy on the MIOvision Traffic Camera Dataset and the Beijing Institute of Technology Vehicle Dataset. The obtained results are effective compared to the standard existing benchmark techniques on both datasets

Transcriptional profiling of heat stress responsive genes in different developmental stages of bread wheat (<i>Triticum aestivum </i> L<i>.</i>)

467-476Transcriptional regulation of heat stress response is a complex phenomenon in crop plants, which needs to be evaluated at molecular level for its proper understanding. The present study aimed at identifying differentially expressed heat responsive genes in a thermo-tolerant Indian wheat cultivar Raj3765. Wheat plants of Raj3765 and HD2967 (heat susceptible genotype) were exposed to heat stress at 37°C and 42°C, respectively for different time intervals (30 min to 6 h) at four developmental stages (seedling, tillering, stem elongation &amp; anthesis) and leaf samples collected. Total RNA isolated from leaf samples was used for constructing eight forward subtracted cDNA libraries. A total of 1016 ESTs were generated and assembled into a collection of 377 unigenes, including 114 contigs and 270 singletons. About 23.22 and 20.47% unigenes showed similarities to stress related and abiotic stimulus related genes, respectively; of which 12.59% showed similarities to genes for heat stress responsive functions. Differential expression analysis in response to heat stress of selected six genes by quantitative Real Time-PCR showed up regulation of all the six genes in Raj 3765 as compared to HD2967. The study identified several heat stress responsive genes that can be deployed for development of thermotolerant transgenic wheat

Machine Learning Approach Regarding the Classification and Prediction of Dog Sounds: A Case Study of South Indian Breeds

Barking is a form of vocal communication made by dogs. Each type of bark made by dogs has a distinct context. The classification of dog bark pattern will aid in the understanding of barking action. In this study, a machine learning algorithm is used to analyze the pattern of barking from two different dog species: Rajapalayam Hound and Kombai Hound. The objective is to find the context of the dog barking pattern based on various real-time scenarios, including whether the dogs are alone, looking at strangers, or showing an eagerness to fight. The barks of the dogs were recorded inside the house under different scenarios, such as while identifying the owner or strangers. Machine learning algorithms, such as the reinforcement learning method, were used in predicting and classifying the dog sounds. Q-learning is a reinforcement learning that will generate the next best action for the given state. It is a model-free learning used to find the best course of dog action for the given current state of the dog. The Q-learning algorithm had been used in improving the prediction of dog sounds by updating the values of learning, where the values with the highest reward were taken into consideration. In total, 6171 barks were collected from the dogs chosen for study, and the proposed approach achieved a correct prediction accuracy of 85.19% of the dog sounds

International open trial of uniform multidrug therapy regimen for leprosy patients: Findings & implications for national leprosy programmes

Background & objectives: Uniform therapy for all leprosy patients will simplify leprosy treatment. In this context, we evaluated six-month multidrug therapy (MDT) currently recommended for multibacillary (MB) patients as uniform MDT (U-MDT) in a single-arm open trial under programme conditions. Primary objective was to determine efficacy to prevent five-year cumulative five per cent relapse. Secondary objectives were to assess acceptability, safety and compliance. Methods: Newly detected, treatment-naive leprosy patients were enrolled in India (six sites) and P. R. China (two sites). Primary outcome was clinically confirmed relapse of occurrence of one or more new skin patches consistent with leprosy, without evidence of reactions post-treatment. Event rates per 100 person years as well as five-year cumulative risk of relapse, were calculated. Results: A total of 2091 paucibacillary (PB) and 1298 MB leprosy patients were recruited from the 3437 patients screened. Among PB, two relapsed (rate=0.023; risk=0.11%), eight had suspected adverse drug reactions (ADRs) (rate=0.79) and rate of new lesions due toreactions was 0.24 (n=23). Rates of neuritis, type 1 and type 2 reactions were 0.39 (n=37), 0.54 (n=51) and 0.03 (n=3), respectively. Among MB, four relapsed (rate=0.07; risk=0.37%) and 16 had suspected ADR (rate=2.64). Rate of new lesions due to reactions among MB was 1.34 (n=76) and rates of neuritis, type 1 and type 2 reactions were 1.37 (n=78), 2.01 (n=114) and 0.49 (n=28), respectively. Compliance to U-MDT was 99 per cent. Skin pigmentation due to clofazimine was of short duration and acceptable. Interpretation & conclusions: We observed low relapse, minimal ADR and other adverse clinical events. Clofazimine-related pigmentation was acceptable. Evidence supports introduction of U-MDT in national leprosy programmes. [CTRI No: 2012/ 05/ 002696