Ethnobotanical studies of weed plants in rice field ecosystem

Tamilnadu is one of the leading states in rice production in India. Weeds are widely found and are tremendously grown everywhere on paddy fields. Ethnobotany have gained importance during recent years. Based on this, an ethnobotanical exploration has been carried to find out the medicinal values of weed plants growing in the paddy fields of Annamalai Nagar, Cuddalore district. The study reveals the importance of the weed plants associated with paddy fields, a total of 40 species of weeds belonging to 22 families has been recorded in meeting the multiple requirements of human beings

Ethnobotanical studies of weed plants in rice field ecosystem

Tamilnadu is one of the leading states in rice production in India. Weeds are widely found and are tremendously grown everywhere on paddy fields. Ethnobotany have gained importance during recent years. Based on this, an ethnobotanical exploration has been carried to find out the medicinal values of weed plants growing in the paddy fields of Annamalai Nagar, Cuddalore district. The study reveals the importance of the weed plants associated with paddy fields, a total of 40 species of weeds belonging to 22 families has been recorded in meeting the multiple requirements of human beings

Exploring the landscape of dysarthric speechrecognition: a survey of literature

Automatic speech recognition (ASR) is a valued tool for individuals with dysarthria, a speech impairment characterized by various pathological traits that differ from healthy speech. However, recognizing dysarthric speech, which is spoken by individuals with speech impairments, poses unique challenges due to its diverse characteristics such as rugged pronunciation, loudness that varies at different intervals, speech that has lot of delays, pauses that are inpredictable, excessive nasal sounds, explosive pronunciation, and airflow noise. The survey reveals the various models for dysarthric speech recognition. Deep learning technologies, unfurls an improved ASR performance leaps and bounds breaking the fluency and pronunciation barriers. Various feature extractions and identification of different types of dysarthria, including spastic, mixed, ataxic, hypokinetic, and hyperkinetic are explored. The performance of contemporary deep learning approaches in dysarthric speaker recognition (DSR) is tested using various datasets to determine accuracy. In conclusion the most effective DSR strategies are identified and areas for future investigation is suggested. However, speaker-dependent difficulties restrict the generalizability of acoustic models, and a lack of speech data impedes training on large datasets. The study throws light on how the effectiveness of ASR for dysarthric speech can be improved and further areas of research in the area are highlighted

A locus-based paradigm for generating systems biological inferences from large scale functional genomics datasets

Genomics data is growing at a exponential rate. The ability to integrate new results with existing knowledge about genomic biology is rapidly becoming the limiting factor as there no universal language with which to describe genomic functional elements. To integrate and compare new and existing genomic data, we define our basic functional unit of a genome to be a locus -- a set of positional coordinates along any genome with an arbitrary amount of functional annotations attached. The locus concept enables addressing genomic elements and annotations at any level of granularity from entire swaths of chromosomes to single base-positions. We define a locus-based framework to compare a given set of genomic elements to any of existing genomic annotations. We use this to build a tool to find genomic annotations significantly and frequently overlaping with a set. We also use this to build a tool to infer functional interactions from locus intersections and show how the inference of regulatory interactions from genomics data and the analysis of the topological properties of genomic networks can provide useful biological insights

Automated Tool for NoSQL to SQL Migration

Choosing which database to use is one of the most important decisions an organization needs to make when working on a new microservice. When deciding on a modern database, one of the biggest decisions is to select the correct type of (relational or non-relational) database. Organizations make this decision based on the application scenario before the development starts. However, sometimes due to the changes in requirements, developers need to switch between database systems after the development starts. Switching between database systems can be a tedious and time-consuming task. In this study, we propose a tool that will automate the process of schema and data migration from MongoDB to MySQL database. The tool has been developed using Python programming language and gives users the ability to convert the database structures while maintaining the relationships between the data fashion accurately and consistently

Role clarity, perceived cohesion and felt responsibility as antecedents of altruism and conscientiousness among college teachers in Kerala

Purpose – Literature evidences that altruism and conscientiousness are very important discretionary behaviours within the broader framework of Organizational Citizenship Behaviour (OCB) among teaching community. The present study is intended to examine the effect of role clarity, perceived cohesion and felt responsibility on altruism and conscientiousness among college teachers in Kerala. Design/methodology/approach – A questionnaire-based survey was conducted among 354 college teachers, and the causal effect was examined using Partial Least Square-based structural equation modelling. Findings – Validity and reliability of the model were established through measurement model evaluation. Explanatory power of the model was established. Cohesion and felt responsibility significantly predicted altruism, but the effect of role clarity on altruism was not significant. Effect of cohesion, felt responsibility and role clarity on conscientiousness was significant. Originality/value – The study contributed to the existing theory on antecedents of OCB. The model has high levels of predictive accuracy – role clarity, cohesiveness and felt responsibility – capable of explaining the discretionary behaviour among college teachers

AI-Based Automated Speech Therapy Tools for persons with Speech Sound Disorders: A Systematic Literature Review

This paper presents a systematic literature review of published studies on AI-based automated speech therapy tools for persons with speech sound disorders (SSD). The COVID-19 pandemic has initiated the requirement for automated speech therapy tools for persons with SSD making speech therapy accessible and affordable. However, there are no guidelines for designing such automated tools and their required degree of automation compared to human experts. In this systematic review, we followed the PRISMA framework to address four research questions: 1) what types of SSD do AI-based automated speech therapy tools address, 2) what is the level of autonomy achieved by such tools, 3) what are the different modes of intervention, and 4) how effective are such tools in comparison with human experts. An extensive search was conducted on digital libraries to find research papers relevant to our study from 2007 to 2022. The results show that AI-based automated speech therapy tools for persons with SSD are increasingly gaining attention among researchers. Articulation disorders were the most frequently addressed SSD based on the reviewed papers. Further, our analysis shows that most researchers proposed fully automated tools without considering the role of other stakeholders. Our review indicates that mobile-based and gamified applications were the most frequent mode of intervention. The results further show that only a few studies compared the effectiveness of such tools compared to expert Speech-Language Pathologists (SLP). Our paper presents the state-of-the-art in the field, contributes significant insights based on the research questions, and provides suggestions for future research directions.Comment: This article has been accepted for publication in Speech, Language and Hearing, published by Taylor & Franci

POTENTIAL ANTIMICROBIAL, ANTHELMINTIC AND ANTIOXIDANT ACTIVITIES OF MYRISTICA DACTYLOIDES GAETRN BARK

Objective: The present study was undertaken to determine the antimicrobial, anthelmintic and antioxidant activities of bark extracts of Myristica dactyloides Gaetrn.Methods: The antimicrobial activity of the petroleum ether, ethyl acetate and methanol extracts were evaluated by the Agar well diffusion method against different gram-positive, gram-negative bacteria and fungi. Different extracts of the plant were taken for anthelmintic activity against Indian earthworm Pheretima Posthuma. DPPH radical scavenging activity was measured by the DPPH antioxidant assay method using ascorbic acid as standard and the total phenolic content was estimated spectrophotometrically using Folin-Ciocalteu method.Results: Petroleum ether extract exhibited significant antifungal activity, anthelmintic activity and considerable DPPH radical scavenging activity with an IC50 value of 10.97±0.07µg/ml. Whereas methanol extract exhibited significant antibacterial activity against both gram positive and gram negative bacteria and it is the richest source of phenolics with a total phenolic content of 95.11±2.14 mg of Catechol equivalents/100 mg dried extract. Preliminary phytochemical screening revealed the presence of alkaloids, flavonoids, tannins/phenolics, steroids/triterpenoids and saponins which may be the reason for its biological properties.Conclusion: The findings of this study indicate that this plant is medicinal with prominent antioxidant, antimicrobial and anthelmintic property. The plant can be considered as promising plant species with high potential value for drug preparation.Â