Quality of Life using AQLQ (S), ACT and GINA in patients with bronchial asthma in South India

Asthma has been notified as a chronic illness that impacts a large number of individuals and affects their quality of life. Aim: To measure the Quality of Life in patients with Bronchial Asthma in a tertiary care setting in South India. Method: Structured face to face interviews were conducted using standardized tools i.e. Standardized version of Juniper‟s Asthma Quality of Life Questionnaire and the responses were classified under the domains of activity limitations, symptoms, emotions and exposure to environmental stimuli. The Asthma Control Test was also used categorizing respondents as demonstrating total control, well controlled or uncontrolled asthma. GINA guidelines was used to classify the patients based on severity of Asthma as intermittent, mild persistent, moderate persistent and severe persistent. Result: 200 physician diagnosed patients with Bronchial Asthma participated in the study. Majority were male (n=115) and rest female (n=85). 143 were married and many were graduates (n=52). The mean QOL of the patients was 4.83 on 7 point scale. More than half of the sample population (57% n=114) were found to experience uncontrolled asthma. The average score received in Asthma Control Test was 17 against a maximum of 25. Less than half the patients (37.5% n =75) in the study were classified as having moderate Asthma. Conclusion: The findings suggest that there is a need to control asthma and the environmental factors that trigger it. Educating patients on treatment and precautionary measures may be a potential solution to enhance the overall sense of well-being in patients with bronchial asthma

Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text

Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark

DravidianCodeMix: Sentiment Analysis and Offensive Language Identification Dataset for Dravidian Languages in Code-Mixed Text

This paper describes the development of a multilingual, manually annotated dataset for three under-resourced Dravidian languages generated from social media comments. The dataset was annotated for sentiment analysis and offensive language identification for a total of more than 60,000 YouTube comments. The dataset consists of around 44,000 comments in Tamil-English, around 7,000 comments in Kannada-English, and around 20,000 comments in Malayalam-English. The data was manually annotated by volunteer annotators and has a high inter-annotator agreement in Krippendorff's alpha. The dataset contains all types of code-mixing phenomena since it comprises user-generated content from a multilingual country. We also present baseline experiments to establish benchmarks on the dataset using machine learning methods. The dataset is available on Github (https://github.com/bharathichezhiyan/DravidianCodeMix-Dataset) and Zenodo (https://zenodo.org/record/4750858\#.YJtw0SYo\_0M).Comment: 36 page

MEDDB: A medicinal plant database developed with the information gathered from tribal people in and around Madurai, Tamil Nadu

Tribal peoples are endowed with enriched traditional wisdom to use available nature resources around them. They are well versed in the usage of plant for treating various diseases. They have used powder or extract or paste form of the plant parts such as root, shoot, whole plant, fruits and leaves etc. The recipe known by the tribal people was passed on only to their family members and community through mouth to mouth practice. Hence, the knowledge is confined to particular people alone. It is always expedient to store information in the database, so that it will be accessible by everyone from everywhere. To achieve this, MEDDB has been developed, which stores the details of 110 plant species that are commonly used by tribes for fever, asthma, cold, cough, diabetes, diarrhea, dysentery, eye infections, stomach ache, wounds and snake bite. The details of each plant were collected from the literature and through web search to give comprehensive and exhaustive information. Each plant entry is compiled under the subheadings viz., common name, classification, physical characteristics, medicinal uses, active constituents, and references

Whole genome sequence analysis of NDM-1, CMY-4, and SHV-12 coproducing salmonella enterica serovar typhimurium isolated from a case of fatal burn wound infection

Salmonella species are frequently associated with gastrointestinal infections such as diarrhea. However, extraintestinal Salmonella infections, including burn infections, have been described. Here, we report the first case of a carbapenem-resistant and metallo-β-lactamase (New Delhi metallo-β-lactamase), extended-spectrum β-lactamase (SHV-12), and AmpC β-lactamase (CMY-4) coproducing Salmonella Typhimurium isolated from a fatal case of burn wound infection. The publication highlights the necessity for the rational use of antibiotics (particularly the rational use of last-resort antibiotics such as carbapenems) in hospitals and burn units, as well as the need for systematic screening of Salmonella spp. (including Salmonella enterica serovar Typhimurium) for resistance to carbapenem antibiotics

Multilingual multimodal machine translation for Dravidian languages utilizing phonetic transcription

Multimodal machine translation is the task of translating from a source text into the target language using information from other modalities. Existing multimodal datasets have been restricted to only highly resourced languages. In addition to that, these datasets were collected by manual translation of English descriptions from the Flickr30K dataset. In this work, we introduce MMDravi, a Multilingual Multimodal dataset for under-resourced Dravidian languages. It comprises of 30,000 sentences which were created utilizing several machine translation outputs. Using data from MMDravi and a phonetic transcription of the corpus, we build an Multilingual Multimodal Neural Machine Translation system (MMNMT) for closely related Dravidian languages to take advantage of multilingual corpus and other modalities. We evaluate our translations generated by the proposed approach with human-annotated evaluation dataset in terms of BLEU, METEOR, and TER metrics. Relying on multilingual corpora, phonetic transcription, and image features, our approach improves the translation quality for the underresourced languages.This work is supported by a research grant from Science Foundation Ireland, co-funded by the European Regional Development Fund, for the Insight Centre under Grant Number SFI/12/RC/2289 and the European Union’s Horizon 2020 research and innovation programme under grant agreement No 731015, ELEXIS - European Lexical Infrastructure and grant agreement No 825182, Pret- ˆ a-` LLOD.non-peer-reviewe

Corpus creation for sentiment analysis in code-mixed Tamil-English text

Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmarkThis publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 (Insight), SFI/12/RC/2289 P2 (Insight 2), co-funded by the European Regional Development Fund as well as by the EU H2020 programme under grant agreements 731015 (ELEXIS-European Lexical Infrastructure), 825182 (Pret- ˆ a-LLOD), and Irish Research Council ` grant IRCLA/2017/129 (CARDAMOM-Comparative Deep Models of Language for Minority and Historical Languages)