Impact of Celebrity Suicides on mental health of vulnerable population

Suicide is culminating into a grave public health concern. Approximately 800,000 people worldwide commit suicide annually, with 3/4th owing to low- middle-income countries.(1) In 2016, the suicide rate in India was 16.5, exceeding the global average of 10.5/1,00,000.(1) Suicide is the deliberate ending of one's own life(2) and primarily done due to persistent sense of despair, depression, drug misuse, and various personal and financial stress factors. One such trigger is suicide by an eminent figure, also known as werthering effect, modelling effect, or copycat suicide. This phenomenon commonly affects the adolescent and younger adults. In India, the 15-29 age group were found most vulnerable.(1) Nearly 5% of consecutive suicides occur after a celebrity death primarily among young, female, and unemployed without being prompted by adverse life circumstances.(3)  Given the global gravity of suicide and India's contribution to it, it is critical to identify the psychopathology and risk factors behind it

Rapport Building in community settings

Establishing good rapport and interpersonal relationship is crucial aspect for public health researcher to embark upon authentic information and concerns of local community that later might have a bigger public health impact in policy making. Despite of its importance, researchers often fail to favourably present themselves in front of participants. The active listening, maintaining eye contact, self disclosure, tuning in, sharing expectations and intentions, non verbal cues, persistent contact and being empathetic are some of the most common techniques of rapport building discussed in literatures. Despite the fact that they have been proved to be beneficial, they do not provide clarity on “what, when, or where”. As a result, this article suggests a step-by-step approach that a researcher might use when conducting community-based research

Study of risk factors for preterm deliveries in a tertiary hospital

Background: Preterm labour and preterm deliveries are very challenging obstetric complications. Early identification of risk factors may help identify women at risk for preterm deliveries.Methods: A one-year observational study was conducted in the department of obstetrics and gynecology, IGMC Shimla, Himachal Pradesh from 1st August 2017 to 31st July 2018. All mothers who delivered between 24 to 37 weeks were subjected to a detailed history with respect to age, parity, previous pregnancy outcomes and to identify the presence of any risk factors. A thorough obstetric and systemic examination was done. Parametric and non-parametric test of significance were used to find the association between different quantitative and qualitative variable.Results: Incidence of preterm deliveries was 11.4%. Maximum cases were of age group 25-30 years. 71.7% belonged to lower socio-economic status. 54% cases were seen in multigravida. History of previous abortion was seen in 18.4% and 9.7% had history of preterm deliveries. 12% cases had history of 1st trimester bleeding.  Spontaneous onset of preterm labour was seen in 55.1%. The significant risk factors associated were PIH and genitourinary infections.Conclusions: The risk factors of preterm birth to a large extent can be identified in antenatal period. Adolescent health education including good nutrition, good hygiene, counselling for contraception to reduce unintended pregnancies and birth spacing can lower the preterm birth rate. Better prenatal care, early identification of risk factors and complicated cases, regular follow up and proper management can help us in reducing preterm births

Generating Linked-Data based Domain-Specific Sentiment Lexicons from Legacy Language and Semantic Resources

We present a methodology for legacy language resource adaptation that generates domain-specific sentiment lexicons organized around domain entities described with lexical information and sentiment words described in the context of these entities. We explain the steps of the methodology and we give a working example of our initial results. The resulting lexicons are modelled as Linked Data resources by use of established formats for Linguistic Linked Data (lemon, NIF) and for linked sentiment expressions (Marl), thereby contributing and linking to existing Language Resources in the Linguistic Linked Open Data cloud

Upcoming 4m ILMT facility and data reduction pipeline testing

peer reviewedThe 4m international liquid mirror telescope (ILMT) installation activities have recently been completed at the Devasthal observatory (Uttarakhand, India). The ILMT will perform continuous observation of a narrow strip of the sky (∼ 27′) passing over the zenith in the SDSS g′, r′ and i′ bands. In combination with a highly efficient 4 k × 4 k CCD camera and an optical corrector, the images will be secured at the prime focus of the telescope using the time delayed integration technique. The ILMT will reach ∼ 22.5 mag (g′-band) in a single scan and this limiting magnitude can be further improved by co-adding the nightly images. The uniqueness of the one-day cadence and deeper imaging with the ILMT will make it possible to discover and study various galactic and extra-galactic sources, specially variable ones. Here, we present the latest updates of the ILMT facility and discuss the preparation for the first light, which is expected during early 2022. We also briefly explain different steps involved in the ILMT data reduction pipeline

Whole genome expression and biochemical correlates of extreme constitutional types defined in Ayurveda

<p>Abstract</p> <p>Background</p> <p>Ayurveda is an ancient system of personalized medicine documented and practiced in India since 1500 B.C. According to this system an individual's basic constitution to a large extent determines predisposition and prognosis to diseases as well as therapy and life-style regime. Ayurveda describes seven broad constitution types (<it>Prakriti</it>s) each with a varying degree of predisposition to different diseases. Amongst these, three most contrasting types, <it>Vata</it>, <it>Pitta</it>, <it>Kapha</it>, are the most vulnerable to diseases. In the realm of modern predictive medicine, efforts are being directed towards capturing disease phenotypes with greater precision for successful identification of markers for prospective disease conditions. In this study, we explore whether the different constitution types as described in Ayurveda has molecular correlates.</p> <p>Methods</p> <p>Normal individuals of the three most contrasting constitutional types were identified following phenotyping criteria described in Ayurveda in Indian population of Indo-European origin. The peripheral blood samples of these individuals were analysed for genome wide expression levels, biochemical and hematological parameters. Gene Ontology (GO) and pathway based analysis was carried out on differentially expressed genes to explore if there were significant enrichments of functional categories among <it>Prakriti </it>types.</p> <p>Results</p> <p>Individuals from the three most contrasting constitutional types exhibit striking differences with respect to biochemical and hematological parameters and at genome wide expression levels. Biochemical profiles like liver function tests, lipid profiles, and hematological parameters like haemoglobin exhibited differences between <it>Prakriti </it>types. Functional categories of genes showing differential expression among <it>Prakriti </it>types were significantly enriched in core biological processes like transport, regulation of cyclin dependent protein kinase activity, immune response and regulation of blood coagulation. A significant enrichment of housekeeping, disease related and hub genes were observed in these extreme constitution types.</p> <p>Conclusion</p> <p>Ayurveda based method of phenotypic classification of extreme constitutional types allows us to uncover genes that may contribute to system level differences in normal individuals which could lead to differential disease predisposition. This is a first attempt towards unraveling the clinical phenotyping principle of a traditional system of medicine in terms of modern biology. An integration of Ayurveda with genomics holds potential and promise for future predictive medicine.</p

Suggestion mining from text

With the ever growing availability of opinions on the web, opinion mining has become a popular area of research in the fields of natural language processing and applied machine learning. We argue that opinion summaries based solely on the sentiment polarity of text towards an entity of interest, excludes explicit recognition and extraction of information appearing in the form of recommendations, tips, and advice. Suggestions and advice are often sought by different stakeholders through surveys and suggestion forms, where readers are explicitly asked to provide suggestions. On the other hand, customers tend to spontaneously mention suggestions for new features in the product reviews or tweets about the product, and similarly recommendations for nearby places to eat are often spotted in the hotel reviews. Yet sentiment analysis remains the most popular opinion mining task performed on these data sources. In this thesis, we investigate the automatic extraction of suggestions from text, which is referred to as Suggestion Mining. Suggestion Mining is framed as a sentence classification task, where sentences in a given text are to be automatically labeled as suggestions and non-suggestions. Given the very limited amount of related work, suggestion mining can be considered as a young research problem. Therefore, research questions investigated in this dissertation address some of the core aspects of suggestion mining. This includes, task definition where the scope of suggestion and non-suggestion classes is formally defined, benchmark datasets are developed, manually identified features for supervised learning methods, as well as representation learning are evaluated, and distant supervision approaches under the lack of domain specific training datasets are introduced. While covering these aspects, this thesis primarily revolves around two computational tasks, sentence classification and representation learning. The thesis adopts some of the popular deep learning concepts and methods for suggestion mining, like Word Embeddings and Long Short Term Memory Networks. This also opens up a young sentence classification task, and corresponding benchmark datasets to the deep learning community. The contributions of this thesis are manifold. A formal task definition for suggestion mining is provided, which accompanies qualitative and quantitative analysis of suggestions and a formal definition of suggestions in the context of suggestion mining. Benchmark datasets from multiple domains are developed and released, accompanied by a robust data annotation methodology which balances the cost and quality of manual annotations. An in-depth evaluation of the method of using manually selected features with Support Vector Machine classifiers is performed in domain specific, domain independent, and cross domain training scenarios. It is demonstrated that a combination of features from the related work and our newly proposed features outperform the models which use either of them. It is also discovered that the syntactic features consistently remain the top performing features in all the experiments. A major contribution of the thesis is creation of a large silver standard dataset composed of sentences from Wikihow and Wikipedia, and validation of a method to use this dataset for automatically learning features, i.e., representation learning. Experiments comparing manually selected features with automatic feature learning, i.e. word embeddings prove that the embeddings which represent part of speech tags outperform the state of the art pre-trained word embeddings for this task. This thesis performs an end to end exploration of suggestion mining, with all evaluations performed for domain specific, open domain and cross domain classification scenarios