55 research outputs found
The corruption of a republic
No full textEminent Indian psychoanalyst and social commentator Dr Ashis Nandy found himself in the middle of a controversy recently after he made a few remarks on corruption at a session entitled ‘The Republic of Ideas’ at the Jaipur literary festival, 24– 28 January 2013. Author and publisher of Tehelka magazine Tarun Tejpal spoke of corruption as an equalising force, to which Dr Nandy said:
Just a response to this part, very briefly. He’s not saying the most important part of the story, which will shock you and it will be a very undignified and, how should I put it, almost vulgar statement on my part. It is a fact that most of the corrupt come from the OBCs (Other Backward Classes) and the Scheduled Castes and now increasingly Scheduled Tribes and as long as this is the case, the Indian republic will survive.
A journalist present at the panel took up this statement, which was later endlessly replayed on a 24-hour television news channel. Dalit organisations and activists protested against Dr Nandy. Not surprisingly, considering the upcoming elections in some key states, some politicians jumped into the fray and called for Dr Nandy’s arrest. In India anti-Dalit speech is punishable under the Scheduled Caste and Scheduled Tribes (Prevention of Atrocities) Act 1989, and is a non-bailable offence. There were demonstrations and police complaints were filed against him in three different locations. Fearing physical harm and the possibility of imprisonment, Dr Nandy and his family went to the Supreme Court. The Supreme Court of India did grant a stay order on the arrest warrants against him, but at the same time the Chief Justice of India told Dr Nandy’s lawyer ‘Whatever your intent, you can’t go on making statements. Tell your client he has no license to make such comments.’
The Indian social media and blogsphere exploded, with various arguments emerging on behalf of and against Dr Nandy. The most common complaint against Dr Nandy is that he was casteist, and that he had stereotyped Dalits. Such complaints came even from those defending him. A passionate critique by Anoop Kumar outlined Dalit oppression in India and accused specific media personalities of defending Dr Nandy instead of interrogating ‘upper caste anxieties’. There are blogs that, while disagreeing with Dr Nandy, argue for his right to express his opinion and to ‘be wrong’. There are those who argue that his remarks were made in humour, and lament the dearth of an understanding of wit, satire or irony.6 While the case seems to be closed after the Supreme Court judgment, there is still debate about whether this was a victory for freedom of speech or another instance of the way in which the upper castes in India can get away with any derogatory statement against the lower castes.
The freedom of speech argument is unsatisfying. The difference between ‘provocative speech that forces you to think’ and ‘provocative speech that is intended to hurt, denigrate or provoke’ is very context dependent. The intention of any speaker is not only difficult to prove, but also difficult to know. I would like to base my defence of Dr Nandy neither on his right to say what was on his mind, nor on his intention. Instead, I would suggest that his remarks should be understood through a discussion of corruption, and the way in which Dr Nandy uses the term.
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Shvetal Vyas is a PhD student in the International Centre for Muslim and non-Muslim Understanding, University of South Australia
Supplementary materials to: Using pointwise mutual information for breast cancer health disparities research with SEER-Medicare claims
No full textSupplementary materials to: Egleston, B. L., Chanda, A. K., Bai, T., Fang, C. Y., Bleicher, R. J., & Vucetic, S. (2023). Using pointwise mutual information for breast cancer health disparities research with SEER-Medicare claims. Methodology, 19(1). https://doi.org/10.5964/meth.8535The supplementary materials provide the Supplementary Tables and Figures
Myxobolidae Thelohan 1892
No full textKey to tailed genera of Myxobolidae <p>1a. Spores ovoid or pyriform, with two polar capsules and a single tail-like caudal process................................. 2</p> <p>1b. Spores with two or more tail-like caudal processes or bifurcated tail; composed of same materials as shell valve......3</p> <p> 2a. Single tail-like caudal process; caudal process symmetrically located at the posterior end........................................................................................ <i>Unicauda</i> Davis, 1944</p> <p> 2b. Single tail-like caudal process, which may be forked at the very end, originating posterolaterally and asymmetrically.................. <i>Laterocaudata</i> Chen et Hsieh, 1984</p> <p> 3a. Spores tear-shaped shaped, with a single polar capsule, caudal process bifurcated..... <i>Phlogospora</i> Qadri, 1962</p> <p>3b. Spores with two polar capsules and 2 caudal processes... 4</p> <p>3c. Spores with two polar capsules and 4 caudal processes... 5</p> <p> 4a. Spores with two caudal processes.......................................................................... <i>Henneguya</i> Thélohan, 1892</p> <p> 4b. Spores with two caudal processes, asymmetric...................... <i>Hennegoides</i> Lom, Tonguthai et Dyková, 1991</p> <p> 4c. Spores with two caudal processes, extending in opposite directions............... <i>Dicauda</i> Hoffman et Walker, 1978</p> <p> 5a. Spores ellipsoidal with two caudal processes at each end of the spore; polar capsules situated asymmetrically............................................. <i>Neohenneguya</i> Tripathi, 1953</p> <p> 5b. Spores with four posterolateral caudal processes, two from each shell valve........................................................................... <i>Tetrauronema</i> Wu, Wang et Jiang, 1988</p> <p> 5c. Spores rhomboidal with four caudal processes pointing in the opposite direction, connected by transverse filaments............................ <i>Trigonosporus</i> Hoshina, 1952</p>Published as part of <i>Barman, Gyan Deb, Chanda, Sukanya, Panigrahi, Ashis Kumar & Eiras, J. C., 2022, Synopsis of tailed Myxobolidae (Cnidaria, Myxozoa, Myxosporea) infecting Indian fishes, pp. 1-5 in Folia Parasitologica (030) (030) 69</i> on pages 1-3, DOI: 10.14411/fp.2022.030, <a href="http://zenodo.org/record/8143700">http://zenodo.org/record/8143700</a>
Comparative analysis of contextual and context-free embeddings in disaster prediction from Twitter data
No full textTwitter is a social media site where people post their personal experiences, opinions, and news. Due to the ubiquitous real-time data availability, many rescue agencies monitor this data regularly to identify disasters, reduce risk, and save lives. However, it is impossible for humans to manually check the mass amount of data and identify disasters in real-time. For this purpose, many research have been proposed to present words in machine-understandable representations and apply machine learning methods on the word representations to identify the sentiment of a text. The previous research methods provide a single vector representation or embedding of a word from a given document. However, the recent advanced contextual embedding method (BERT — Bidirectional Encoder Representations from Transformers) constructs different vectors for the same word in different contexts. The BERT embeddings have been used successfully in various Natural Language Processing (NLP) tasks, yet there is no concrete analysis of how these representations are helpful in disaster-type tweet analysis. This research study explores the efficacy of the BERT embeddings on predicting disaster from Twitter data and compares these to traditional context-free word embedding methods. We provide both quantitative and qualitative results for this study. The results show that the contextual embeddings have the best results in disaster prediction task than the traditional word embeddings. Furthermore, we discuss the opportunities and challenges of contextual embeddings on sentiment analysis of Twitter data.Temple University. College of Science and TechnologyComputer and Information Science
A Critical Analysis of the Post-structuralist Thought with Reference to ‘The Death of the Author’ by Roland Barthes
No full textRoland Barthes in his famous essay “The Death of the Author” from a post-structuralist position took a stand against the notion of authority in a text. He while referring to the myth of Sarrasine in Balzac asks certain essential question regarding the position of authorship. For him the author only is a participant in the existing discourse of the time—a mere explorer of the existing symbols and pre-existing linguistic and literary systems. One the other hand he only narrates the events through the existing codes but never participates in it. It is here where Barthes connotes that the author might be praised for his mastery over the existing codes but not for his genius. Likewise, Barthes explores various concepts of post-enlightenment to give his concept of the death of the author not in a literary sense where the work is found importance rather than the author who is the product of the industrial strategy and his position changes over time according to the changes in society
Quantitative proteomics to reveal the composition of Southern India spectacled cobra (Naja naja) venom and its immunological cross-reactivity towards commercial antivenom
No full textIndian cobra (Naja naja) envenomation is frequently reported across Indian subcontinent. Geographical differences in the venom composition of a particular species of snake often leads to inconsistencies in the antivenom neutralization. Consequently, determining the venom proteome from every locale is necessary for the production of effective antivenom. In this study, we deciphered the proteome composition of N. naja venom (NnV) from southern India (SI) by label-free quantitative proteomics that identified 45 proteins (toxins) belonging to 14 venom protein families when searched against Elapidae (taxid: 8602) protein entries in the non-redundant NCBI database. Low molecular mass (<15 kDa) toxins such as PLA2 (18.2%) and 3FTx (37.4%) are the most abundant enzymatic and non-enzymatic proteins, respectively, in SI NnV. Nevertheless, the relative abundance of 3FTxs in SI NnV was found to be lower than the relative abundance of these toxins in previously determined eastern and western India NnV samples. Immuno-recognition and in vitro neutralization of some enzymatic activities and pharmacological properties of SI NnV by commercial polyvalent antivenom evidently demonstrated poor recognition of the most abundant low molecular mass toxins of SI NnV. This finding points to the need for new strategies for antivenom production for the successful treatment of cobra bite
Mass spectrometric analysis to unravel the venom proteome composition of Indian snakes: opening new avenues in clinical research
No full textThe 'Big Four' venomous snakes - Daboia russelii, Naja naja, Bungarus caeruleus, and Echis carinatus - are primarily responsible for the majority of snake envenomation in India. Several other lesser-known venomous snake species also inflict severe envenomation in the country
Improving medical term embeddings using UMLS Metathesaurus
No full textBackground: Health providers create Electronic Health Records (EHRs) to describe the conditions and procedures used to treat their patients. Medical notes entered by medical staff in the form of free text are a particularly insightful component of EHRs. There is a great interest in applying machine learning tools on medical notes in numerous medical informatics applications. Learning vector representations, or embeddings, of terms in the notes, is an important pre-processing step in such applications. However, learning good embeddings is challenging because medical notes are rich in specialized terminology, and the number of available EHRs in practical applications is often very small. Methods: In this paper, we propose a novel algorithm to learn embeddings of medical terms from a limited set of medical notes. The algorithm, called definition2vec, exploits external information in the form of medical term definitions. It is an extension of a skip-gram algorithm that incorporates textual definitions of medical terms provided by the Unified Medical Language System (UMLS) Metathesaurus. Results: To evaluate the proposed approach, we used a publicly available Medical Information Mart for Intensive Care (MIMIC-III) EHR data set. We performed quantitative and qualitative experiments to measure the usefulness of the learned embeddings. The experimental results show that definition2vec keeps the semantically similar medical terms together in the embedding vector space even when they are rare or unobserved in the corpus. We also demonstrate that learned vector embeddings are helpful in downstream medical informatics applications. Conclusion: This paper shows that medical term definitions can be helpful when learning embeddings of rare or previously unseen medical terms from a small corpus of specialized documents such as medical notes.Temple University. College of Science and TechnologyComputer and Information Science
Studies of viscous antagonism, excess molar volumes, viscosity deviation and isentropic compressibility of ternary mixtures containing N,N-dimethylformamide, benzene and some ethers at 298.15 K
No full textThe densities (r) and viscosities (h) for ternary liquid mixtures of N,N-dimethylformamide + benzene + an ether were measured as a function of composition at 298.15 K. From experimental measurements, the excess molar volumes (VE), viscosity deviation (Δh), antagonic interaction index (IA) and Gibbs free energy of activation for viscous flow (ΔGE) were evaluated. The speeds of sound were also measured and excess isentropic compressibilities (KsE) were calculated at the experimental temperature. The results are discussed and interpreted in terms of molecular package and specific interaction predominated by hydrogen bonding
NOVEL DATA MINING ALGORITHMS FOR ANALYSIS OF ELECTRONIC HEALTH RECORDS
No full textMedical health providers use electronic health records (EHRs) to store information about patient treatment to support patient care management and securely share health information among healthcare organizations. EHRs have also been used in healthcare research in problems such as patient phenotyping, health risk prediction, and medical entity extraction. In this thesis, we focus on several important issues: (1) how to convert natural text from medical notes to vector representations suitable for deep learning algorithms, (2) how to help healthcare researchers select a patient cohort from EHRs, and (3) how to use EHRs to identify patient diagnoses and treatments.
In the first part of the thesis, we present a new method for learning vector representations of medical terms. Learning vector representations of words is an important pre-processing step in many natural language processing applications. For example, EHRs contain clinical notes that describe patient health conditions and course of treatment in a narrative style. The notes contain specialized medical terminology and many abbreviations. Learning good vector representations of specialized medical terms can improve the quality of downstream data analysis tasks on EHR data. However, the traditional approaches struggle to learn vector representations of rarely used medical terms. To overcome this problem, we developed a neural network-based approach, called definition2vec, that uses external knowledge contained in medical vocabularies. We performed quantitative and qualitative analysis to measure the usefulness of the learned representations. The results demonstrate that definition2vec is superior to the state-of-the-art algorithms.
In the second part of the thesis, we describe a new visual interface that helps healthcare researchers select patient cohorts from EHR data. Process of identifying patients of interest for observational studies from EHR data is known as cohort selection, a challenging research problem. We considered a problem of cohort selection from medical claim data, which requires identifying a set of medical codes for selection. However, there are tens of thousands of unique medical codes, and it becomes very difficult for any human to decide which codes identify patients of interest. To help users in defining a set of codes for cohort identification, we developed an interactive system, called Medical Claim Visualization system (MedCV), which visualizes medical code representations. MedCV analyzes a medical claim database and allows users to reason about medical code relationships and define inclusion rules for the selection by visualizing medical codes, claims, and patient timelines. Evaluation of our system through a user study indicates that MedCV enables domain experts to define inclusion rules efficiently and with high quality.
The third part of the thesis is a study of the definition of acute kidney injury (AKI), which is a condition where kidneys suddenly cannot filter waste from the blood. AKI is a major cause of patient death in intensive care units (ICU) and it is critical to detect it early. Recently published KDIGO medical guideline proposed a clinical definition of AKI using blood serum creatinine and urine output. The KDIGO definition was developed based on the expert knowledge, but very little is known about how well it matches the medical practice. In this study, we investigated publicly available EHR data from 47,499 ICU admissions to determine the concordance between the KDIGO definition and AKI determination by the medical provider. We show that it is possible to find a formula using machine learning with much higher concordance with the medical provider AKI coding than KDIGO and discuss the medical relevance of this finding.Computer and Information Scienc
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