Am I hurt?: Evaluating Psychological Pain Detection in Hindi Text using Transformer-based Models

The automated evaluation of pain is critical for developing effective pain management approaches that seek to alleviate while preserving patients’ functioning. Transformer-based models can aid in detecting pain from Hindi text data gathered from social media by leveraging their ability to capture complex language patterns and contextual information. By understanding the nuances and context of Hindi text, transformer models can effectively identify linguistic cues, sentiment and expressions associated with pain enabling the detection and analysis of pain-related content present in social media posts. The purpose of this research is to analyse the feasibility of utilizing NLP techniques to automatically identify pain within Hindi textual data, providing a valuable tool for pain assessment in Hindi-speaking populations. The research showcases the HindiPainNet model, a deep neural network that employs the IndicBERT model, classifying the dataset into two class labels {pain, no_pain} for detecting pain in Hindi textual data. The model is trained and tested using a novel dataset, दर्द-ए-शायरी (pronounced as Dard-e-Shayari) curated using posts from social media platforms. The results demonstrate the model’s effectiveness, achieving an accuracy of 70.5%. This pioneer research highlights the potential of utilizing textual data from diverse sources to identify and understand pain experiences based on psychosocial factors. This research could pave the path for the development of automated pain assessment tools that help medical professionals comprehend and treat pain in Hindi speaking populations. Additionally, it opens avenues to conduct further NLP-based multilingual pain detection research, addressing the needs of diverse language communities

Framework for Personalized Chronic Pain Management: Harnessing AI and Personality Insights for Effective Care

This paper introduces a cutting-edge framework for personalized chronic pain management, leveraging the power of artificial intelligence (AI) and personality insights. It explores the intricate relationship between personality traits and pain perception, expression, and management, identifying key correlations that influence an individual's experience of pain. By integrating personality psychology with AI-driven personality assessment, this framework offers a novel approach to tailoring chronic pain management strategies for each patient's unique personality profile. It highlights the relevance of well-established personality theories such as the Big Five and the Myers-Briggs Type Indicator (MBTI) in shaping personalized pain management plans. Additionally, the paper introduces multimodal AI-driven personality assessment, emphasizing the ethical considerations and data collection processes necessary for its implementation. Through illustrative case studies, the paper exemplifies how this framework can lead to more effective and patient-centered pain relief, ultimately enhancing overall well-being. In conclusion, the paper positions the need of an "AI-Powered Holistic Pain Management Initiative" which has the potential to transform chronic pain management by providing personalized, data-driven solutions and create a multifaceted research impact influencing clinical practice, patient outcomes, healthcare policy, and the broader scientific community's understanding of personalized medicine and AI-driven interventio

Case Studies on the Exploitation of Crowd-Sourcing with Web 2.0 Functionalities

Crowd-sourcing appears more promising with Web 2.0 functionality and businesses have started using it for a wide range of activities, that would be better completed by a crowd rather than any specific pool of knowledge workers. However, relatively little is known about how a business can leverage on collective intelligence and capture the user- generated value for competitive advantage. This empirical study uses the principle of interpretive field research to validate the case findings with a descriptive multiple case study methodology. An extended theoretical framework to identify the important considerations at strategic and functional levels for the effective use of crowd-sourcing is proposed. The analytic framework uses five Business Strategy Components: Vision and Strategy, Human Capital, Infrastructure, Linkage and Trust, and External Environment. It also uses four Web 2.0 Functional Components: Social Networking, Interaction Orientation, Customization & Personalization, and User- added Value. By using these components as analytic lenses, the case research examines how successful e-commerce firms may deploy Web 2.0 functionalities for effective use of crowd-sourcing. Prioritization of these functional considerations might be favorable in some cases for the best fit of situations and limitations. In conclusion, it is important that the alignment between strategy and functional components is maintained

Effective Asset Allocation Using Fuzzy Logic

ABSTRACT With the rising inflation it becomes important to get maximize

Verrucous Hyperplasia : Case report and differential diagnosis

Verrucous hyperplasia (VH) is a rare exophytic oral mucosal lesion which can transform into verrucous carcinoma (VC), its malignant but clinically similar counterpart. These entities can be distinguished by the lack of invasive growth in VH cases; as such, it is essential to include a margin with adequate depth whenperforming a biopsy of the epithelium of the lesion. We report an 80-year-old male patient who presented to the Bapuji Dental College & Hospital, Davangere, Karanataka, India, in 2011 with a warty whitish-pink growth on the inside of his cheek. The patient was treated with wide surgical excision of the lesion and a diagnosis of VH was made based on histopathological features. There was no evidence of recurrence at a five-year follow-up. This report highlights the histological variations, pathogenesis and differential diagnosis of VH

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Thirty Years Later: A Community Memoir of the 1984 Sikh Massacres

Thirty Years Later is a work of creative nonfiction based on interviews with members of the Portland-area Sikh community regarding their experiences during the 1984 Sikh massacres in India. The narrative, which is set in scene thirty years ago, weaves together 17 testimonies to paint an intimate picture of a people while endeavoring to tell an alternative, subjective history to counter official accounts

Investigating Chemical Transformations of Multicomponent Levitated Particles Using a Linear Quadrupole Electrodynamic Balance Coupled With Mass Spectrometry

Aerosol particles are ubiquitous in the Earth’s atmosphere and play an important role in climate, air quality and health. The chemical composition of aerosol particles is complex and primarily associated with their source of origin. Once in the atmosphere, the composition is continuously evolving as a result of photochemical and heterogeneous chemical reactions. Consequently, composition-dependent properties that define the physical state and optical properties are also continuously changing, with important consequences for the dynamics and impacts of aerosols in the atmosphere. Laboratory-based methods have allowed an extensive exploration of the microphysical properties and chemical transformations of atmospheric aerosols. Single particle levitation methods are used to measure the microphysical properties of particles and connect with their evolving chemical composition. Properties such as size, hygroscopicity, phase morphology, and composition can be precisely measured by coupling levitation methods with analytical tools, such as mass spectrometry.This dissertation describes the development of a new experimental platform connecting a linear quadrupole electrodynamic balance (LQ-EDB) with mass spectrometry (MS) that is capable of interrogating single levitated particles undergoing atmospherically relevant transformations. Two sampling methods, paper spray (PS) and an open port sampling interface (OPSI), were developed to transfer and ionize chemical analytes from levitated particles to the MS for compositional analysis. Laboratory studies were carried out to understand both non-reactive transformations and heterogeneous oxidation of levitated particles by precisely measuring their physical and optical properties as a function of evolving chemical composition. Non-reactive transformations include the particle-to-gas partitioning of semi-volatile species, while heterogeneous oxidation includes ozonolysis measurements on oleic and elaidic acid particles to understand the influence of particle phases on transformation kinetics and product distribution.A key objective of this dissertation is to gain molecular-level understanding of how aerosol particles evolve due to chemical transformations and provide size-resolved detailed knowledge of their characteristics. It is essential to link the microphysical properties, composition, and reaction timescales for particles to quantify these evolving properties. Ultimately, the developments and measurements reported in this dissertation provide a foundation for exploring the chemistry of aerosol particles of complex composition and morphology to better understand their role in the environment