Understanding K-Pop Twitter as a site of Transnational Social Media Activism

This project aims to examine social media as a platform for political organization and social change beyond geographical boundaries in the context of K-pop fans and their transnational online communities. Social media, and twitter specifically, have long been a site of activism and popular music has always had a place in social commentary. In this paper, I seek to understand this phenomenon in the context of the large and ever-growing global community of Korean pop fans. In 2020, largely through the Black Lives Matter movement, we have seen, to the shock of many, a rise in political engagement from the K-pop fan community in a number of highly sensationalized events. Using data collected from twitter I will study the expressive sentiments and strategic organization of this population as well as how said action is perceived and engaged with by the media and general public. Additionally, I will study the transnational networks that facilitate this communal activism and the cross-cultural communication required for this level of organizational success and notoriety. My findings expose how, similar to other twitter-based New Social Movements, K-pop community action consists largely of expressive content with consistent efforts by ingroup members to define and monitor the scope of the movement and the rules of engagement. I note the importance of individual accounts with large spheres of influence in creating important community structures for content dissemination. I observe that, in these fan communities, actual transnational mobilization requires very little explicit instruction as these networks were built on shared affinity and thus have built in expectations of mutual aid. In the context of all of my findings, I reaffirm the importance of studying critically social media based community action and the positive as well as negative processes it can represent

Methodologies used in cost-effectiveness models for evaluating treatments in major depressive disorder: a systematic review

<p>Abstract</p> <p>Background</p> <p>Decision makers in many jurisdictions use cost-effectiveness estimates as an aid for selecting interventions with an appropriate balance between health benefits and costs. This systematic literature review aims to provide an overview of published cost-effectiveness models in major depressive disorder (MDD) with a focus on the methods employed. Key components of the identified models are discussed and any challenges in developing models are highlighted.</p> <p>Methods</p> <p>A systematic literature search was performed to identify all primary model-based economic evaluations of MDD interventions indexed in MEDLINE, the Cochrane Library, EMBASE, EconLit, and PsycINFO between January 2000 and May 2010.</p> <p>Results</p> <p>A total of 37 studies were included in the review. These studies predominantly evaluated antidepressant medications. The analyses were performed across a broad set of countries. The majority of models were decision-trees; eight were Markov models. Most models had a time horizon of less than 1 year. The majority of analyses took a payer perspective. Clinical input data were obtained from pooled placebo-controlled comparative trials, single head-to-head trials, or meta-analyses. The majority of studies (24 of 37) used treatment success or symptom-free days as main outcomes, 14 studies incorporated health state utilities, and 2 used disability-adjusted life-years. A few models (14 of 37) incorporated probabilities and costs associated with suicide and/or suicide attempts. Two models examined the cost-effectiveness of second-line treatment in patients who had failed to respond to initial therapy. Resource use data used in the models were obtained mostly from expert opinion. All studies, with the exception of one, explored parameter uncertainty.</p> <p>Conclusions</p> <p>The review identified several model input data gaps, including utility values in partial responders, efficacy of second-line treatments, and resource utilisation estimates obtained from relevant, high-quality studies. It highlighted the differences in outcome measures among the trials of MDD interventions, which can lead to difficulty in performing indirect comparisons, and the inconsistencies in definitions of health states used in the clinical trials and those used in utility studies. Clinical outcomes contributed to the uncertainty in cost-effectiveness estimates to a greater degree than costs or utility weights.</p

Adapting cognitive diagnosis computerized adaptive testing item selection rules to traditional item response theory

Currently, there are two predominant approaches in adaptive testing. One, referred to as cognitive diagnosis computerized adaptive testing (CD-CAT), is based on cognitive diagnosis models, and the other, the traditional CAT, is based on item response theory. The present study evaluates the performance of two item selection rules (ISRs) originally developed in the CD-CAT framework, the double Kullback-Leibler information (DKL) and the generalized deterministic inputs, noisy and gate model discrimination index (GDI), in the context of traditional CAT. The accuracy and test security associated with these two ISRs are compared to those of the point Fisher information and weighted KL using a simulation study. The impact of the trait level estimation method is also investigated. The results show that the new ISRs, particularly DKL, could be used to improve the accuracy of CAT. Better accuracy for DKL is achieved at the expense of higher item overlap rate. Differences among the item selection rules become smaller as the test gets longer. The two CD-CAT ISRs select different types of items: items with the highest possible a parameter with DKL, and items with the lowest possible c parameter with GDI. Regarding the trait level estimator, expected a posteriori method is generally better in the first stages of the CAT, and converges with the maximum likelihood method when a medium to large number of items are involved. The use of DKL can be recommended in low-stakes settings where test security is less of a concern

Mobile-based Skin Lesions Classification Using Convolution Neural Network

This research work is aimed at investing skin lesions classification problem using Convolution Neural Network (CNN) using cloud-server architecture. Using the cloud services and CNN, a real-time mobile-enabled skin lesions classification expert system “i-Rash” is proposed and developed. i-Rash aimed at early diagnosis of acne, eczema and psoriasis at remote locations. The classification model used in the “i-Rash” is developed using the CNN model “SqueezeNet”. The transfer learning approach is used for training the classification model and model is trained and tested on 1856 images. The benefit of using SqueezeNet results in a limited size of the trained model i.e. only 3 MB. For classifying new image, cloud-based architecture is used, and the trained model is deployed on a server. A new image is classified in fractions of seconds with overall accuracy, sensitivity and specificity of 97.21%, 94.42% and 98.14% respectively. i-Rash can serve in initial classification of skin lesions, hence, can play a very important role early classification of skin lesions for people living in remote areas

Predictors of anemia in preschool children: Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project.

Background: A lack of information on the etiology of anemia has hampered the design and monitoring of anemia-control efforts.Objective: We aimed to evaluate predictors of anemia in preschool children (PSC) (age range: 6-59 mo) by country and infection-burden category.Design: Cross-sectional data from 16 surveys (n = 29,293) from the Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project were analyzed separately and pooled by category of infection burden. We assessed relations between anemia (hemoglobin concentration &lt;110 g/L) and severe anemia (hemoglobin concentration &lt;70 g/L) and individual-level (age, anthropometric measures, micronutrient deficiencies, malaria, and inflammation) and household-level predictors; we also examined the proportion of anemia with concomitant iron deficiency (defined as an inflammation-adjusted ferritin concentration &lt;12 μg/L). Countries were grouped into 4 categories on the basis of risk and burden of infectious disease, and a pooled multivariable logistic regression analysis was conducted for each group.Results: Iron deficiency, malaria, breastfeeding, stunting, underweight, inflammation, low socioeconomic status, and poor sanitation were each associated with anemia in &gt;50% of surveys. Associations between breastfeeding and anemia were attenuated by controlling for child age, which was negatively associated with anemia. The most consistent predictors of severe anemia were malaria, poor sanitation, and underweight. In multivariable pooled models, child age, iron deficiency, and stunting independently predicted anemia and severe anemia. Inflammation was generally associated with anemia in the high- and very high-infection groups but not in the low- and medium-infection groups. In PSC with anemia, 50%, 30%, 55%, and 58% of children had concomitant iron deficiency in low-, medium-, high-, and very high-infection categories, respectively.Conclusions: Although causal inference is limited by cross-sectional survey data, results suggest anemia-control programs should address both iron deficiency and infections. The relative importance of factors that are associated with anemia varies by setting, and thus, country-specific data are needed to guide programs

Cross-Cultural Measurement Invariance in the Personality Inventory for DSM-5

The validity of cross-cultural comparisons of test scores requires that scores have the same meaning across cultures, which is usually tested by checking the invariance of the measurement model across groups. In the last decade, a large number of studies were conducted to verify the equivalence across cultures of the dimensional Alternative Model of Personality Disorders (DSM-5 Section III). These studies have provided information on configural invariance (i.e., the facets that compose the domains are the same) and metric invariance (i.e., facet-domain relationships are equal across groups), but not on the stricter scalar invariance (i.e., the baseline levels of the facets are the same), which is a prerequisite for meaningfully comparing group means. The present study aims to address this gap. The Personality Inventory for DSM-5 (PID-5) was administered to five samples differing on country and language (Belgium, Catalonia, France, Spain, and Switzerland), with a total of 4,380 participants. Configural and metric invariance were supported, denoting that the model structure was stable across samples. Partial scalar invariance was supported, being minimal the influence of non-invariant facets. This allowed cross-cultural mean comparisons. Results are discussed in light of the sample composition and a possible impact of culture on development of psychopathologyPreparation of this manuscript was supported by Grant PSI2017–85022-P (Ministerio de Ciencia, Innovacion ´ y Universidades, Spain) and the UAM-IIC Chair "Psychometric Models and Applications

Adjusting ferritin concentrations for inflammation: Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project

Background: The accurate estimation of iron deficiency is important in planning and implementing interventions. Ferritin is recommended as the primary measure of iron status, but interpretability is challenging in settings with infection and inflammation

Persistent anthrax as a major driver of wildlife mortality in a tropical rainforest

Anthrax is a globally important animal disease and zoonosis. Despite this, our current knowledge of anthrax ecology is largely limited to arid ecosystems, where outbreaks are most commonly reported. Here we show that the dynamics of an anthrax-causing agent, Bacillus cereus biovar anthracis, in a tropical rainforest have severe consequences for local wildlife communities. Using data and samples collected over three decades, we show that rainforest anthrax is a persistent and widespread cause of death for a broad range of mammalian hosts. We predict that this pathogen will accelerate the decline and possibly result in the extirpation of local chimpanzee (Pan troglodytes verus) populations. We present the epidemiology of a cryptic pathogen and show that its presence has important implications for conservation