“…Take up thy Bed, and Vote” Measuring the Relationship between Voting Behaviour and Indicators of Health

Individuals experiencing poor health are less likely to vote at election time, despite being the ones most affected by health policies implemented by the successful party. This paper investigates the relationship between health and voter turnout and political party choice in the 1979, 1987 and 1997 British general elections using the National Child Development Study (NCDS). It finds that poor health is associated with lower turnout, as the perceived costs of voting, such as the physical and mental effort involved, are greater than the perceived benefits, which are derived from the policy implications of the election outcome. In addition, the subset of unhealthy individuals who do vote at election time generally support Labour, as such voters are more likely to utilise the NHS and hence support parties that advocate public provision of health services. Given the low participation rates of the unhealthy, a political party which formulates an attractive policy package aimed at such potential voters could therefore mobilise a previously untapped source of the electorate.Health Status, Voter Turnout, Political Party Choice

Political Interest, Cognitive Ability and Personality - Determinants of Voter Turnout in Britain

This paper uses longitudinal data from the National Cohort Development Study (NCDS) to investigate the determinants of voter turnout in the 1997 British General Election. It introduces measures of cognitive ability and personality into models of electoral participation and finds that firstly, their inclusion reduces the impact of education and secondly, that standard turnout models may be biased by the inclusion of the much used “interest in politics” measure. A bivariate probit model of turnout and interest then shows that individuals with high ability, an aggressive personality and a sense of civic duty are more likely to both turn out to vote and to have an interest in politics.Turnout, Education, Ability, Personality

Returns to basic skills in Central and Eastern Europe - a semi-parametric approach

This paper uses semi-parametric econometric techniques to investigate the relationship between basic skills and earning in three post-communist countries - the Czech Republic, Hungary and Slovenia using the IALS dataset. While the large increases in the returns to education in the new market economies has been well documented in the literature, no study to date has examined the impact of basic skills and schooling on income. Estimating a Mincer human capital model we find that including a measure of basic skills reduces the returns to education. In addition, using a partial linear model in which log earnings is linear in schooling, but is an arbitrary function of basic skills, we find that this relationship is not well described by the common assumption of linearity at the tails of the distribution.Earnings, Education, Basic skills, Transition

Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification

Graph-based neural network models are gaining traction in the field of representation learning due to their ability to uncover latent topological relationships between entities that are otherwise challenging to identify. These models have been employed across a diverse range of domains, encompassing drug discovery, protein interactions, semantic segmentation, and fluid dynamics research. In this study, we investigate the potential of Graph Neural Networks (GNNs) for medical image classification. We introduce a novel model that combines GNNs and edge convolution, leveraging the interconnectedness of RGB channel feature values to strongly represent connections between crucial graph nodes. Our proposed model not only performs on par with state-of-the-art Deep Neural Networks (DNNs) but does so with 1000 times fewer parameters, resulting in reduced training time and data requirements. We compare our Graph Convolutional Neural Network (GCNN) to pre-trained DNNs for classifying MedMNIST dataset classes, revealing promising prospects for GNNs in medical image analysis. Our results also encourage further exploration of advanced graph-based models such as Graph Attention Networks (GAT) and Graph Auto-Encoders in the medical imaging domain. The proposed model yields more reliable, interpretable, and accurate outcomes for tasks like semantic segmentation and image classification compared to simpler GCNN

Connecting the Dots: Graph Neural Network Powered Ensemble and Classification of Medical Images

Deep learning models have demonstrated remarkable results for various computer vision tasks, including the realm of medical imaging. However, their application in the medical domain is limited due to the requirement for large amounts of training data, which can be both challenging and expensive to obtain. To mitigate this, pre-trained models have been fine-tuned on domain-specific data, but such an approach can suffer from inductive biases. Furthermore, deep learning models struggle to learn the relationship between spatially distant features and their importance, as convolution operations treat all pixels equally. Pioneering a novel solution to this challenge, we employ the Image Foresting Transform to optimally segment images into superpixels. These superpixels are subsequently transformed into graph-structured data, enabling the proficient extraction of features and modeling of relationships using Graph Neural Networks (GNNs). Our method harnesses an ensemble of three distinct GNN architectures to boost its robustness. In our evaluations targeting pneumonia classification, our methodology surpassed prevailing Deep Neural Networks (DNNs) in performance, all while drastically cutting down on the parameter count. This not only trims down the expenses tied to data but also accelerates training and minimizes bias. Consequently, our proposition offers a sturdy, economically viable, and scalable strategy for medical image classification, significantly diminishing dependency on extensive training data sets.Comment: Our code is available at https://github.com/aryan-at-ul/AICS_2023_submissio

Inhibition of HSP90 distinctively modulates the global phosphoproteome of Leishmania mexicana developmental stages

Heat shock protein 90 (HSP90) is an evolutionarily conserved chaperone protein that plays a central role in the folding and maturation of a large array of client proteins. In the unicellular parasite Leishmania, the etiological agent of the neglected tropical disease leishmaniasis, treatment with HSP90 inhibitors leads to differentiation from promastigote to amastigote stage, resembling the effects of established environmental triggers, low pH and heat shock. This indicates a crucial role for HSP90 in the life cycle control of Leishmania. However, the underlying molecular mechanisms remain unknown. Using a combination of treatment with the classical HSP90 inhibitor tanespimycin, phosphoproteome enrichment, and tandem mass tag (TMT) labeling-based quantitative proteomic mass spectrometry (MS), we systematically characterized the perturbing effect of HSP90 inhibition on the global phosphoproteome of Leishmania mexicana across its life cycle stages and showed that the HSP90 inhibition causes substantially distinct molecular effects in promastigote and amastigote forms.While phosphorylation of HSP90 and its co-chaperone HSP70 was decreased in amastigote, the opposite effect was observed in promastigotes. Our results showed that kinase activity and microtubule motor activity are highly represented in the negatively affected phosphoproteins of the promastigotes, whereas ribosomal proteins, protein folding, and proton channel activity are preferentially enriched in the perturbed amastigote phosphoproteome. Additionally, cross-comparison of our results with HSP90 inhibition-affected RNA-binding proteins showed that RNA helicase domains were distinctively enriched among the upregulated amastigote phosphoproteins. In addition to providing robust identification and quantification of 1,833 phosphorylated proteins across three life cycle stages of L. mexicana, this study reveals the dramatically different ways the HSP90 inhibition stress modulates the phosphoproteome of the pathogenic amastigote and provides in-depth insight into the scope of selective molecular targeting in the therapeutically relevant amastigote stage