Protein Microarrays and Biomarkers of Infectious Disease

Protein microarrays are powerful tools that are widely used in systems biology research. For infectious diseases, proteome microarrays assembled from proteins of pathogens will play an increasingly important role in discovery of diagnostic markers, vaccines, and therapeutics. Distinct formats of protein microarrays have been developed for different applications, including abundance-based and function-based methods. Depending on the application, design issues should be considered, such as the need for multiplexing and label or label free detection methods. New developments, challenges, and future demands in infectious disease research will impact the application of protein microarrays for discovery and validation of biomarkers

Contemporary migration patterns in the prevalence of Helicobacter pylori infection: a systematic review

Background: A rapid growth in the number of international migrants over the past years has occurred with most traveling to more affluent settings. As Helicobacter pylori infects over half of the adult population and its prevalence is higher in developing countries, understanding the prevalence of infection in migrants can provide insight into future trends in the burden and management of infection. We aimed to describe the prevalence of H. pylori among migrants through a systematic literature review. Methods: We searched PubMed® from inception to September 2015 to identify studies reporting the prevalence of H. pylori in international migrants according to country of birth for first‐generation, and country of birth and parents' nationality for successive generations. Comparable data from origin and destination populations were obtained from the same studies or, when not present, from a previous systematic review on H. pylori worldwide. Results: A total of 28 eligible studies were identified with data for 29 origin and 12 destination countries. Two studies that evaluated refugees presented prevalences of infection higher than both the origin and destination countries. Otherwise, the prevalences among migrants were generally similar or below that of the origin and higher than the destination. Second‐ or more generation had lower prevalences compared to first‐generation migrants. Conclusions: Our study findings are consistent with what would be expected based on the prevalence of H. pylori worldwide. The results of this review show that migrants are particularly at risk of infection and help to identify gaps in the knowledge of migrants' prevalence of infection globally.This work was supported by “Fundo Europeu de Desenvolvimento Regional” (FEDER) funds through the “Programa Operacional Factores de Competitividade” (POFC) – COMPETE (FCOMP‐01‐0124‐FEDER‐021181) and by national funds through the “Fundação para a Ciência e a Tecnologia” (PTDC/SAU‐EPI/122460/2010) and the Epidemiology Research Unit – Institute of Public Health, University of Porto supported by the “Fundação para a Ciência e a Tecnologia” (UID/DTP/047507/2013). Individual grants attributed to SM (SFRH/BD/102585/2014), ARC (SFRH/BD/102181/2014), AF (PD/BD/105823/2014), and BP (SFRH/BPD/75918/2011 and SFRH/BPD/108751/2015) were supported by “Fundação para a Ciência e a Tecnologia.” The funders had no role in study design, data collection, decision to publish, or preparation of the manuscript

Electoral rules and elite recruitment: A comparative analysis of the Bundestag and the U.S. House of Representatives

In this research, I analyze the effects of candidate nomination rules and campaign financing rules on elite recruitment into the national legislatures of Germany and the United States. This dissertation is both theory-driven and constitutes exploratory research, too. While the effects of electoral rules are frequently studied in political science, the emphasis is thereby on electoral rules that are set post-election. My focus, in contrast, is on electoral rules that have an effect prior to the election. Furthermore, my dissertation is comparative by design. The research question is twofold. Do electoral rules have an effect on elite recruitment, and does it matter? To answer these question, I create a large-N original data set, in which I code the behavior and recruitment paths and patterns of members of the American House of Representatives and the German Bundestag. Furthermore, I include interviews with members of the said two national legislatures. Both the statistical analyses and the interviews provide affirmative evidence for my working hypothesis that differences in electoral rules lead to a different type of elite recruitment. To that end, I use the active-politician concept, through which I dichotomously distinguish the economic behavior of politicians. Thanks to the exploratory nature of my research, I also discover the phenomenon of differential valence of local and state political office for entrance into national office in comparative perspective. By statistically identifying this hitherto unknown paradox, as well as evidencing the effects of electoral rules, I show that besides ideology and culture, institutional rules are key in shaping the ruling elite. The way institutional rules are set up, in particular electoral rules, does not only affect how the electorate will vote and how seats will be distributed, but it will also affect what type of people will end up in elected office

Recognizing Touch Gestures for Social Human-Robot Interaction

In this study, we performed touch gesture recognition on two sets of data provided by "Recognition of Social Touch Gestures Challenge 2015". For the first dataset, dubbed Corpus of Social Touch (CoST), touch is performed on a mannequin arm, whereas for the second dataset (Human-Animal Affective Robot TouchλHAART) touch is performed in a human-pet interaction setting. CoST includes 14 gestures and HAART includes 7 gestures. We used the pressure data, image features, Hurst exponent, Hjorth parameters and autoregressive model coefficients as features, and performed feature selection using sequential forward floating search. We obtained classification results around 60%-70% for the HAART dataset. For the CoST dataset, the results range from 26% to 95% depending on the selection of the training/test sets