Breast cancer is the most common cancer among women worldwide. Early detection is the most critical element, as the survival rate increases by up to 99%. Although there are conventional screening techniques such as mammography, breast cancer is still the second leading cause of cancer death in women. One of the holy grails in cancer research is to develop a blood test for early detection. Sensitive, non-invasive, and easy-to-use liquid biopsy techniques will revolutionize cancer diagnosis and treatment monitoring. The goal of this project is to develop a complete platform for the isolation and label-free optical characterization of biological nanoparticles called exosomes extracted from blood. Exosomes are secreted from the cells into the body fluids and play an important role in intercellular communication. These particles are emerged as potential biomarkers for liquid biopsy applications, as they carry information about their original cell. However, the translation of exosome-based analyses from research labs to clinical settings is limited due to the lack of efficient isolation and quantification tools available. We aim to develop a novel exosome characterization platform, named multimodal Interferometric-Raman imaging platform to detect and quantify exosomes at a single-particle level without using any labels. The combination of the label-free imaging capability of interferometric microscopy and the chemical content information provided by Raman spectroscopy will be an important step for exosome-based diagnostics applications. The proposed system will be integrated into a state-of-the-art isolation tool, creating complete isolation and multiparameter characterization platform. The proposed platform will be tested with the exosomes isolated from tumor cells to determine possible biomarkers that can be used for diagnosis. The diagnostic capability of the system will be validated with healthy donors and breast cancer patients(30 persons each)

We currently witness democratic backsliding around the world where political rights, civil liberties, and free and fair elections are under assault. At the same time, politics in many countries are increasingly characterized by hostility and distrust across partisan lines; a phenomenon called affective polarization. Could there be a causal link between the two? Despite several theoretical conjectures about how affective polarization erodes democracy through its negative effects on citizens’ democratic attitudes, we lack the evidence to support them. We also do not have reliable interventions to reduce affective polarization that are generalizable to different contexts. DEPOLARIZE addresses these puzzles around affective polarization with two objectives. First, it will identify reliable and generalizable experimental interventions to reduce affective polarization in multiple contexts. Second, it will establish any causal relationship between affective polarization and changes in democratic attitudes through novel empirical approaches. DEPOLARIZE will achieve these goals by producing and analysing high-quality observational and experimental data from multiple waves of surveys in Hungary, Poland, Turkey, and the US. These countries are selected for analyses because they have recently experienced democratic backsliding to different degrees, and they are also examples of high levels of affective polarization in society. The stakes for identifying factors contributing to democratic backsliding could not be any higher. DEPOLARIZE will contribute to this effort by establishing whether affective polarization is a factor driving democratic backsliding using state-of-the-art causal inference methods. DEPOLARIZE is also one of the first projects in political science to employ comprehensive measures for cumulative learning: coordinated and comparable randomized controlled trials in multiple contexts with pre-registration of experimental interventions.

The International Monetary Fund (IMF) is frequently argued to be an agent of its most powerful shareholders. Challenging the common belief that strategic allies of the US and/or G5 countries always get better deals from the IMF, whereas it is the IMF staff who has the main leverage over the design of conditionality when low-income countries are borrowing from the Fund, this project will develop a novel framework, drawing upon existing literature on the IMF, in order to present a comprehensive model that takes into account all actors having an impact on IMF program design. The following questions will be at the core of this research: What factors influence the terms of an IMF program? And how do those factors play into shaping the design of the programs? Through creating an original framework, the project will aim to provide an indispensable and extensible tool for international political economy researchers, policymakers, governments and IMF staff to model the program design and implementation process with high predictive power of the outcomes. The project will make a major contribution to the literature by creating a machine learning (ML) model for predicting the loan size, number of IMF conditions and waivers during program implementation, which complements traditional statistical models by integrating a larger number of variables and providing high accuracy of prediction. The project will also create a natural language processing (NLP) tool for automated, fast analysis of the IMF’s Executive Board meeting minutes, which is able to capture elements including individual board member sentiments, alliance between representatives of different countries and G5 stance with high accuracy. The research will take on board an eclectic approach, using mixed methods involving ML, NLP, statistical analyses, and process tracing of in-depth case studies (namely Romania and Greece) to account for the variation in the terms of IMF programs.