An orifice shape-based reduced order model of patient-specific mitral valve regurgitation

Mitral valve regurgitation (MR) is one of the most prevalent valvular heart diseases. Its quantitative assessment is challenging but crucial for treatment decisions. Using computational fluid dynamics (CFD), we developed a reduced order model (ROM) describing the relationship between MR flow rates, transvalvular pressure differences, and the size and shape of the regurgitant valve orifice. Due to its low computational cost, this ROM could easily be implemented into clinical workflows to support the assessment of MR. We reconstructed mitral valves of 43 patients from 3D transesophageal echocardiographic images and estimated the 3D anatomic regurgitant orifice areas using a shrink-wrap algorithm. The orifice shapes were quantified with three dimensionless shape parameters. Steady-state CFD simulations in the reconstructed mitral valves were performed to analyse the relationship between the regurgitant orifice geometry and the regurgitant hemodynamics. Based on the results, three ROMs with increasing complexity were defined, all of which revealed very good agreement with CFD results with a mean bias below 3% for the MR flow rate. Classifying orifices into two shape groups and assigning group-specific flow coefficients in the ROM reduced the limit of agreement predicting regurgitant volumes from 9.0 ml to 5.7 ml at a mean regurgitant volume of 57 ml

Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty

This study explores how researchers’ analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers’ expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team’s workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers’ results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings

Deterring Dictatorship: Explaining Democratic Resilience since 1900

Democracy is under threat globally from democratically elected leaders engaging in erosion of media freedom, civil society, and the rule of law. What distinguishes democracies that prevail against the forces of autocratization? This article breaks new ground by conceptualizing democratic resilience as a two-stage process, whereby democracies first exhibit resilience by avoiding autocratization altogether and second, by avoiding democratic breakdown given that autocratization has occurred. To model this two-stage process, we introduce the Episodes of Regime Transformation (ERT) dataset tracking autocratization since 1900. These data demonstrate the extraordinary nature of the current wave of autocratization: Fifty-nine (61%) episodes of democratic regression in the ERT began after 1992. Since then, autocratization episodes have killed an unprecedented 36 democratic regimes. Using a selection-model, we simultaneously test for factors that make democracies more prone to experience democratic regression and, given this, factors that explain democratic breakdown. Results from the explanatory analysis suggest that constraints on the executive are positively associated with a reduced risk of autocratization. Once autocratization is ongoing, we find that a long history of democratic institutions, durable judicial constraints on the executive, and more democratic neighbours are factors that make democracy more likely to prevail.We recognize support by the Swedish Research Council, Grant 2018-01614, PI: Anna Lührmann; by Knut and Alice Wallenberg Foundation to Wallenberg Academy Fellow Staffan I. Lindberg, Grant 2018.0144; by European Research Council, Grant 724191, PI: Staffan I. Lindberg; as well as by internal grants from the Vice- Chancellor’s office, the Dean of the College of Social Sciences, and the Department of Political Science at University of Gothenburg. The computations of expert data were enabled by the Swedish National Infrastructure for Computing (SNIC) at National Supercomputer Centre, Linköping University, partially funded by the Swedish Research Council through grant agreement no. 2019/3-516

A Framework for Understanding Regime Transformation: Introducing the ERT Dataset

Gradual processes of democratization and autocratization have gained increased attention in the literature. Assessing such processes in a comparative framework remains a challenge, however, due to their under-conceptualization and a bifurcation of the democracy and autocracy literatures. This article provides a new conceptualization of regime transformation as substantial and sustained changes in democratic institutions and practices in either direction. This allows for studies to address both democratization and autocratization as related obverse processes. Using this framework, the article introduces a dataset that captures 680 unique episodes of regime transformation (ERT) from 1900 to 2019. These data provide novel insights into regime change over the past 120 years, illustrating the value of developing a unified framework for studying regime transformation. Such transformations, while meaningfully altering the qualities of the regime, only produce a regime transition about 32% of the time. The majority of episodes either end before a transition takes place or do not have the potential for such a transition (i.e. constituted further democratization in democratic regimes or further autocratization in autocratic regimes). The article also provides comparisons to existing datasets and illustrative case studies for face validity. It concludes with a discussion about how the ERT framework can be applied in peace research.This research project was principally supported by European Research Council, Consolidator Grant 724191, PI: Staffan I. Lindberg; but also by Knut and Alice Wallenberg Foundation to Wallenberg Academy Fellow Staffan I. Lindberg, Grant 2018.0144; as well as by co-funding from the Vice-Chancellor’s office, the Dean of the College of Social Sciences, and the Department of Political Science at University of Gothenburg

An orifice shape-based reduced order model of patient-specific mitral valve regurgitation

Mitral valve regurgitation (MR) is one of the most prevalent valvular heart diseases. Its quantitative assessment is challenging but crucial for treatment decisions. Using computational fluid dynamics (CFD), we developed a reduced order model (ROM) describing the relationship between MR flow rates, transvalvular pressure differences, and the size and shape of the regurgitant valve orifice. Due to its low computational cost, this ROM could easily be implemented into clinical workflows to support the assessment of MR. We reconstructed mitral valves of 43 patients from 3D transesophageal echocardiographic images and estimated the 3D anatomic regurgitant orifice areas using a shrink-wrap algorithm. The orifice shapes were quantified with three dimensionless shape parameters. Steady-state CFD simulations in the reconstructed mitral valves were performed to analyse the relationship between the regurgitant orifice geometry and the regurgitant hemodynamics. Based on the results, three ROMs with increasing complexity were defined, all of which revealed very good agreement with CFD results with a mean bias below 3% for the MR flow rate. Classifying orifices into two shape groups and assigning group-specific flow coefficients in the ROM reduced the limit of agreement predicting regurgitant volumes from 9.0 ml to 5.7 ml at a mean regurgitant volume of 57 ml