Alternative Views on Portuguese Stagnation: From the Euro’s Inception to the COVID-19 Pandemic

Over the past two decades, real GDP per capita in Portugal has nearly stagnated. The conventional account attributes this to the mismanagement of public finances and the lack of structural reforms in labor and product markets prior to the “adjustment program” agreed with the troika in the early 2010s. In the same vein, the neoclassical-inspired interpretation explains the subsequent recovery based on supply-side and fiscal reforms implemented during the troika years, which would account for the reduced fiscal deficits, external equilibrium and employment growth registered in the pre-COVID-19 period. In this article, we challenge this optimistic view, identifying structural weaknesses of the Portuguese economy that would have soon become apparent even if the pandemic had not happened. Addressing these weaknesses requires changes that go beyond the EU and national responses to the pandemic crisis.

GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research

Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experimental drug screening is expensive and time consuming, and it is desirable to carry out large scale docking calculations in a high-throughput manner to narrow the experimental search space. Few of the existing computational docking tools were designed with high performance computing in mind. Therefore, optimizations to maximize use of high-performance computational resources available at leadership-class computing facilities enables these facilities to be leveraged for drug discovery. Here we present the porting, optimization, and validation of the AutoDock-GPU program for the Summit supercomputer, and its application to initial compound screening efforts to target proteins of the SARS-CoV-2 virus responsible for the current COVID-19 pandemic.

Integrating Social Determinants in Decision-Making Processes for Health: Insights from Conceptual Frameworks-the 3-D Commission.

The inclusion of social determinants of health offers a more comprehensive lens to fully appreciate and effectively address health. However, decision-makers across sectors still struggle to appropriately recognise and act upon these determinants, as illustrated by the ongoing COVID-19 pandemic. Consequently, improving the health of populations remains challenging. This paper seeks to draw insights from the literature to better understand decision-making processes affecting health and the potential to integrate data on social determinants. We summarised commonly cited conceptual approaches across all stages of the policy process, from agenda-setting to evaluation. Nine conceptual approaches were identified, including two frameworks, two models and five theories. From across the selected literature, it became clear that the context, the actors and the type of the health issue are critical variables in decision-making for health, a process that by nature is a dynamic and adaptable one. The majority of these conceptual approaches implicitly suggest a possible role for data on social determinants of health in decision-making. We suggest two main avenues to make the link more explicit: the use of data in giving health problems the appropriate visibility and credibility they require and the use of social determinants of health as a broader framing to more effectively attract the attention of a diverse group of decision-makers with the power to allocate resources. Social determinants of health present opportunities for decision-making, which can target modifiable factors influencing health-i.e. interventions to improve or reduce risks to population health. Future work is needed to build on this review and propose an improved, people-centred and evidence-informed decision-making tool that strongly and explicitly integrates data on social determinants of health.

GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research

Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experimental drug screening is expensive and time consuming, and it is desirable to carry out large scale docking calculations in a high-throughput manner to narrow the experimental search space. Few of the existing computational docking tools were designed with high performance computing in mind. Therefore, optimizations to maximize use of high-performance computational resources available at leadership-class computing facilities enables these facilities to be leveraged for drug discovery. Here we present the porting, optimization, and validation of the AutoDock-GPU program for the Summit supercomputer, and its application to initial compound screening efforts to target proteins of the SARS-CoV-2 virus responsible for the current COVID-19 pandemic.