"Pandemics know no borders," but Responses to Pandemics Do: Global Health, COVID-19, and Latin America.

This article focuses on Brazil and Peru, the Latin American epicenter of the coronavirus pandemic during 2020 and 2021. The pandemic magnified the legacy of years of neoliberal policies, corruption and racism in these countries, the limitations of their poverty-reduction programs, the fragility of their democratic systems, and the insufficient political regard for public health and basic sanitation. I rely on the concepts of negligence and necropolitics.

Covid-19 as an issue of memory, truth, and justice: an interview with Deisy Ventura.

This interview with Deisy Ventura, professor at the Faculty of Public Health of the Universidade de São Paulo, discusses the political dimension of the covid-19 pandemic in Brazil. She has become a leading reference on the subject due to her extensive knowledge of international law, with a focus on health. In this interview, Deisy Ventura offers some reflections on global health and discusses the handling of the pandemic in Brazil and its human rights implications.

'Eradication was a dirty word': Anti- campaign, between cooperation and rivalry (1938-1940).

This article analyzes the efforts of the International Health Division of the Rockefeller Foundation (IHDRF) in its project initiative that resulted in the extermination of the African mosquito from Brazil in 1940. This species, which originated in Dakar, Senegal, was identified in the Brazilian city of Natal in 1930, where insufficient local emergency sanitation actions enabled it to spread into the interior of the Brazilian northeast, causing an unprecedented malaria epidemic in the Americas in 1938, after years of silent spread. We will analyse the formation of Brazil's Malaria Service of the Northeast (MSNE), discussing its political and scientific controversies and how the transition from the idea of extermination to the idea of eradication was consolidated in the political process of creating this successful sanitation campaign.

Data-Driven Analysis of Hole-Transporting Materials for Perovskite Solar Cells Performance.

We have created a dataset of 269 perovskite solar cells, containing information about their perovskite family, cell architecture, and multiple hole-transporting materials features, including fingerprints, additives, and structural and electronic features. We propose a predictive machine learning model that is trained on these data and can be used to screen possible candidate hole-transporting materials. Our approach allows us to predict the performance of perovskite solar cells with reasonable accuracy and is able to successfully identify most of the top-performing and lowest-performing hole-transporting materials in the dataset.

Limitations of machine learning models when predicting compounds with completely new chemistries: possible improvements applied to the discovery of new non-fullerene acceptors.

We try to determine if machine learning (ML) methods, applied to the discovery of new materials on the basis of existing data sets, have the power to predict completely new classes of compounds (extrapolating) or perform well only when interpolating between known materials. We introduce the leave-one-group-out cross-validation, in which the ML model is trained to explicitly perform extrapolations of unseen chemical families. This approach can be used across materials science and chemistry problems to improve the added value of ML predictions, instead of using extrapolative ML models that were trained with a regular cross-validation.

High-throughput virtual screening for organic electronics: a comparative study of alternative strategies.

We present a review of the field of high-throughput virtual screening for organic electronics materials focusing on the sequence of methodological choices that determine each virtual screening protocol. These choices are present in all high-throughput virtual screenings and addressing them systematically will lead to optimised workflows and improve their applicability. We consider the range of properties that can be computed and illustrate how their accuracy can be determined depending on the quality and size of the experimental datasets.

Determining usefulness of machine learning in materials discovery using simulated research landscapes.

When existing experimental data are combined with machine learning (ML) to predict the performance of new materials, the data acquisition bias determines ML usefulness and the prediction accuracy. In this context, the following two conditions are highly common: (i) constructing new unbiased data sets is too expensive and the global knowledge effectively does not change by performing a limited number of novel measurements; (ii) the performance of the material depends on a limited number of physical parameters, much smaller than the range of variables that can be changed, albeit such parameters are unknown or not measurable. To determine the usefulness of ML under these conditions, we introduce the concept of simulated research landscapes, which describe how datasets of arbitrary complexity evolve over time.

Backlash in global health and the end of AIDS' exceptionalism in Brazil, 2007-2019.

This paper examines the decline of the AIDS Programme in Brazil, the Latin American country most affected by the epidemic, with emphasis in the second decade of the twenty-first century. For many years, Brazil served as a model in Global Health due to a comprehensive preventive policy, a partnership between the government and health activists and the support of life-saving drugs as public goods rather than commodities. The regression of AIDS policies in Brazil interacted with developments in the United States as well as with multilateral agencies like UNAIDS that emphasised biomedicalisation in the response to the disease where broad human-rights programmes and alliance with activists were not priorities.

Prominent out-of-plane diffraction in helium scattering from a methyl-terminated Si(111) surface.

Due to their electrochemical and oxidative stability, organic-terminated semiconductor surfaces are well suited to applications in, for example, photoelectrodes and electrochemical cells, which explains the lively interest in their detailed characterization. Helium atom scattering (HAS) is a useful tool to carry out such characterization. Here, we have simulated HAS in He/CH3-Si(111) based on density functional theory (DFT) potential energy surfaces (PESs) and multi-configuration time-dependent Hartree (MCTDH) dynamics.