Understanding Brazil’s catastrophic fires : causes, consequences and policy needed to prevent future tragedies

Brazil has experienced unprecedented wildfires in the last decade. Images ofimmense burnt areas or dead animals that failed to escape the 2020 wildfires have shocked the world. To prevent or minimize further similardisasters wemustunderstandthe factors thathave ledto these catastrophic events. The causes and consequences of wildfires entail complex interactions between the biophysical and sociocultural spheres, and suitable management decisions require a sound scientific base. We present the recent panorama of increasing fire outbreaks in the Brazilian biomes, and discuss the causes that have contributed to such fires, their impacts on the environment and overall consequences for human well-being, based on reviewing the extensive specialist literature, on authors’ expert knowledge and information provided by environmental managers, researchers and politicians during a workshop organized to debate the wildfire issue in Brazil. Our up-to-date review is aimed at the academic public, environmental managers and decision- and policy-makers. First, we present evidence on the contrasting effects of fire on different ecosystems. Second, we outline the historic perceptions and policies related to fire use and management in Brazil since its colonization to the present date. Third, we propose means to advance fire prevention and develop successful management strategies. Finally, we answer frequently asked questions to clarify and/or demystify some fire-related issues not always properly addressed in the media

Evaluation of machine-learning methods for ligand-based virtual screening

Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed