The widespread use of pesticides in agriculture can lead to water contamination and cause adverse effects on non-target organisms. Brazil has been the world's top pesticide market consumer since 2008, with 381 approved pesticides for crop use. This study provides a comprehensive literature review on the occurrence of pesticide residues in Brazilian freshwaters. We searched for information in official agency records and peer-reviewed scientific literature. Risk quotients were calculated to assess the potential risk posed to aquatic life by the individual pesticides based on their levels of water contamination. Studies about the occurrence of pesticides in freshwaters in Brazil are scarce and concentrated in few sampling sites in 5 of the 27 states. Herbicides (21) accounted for the majority of the substances investigated, followed by fungicides (11), insecticides (10) and plant growth regulators (1). Insecticides are the class of major concern. Brazil would benefit from the implementation of a nationwide pesticide freshwater monitoring program to support preventive, remediation and enforcement actions.

Objectives: Automated computational segmentation of the lung and its lobes and findings in X-Ray based computed tomography (CT) images is a challenging problem with important applications, including medical research, surgical planning, and diagnostic decision support. With the increase in large imaging cohorts and the need for fast and robust evaluation of normal and abnormal lungs and their lobes, several authors have proposed automated methods for lung assessment on CT images. In this paper we intend to provide a comprehensive summarization of these methods. Methods: We used a systematic approach to perform an extensive review of automated lung segmentation methods. We chose Scopus, PubMed, and Scopus to conduct our review and included methods that perform segmentation of the lung parenchyma, lobes or internal disease related findings. The review was not limited by date, but rather by only including methods providing quantitative evaluation. Results: We organized and classified all 234 included articles into various categories according to methodological similarities among them. We provide summarizations of quantitative evaluations, public datasets, evaluation metrics, and overall statistics indicating recent research directions of the field. Conclusions: We noted the rise of data-driven models in the last decade, especially due to the deep learning trend, increasing the demand for high-quality data annotation. This has instigated an increase of semi-supervised and uncertainty guided works that try to be less dependent on human annotation. In addition, the question of how to evaluate the robustness of data-driven methods remains open, given that evaluations derived from specific datasets are not general.