Fumonisin Contamination and Fusarium Incidence in Corn from Santa Catarina, Brazil

In Brazil, the southern region has the highest incidence of esophageal cancer and also the highest production and consumption of corn (Zea mays) products. Corn samples intended for human consumption from the western, northern, and southern regions of the state of Santa Catarina, southern Brazil, had mean total fumonisin B (B1, B2, and B3) levels of 3.2, 3.4, and 1.7 mg/kg, respectively. Fusarium verticillioides, the predominant fungus in the corn samples, had mean incidences (percent of kernels infected) of 14, 11, and 18% for the three regions, respectively. Additional corn samples intended for animal feed from the southern region had a mean total fumonisin level of 1.5 mg/kg and a mean F. verticillioides incidence of 10%. The fumonisin levels in corn from the state of Santa Catarina, Brazil, were similar to the high levels determined in other high esophageal cancer incidence regions of the world

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset