REVIEW | doi:10.20944/preprints202101.0426.v1
Subject: Mathematics & Computer Science, Algebra & Number Theory Keywords: deep learning; machine learning; ischemic stroke; demyelinating disease; image processing; computer aided diagnostics; brain MRI; CNN; White Matter Hyperintensities; VOSViewer
Online: 21 January 2021 (14:55:05 CET)
Medical brain image analysis is a necessary step in the Computers Assisted /Aided Diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of White Matter Hyperintensities (WMHs) of brain MRI images specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used the bibliometric networks. Out of a total of 140 documents we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and proposal of new models of deep learning to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with indicators (Dice Score, DSC: 0.99) were found, however with little practical application due to the uses of small datasets and lack of reproducibility. Therefore, the main conclusion is to establish multidisciplinary research groups to overcome the gap between CAD developments and their complete utilization in the clinical environment.
ARTICLE | doi:10.20944/preprints202108.0381.v2
Subject: Biology, Agricultural Sciences & Agronomy Keywords: quinoa; genotype; nutritional traits; seed quality
Online: 8 September 2021 (12:37:08 CEST)
Exploiting the relationship between the nutritional properties of seeds and the genetic background, constitutes an essential analysis which contributes to broadening our knowledge regarding the control of the nutritional quality of seeds or any other edible plant structure. This constitutes an important aspect when aiming at improving the nutritional characteristics properties of crops, including those of Chenopodium quinoa Willd (quinoa) which is intended to be one of the main nutrient sources ensuring food security worldwide. Changes in the nutritional properties of quinoa seeds due to the influence exerted by the environment, the genotype, or their interaction, have been already described in previous works, but there is an important limitation in the analyses carried out, including the outcomes that can be translated into agronomical practices by which quality can be improved selecting the most adequate genotype. In the present study, several seed nutritional-related parameters from fifteen quinoa cultivars grown in a particular environmental context were analyzed aiming at targeting compounds that can be determinants of seed quality. Important agronomical and nutritional differences were found among cultivars such as distinct mineral or protein contents and seed viability. More importantly, our analyses revealed key correlations between seed quality-related traits in some cultivars, including those that relate yield and antioxidants or the germination rate. These results highlight the importance of considering the genotypic variation in quinoa when selecting improved quinoa varieties with the best nutritional characteristics for new cultivation environments.