Impact of weathering on the chemical identification of microplastics from usual packaging polymers in the marine environment.

Most of the plastics produced worldwide are finally disposed into the environment, most of them being one-use plastic packaging. Once released, plastics may undergone degradation through several agents, such as solar radiation, mechanical forces, and microbial action. Weathered plastics and microplastics (MPs) collected from the marine environment show considerable physical and chemical differences regarding their pristine counterparts; most notably on their surface, where spectrometric measurements are done. Hence, it is crucial to consider aging for their correct identification and quantification in environmental monitoring. Five of the most common polymers employed worldwide for packaging (LDPE, HDPE, PS, PP and PET) were weathered in a pilot-scale system simulating dry and marine conditions for more than 10 weeks. Aliquots were withdrawn periodically to monitor their weathering processes by means of infrared spectrometry and scanning electron microscopy; their spectra were compared and band ratios calculated. Results showed that an individual study of each polymer is necessary since degradation pathways and products depend on the polymer type. Moreover, including spectra of weathered polymers in the spectral libraries to obtain reliable identifications in microplastics pollution studies was critical.

Classification of radiologically isolated syndrome and clinically isolated syndrome with machine-learning techniques.

BACKGROUND AND PURPOSE: The unanticipated detection by magnetic resonance imaging (MRI) in the brain of asymptomatic subjects of white matter lesions suggestive of multiple sclerosis (MS) has been named radiologically isolated syndrome (RIS). As the difference between early MS [i.e. clinically isolated syndrome (CIS)] and RIS is the occurrence of a clinical event, it is logical to improve detection of the subclinical form without interfering with MRI as there are radiological diagnostic criteria for that. Our objective was to use machine-learning classification methods to identify morphometric measures that help to discriminate patients with RIS from those with CIS. METHODS: We used a multimodal 3-T MRI approach by combining MRI biomarkers (cortical thickness, cortical and subcortical grey matter volume, and white matter integrity) of a cohort of 17 patients with RIS and 17 patients with CIS for single-subject level classification. RESULTS: The best proposed models to predict the diagnosis of CIS and RIS were based on the Naive Bayes, Bagging and Multilayer Perceptron classifiers using only three features: the left rostral middle frontal gyrus volume and the fractional anisotropy values in the right amygdala and right lingual gyrus. The Naive Bayes obtained the highest accuracy [overall classification, 0.765; area under the receiver operating characteristic (AUROC), 0.782]. CONCLUSIONS: A machine-learning approach applied to multimodal MRI data may differentiate between the earliest clinical expressions of MS (CIS and RIS) with an accuracy of 78%.

Hybrid model based on Genetic Algorithms and SVM applied to variable selection within fruit juice classification.

Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected.