Abstract
This paper introduces a sort of automata and associated languages, often arising in modelling natural phenomena, in which both vagueness and simultaneity are taken as first class citizens. This requires a fuzzy semantics assigned to transitions and a precise notion of a synchronous product to enforce the simultaneous occurrence of actions. The expected relationships between automata and languages are revisited in this setting; in particular it is shown that any subset of a fuzzy synchronous language with the suitable signature forms a synchronous Kleene algebra.

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Abstract
Magnetic resonance imaging (MRI) is the most commonly used exam for diagnosis and follow-up of neurodegenerative diseases, such as multiple sclerosis (MS). MS is a neuroinflammatory and neurodegenerative disease characterized by demyelination of neuron axon. This demyelination process causes lesions in white matter that can be observed in vivo by MRI. Such lesions may provide quantitative assessments of the inflammatory activity of the disease. Quantitative measures based on various features of lesions have been shown to be useful in clinical trials for evaluating therapies. Although manual segmentations are considered as the gold standard, this process is time consuming and error prone. Therefore, automated lesion identification and quantification of the MRI are active areas in MS research. The purpose of this study was to perform the brain lesions volumetric quantification in MS patients, after segmentation via a convolutional neural network (CNN) model. Initially, MRI was rigidly registered, skullstripped and bias corrected. After, we use the CNN for brain lesions segmentation, which used training data to identify lesions within new test subjects. Finally, volume quantification was performed with a count of segmented voxels and represented by mm(3). We did not observe a statistical difference between the volume of brain lesion automatically identified and the volume manually segmented. The use of deep learning techniques in health is constantly developing. We observed that the use of these computational method for segmentation and quantification of brain lesions can be applied to aid in diagnosis and follow-up of MS.

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Abstract
With the advent of personalized medicine, there is a movement to develop "smaller" and "smarter" microdevices that are able to distinguish similar cancer subtypes. Tumor cells display major differences when compared to their natural counterparts, due to alterations in fundamental cellular processes such as glycosylation. Glycans are involved in tumor cell biology and they have been considered to be suitable cancer biomarkers. Thus, more selective cancer screening assays can be developed through the detection of specific altered glycans on the surface of circulating cancer cells. Currently, this is only possible through time-consuming assays. In this work, we propose the "intelligent" Lab on Fiber (iLoF) device, that has a high-resolution, and which is a fast and portable method for tumor single-cell type identification and isolation. We apply an Artificial Intelligence approach to the back-scattered signal arising from a trapped cell by a micro-lensed optical fiber. As a proof of concept, we show that iLoF is able to discriminate two human cancer cell models sharing the same genetic background but displaying a different surface glycosylation profile with an accuracy above 90% and a speed rate of 2.3 seconds. We envision the incorporation of the iLoF in an easy-to-operate microchip for cancer identification, which would allow further biological characterization of the captured circulating live cells.

Abstract