Abstract

Abstract
BACKGROUND: Freezing represents an important storage method for vegetal foodstuffs, such as cowpea pods, and thus the impact of this process on the chemical composition of these matrices arises as a prominent issue. In this sense, the phytochemical contents in frozen cowpea pods (i.e. at 6 and 9 months) have been compared with fresh cowpea pods material, with the samples being concomitantly assessed by Fourier-transform infrared spectroscopy (FTIR), both mid-infrared (MIR) and near infrared (NIR), aiming to evaluate the potential of these techniques as a rapid tool for the traceability of these matrices. RESULTS: A decrease in phytochemical contents during freezing was observed, allowing the classification of samples according to the freezing period based on such variations. Also, MIR and NIR allowed discrimination of samples: the use of the first derivative demonstrated a better performance for this purpose, whereas the use of the normalized spectra gave the best correlations between the spectra and specific contents. In both cases, NIR displayed the best performance. CONCLUSION: Freezing of cowpea pods leads to a decrease of phytochemical contents, which can be monitored by FTIR spectroscopy, both within the MIR and NIR ranges, whereas the use of this technique, in tandem with chemometrics, constitutes a suitable methodology for the traceability of these matrices. (C) 2017 Society of Chemical Industry

Abstract
Rheumatic Fever and Rheumatic Heart Disease remain a major burden among children in developing countries. Echocardiography with colour flow Doppler is key to early diagnosis. However, the technique requires time and experienced operators, which are scarce resources in the affected areas. Automatic segmentation of colour Doppler regurgitation jets could, potentially, reduce the cost of screening, and spread diagnostic accessibility for a larger number of patients. Ultrasound processing is very challenging due to speckle noise and similarity of representation of all kinds of tissue. Region-based active contours are suitable tools for the segmentation in cases of intensity heterogeneities, which makes them interesting algorithms for left atrium segmentation. HSV colour space describes colour in terms of hues and saturation, which may facilitate the translation of medical interpretation of the Doppler pseudo-colour into mathematical expression for colour segmentation. A total of 979 frames from 20 sequences were manually annotated and used to validate the proposed pipeline. Overall, the results for colour pattern segmentation are promising (sensitivity=0.91 false detection rate=0.10), but further developments are required for the atrium segmentation (sensitivity=0.80, false detection rate=0.28).

Abstract
The incidence of chronic diseases is increasing and monitoring patients in a home environment is recommended. Noncompliance with prescribed medication regimens is a concern, especially among older people. Heart failure is a chronic disease that requires patients to follow strict medication plans permanently. With the objective of helping these patients managing information about their medicines and increasing adherence, the personal medication advisor CARMIE was developed as a conversational agent capable of interacting, in Portuguese, with users through spoken natural language. The system architecture is based on a language parser, a dialog manager, and a language generator, integrated with already existing tools for speech recognition and synthesis. All modules work together and interact with the user through an Android application, supporting users to manage information about their prescribed medicines. The authors also present a preliminary usability study and further considerations on CARMIE.

Abstract
Background and purpose We evaluated two methods to identify mesial temporal sclerosis (MTS): visual inspection by experienced epilepsy neuroradiologists based on structural magnetic resonance imaging sequences and automated hippocampal volumetry provided by a processing pipeline based on the FMRIB Software Library. Methods This retrospective study included patients from the epilepsy monitoring unit database of our institution. All patients underwent brain magnetic resonance imaging in 1.5T and 3T scanners with protocols that included thin coronal T2, T1 and fluid-attenuated inversion recovery and isometric T1 acquisitions. Two neuroradiologists with experience in epilepsy and blinded to clinical data evaluated magnetic resonance images for the diagnosis of MTS. The diagnosis of MTS based on an automated method included the calculation of a volumetric asymmetry index between the two hippocampi of each patient and a threshold value to define the presence of MTS obtained through statistical tests (receiver operating characteristics curve). Hippocampi were segmented for volumetric quantification using the FIRST tool and fslstats from the FMRIB Software Library. Results The final cohort included 19 patients with unilateral MTS (14 left side): 14 women and a mean age of 43.4 ± 10.4 years. Neuroradiologists had a sensitivity of 100% and specificity of 73.3% to detect MTS (gold standard, k = 0.755). Automated hippocampal volumetry had a sensitivity of 84.2% and specificity of 86.7% (k = 0.704). Combined, these methods had a sensitivity of 84.2% and a specificity of 100% (k = 0.825). Conclusions Automated volumetry of the hippocampus could play an important role in temporal lobe epilepsy evaluation, namely on confirmation of unilateral MTS diagnosis in patients with radiological suggestive findings. © SAGE Publications.

Abstract
Online recommender systems often deal with continuous, potentially fast and unbounded flows of data. Ensemble methods for recommender systems have been used in the past in batch algorithms, however they have never been studied with incremental algorithms that learn from data streams. We evaluate online bagging with an incremental matrix factorization algorithm for top-N recommendation with positiveonly user feedback, often known as binary ratings. Our results show that online bagging is able to improve accuracy up to 35% over the baseline, with small computational overhead.