Validation and quality assurance applied to goat milk chemical composition: minerals and trace elements measurements

In the present study, quality assurance programmes were implemented to validate and control the analytical methodologies used for the characterization of minerals and trace elements in goat milk from Portuguese breeds. With the exception of chloride that was determined by potentiometric titration, all the other elements were determined by spectroscopic techniques after different sample decomposition: P was measured by ultraviolet-visible molecular absorption spectrometry, Ca, Fe, K, Mg, Mn, Na and Zn by flame atomic absorption spectrometry and Cd, Co, Cr, Cu, Mo, Ni and Pb by electrothermal atomic absorption spectrometry. The methods performance characteristics, namely specificity, limit of detection, limit of quantification, working range, precision and trueness were evaluated. Measurement uncertainty was expressed in terms of precision and trueness. Precision under intralaboratory reproducibility conditions was estimated from triplicate analysis, and the trueness component was estimated in terms of overall recovery using either skim milk powder certified reference materials or spiked samples. The results obtained are discussed on the basis of the performance criteria required by EC regulations to verify when a method is suitable for food control. The methods used for the characterization of minerals and trace elements in goat milk complied with EC requirements since there was no matrix influence, the Horrat values were < 2.0, recoveries were within the interval 1.00 ± 0.10 for minerals and 1.00 ± 0.20 for trace elements and the combined uncertainty of the results were lower than the maximum standard uncertainty calculated using the uncertainty function approach. In relation to the limits of detection and quantification, the limits obtained for Pb were lower than those specified by EC regulation

Eyes with Large Disc Cupping and Normal Intraocular Pressure: Using Optical Coherence Tomography to Discriminate Those With and Without Glaucoma

We evaluated the ability of spectral-domain optic coherence tomography (SD-OCT) to differentiate large physiological optic disc cupping (LPC) from glaucomatous cupping in eyes with intraocular pressure (IOP) within the normal range.  We prospectively enrolled patients with glaucoma or presumed LPC. Participants  had optic discs with confirmed or suspected glaucomatous damage (defined as a vertical cup-to-disc ratio≥0.6), and all eyes had known untreated IOP&lt;21 mmHg. For glaucomatous eyes, a reproducible glaucomatous visual field (VF) defect was required. LPC eyes required normal VF and no evidence of progressive glaucomatous neuropathy (follow-up≥30 months). Peripapillary retinal nerve fiber layer (pRNFL) and macular ganglion cell complex (GCC) thicknesses were obtained using SD-OCT. For all studied parameters of pRNFL and GCC thicknesses, eyes with glaucoma (n=36) had significantly thinner values compared to eyes with LPC (n=71; P&lt;0.05 for all comparisons). In addition, pRNFL parameters had sensitivity of 66.7% and specificity of 83.1%, and GCC parameters had sensitivity of 61.2% and specificity of 81.7%. The combination of the two analyses increased the sensitivity to 80.6%. In conclusion, while evaluating patients with large optic disc cupping and IOP in the statistically normal range, SD-OCT had only limited diagnostic ability to differentiate those with and without glaucoma. Although the diagnostic ability of the pRNFL and the GCC scans were similar, these parameters yielded an increase in sensitivity when combined, suggesting that both parameters could be considered simultaneously in these cases

An on-line system for remote treatment of aphasia

Aphasia treatment for the recovery of lost communication functionalities is possible through frequent and intense speech therapy sessions. In this sense, speech and language technology may provide important support in improving the recovery process. The aim of the project Vithea (Virtual Therapist for Aphasia Treatment) is to develop an on-line system designed to behave as a virtual therapist, guiding the patient in performing training exercises in a simple and intuitive fashion. In this paper, the fundamental components of the Vithea system are presented, with particular emphasis on the speech recognition module. Furthermore, we report encouraging automatic word naming recognition results using data collected from speech therapy sessions.

Low-Resource Unsupervised NMT: Diagnosing the Problem and Providing a Linguistically Motivated Solution

Unsupervised Machine Translation hasbeen advancing our ability to translatewithout parallel data, but state-of-the-artmethods assume an abundance of mono-lingual data. This paper investigates thescenario where monolingual data is lim-ited as well, finding that current unsuper-vised methods suffer in performance un-der this stricter setting. We find that theperformance loss originates from the poorquality of the pretrained monolingual em-beddings, and we propose using linguis-tic information in the embedding train-ing scheme. To support this, we look attwo linguistic features that may help im-prove alignment quality: dependency in-formation and sub-word information. Us-ing dependency-based embeddings resultsin a complementary word representationwhich offers a boost in performance ofaround 1.5 BLEU points compared to stan-dardWORD2VECwhen monolingual datais limited to 1 million sentences per lan-guage. We also find that the inclusion ofsub-word information is crucial to improv-ing the quality of the embedding