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Abstract This study investigated the effect of the inclusion of extruded linseed and hazelnut skin on fatty acid (FA) metabolism in finishing lambs. Forty lambs were divided into 4 groups and fed for 60 d with: a conventional cereal-based diet, or the same diet with 8% of extruded linseed, or 15% of hazelnut skin, or 4% of linseed plus 7.5% of hazelnut skin as partial replacement of maize. Dietary treatments did not affect growth performances, carcass traits, and ruminal fermentation. The combined effect of linseed and hazelnut skin enriched the intramuscular fat with health promoting FA. Particularly, increases in α-linolenic acid (3.75-fold), and very long-chain n-3 poly-unsaturated FA (+ 40%) were attributed to the supplementation with linseed, rich in α-linolenic acid. In addition, increases in rumenic (+ 33%), and vaccenic (+ 59%) acids were attributed to hazelnut skin tannins modulating ruminal biohydrogenation and accumulating intermediate metabolites. The simultaneous inclusion of linseed and hazelnut skin can be a profitable strategy for enriching the intramuscular fat of lambs with health promoting FA, without adverse effects on ruminal fermentation and animal performance.

Abstract Artificial Neural Networks (ANNs) have been used in a multitude of real-world applications given their predictive capabilities, and algorithms based on gradient descent, such as Backpropagation (BP) and variants, are usually considered for their optimisation. However, these algorithms have been shown to get stuck at local optima, and they require a cautious design of the architecture of the model. This paper proposes a novel memetic training method for simultaneously learning the ANNs structure and weights based on the Coral Reef Optimisation algorithms (CROs), a global-search metaheuristic based on corals’ biology and coral reef formation. Three versions based on the original CRO combined with a Local Search procedure are developed: (1) the basic one, called Memetic CRO; (2) a statistically guided version called Memetic SCRO (M-SCRO) that adjusts the algorithm parameters based on the population fitness; (3) and, finally, an improved Dynamic Statistically-driven version called Memetic Dynamic SCRO (M-DSCRO). M-DSCRO is designed with the idea of improving the M-SCRO version in the evolutionary process, evaluating whether the fitness distribution of the population of ANNs is normal to automatically decide the statistic to be used for assigning the algorithm parameters. Furthermore, all algorithms are adapted to the design of ANNs by means of the most suitable operators. The performance of the different algorithms is evaluated with 40 classification datasets, showing that the proposed M-DSCRO algorithm outperforms the other two versions on most of the datasets. In the final analysis, M-DSCRO is compared against four state-of-the-art methods, demonstrating its superior efficacy in terms of overall accuracy and minority class performance.

Abstract Huntington’s disease (HD) is caused by an aberrant expansion of CAG repeats in the HTT gene that mainly affects basal ganglia. Although striatal dysfunction has been widely studied in HD mouse models, other brain areas can also be relevant to the pathology. In this sense, we have special interest on the retina as this is the most exposed part of the central nervous system that enable health monitoring of patients using noninvasive techniques. To establish the retina as an appropriate tissue for HD studies, we need to correlate the retinal alterations with those in the inner brain, i.e., striatum. We confirmed the malfunction of the transgenic R6/1 retinas, which underwent a rearrangement of their transcriptome as extensive as in the striatum. Although tissue-enriched genes were downregulated in both areas, a neuroinflammation signature was only clearly induced in the R6/1 retina in which the observed glial activation was reminiscent of the situation in HD patient’s brains. The retinal neuroinflammation was confirmed in the slow progressive knock-in zQ175 strain. Overall, these results demonstrated the suitability of the mouse retina as a research model for HD and its associated glial activation.

Abstract Modelling extreme values distributions, such as wave height time series where the higher waves are much less frequent than the lower ones, has been tackled from the point of view of the Peak-Over-Threshold (POT) methodologies, where modelling is based on those values higher than a threshold. This threshold is usually predefined by the user, while the rest of values are ignored. In this paper, we propose a new method to estimate the distribution of the complete time series, including both extreme and regular values. This methodology assumes that extreme values time series can be modelled by a normal distribution in a combination of a uniform one. The resulting theoretical distribution is then used to fix the threshold for the POT methodology. The methodology is tested in nine real-world time series collected in the Gulf of Alaska, Puerto Rico and Gibraltar (Spain), which are provided by the National Data Buoy Center (USA) and Puertos del Estado (Spain). By using the Kolmogorov-Smirnov statistical test, the results confirm that the time series can be modelled with this type of mixed distribution. Based on this, the return values and the confidence intervals for wave height in different periods of time are also calculated.

Abstract To compare the effectiveness at ten years of follow-up of radical prostatectomy, brachytherapy and external radiotherapy, in terms of overall survival, prostate cancer-specific mortality and biochemical recurrence. Cohort of men diagnosed with localized prostate cancer (T1/T2 and low/intermediate risk) from ten Spanish hospitals, followed for 10 years. The treatment selection was decided jointly by patients and physicians. Of 704 participants, 192 were treated with open radical retropubic prostatectomy, 317 with 125I brachytherapy alone, and 195 with 3D external beam radiation. We evaluated overall survival, prostate cancer-specific mortality, and biochemical recurrence. Kaplan–Meier estimators were plotted, and Cox proportional-hazards regression models were constructed to estimate hazard ratios (HR), adjusted by propensity scores. Of the 704 participants, 542 patients were alive ten years after treatment, and a total of 13 patients have been lost during follow-up. After adjusting by propensity score and Gleason score, brachytherapy and external radiotherapy were not associated with decreased 10-year overall survival (aHR = 1.36, p = 0.292 and aHR = 1.44, p = 0.222), but presented higher biochemical recurrence (aHR = 1.93, p = 0.004 and aHR = 2.56, p

Abstract Parkinson’s disease is characterised by a decrease in the density of presynaptic dopamine transporters in the striatum. Frequently, the corresponding diagnosis is performed using a qualitative analysis of the 3D-images obtained after the administration of $$^{123}$$ 123 I-ioflupane, considering a binary classification problem (absence or existence of Parkinson’s disease). In this work, we propose a new methodology for classifying this kind of images in three classes depending on the level of severity of the disease in the image. To tackle this problem, we use an ordinal classifier given the natural order of the class labels. A novel strategy to perform feature selection is developed because of the large number of voxels in the image, and a method for generating synthetic images is proposed to improve the quality of the classifier. The methodology is tested on 434 studies conducted between September 2015 and January 2019, divided into three groups: 271 without alteration of the presynaptic nigrostriatal pathway, 73 with a slight alteration and 90 with severe alteration. Results confirm that the methodology improves the state-of-the-art algorithms, and that it is able to find informative voxels outside the standard regions of interest used for this problem. The differences are assessed by statistical tests which show that the proposed image ordinal classification could be considered as a decision support system in medicine.