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Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the digestive tract usually characterized by diarrhea, rectal bleeding, and abdominal pain. IBD includes Crohn's disease and ulcerative colitis as the main entities. IBD is a debilitating condition that can lead to life-threatening complications, involving possible malignancy and surgery. The available therapies aim to achieve long-term remission and prevent disease progression. Biologics are bioengineered therapeutic drugs that mainly target proteins. Although they have revolutionized the treatment of IBD, their potential therapeutic benefits are limited due to large interindividual variability in clinical response in terms of efficacy and toxicity, resulting in high rates of long-term therapeutic failure. It is therefore important to find biomarkers that provide tailor-made treatment strategies that allow for patient stratification to maximize treatment benefits and minimize adverse events. Pharmacogenetics has the potential to optimize biologics selection in IBD by identifying genetic variants, specifically single nucleotide polymorphisms (SNPs), which are the underlying factors associated with an individual's drug response. This review analyzes the current knowledge of genetic variants associated with biological agent response (infliximab, adalimumab, ustekinumab, and vedolizumab) in IBD. An online literature search in various databases was conducted. After applying the inclusion and exclusion criteria, 28 reports from the 1685 results were employed for the review. The most significant SNPs potentially useful as predictive biomarkers of treatment response are linked to immunity, cytokine production, and immunorecognition. [ABSTRACT FROM AUTHOR]

Due to the constrained processing capabilities of real-time detection techniques in remote sensing applications, it is often difficult to obtain detection results with high accuracy in practice. To address this problem, we introduce a new real-time anomaly detection algorithm for hyperspectral images called cloud–edge RX (CE-RX). The algorithm combines the advantages of cloud and edge computing. During the data acquisition process, the edge performs real-time detection on the data just captured to obtain a coarse result and find the suspicious anomalies. At regular intervals, the suspicious anomalies are sent to the cloud for further detection with a highly accurate algorithm, then the cloud sends back the (high-accuracy) results to the edge for information updating. After receiving the results from the cloud, the edge updates the information of the detector in the real-time algorithm to improve the detection accuracy of the next acquired piece of data. Our experimental results demonstrate that the proposed cloud–edge collaborative algorithm can obtain more accurate results than existing real-time detection algorithms. [ABSTRACT FROM AUTHOR]

The characterization of quality brand meat products, such as "Chorizo Zamorano" dry fermented sausages, involves a wide range of data which makes it necessary to use alternative statistical methodologies. In this study, the feasibility of the Categorical Principal Components Analysis as a multivariate non-linear technique for the characterization of "Chorizo Zamorano" was assessed. The data analyzed were those of eight commercial brands covered by the quality mark over an eight-year period (2013–2020) and included parameters of the physicochemical composition and organoleptic properties of the product. The results showed that "Chorizo Zamorano" has an average moisture content (28.28%), high protein (38.38%) and fat (51.05%) contents, and a very low carbohydrate concentration (1.52%). Results showed that the fat and protein content and the sensory parameters related to external and internal odor appeared to be the studied variables with the greatest influence on the classification of the products according to their quality. [ABSTRACT FROM AUTHOR]

Simple Summary: Hormonal treatments used to synchronize estrus in sheep artificial insemination procedures can cause several physiological problems that can affect negatively fertility and animal health; however, alternative protocols based on the detection of natural estrus offer a more sustainable option while reaching successful fertility results. Therefore, in this study, an artificial insemination protocol at natural estrus in organic Churra sheep was performed. In the protocol design phase, 125 ewes were exocervically inseminated, and their fertility was assessed based on the number of inseminations, physiological state, body condition, estrus detection–insemination interval and vaginal fluids in cervix. That protocol was repeated in six consecutive years. In all ewes, fertilities based on the timing of insemination after estrus detection were very high. Lactating ewes produced better results than did dry ewes, which was probably because of the better feeding of the former. In addition, double insemination increased the fertility of ewes, whose estrus was detected within 16 h of onset. Body condition and amount of vaginal fluid were correlated with fertility. In conclusion, exocervical inseminations at natural estrus can produce acceptable fertility and prolificity in Churra ewes, avoiding physiological problems derived from the use of hormonal treatments. Hormonal treatments used in the artificial insemination (AI) of sheep can cause several physiological problems that can affect negatively fertility and animal health; however, AI protocols based on the detection of natural estrus offer a more sustainable option and can achieve high fertility. In this study, an AI protocol at natural estrus in organic Churra sheep was performed. In the first phase (AI protocol development), 125 ewes were exocervically inseminated, and their fertility was assessed based on the following factors: number of AI, physiological state, body condition, estrus detection–AI interval, and vaginal fluids in cervix. That protocol was repeated for six consecutive years. In all individuals, fertilities based on the timing of insemination after estrus detection were very high. Lactating ewes produced better results than did dry ewes, which was probably because of the better feeding of the former. In addition, double insemination increased the fertility of ewes whose estrus was detected within 16 h of onset. Body condition and amount of vaginal fluid were correlated with fertility. Exocervical inseminations at natural estrus can produce acceptable fertility and prolificity in Churra ewes. [ABSTRACT FROM AUTHOR]

Insect larval meal is an increasingly common protein source in poultry systems. In this study, the effect of replacing soybean meal with Tenebrio molitor larval meal on the performance of slow-growing chickens was assessed. A total of 128 one-day-old chickens (Colorield) were randomly divided into a control group (C) (n = 64), fed with soybean meal, and an experimental group (TM) (n = 64), fed with T. molitor larvae meal. The chicks were slaughtered after 95 days. Three different isoenergetic and isoproteic diets (F1, F2 and F3) were used for each group. The F1 diet resulted in higher body weight gain and higher feed and water intakes in group C, but a lower feed conversion ratio. Contrarily, diets F2 and F3 did not produce differences in the studied parameters between the two groups, except for body weight gain in the case of diet F2, which was highest in group C. Therefore, weight gain and feed and water intakes were significantly higher in group C, but there were no differences in feed conversion ratio or live weight. In conclusion, the total replacement of soybean meal with T. molitor larvae meal resulted in a reduction in feed intake and a consequent reduction in weight. During this period, partial rather than total substitution may be recommended. [ABSTRACT FROM AUTHOR]

Since the discovery of the presence of fetal DNA in maternal blood, non-invasive fetal sex determination has been the test most widely translated into clinical practice. To date there is no agreement between the different laboratories performing such tests in relation to which is the best protocol. As a consequence there are almost as many protocols as laboratories offering the service, using different methodologies and thus obtaining different diagnostic accuracies. By the end of 2007, after a validation study performed in 316 maternal samples collected between the 5th and 12th week of gestation, the fetal sex determination was incorporated into clinical practice in our Service. The test is performed in the first trimester of pregnancy, and it is offered as part of the genetic counseling process for couples at risk of X-linked disorders. As a general rule and in order to avoid misdiagnosis, two samples at different gestational ages are tested per patient. The analysis is performed by the study of the SRY gene by RT-PCR. Two hundred and twenty six pregnancies have been tested so far in these 5 years. Neither false positives nor false negatives diagnoses have been registered, thus giving a diagnostic accuracy of 100%. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging is a new remote sensing technique that generates hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. Supervised classification of hyperspectral image data sets is a challenging problem due to the limited availability of training samples (which are very difficult and costly to obtain in practice) and the extremely high dimensionality of the data. In this paper, we explore the use of multi-channel morphological profiles for feature extraction prior to classification of remotely sensed hyperspectral data sets using support vector machines (SVMs). In order to introduce multi-channel morphological transformations, which rely on ordering of pixel vectors in multidimensional space, several vector ordering strategies are investigated. A reduced implementation which builds the multi-channel morphological profile based on the first components resulting from a dimensional reduction transformation applied to the input data is also proposed. Our experimental results, conducted using three representative hyperspectral data sets collected by NASA's Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) sensor and the German Digital Airborne Imaging Spectrometer (DAIS 7915), reveal that multi-channel morphological profiles can improve single-channel morphological profiles in the task of extracting relevant features for classification of hyperspectral data using small training sets. [ABSTRACT FROM AUTHOR]

The accurate identification of crops is essential to help environmental sustainability and support agricultural policies. This study presents the use of a Spanish radar mission, PAZ, to classify agricultural areas with a very high spatial resolution. PAZ was recently launched, and it operates at X band, joining the synthetic aperture radar (SAR) constellation along with TerraSAR-X and TanDEM-X satellites. Owing to its novelty and its ability to classify crop areas (both taking individually its time series and blending with the Sentinel-1 series), it has been tested in an agricultural area of the central-western part of Spain during 2020. The random forest algorithm was selected to classify the time series under five alternatives of standalone/fused data. The map accuracy resulting from the PAZ series standalone was acceptable, but it highlighted the need for a denser time-series of data. The overall accuracy provided by eight PAZ images or by eight Sentinel-1 images was below 60%. The fusion of both sets of eight images improved the overall accuracy by more than 10%. In addition, the exploitation of the whole Sentinel-1 series, with many more observations (up to 40 in the same temporal window) improved the results, reaching an overall accuracy around 76%. This overall performance was similar to that obtained by the joint use of all the available images of the two frequency bands (C and X). [ABSTRACT FROM AUTHOR]

The capability of UAVs imagery to monitor and predict the evolution of several forage associations was assessed during the whole growing cycle of 2019–20. For this purpose, eight different forage associations grown in triplicate were used: vetch-barley-triticale (VBT), vetch-triticale (VT), vetch-rye (VR), vetch-oats (VO), pea-barley-triticale (PBT), pea-triticale (PT), pea-rye (PR) and pea-oats (PO). Six biophysical parameters were monitored through six vegetation indices on seven measurements dates distributed along the growing cycle. The experiments were carried out on the organic farm "Gallegos de Crespes" located in the municipality of Larrodrigo (Salamanca, Spain). The results obtained in the exploratory and the correlation analysis suggested that a predictive model (PLS regression) could be performed. Overall, vetch-based associations showed slightly higher values for both the field parameters and the vegetation indices than pea-based ones. Correlations were very strong and significant for each association throughout their growing cycle, suggesting that the evolution of the associations would be monitored from the spectral indices. Integrating these multispectral observations in the PLS model, the agronomic parameters of forage associations were predicted with a reliability of more than 50%. A single combination of VNIR (or even only visible) bands was able to feed the regression model, leading to a successful prediction of the agronomic parameters. [ABSTRACT FROM AUTHOR]

Nowadays, there is a growing demand for high-quality vegetal protein food products, such as pulses and lentils in particular. However, there is no scientific evidence on the nutritional and morphometric characterization of the main lentil cultivars in the Western Mediterranean area. For this reason, the aim of this work is to carry out a morphometric and nutritional characterization of the main Spanish lentil cultivars. Nutrient content assessment was performed on dry matter. The results showed that all studied cultivars are large and heavy lentils, except for the cultivar "Pardina". They have high protein levels, ranging from 21% to 25%, which is higher than those found in any other pulse, as well as a high carbohydrate content, greater than 59% in all cases. Fiber content was higher than expected in "Armuña" and "Rubia Castellana" cultivars, ranging from 6% to 6.6%, and exceptionally high in the case of the cultivar "Pardina", which reached 7.8%. Conversely, very low values were found for fat content, varying between 0.5% and 0.9%. Ca, Fe and Mg levels were remarkably higher (from 550 ppm to 851 ppm, from 98 ppm to 139 ppm and from 790 ppm to 989 ppm, respectively) than those found for other lentil cultivars, especially the high Mg content in the cultivars "Jaspeada" and "Microjaspeada", both above 955 ppm. Clear differentiation was found between the cultivars "Rubia Castellana", "Pardina" and those included in the Protected Geographical Indication (PGI) "Lenteja de la Armuña". Overall, lentil cultivars included in the PGI "Lenteja de la Armuña" showed better morphometric and nutritional characteristics than cultivars "Pardina" or "Rubia Castellana". [ABSTRACT FROM AUTHOR]

Simple Summary: In order to assess the effect of grazing level on milk quality and indicators related to the carbon footprint of dairy sheep farms, monthly data collection was carried out for 1 year on 17 farms in the region of Castilla y León (Spain). These data were analysed using a multivariate statistical procedure that allowed the association of the mentioned indicators with the grazing level and identifying the management system of the farms. It was shown that farms with higher grazing levels were more environmentally sustainable, as indirect gas emissions and energy consumption were much lower. Milk quality from these farms was higher in terms of total protein, fat, omega 3 fatty acids, conjugated linoleic acid and α-tocopherol levels. Currently, there are very few studies in the dairy sheep sector associating milk quality and indicators regarding carbon footprint and their link to grazing levels. For 1 year, monthly milk samples and records related to environmental emissions and management systems were collected through surveys from 17 dairy sheep farms in the region of Castilla y León (Spain), in order to relate this information to the use of natural pastures under free grazing. Indicators were constructed on the collected data and subjected to a multivariate statistical procedure that involved a factor analysis, a cluster analysis and a population canonical analysis. By applying multivariate statistical techniques on milk quality and carbon footprint indicators, it was possible to identify the management system of the farms. From an environmental point of view, farms with a higher grazing level (cluster 4) were more sustainable, as they had the lowest carbon footprint (lower CO2, N2O and CO2 equivalent emissions per sheep and year) and the lowest energy consumption levels, which were gradually lower than those of farms in cluster 3; both indicators were much lower than those of farms in clusters 1 and 2. The milk quality of cluster 1 and 2 farms was significantly lower in terms of total protein and fat content, dry extract, omega-3 fatty acid levels and α-tocopherol content than farms in clusters 3 and 4, which had higher accessibility to grazing resources. In sum, the higher the use of natural resources, the lower the external inputs the farms required and the lower environmental impact and energy costs they have. [ABSTRACT FROM AUTHOR]

Simple Summary: There is an increasing demand for meat products with high nutritional quality and produced through eco-friendly and sustainable systems, such as organic production. Therefore, in this work, it was proposed to study how the main characteristics of nutritional quality of beef are modified according to the feed management system, in terms of grazing level, and the ageing time of the meat. We found that the increase in pasture intake caused calves' meat to present lower values of fat oxidation and, simultaneously, higher values of healthy fatty acids for humans. In the case of samples from organic farming, these showed a higher fat content and lower moisture, besides being the darkest samples and those with the lowest score regarding flavour quality in the tasting panel. Furthermore, it was proved that the increase in ageing time resulted in a general improvement of the sensory characteristics of the meat samples, especially those from the animals that had consumed more pasture. Therefore, we suggest extending the ageing period of beef since there is a clear tendency to increase the tenderness and juiciness of beef meat. This study investigated the influence of the production system (conventional vs. organic), the grass consumption level and the ageing period (7 and 14 days) on beef quality. Three groups of samples from Limousin × Avileña calves were analysed: F100, formed by animals fed 100% on forage; F74, formed by animals fed on an average amount of forage of 74%; and F35, formed by animals fed on straw fodder (35%) and concentrate (65%). The results showed that the higher the grass content, the lower the fat oxidation and the higher the n-3 content, but also the higher the SFA level, the initial Warner-Bratzler shear force (WBSF), and the more residue it leaves on chewing. As for the effect of production system, organic samples showed higher intramuscular fat content and lower moisture and MUFA content. These samples were darker and showed lower values for flavour quality. Among the organic samples, F100 had higher n-3 and CLA content and higher values for colour, hardness, odour and flavour quality. Increased ageing time may improve the sensory characteristics of the meat, especially in the case of the F100 samples. The results pointed out that F100 samples aged at least 14 days showed the best physico-chemical, nutritional and sensory characteristics. [ABSTRACT FROM AUTHOR]

Endmember estimation plays a key role in hyperspectral image unmixing, often requiring an estimation of the number of endmembers and extracting endmembers. However, most of the existing extraction algorithms require prior knowledge regarding the number of endmembers, being a critical process during unmixing. To bridge this, a new maximum distance analysis (MDA) method is proposed that simultaneously estimates the number and spectral signatures of endmembers without any prior information on the experimental data containing pure pixel spectral signatures and no noise, being based on the assumption that endmembers form a simplex with the greatest volume over all pixel combinations. The simplex includes the farthest pixel point from the coordinate origin in the spectral space, which implies that: (1) the farthest pixel point from any other pixel point must be an endmember, (2) the farthest pixel point from any line must be an endmember, and (3) the farthest pixel point from any plane (or affine hull) must be an endmember. Under this scenario, the farthest pixel point from the coordinate origin is the first endmember, being used to create the aforementioned point, line, plane, and affine hull. The remaining endmembers are extracted by repetitively searching for the pixel points that satisfy the above three assumptions. In addition to behaving as an endmember estimation algorithm by itself, the MDA method can co-operate with existing endmember extraction techniques without the pure pixel assumption via generalizing them into more effective schemes. The conducted experiments validate the effectiveness and efficiency of our method on synthetic and real data. [ABSTRACT FROM AUTHOR]

One of the most important tasks in hyperspectral imaging is the classification of the pixels in the scene in order to produce thematic maps. This problem can be typically solved through machine learning techniques. In particular, deep learning algorithms have emerged in recent years as a suitable methodology to classify hyperspectral data. Moreover, the high dimensionality of hyperspectral data, together with the increasing availability of unlabeled samples, makes deep learning an appealing approach to process and interpret those data. However, the limited number of labeled samples often complicates the exploitation of supervised techniques. Indeed, in order to guarantee a suitable precision, a large number of labeled samples is normally required. This hurdle can be overcome by resorting to unsupervised classification algorithms. In particular, autoencoders can be used to analyze a hyperspectral image using only unlabeled data. However, the high data dimensionality leads to prohibitive training times. In this regard, it is important to realize that the operations involved in autoencoders training are intrinsically parallel. Therefore, in this paper we present an approach that exploits multi-core and many-core devices in order to achieve efficient autoencoders training in hyperspectral imaging applications. Specifically, in this paper, we present new OpenMP and CUDA frameworks for autoencoder training. The obtained results show that the CUDA framework provides a speed-up of about two orders of magnitudes as compared to an optimized serial processing chain. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have emerged as a relevant tool for the classification of remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) being the current state-of-the-art in many classification tasks. However, deep CNNs present several limitations in the context of HSI supervised classification. Although deep models are able to extract better and more abstract features, the number of parameters that must be fine-tuned requires a large amount of training data (using small learning rates) in order to avoid the overfitting and vanishing gradient problems. The acquisition of labeled data is expensive and time-consuming, and small learning rates forces the gradient descent to use many small steps to converge, slowing down the runtime of the model. To mitigate these issues, this paper introduces a new deep CNN framework for spectral-spatial classification of HSIs. Our newly proposed framework introduces shortcut connections between layers, in which the feature maps of inferior layers are used as inputs of the current layer, feeding its own output to the rest of the the upper layers. This leads to the combination of various spectral-spatial features across layers that allows us to enhance the generalization ability of the network with HSIs. Our experimental results with four well-known HSI datasets reveal that the proposed deep&dense CNN model is able to provide competitive advantages in terms of classification accuracy when compared to other state-of-the-methods for HSI classification. [ABSTRACT FROM AUTHOR]