Delayed diagnosis of Loeys-Dietz syndrome revealed through atrial septal defect with pulmonary artery dilation.

Loeys-Dietz syndrome (LDS) is an autosomal-dominant connective tissue disorder associated with mutations in the transforming growth factor ß receptor. It is characterized by distinctive craniofacial changes, skeletal features, and cardiovascular complications. We present a case of a 24-year-old male with development delay and a one-year history of progressively worsening dyspnea on moderate exertion and orthopnea. Echocardiography revealed right atrial and right ventricle dilation, right ventricle hypertrophy, atrial septal defect, and aneurysmal dilation of the pulmonary artery trunk. This case underscores the importance of early detection and comprehensive imaging in patients suspected of having LDS, particularly considering the potential for atypical vascular manifestations.

Influence of Oxygen Management on Color and Phenolics of Red Wines.

Winemaking involves contact at different stages with atmospheric oxygen, the consumption of which determines its final properties. The chemical analysis of red wines subjected to consecutive cycles of air saturation has been extensively researched; however, the capacity to consume different doses of oxygen before bottling is an aspect that has been little studied. In this work, the effect of saturation of different levels of oxygen on the final characteristics of different wines made from Tempranillo and Garnacha grape extracts was studied. For this purpose, the wines were subjected to controlled oxygen saturation levels to simulate their possible oxygenation before bottling. The only difference was the phenolic composition of grape extracts that were reconstituted under the same conditions to avoid the interferences inherent to the fermentation process and the additives added in the winery. The kinetics of oxygen consumption was then evaluated and its effect on the color, antioxidant capacity, and phenols of three different wines was analyzed. This work shows the relationship between the oxidation state of wine and changes in its chemical composition. In addition, it provides insight into the effect of oxygen consumption before bottling on the properties of wines subjected to high and single doses of oxygen.

Development of a New Strategy for Studying the Oxygen Consumption Potential of Wine through the Grape Extract Evaluation.

The development of a method to determine the aging potential of wine at the time of harvest, through the evaluation of its oxygen avidity, is a potential tool for the winemaking sector. To this end, it is necessary to formulate a potential wine with this grape prior to alcoholic fermentation. The main objective of this method was to optimize a formulation of the potential wine, based on the grape extracts (GEs), to subsequently evaluate its oxygen consumption kinetics, guaranteeing maximum differentiation between the different GEs. The optimization was carried out with a Taguchi orthogonal matrix design, which optimized the variables to be used in the GE reconstitution. The variables studied were pH, Fe2+, Cu2+, Mn2+, alcohol content and acetaldehyde. The evaluation of the characteristic parameters of the consumption kinetics of each of the GEs allowed us to know the different reconstitution conditions that most influence the differentiation of the oxygen consumption kinetics of very similar GEs. The reconstitution conditions chosen were pH 3.3; 1 mg/L Fe2+; 0.1 mg/L Cu2+; 1 mg/L Mn2+; 12% (v/v) alcoholic strength and 10 mg/L acetaldehyde, with pH, Fe2+ and Mn2+ being the significant conditions. The kinetics of reconstituted GE could be a tool for the classification and evaluation of grapes according to their aging potential or shelf life of the wine made.

Air saturation methodology proposal for the analysis of wine oxygen consumption kinetics.

The great heterogeneity currently present when characterizing wine consumption kinetics means that a saturation method, as well as different parameters that allow comparison between wines, need to be established. The aim of this work was to establish a robust method for a wine saturation protocol and compare different fitting models to approximate the oxygen consumption kinetics. To differentiate wines, parameters extracted from the oxygen consumption curves were studied and proposed. 72 young commercial wines (red, white and rosé) from different Spanish appellations of origin, varieties and vintages were used. The results revealed that 5 min was enough to saturate wines up to the maximum level for each one at 35 °C. The inverse curve fitting model showed the best results for all wines. Oxygen at half consumption time (Omid) and time required to consume from 90% to 10% of the oxygen initially available (ΔtO_90_10) were the parameters that differentiated wines the most.

Artificial Intelligence Methods for Constructing Wine Barrels with a Controlled Oxygen Transmission Rate.

Oxygen is an important factor in the wine aging process, and the oxygen transmission rate (OTR) is the parameter of the wood that reflects its oxygen permeation. OTR has not been considered in the cooperage industry yet; however, recent studies proposed a nondestructive method for estimating the OTR of barrel staves, but an efficient method to combine these staves to build barrels with a desired OTR is needed to implement it in the industry. This article proposes artificial intelligence methods for selecting staves for the construction of barrel heads or bodies with a desired target OTR. Genetic algorithms were used to implement these methods in consideration of the known OTR of the staves and the geometry of the wine barrels. The proposed methods were evaluated in several scenarios: homogenizing the OTR of the actual constructed barrels, constructing low-OTR and high-OTR barrels based on a preclassification of the staves and implementing the proposed method in real cooperage conditions. The results of these experiments suggest the suitability of the proposed methods for their implementation in a cooperage in order to build controlled OTR barrels.

Study of the role of oxygen in the evolution of red wine colour under different ageing conditions in barrels and bottles.

Wine ageing in barrels is conditioned, among other factors, by the amount of oxygen received during this process, which thus impacts its final properties. The aim of this study was to evaluate the effect of oxygen on wine colour during ageing in barrels and bottles during different times. The use of barrels with different and known rates of oxygenation allows the effect of different oxygenation conditions throughout the process in barrels and its later evolution in bottles. A simulation process of ageing in bottles was used to study the impact of bottling in wines after differing ageing periods in barrels. The study of winés oxygen consumption capacity has been tied to colour modifications during ageing in barrels and bottles. Wines aged in barrels with a high oxygenation rate showed greater avidity to consume oxygen taking less time to consume that available, which is reflected in a greater increase in colour intensity.

Prediction Models to Control Aging Time in Red Wine.

A combination of physical-chemical analysis has been used to monitor the aging of red wines from D.O. Toro (Spain). The changes in the chemical composition of wines that occur over the aging time can be used to distinguish between wine samples collected after one, four, seven and ten months of aging. Different computational models were used to develop a good authenticity tool to certify wines. In this research, different models have been developed: Artificial Neural Network models (ANNs), Support Vector Machine (SVM) and Random Forest (RF) models. The results obtained for the ANN model developed with sigmoidal function in the output neuron and the RF model permit us to determine the aging time, with an average absolute percentage deviation below 1%, so it can be concluded that these two models have demonstrated their capacity to predict the age of wine.

Leaf and canopy reflectance spectrometry applied to the estimation of angular leaf spot disease severity of common bean crops.

This study is aimed at (i) estimating the angular leaf spot (ALS) disease severity in common beans crops in Brazil, caused by the fungus Pseudocercospora griseola, employing leaf and canopy spectral reflectance data, (ii) evaluating the informative spectral regions in the detection, and (iii) comparing the estimation accuracy when the reflectance or the first derivative reflectance (FDR) is employed. Three data sets of useful spectral reflectance measurements in the 440 to 850 nm range were employed; measurements were taken over the leaves and canopy of bean crops with different levels of disease. A system based in Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) was developed to estimate the disease severity from leaf and canopy hyperspectral reflectance spectra. Levels of disease to be taken as true reference were determined from the proportion of the total leaf surface covered by necrotic lesions on RGB images. When estimating ALS disease severity in bean crops by using hyperspectral reflectance spectrometry, this study suggests that (i) successful estimations with coefficients of determination up to 0.87 can be achieved if the spectra is acquired by the spectroradiometer in contact with the leaves, (ii) unsuccessful estimations are obtained when the spectra are acquired by the spectroradiometer from one or more meters above the crop, (iii) the red to near-infrared spectral region (630-850 nm) offers the same precision in the estimation as the blue to near-infrared spectral region (440-850), and (iv) neither significant improvements nor significant detriments are achieved when the input data to the estimation processing system are the FDR spectra, instead of the reflectance spectra.

On-line monitoring of oxygen as a method to qualify the oxygen consumption rate of wines.

Measuring the oxygen content during winemaking and bottle storage has become increasingly popular due to its impact on the sensory quality and longevity of wines. Nevertheless, only a few attempts to describe the kinetics of oxygen consumption based on the chemical composition of wines have been published. Therefore, this study proposes firstly a new fitting approach describing oxygen consuming kinetics and secondly the use of an Artificial Neural Network approach to describe and compare the oxygen avidity of wines according to their basic chemical composition (i.e. the content of ethanol, titratable acidity, total sulfur dioxide, total phenolics, iron and copper). The results showed no significant differences in the oxygen consumption rate between white and red wines, and allowed the sorting of the wines studied according to their oxygen consumption rate.

An SVM-based classifier for estimating the state of various rotating components in agro-industrial machinery with a vibration signal acquired from a single point on the machine chassis.

The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM)-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i) accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii) the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii) when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels.

Temperature and relative humidity estimation and prediction in the tobacco drying process using Artificial Neural Networks.

This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed.