Verification of the selectivity of a liquid chromatography method for determination of stilbenes and flavonols in red wines by mass spectrometry.

Quantification of bioactive phenols, like stilbenes and flavonols (SaF), has been conducted to evaluate the nutraceutical potential of red wines. However, there is still a lack of full validated, fast and accessible liquid chromatography methods offering high selectivity and a simple procedure. We present here the use of a high-resolution mass spectrometer to evaluate the selectivity of a feasible and traditional liquid chromatography technique (HPLC? DAD) to analyze markers of aglycone SaF in red wines. The SaF compounds were tested: trans-resveratrol, trans-eviniferin, quercetin, myricetin, and kaempferol, as well as trans-cinnamic acid, one of their precursors. System suitability and validation tests were employed for the selected conditions (octylsilane column, methanol mobile phase, and gradient elution). The validation process ensured the HPLC?DAD method was selective, linear, sensitive, precise, accurate and robust. The method was then applied to red wine samples from the Campanha Gau´cha region, Southern Brazil. The real samples contained different SaF levels, showing that the method is applicable to routine use. Furthermore, this was the first SaF characterization of red wines from the Campanha Gau´cha, contributing to regional and product development. Keywords Bioactive phenols Red wine Liquid chromatography Mass spectrometry Validatio

Land use in the Paraiba Valley through remotely sensed data

A methodology for land use survey was developed and land use modification rates were determined using LANDSAT imagery of the Paraiba Valley (state of Sao Paulo). Both visual and automatic interpretation methods were employed to analyze seven land use classes: urban area, industrial area, bare soil, cultivated area, pastureland, reforestation and natural vegetation. By means of visual interpretation, little spectral differences are observed among those classes. The automatic classification of LANDSAT MSS data using maximum likelihood algorithm shows a 39% average error of omission and a 3.4% error of inclusion for the seven classes. The complexity of land uses in the study area, the large spectral variations of analyzed classes, and the low resolution of LANDSAT data influenced the classification results

A Novel Transformer-Based IMU Self-Calibration Approach through On-Board RGB Camera for UAV Flight Stabilization

During flight, unmanned aerial vehicles (UAVs) need several sensors to follow a predefined path and reach a specific destination. To this aim, they generally exploit an inertial measurement unit (IMU) for pose estimation. Usually, in the UAV context, an IMU entails a three-axis accelerometer and a three-axis gyroscope. However, as happens for many physical devices, they can present some misalignment between the real value and the registered one. These systematic or occasional errors can derive from different sources and could be related to the sensor itself or to external noise due to the place where it is located. Hardware calibration requires special equipment, which is not always available. In any case, even if possible, it can be used to solve the physical problem and sometimes requires removing the sensor from its location, which is not always feasible. At the same time, solving the problem of external noise usually requires software procedures. Moreover, as reported in the literature, even two IMUs from the same brand and the same production chain could produce different measurements under identical conditions. This paper proposes a soft calibration procedure to reduce the misalignment created by systematic errors and noise based on the grayscale or RGB camera built-in on the drone. Based on the transformer neural network architecture trained in a supervised learning fashion on pairs of short videos shot by the UAV’s camera and the correspondent UAV measurements, the strategy does not require any special equipment. It is easily reproducible and could be used to increase the trajectory accuracy of the UAV during the flight

Dynamic instance generation for few-shot handwritten document layout segmentation (short paper)

Historical handwritten document analysis is an important activity to retrieve information about our past. Given that this type of process is slow and time-consuming, the humanities community is searching for new techniques that could aid them in this activity. Document layout analysis is a branch of machine learning that aims to extract semantic informations from digitised documents. Here we propose a new framework for handwritten document layout analysis that differentiates from the current state-of-the-art by the fact that it features few-shot learning, thus allowing for good results with little manually labelled data and the dynamic instance generation process. Our results were obtained using the DIVA - HisDB dataset

Chemokine CCL2 and its receptor CCR2 are increased in the hippocampus following pilocarpine-induced status epilepticus

<p>Abstract</p> <p>Background</p> <p>Neuroinflammation occurs after seizures and is implicated in epileptogenesis. CCR2 is a chemokine receptor for CCL2 and their interaction mediates monocyte infiltration in the neuroinflammatory cascade triggered in different brain pathologies. In this work CCR2 and CCL2 expression were examined following status epilepticus (SE) induced by pilocarpine injection.</p> <p>Methods</p> <p>SE was induced by pilocarpine injection. Control rats were injected with saline instead of pilocarpine. Five days after SE, CCR2 staining in neurons and glial cells was examined using imunohistochemical analyses. The number of CCR2 positive cells was determined using stereology probes in the hippocampus. CCL2 expression in the hippocampus was examined by molecular assay.</p> <p>Results</p> <p>Increased CCR2 was observed in the hippocampus after SE. Seizures also resulted in alterations to the cell types expressing CCR2. Increased numbers of neurons that expressed CCR2 was observed following SE. Microglial cells were more closely apposed to the CCR2-labeled cells in SE rats. In addition, rats that experienced SE exhibited CCR2-labeling in populations of hypertrophied astrocytes, especially in CA1 and dentate gyrus. These CCR2+ astroctytes were not observed in control rats. Examination of CCL2 expression showed that it was elevated in the hippocampus following SE.</p> <p>Conclusion</p> <p>The data show that CCR2 and CCL2 are up-regulated in the hippocampus after pilocarpine-induced SE. Seizures also result in changes to CCR2 receptor expression in neurons and astrocytes. These changes might be involved in detrimental neuroplasticity and neuroinflammatory changes that occur following seizures.</p

A RAPD-PCR-based genetic diversity analysis of Helicoverpa armigera and H. zea populations in Brazil.

Therefore, the aim of this study was to determine the genetic diversity of H. armigera and H. zea populations by RAPD-PCR analysis. The most important result was the clustering of one H. armigera population in a group predominantly formed by H. zea. It could indicate a possible occurrence of an interspecific cross between these species. This is a concern to Brazilian agriculture due to the possibility of selection of hybrids well adapted to the American environment, which would be inherited from H. zea

Nowcasting of thunderstorm severity with Machine Learning in the Alpine Region

Presentación realizada en la 3rd European Nowcasting Conference, celebrada en la sede central de AEMET en Madrid del 24 al 26 de abril de 2019

Application of LANDSAT data to the study of urban development in Brasilia

The urban growth of Brasilia within the last ten years is analyzed with special emphasis on the utilization of remote sensing orbital data and automatic image processing. The urban spatial structure and the monitoring of its temporal changes were examined in a whole and dynamic way by the utilization of MSS-LANDSAT images for June (1973, 1978 and 1983). In order to aid data interpretation, a registration algorithm implemented in the Interactive Multispectral Image Analysis System (IMAGE-100) was utilized aiming at the overlap of multitemporal images. The utilization of suitable digital filters, combined with the images overlap, allowed a rapid identification of areas of possible urban growth and oriented the field work. The results obtained in this work permitted an evaluation of the urban growth of Brasilia, taking as reference the proposal stated for the construction of the city in the Pilot Plan elaborated by Lucio Costa

A Novel GAN-Based Anomaly Detection and Localization Method for Aerial Video Surveillance at Low Altitude

The last two decades have seen an incessant growth in the use of Unmanned Aerial Vehicles (UAVs) equipped with HD cameras for developing aerial vision-based systems to support civilian and military tasks, including land monitoring, change detection, and object classification. To perform most of these tasks, the artificial intelligence algorithms usually need to know, a priori, what to look for, identify. or recognize. Actually, in most operational scenarios, such as war zones or post-disaster situations, areas and objects of interest are not decidable a priori since their shape and visual features may have been altered by events or even intentionally disguised (e.g., improvised explosive devices (IEDs)). For these reasons, in recent years, more and more research groups are investigating the design of original anomaly detection methods, which, in short, are focused on detecting samples that differ from the others in terms of visual appearance and occurrences with respect to a given environment. In this paper, we present a novel two-branch Generative Adversarial Network (GAN)-based method for low-altitude RGB aerial video surveillance to detect and localize anomalies. We have chosen to focus on the low-altitude sequences as we are interested in complex operational scenarios where even a small object or device can represent a reason for danger or attention. The proposed model was tested on the UAV Mosaicking and Change Detection (UMCD) dataset, a one-of-a-kind collection of challenging videos whose sequences were acquired between 6 and 15 m above sea level on three types of ground (i.e., urban, dirt, and countryside). Results demonstrated the effectiveness of the model in terms of Area Under the Receiving Operating Curve (AUROC) and Structural Similarity Index (SSIM), achieving an average of 97.2% and 95.7%, respectively, thus suggesting that the system can be deployed in real-world applications