Inclusion of [H3PW12O40] and [H4SiW12O40] into a silica gel matrix via "sol-gel" methodology

Indexación: Web of Science; Scopus.Here we report the inclusion of two Keggin Polyoxometalates (POMs), [H3PW12O40] and [H4SiW12O40], into silica gels by integrating them during the preparation of the SiO2 matrix via "sol-gel" methods. Aerogels were produced by supercritical drying of the wet gels impregnated with the POMs, and lyogels were obtained by means of a lyophilization process. These materials were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transformed infrared (FT-IR) spectroscopy and thermoanalytical techniques (TGA-DSC). We found that a large fraction of POMs are lost during the aging time, and solvent exchange for lyophilization. However the thermal stability of the bare matrix is modified by the inclusion of POMs. Some aggregates with a high content of POMs were found via SEM-EDX.http://ref.scielo.org/3fg9t

GPCA vs. PCA in Recognition and 3-D Localization of Ultrasound Reflectors

In this paper, a new method of classification and localization of reflectors, using the time-of-flight (TOF) data obtained from ultrasonic transducers, is presented. The method of classification and localization is based on Generalized Principal Component Analysis (GPCA) applied to the TOF values obtained from a sensor that contains four ultrasound emitters and 16 receivers. Since PCA works with vectorized representations of TOF, it does not take into account the spatial locality of receivers. The GPCA works with two-dimensional representations of TOF, taking into account information on the spatial position of the receivers. This report includes a detailed description of the method of classification and localization and the results of achieved tests with three types of reflectors in 3-D environments: planes, edges, and corners. The results in terms of processing time, classification and localization were very satisfactory for the reflectors located in the range of 50–350 cm

DPDnet: A Robust People Detector using Deep Learning with an Overhead Depth Camera

In this paper we propose a method based on deep learning that detects multiple people from a single overhead depth image with high reliability. Our neural network, called DPDnet, is based on two fully-convolutional encoder-decoder neural blocks based on residual layers. The Main Block takes a depth image as input and generates a pixel-wise confidence map, where each detected person in the image is represented by a Gaussian-like distribution. The refinement block combines the depth image and the output from the main block, to refine the confidence map. Both blocks are simultaneously trained end-to-end using depth images and head position labels. The experimental work shows that DPDNet outperforms state-of-the-art methods, with accuracies greater than 99% in three different publicly available datasets, without retraining not fine-tuning. In addition, the computational complexity of our proposal is independent of the number of people in the scene and runs in real time using conventional GPUs

InSAR-Based Mapping to Support Decision-Making after an Earthquake

It has long been recognized that earthquakes change the stress in the upper crust around the fault rupture and can influence the behaviour of neighbouring faults and volcanoes. Rapid estimates of these stress changes can provide the authorities managing the post-disaster situation with valuable data to identify and monitor potential threads and to update the estimates of seismic and volcanic hazard in a region. Here we propose a methodology to evaluate the potential influence of an earthquake on nearby faults and volcanoes and create easy-to-understand maps for decision-making support after large earthquakes. We apply this methodology to the Mw 7.8, 2016 Ecuador earthquake. Using Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) and continuous GPS data, we measure the coseismic ground deformation and estimate the distribution of slip over the fault rupture. We also build an alternative source model using the Global Centroid Moment Tensor (CMT) solution. Then we use these models to evaluate changes of static stress on the surrounding faults and volcanoes and produce maps of potentially activated faults and volcanoes. We found, in general, good agreement between our maps and the seismic and volcanic events that occurred after the Pedernales earthquake. We discuss the potential and limitations of the methodology.This work is supported by the European Commission, Directorate-General Humanitarian Aid and Civil Protection (ECHO) under the SAFETY (Sentinel for Geohazards regional monitoring and forecasting) project (ECHO/SUB/2015/718679/Prev02) and by the Spanish Ministry of Economy and Competitiveness under INTERGEOSIMA (CGL2013-47412) and ACTIVESTEP (CGL2017-83931-C3), QUAKESTEP (1-P) + 3GEO(2-P) + GEOACTIVA (3-P) projects

CDK11 Promotes Cytokine-Induced Apoptosis in Pancreatic Beta Cells Independently of Glucose Concentration and Is Regulated by Inflammation in the NOD Mouse Model

Background: Pancreatic islets are exposed to strong pro-apoptotic stimuli: inflammation and hyperglycemia, during the progression of the autoimmune diabetes (T1D). We found that the Cdk11(Cyclin Dependent Kinase 11) is downregulated by inflammation in the T1D prone NOD (non-obese diabetic) mouse model. The aim of this study is to determine the role of CDK11 in the pathogenesis of T1D and to assess the hierarchical relationship between CDK11 and Cyclin D3 in beta cell viability, since Cyclin D3, a natural ligand for CDK11, promotes beta cell viability and fitness in front of glucose. Methods: We studied T1D pathogenesis in NOD mice hemideficient for CDK11 (N-HTZ), and, in N-HTZ deficient for Cyclin D3 (K11HTZ-D3KO), in comparison to their respective controls (N-WT and K11WT-D3KO). Moreover, we exposed pancreatic islets to either pro-inflammatory cytokines in the presence of increasing glucose concentrations, or Thapsigargin, an Endoplasmic Reticulum (ER)-stress inducing agent, and assessed apoptotic events. The expression of key ER-stress markers (Chop, Atf4 and Bip) was also determined. Results: N-HTZ mice were significantly protected against T1D, and NS-HTZ pancreatic islets exhibited an impaired sensitivity to cytokine-induced apoptosis, regardless of glucose concentration. However, thapsigargin-induced apoptosis was not altered. Furthermore, CDK11 hemideficiency did not attenuate the exacerbation of T1D caused by Cyclin D3 deficiency. Conclusions: This study is the first to report that CDK11 is repressed in T1D as a protection mechanism against inflammation-induced apoptosis and suggests that CDK11 lies upstream Cyclin D3 signaling. We unveil the CDK11/Cyclin D3 tandem as a new potential intervention target in T1D

High-throughput phenotyping and improvements in breeding cassava for increased carotenoids in the roots

Past research developed reliable equations to base selections for high β-carotene on near-infrared spectroscopy (NIR) predictions (100 genotypes d−1) rather than with high-performance liquid chromatography (HPLC) (<10 samples d−1). During recent harvest, CIAT made selections based on NIR predictions for the first time. This innovation produced valuable information that will help other cassava (Manihot esculenta Crantz) breeding programs. A total of 284 samples were analyzed with NIR and HPLC for total β-carotene (TBC) and by the oven method for dry matter content (DMC). Results indicated that NIR reliably predicted TBC and DMC. In addition, 232 genotypes grown in preliminary yield trials (PYTs) were harvested at 8.5 and 10.5 mo after planting (one plant per genotype and age) and root quality traits analyzed (by NIR only). Repeatability of results at the two ages was excellent, suggesting reliable results from NIR. In contrast to previous reports, age of the plant did not influence carotenoids content in the roots. The availability of a high-throughput NIR protocol allowed comparing results (for the first time) from seedling and cloned plants from the same genotype. Results showed very little relationship for DMC between seedling and cloned plants (R2 = 0.09). There was a much better association for TBC (R2 = 0.48) between seedling and cloned plants. It is postulated that variation in the environmental conditions when seedling and cloned plants (from the same genotype) may be responsible for these weak associations. Important changes in selection strategies have been implemented to overcome problems related to a lengthy harvesting season. (Résumé d'auteur