ALBERT

Description: Hypertension and smoking are related with oxidative stress (OS), which in turn reports on cellular aging. Zinc is an essential element involved in an individual’s physiology. The aim of this study was to evaluate the relation of zinc levels in serum and urine with OS and cellular aging and its effect on the development of hypertension. In a Spanish sample with 1500 individuals, subjects aged 20–59 years were selected, whose zinc intake levels fell within the recommended limits. These individuals were classified according to their smoking habits and hypertensive condition. A positive correlation was found (Pearson’s C = 0.639; p = 0.01) between Zn serum/urine quotient and oxidized glutathione levels (GSSG). Finally, risk of hypertension significantly increased when the GSSG levels exceeded the 75 percentile; OR = 2.80 (95%CI = 1.09–7.18) and AOR = 3.06 (95%CI = 0.96–9.71). Low zinc levels in serum were related with OS and cellular aging and were, in turn, to be a risk factor for hypertension.

Description: Forests, Vol. 9, Pages 190: Developing Models to Predict the Number of Fire Hotspots from an Accumulated Fuel Dryness Index by Vegetation Type and Region in Mexico Forests doi: 10.3390/f9040190 Authors: D. Vega-Nieva J. Briseño-Reyes M. Nava-Miranda E. Calleros-Flores P. López-Serrano J. Corral-Rivas E. Montiel-Antuna M. Cruz-López M. Cuahutle R. Ressl E. Alvarado-Celestino A. González-Cabán E. Jiménez J. Álvarez-González A. Ruiz-González R. Burgan H. Preisler Understanding the linkage between accumulated fuel dryness and temporal fire occurrence risk is key for improving decision-making in forest fire management, especially under growing conditions of vegetation stress associated with climate change. This study addresses the development of models to predict the number of 10-day observed Moderate-Resolution Imaging Spectroradiometer (MODIS) active fire hotspots—expressed as a Fire Hotspot Density index (FHD)—from an Accumulated Fuel Dryness Index (AcFDI), for 17 main vegetation types and regions in Mexico, for the period 2011–2015. The AcFDI was calculated by applying vegetation-specific thresholds for fire occurrence to a satellite-based fuel dryness index (FDI), which was developed after the structure of the Fire Potential Index (FPI). Linear and non-linear models were tested for the prediction of FHD from FDI and AcFDI. Non-linear quantile regression models gave the best results for predicting FHD using AcFDI, together with auto-regression from previously observed hotspot density values. The predictions of 10-day observed FHD values were reasonably good with R2 values of 0.5 to 0.7 suggesting the potential to be used as an operational tool for predicting the expected number of fire hotspots by vegetation type and region in Mexico. The presented modeling strategy could be replicated for any fire danger index in any region, based on information from MODIS or other remote sensors.

Description: Forests, Vol. 9, Pages 393: Is Mechanized Harvesting of Shrubs Grown for Energy Purposes Environmentally Sustainable? Forests doi: 10.3390/f9070393 Authors: Borja D. González-González Hortensia Sixto Isabel González Isabel Cañellas Mechanized harvesting of shrub formations as part of sustainable forest management not only helps reduce the risk of forest fires in Mediterranean environments but also provides economic benefit from the extracted biomass, contributing to the development of the bioeconomy. However, these mechanized operations require an environmental impact assessment to identify the short-term impacts, both positive and negative. This is especially important in the Mediterranean basin given the specific climatic conditions which exist. In this study, the environmental impact of mechanized shrub harvesting is analyzed in relation to (i) changes in biodiversity and in the presence and growth of species; (ii) physical and chemical properties of the soil; and (iii) changes in forest fire risk. For this purpose, a pre-harvest inventory was conducted and post-harvest monitoring schedules of 1- and 2-year durations were established in three characteristic Mediterranean shrubland formations located in the northern–central area of the Iberian Peninsula. Our results reveal that the recovery rates in biodiversity indices after harvesting were very high, with values ranging from 30 to 70% depending on the site. Two years after harvesting, the species coverage was similar to the pre-harvest scenario in some locations, although not with regards to height, the ericaceous species being those with the greatest sprouting capacity. Significant changes in the physical and chemical properties of soils were also observed. In this regard, negative impacts such as soil compaction or slight acidification were identified at some sites. However, positive effects were also found such as an increment in carbon and nitrogen content after harvesting, along with increased litter quantity a year from the clearing operation due to biomass residue left on the ground after harvesting. Furthermore, mechanical harvesting effectively modified fire behavior in all the shrub formations 2 years after clearing, with a notable reduction in fire risk at all the studied sites.

Description: Ecological measurements in marine settings are often constrained in space and time, with spatial heterogeneity obscuring broader generalisations. While advances in remote sensing, integrative modelling and meta-analysis enable generalisations from field observations, there is an underlying need for high-resolution, standardised and geo-referenced field data. Here, we evaluate a new approach aimed at optimising data collection and analysis to assess broad-scale patterns of coral reef community composition using automatically annotated underwater imagery, captured along 2 km transects. We validate this approach by investigating its ability to detect spatial (e.g., across regions) and temporal (e.g., over years) change, and by comparing automated annotation errors to those of multiple human annotators. Our results indicate that change of coral reef benthos can be captured at high resolution both spatially and temporally, with an average error below 5%, among key benthic groups. Cover estimation errors using automated annotation varied between 2% and 12%, slightly larger than human errors (which varied between 1% and 7%), but small enough to detect significant changes among dominant groups. Overall, this approach allows a rapid collection of in-situ observations at larger spatial scales (km) than previously possible, and provides a pathway to link, calibrate, and validate broader analyses across even larger spatial scales (10–10,000 km2).

Description: 222Rn has been detected in 28 groundwater samples from the northeast of Gran Canaria (Canary Islands, Spain) utilizing a closed loop system consisting of an AlphaGUARD monitor that measures radon activity concentration in the air by means of an ionization chamber, and an AquaKIT set that transfers dissolved radon in the water samples to the air within the circuit. Radon concentration in the water samples studied varies between 0.3 and 76.9 Bq/L. Spanish radiological protection regulations limit the concentration of 222Rn for drinking water to 100 Bq/L, therefore the values obtained for all the analyzed samples are below this threshold. The hydrogeological study reveals a significant correspondence between the radon activity concentration and the material characteristics of the aquifer. For a selected group of samples with high radon concentrations, gross alpha activity has been determined to have values higher than the prescriptive screening level (0.1 Bq/L).

Description: In this article nickel interdigital capacitors were fabricated on top of silicon substrates. The capacitance of the interdigital capacitor was optimized by coating the electrodes with a 60 nm layer of HfO2. An analytical solution of the capacitance was compared to electromagnetic simulations using COMSOL and with experimental measurements. Results show that modeling interdigital capacitors using Finite Element Method software such as COMSOL is effective in the design and electrical characterization of these transducers.

Description: During the last years, proteomics has facilitated biomarker discovery by coupling high-throughput techniques with novel nanosensors. In the present review, we focus on the study of label-based and label-free detection systems, as well as nanotechnology approaches, indicating their advantages and applications in biomarker discovery. In addition, several disease biomarkers are shown in order to display the clinical importance of the improvement of sensitivity and selectivity by using nanoproteomics approaches as novel sensors.

Description: In recent years, there have been major advances in the development of new and more powerful perception systems for agriculture, such as computer-vision and global positioning systems. Due to these advances, the automation of agricultural tasks has received an important stimulus, especially in the area of selective weed control where high precision is essential for the proper use of resources and the implementation of more efficient treatments. Such autonomous agricultural systems incorporate and integrate perception systems for acquiring information from the environment, decision-making systems for interpreting and analyzing such information, and actuation systems that are responsible for performing the agricultural operations. These systems consist of different sensors, actuators, and computers that work synchronously in a specific architecture for the intended purpose. The main contribution of this paper is the selection, arrangement, integration, and synchronization of these systems to form a whole autonomous vehicle for agricultural applications. This type of vehicle has attracted growing interest, not only for researchers but also for manufacturers and farmers. The experimental results demonstrate the success and performance of the integrated system in guidance and weed control tasks in a maize field, indicating its utility and efficiency. The whole system is sufficiently flexible for use in other agricultural tasks with little effort and is another important contribution in the field of autonomous agricultural vehicles.

Description: The ability of bacteria isolated from a fixed-film bioreactor to precipitate phosphate crystals for the treatment of domestic wastewater in both artificial and natural media was studied. When this was demonstrated in artificial solid media for crystal formation, precipitation took place rapidly, and crystal formation began 3 days after inoculation. The percentage of phosphate-forming bacteria was slightly higher than 75%. Twelve major colonies with phosphate precipitation capacity were the dominant heterotrophic platable bacteria growing aerobically in artificial media. According to their taxonomic affiliations (based on partial sequencing of the 16S rRNA), the 12 strains belonged to the following genera of Gram-negative bacteria: Rhodobacter, Pseudoxanthobacter, Escherichia, Alcaligenes, Roseobacter, Ochrobactrum, Agromyce, Sphingomonas and Paracoccus. The phylogenetic tree shows that most of the identified populations were evolutionarily related to the Alphaproteobacteria (91.66% of sequences). The minerals formed were studied by X-ray diffraction, scanning electron microscopy (SEM), and energy dispersive X-ray microanalysis (EDX). All of these strains formed phosphate crystals and precipitated struvite (MgNH4PO4·6H2O), bobierrite [Mg3(PO4)2·8H2O] and baricite [(MgFe)3(PO4)2·8H2O]. The results obtained in this study show that struvite and spherulite crystals did not show any cell marks. Moreover, phosphate precipitation was observed in the bacterial mass but also near the colonies. Our results suggest that the microbial population contributed to phosphate precipitation by changing the media as a consequence of their metabolic activity. Moreover, the results of this research suggest that bacteria play an active role in the mineral precipitation of soluble phosphate from urban wastewater in submerged fixed-film bioreactors.

Description: This work is aimed at optimizing the wind turbine rotor speed setpoint algorithm. Several intelligent adjustment strategies have been investigated in order to improve a reward function that takes into account the power captured from the wind and the turbine speed error. After different approaches including Reinforcement Learning, the best results were obtained using a Particle Swarm Optimization (PSO)-based wind turbine speed setpoint algorithm. A reward improvement of up to 10.67% has been achieved using PSO compared to a constant approach and 0.48% compared to a conventional approach. We conclude that the pitch angle is the most adequate input variable for the turbine speed setpoint algorithm compared to others such as rotor speed, or rotor angular acceleration.