Dynamically Stable Topological Phase of Arsenene

First-principles calculations based on density functional theory (DFT) are used to investigate the electronic structures and topological phase transition of arsenene under tensile and compressive strains. Buckling in arsenene strongly depends on compressive/tensile strain. The phonons band structures reveal that arsenene is dynamically stable up to 18% tensile strain and the frequency gap between the optical and acoustic branches decreases with strain. The electronic band structures show the direct bandgap decreases with tensile strain and then closes at 13% strain followed by band inversion. With spin-orbit coupling (SOC), the 14% strain-assisted topological insulator phase of arsenene is mainly governed by the p-orbitals. The SOC calculated bandgap is about 43 meV. No imaginary frequency in the phonons is observed in the topological phase of arsenene. The dynamically stable topological phase is accessed through Z2 topological invariant ν using the analysis of the parity of the wave functions at the time-reversal invariant momentum points. The calculated ν is shown to be 1, implying that arsenene is a topological insulator which can be a candidate material for nanoelectronic devices.G.R. acknowledges the higher education commission (HEC) of Pakistan under the project ‘electronic structure calculations using density functional theory’ and the GIK Institute for providing supercomputing facilities. V.M.G.S. thanks the Spanish Ministerio de Economía y Competitividad for funding through the project FIS2015-63918-R and the Spanish Ministerio de Ciencia, Innovación y Universidades for funding through the project PGC2018-094783-B-I00.Peer reviewe

Dibenzocycloheptatriene as end-group of Thiele and tetrabenzo-Chichibabin hydrocarbons

The authors are grateful for the financial support from: MICIU/FEDER/AEI, Spain (PG2018-101181-B-I00, PGC2018-095808B-I00, MAT2016-80826-R, FIP-2018-HECTIC-PTM, Prometeo2019/076 and the "Severo Ochoa" Programme for Centres of Excellence in R & D; SEV-2015-0496), the European Research Council (ERC) (677023), DGR (Catalunya) (2017-SGR-918), and SNSF (Switzerland, TS., PZ00P2_174175). We thank the CSIRC-Alhambra and SciCore (Basel, Switzerland) for supercomputing facilities and the Servei de RMN, UAB, for instrument time.Thiele (Th) and tetrabenzo-Chichibabin (TBC) derivatives with terminal dibenzocycloheptatriene (DBHept) units were prepared. A clear correlation between their electronic and molecular structures was stablished. Insights into their closed- or open-shell ground states were gained, where particular contribution of the heptagonal carbocycles as end-groups was proved. Remarkably, a thermally accessible triplet diradical configuration was confirmed for theDBHept-TBCcompound.MICIU/FEDER/AEI, Spain PG2018-101181-B-I00 PGC2018-095808B-I00 MAT2016-80826-R FIP-2018-HECTIC-PTM Prometeo2019/076MICIU/FEDER/AEI, Spain ("Severo Ochoa" Programme for Centres of Excellence in RD) SEV-2015-0496European Research Council (ERC) 677023DGR (Catalunya) 2017-SGR-918Swiss National Science Foundation (SNSF) PZ00P2_17417

Síndrome nefrótico congénito. Reporte de caso y revisión del enfoque diagnóstico

A nephrotic syndrome is defined as the association of massive proteinuria, hypoalbuminemia and hyperlipidemia, which may be associated with edema and hypercoagulability. It originates from an abnormality of the glomerular filtration barrier with a massive protein leak and the consequent side effects. In its primary forms, it occurs with an incidence of 1 - 3 per 100,000 children under 16 years of age. The congenital form is a rare variant of the nephrotic syndrome, which occurs at birth or within the first three months of life and is usually resistant to corticosteroid therapy. Congenital infections and most common related monogenic diseases should be tested. Finally, new generation sequencing must be used to search for mutations in other candidate genes. We present the case of a girl with congenital nephrotic syndrome difficult to control, emphasizing the diagnostic process and support management. The importance of genetic counseling to the family in all cases is highlighted.El síndrome nefrótico se define como la unión de proteinuria masiva, hipoalbuminemia e hiperlipidemia, que pueden asociarse a edemas e hipercoagulabilidad. Se origina de una anormalidad de la barrera de filtración glomerular con una fuga masiva de proteína y los efectos secundarios consecuentes. En sus formas primarias, ocurre con una incidencia de 1-3 por cada 100.000 niños menores de 16 años. La forma congénita es una variante poco frecuente del síndrome nefrótico, la cual se presenta en el nacimiento o dentro de los tres primeros meses de vida, y suele ser resistente a la corticoterapia. Se debe evaluar primero la existencia de infecciones congénitas y luego buscar las enfermedades monogénicas más comunes, finalmente se puede recurrir a la secuenciación de nueva generación para buscar mutaciones en los demás genes candidatos. Se presenta el caso de una niña con síndrome nefrótico congénito de difícil control, enfatizando en el proceso diagnóstico y el manejo de soporte. Se resalta la importancia de la asesoría genética a la familia en todos los casos

Mapping Soil Burn Severity at Very High Spatial Resolution from Unmanned Aerial Vehicles

ArtículoThe evaluation of the effect of burn severity on forest soils is essential to determine the impact of wildfires on a range of key ecological processes, such as nutrient cycling and vegetation recovery. The main objective of this study was to assess the potentiality of different spectral products derived from RGB and multispectral imagery collected by unmanned aerial vehicles (UAVs) at very high spatial resolution for discriminating spatial variations in soil burn severity after a heterogeneous wildfire. In the case study, we chose a mixed-severity fire that occurred in the northwest (NW) of the Iberian Peninsula (Spain) in 2019 that affected 82.74 ha covered by three different types of forests, each dominated by Pinus pinaster, Pinus sylvestris, and Quercus pyrenaica. We evaluated soil burn severity in the field 1 month after the fire using the Composite Burn Soil Index (CBSI), as well as a pool of five individual indicators (ash depth, ash cover, fine debris cover, coarse debris cover, and unstructured soil depth) of easy interpretation. Simultaneously, we operated an unmanned aerial vehicle to obtain RGB and multispectral postfire images, allowing for deriving six spectral indices. Then, we explored the relationship between spectral indices and field soil burn severity metrics by means of univariate proportional odds regression models. These models were used to predict CBSI categories, and classifications were validated through confusion matrices. Results indicated that multispectral indices outperformed RGB indices when assessing soil burn severity, being more strongly related to CBSI than to individual indicators. The Normalized Difference Water Index (NDWI) was the best-performing spectral index for modelling CBSI (R2cv = 0.69), showing the best ability to predict CBSI categories (overall accuracy = 0.83). Among the individual indicators of soil burn severity, ash depth was the one that achieved the best results, specifically when it was modelled from NDWI (R2cv = 0.53). This work provides a useful background to design quick and accurate assessments of soil burn severity to be implemented immediately after the fire, which is a key factor to identify priority areas for emergency actions after forest fires.S

Impact of burn severity on soil properties in a Pinus pinaster ecosystem immediately after fire

P. 1-11We analyse the effects of burn severity on individual soil properties and soil quotients in Mediterranean fire-prone pine forests immediately after a wildfire. Burn severity was measured in the field through the substrate stratum of the Composite Burn Index and soil samples were taken 7–9 days after a wildfire occurred in a Pinus pinaster Ait. ecosystem. In each soil sample, we analysed physical (size of soil aggregates), chemical (pH, organic C, total N and available P) and biological (microbial biomass C, b-glucosidase, urease and acid phosphatase activities) properties. Size of aggregates decreased in the areas affected by high burn severity. Additionally, moderate and high severities were associated with increases in pH and available P concentration and with decreases in organic C concentration. Microbial biomass C showed similar patterns to organic C along the burn severity gradient. The enzymatic activities of phosphatase and b-glucosidase showed the highest sensitivity to burn severity, as they strongly decreased from the low-severity scenarios. Among the studied soil quotients, the C : N ratio, microbial quotient and b-glucosidase : microbial biomass C quotient decreased with burn severity. This work provides valuable information on the impact of burn severity on the functioning of sandy siliceous soils in fire-prone pine ecosystems.S

Relevance of UAV and sentinel-2 data fusion for estimating topsoil organic carbon after forest fire

[EN] The evaluation at detailed spatial scale of soil status after severe fires may provide useful information on the recovery of burned forest ecosystems. Here, we aimed to assess the potential of combining multispectral imagery at different spectral and spatial resolutions to estimate soil indicators of burn severity. The study was conducted in a burned area located at the northwest of the Iberian Peninsula (Spain). One month after fire, we measured soil burn severity in the field using an adapted protocol of the Composite Burn Index (CBI). Then, we performed soil sampling to analyze three soil properties potentially indicatives of fire-induced changes: mean weight diameter (MWD), soil moisture content (SMC) and soil organic carbon (SOC). Additionally, we collected post-fire imagery from the Sentinel-2A MSI satellite sensor (10–20 m of spatial resolution), as well as from a Parrot Sequoia camera on board an unmanned aerial vehicle (UAV; 0.50 m of spatial resolution). A Gram-Schmidt (GS) image sharpening technique was used to increase the spatial resolution of Sentinel-2 bands and to fuse these data with UAV information. The performance of soil parameters as indicators of soil burn severity was determined trough a machine learning decision tree, and the relationship between soil indicators and reflectance values (UAV, Sentinel-2 and fused UAV-Sentinel-2 images) was analyzed by means of support vector machine (SVM) regression models. All the considered soil parameters decreased their value with burn severity, but soil moisture content, and, to a lesser extent, soil organic carbon discriminated at best among soil burn severity classes (accuracy = 91.18 %; Kappa = 0.82). The performance of reflectance values derived from the fused UAV-Sentinel-2 image to monitor the effects of wildfire on soil characteristics was outstanding, particularly for the case of soil organic carbon content (R2 = 0.52; RPD = 1.47). This study highlights the advantages of combining satellite and UAV images to produce spatially and spectrally enhanced images, which may be relevant for estimating main impacts on soil properties in burned forest areas where emergency actions need to be applied.S

Pre-fire aboveground biomass, estimated from LiDAR, spectral and field inventory data, as a major driver of burn severity in maritime pine (Pinus pinaster) ecosystems

100022Background: The characterization of surface and canopy fuel loadings in fire-prone pine ecosystems is critical for understanding fire behavior and anticipating the most harmful ecological effects of fire. Nevertheless, the joint consideration of both overstory and understory strata in burn severity assessments is often dismissed. The aim of this work was to assess the role of total, overstory and understory pre-fire aboveground biomass (AGB), estimated by means of airborne Light Detection and Ranging (LiDAR) and Landsat data, as drivers of burn severity in a megafire occurred in a pine ecosystem dominated by Pinus pinaster Ait. in the western Mediterranean Basin. Results: Total and overstory AGB were more accurately estimated (R2 equal to 0.72 and 0.68, respectively) from LiDAR and spectral data than understory AGB (R2 ¼ 0.26). Density and height percentile LiDAR metrics for several strata were found to be important predictors of AGB. Burn severity responded markedly and non-linearly to total (R2 ¼ 0.60) and overstory (R2 ¼ 0.53) AGB, whereas the relationship with understory AGB was weaker (R2 ¼ 0.21). Nevertheless, the overstory plus understory AGB contribution led to the highest ability to predict burn severity (RMSE ¼ 122.46 in dNBR scale), instead of the joint consideration as total AGB (RMSE ¼ 158.41). Conclusions: This study novelty evaluated the potential of pre-fire AGB, as a vegetation biophysical property derived from LiDAR, spectral and field plot inventory data, for predicting burn severity, separating the contribution of the fuel loads in the understory and overstory strata in Pinus pinaster stands. The evidenced relationships between burn severity and pre-fire AGB distribution in Pinus pinaster stands would allow the implementation of threshold criteria to support decision making in fuel treatments designed to minimize crown fire hazard.S

Thermally enhanced spectral indices to discriminate burn severity in Mediterranean forest ecosystems

P. 1-8Fires are a problematic and recurrent issue in Mediterranean forest ecosystems. Accurate discrimination of burn severity level is fundamental for the rehabilitation planning of affected areas. Though fieldwork is still necessary for measuring post-fire burn severity, remote sensing based techniques are being widely used to predict it because of their computational simplicity and straightforward application. Among them, spectral indices classification (especially difference Normalized Burn Ratio–dNBR- based ones) may be considered the standard remote sensing based method to distinguish burn severity level. In this work we show how this methodology may be improved by using land surface temperature (LST) to enhance the standard spectral indices. We considered a large wildfire in August 2012 in North Western Spain. The Composite Burn Index (CBI) was measured in 111 field plots and grouped into three burn severity levels. Relationship between Landsat 7 Enhanced Thematic Mapper (ETM+) LST-enhanced spectral indices and CBI was evaluated by using the normalized distance between two burn severity levels and spectral dispersion graphs. Inclusion of LST in the spectral index equation resulted in higher discrimination between burn severity levels than standard spectral indices (0.90, 8.50, and 17.52 NIR-SWIR Temperature version 1 vs 0.60, 2.83, and 6.46 NBR). Our results demonstrate the potential of LST for improving burn severity discrimination and mapping. Future research, however, is needed to evaluate the performance of the proposed LST-enhanced spectral indices in other fire regimes, and forest ecosystems.S

Multiple Endmember Spectral Mixture Analysis (MESMA) Applied to the Study of Habitat Diversity in the Fine-Grained Landscapes of the Cantabrian Mountains

P. 1-19 ArtículoHeterogeneous and patchy landscapes where vegetation and abiotic factors vary at small spatial scale (fine-grained landscapes) represent a challenge for habitat diversity mapping using remote sensing imagery. In this context, techniques of spectral mixture analysis may have an advantage over traditional methods of land cover classification because they allow to decompose the spectral signature of a mixed pixel into several endmembers and their respective abundances. In this work, we present the application of Multiple Endmember Spectral Mixture Analysis (MESMA) to quantify habitat diversity and assess the compositional turnover at different spatial scales in the fine-grained landscapes of the Cantabrian Mountains (northwestern Iberian Peninsula). A Landsat-8 OLI scene and high-resolution orthophotographs (25 cm) were used to build a region-specific spectral library of the main types of habitats in this region (arboreal vegetation; shrubby vegetation; herbaceous vegetation; rocks–soil and water bodies). We optimized the spectral library with the Iterative Endmember Selection (IES) method and we applied MESMA to unmix the Landsat scene into five fraction images representing the five defined habitats (root mean square error, RMSE 0.025 in 99.45% of the pixels). The fraction images were validated by linear regressions using 250 reference plots from the orthophotographs and then used to calculate habitat diversity at the pixel ( -diversity: 30 30 m), landscape (-diversity: 1 1 km) and regional ("-diversity: 110 33 km) scales and thecompositional turnover ( - and -diversity) according to Simpson’s diversity index. Richness and evenness were also computed. Results showed that fraction images were highly related to reference data (R2 0.73 and RMSE 0.18). In general, our findings indicated that habitat diversity was highly dependent on the spatial scale, with values for the Simpson index ranging from 0.20 0.22 for -diversity to 0.60 0.09 for -diversity and 0.72 0.11 for "-diversity. Accordingly, we found -diversity to be higher than -diversity. This work contributes to advance in the estimation of ecological diversity in complex landscapes, showing the potential of MESMA to quantify habitat diversity in a comprehensive way using Landsat imageryS