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Cancer is one of the major socio-health problems in the world. The level of education, the profession and/or employment status of the patient and the family can influence the amount of household income, the additional expenditure, and the possible socio-economic impact of the disease. The main objective of the study is to analyse and evaluate the influence of the level of education, the employment status of the patient, and the need for care and how these factors influence the additional expenditure and the possible socio-economic impact. Methods: descriptive cross-sectional randomised observational epidemiological study without replacement at the Hospital Universitario de Salamanca (CAUSA). Results: total sample (n = 365) comprised 53.2% of patients with no education or primary education, 25.8% with secondary education and 21.1% with higher education. Overall, 36.4% of patients were employed, 10.1% were self-employed, 53.0% were not employed, and 38.9% were experiencing other conditions. Significant statistics were found for educational level, employment status of the patient and main caregivers in terms of financial expenditure. Conclusions: Oncology patients with more education spend more on home help and/or accompanying the patient and transfers to the hospital for treatment. Higher incomes are not synonymous with higher expenditure in the sample. The patient’s main caregivers are a fundamental pillar of the patient’s household income and additional spending on orthopaedic material.

We address the possibility of the appearance of a charged kaon condensate in neutron star cores described within a generalized Skyrme model. Our treatment of strange degrees of freedom is based on the Bound State Approach by Callan and Klebanov, which allows to obtain an in-medium effective potential for the $s$-wave kaon condensate. We predict the onset of kaon condensation at a certain threshold density - whose value depends on the parameters of the model, and ranges between 1.5 and 2.5 times saturation density -, and obtain both the particle fractions and equation of state for dense matter in the kaon condensed phase. Finally, we discuss the effect of such condensates on the mass-radius curves and other observable properties of neutron stars with kaon condensed cores., 17 pages, 8 figures

A precise estimation of agricultural production provides relevant information for upcoming seasons, and helps in the assessment of crop losses before harvest in case of adverse situations. The objective of this work is to explore the development of a model capable of estimating barley production of a small agricultural production (127 ha) in Belchite, Spain. Variables adapted to the crop calendar of the growing barley are used to achieve that purpose. The variables have been created with weather data and remote sensing images. These images are acquired in two ranges of the electromagnetic spectrum, i.e., microwaves and optical spectral range, obtained from Sentinel-1 and Sentinel-2, respectively. Models are defined with a multiple linear regression method using all combinations of the independent variables correlated with production. The best linear regression model has a prediction error of 57.38 kg/ha (4%). The use of spectral variables, derived from radar vegetation index Cross Ratio (CR) and optical Inverted Red Edge Chlorophyll Index (IRECI), and climatic variables adapted to the crop calendar and climatic conditioning is revealed as an adequate strategy to obtain adjusted models.

Boson stars represent a hypothetical exotic type of compact stellar object that may be observed from the gravitational signal of coalescing binaries in current and future GW detectors. In this work we show that the moment of inertia $I$, the (dimensionless) angular momentum $\chi$ and the quadrupole moment $Q$ of rotating boson stars obey a universal relation, valid for a wide set of boson star models. Further, the obtained $I-\chi-Q~$ relation clearly differs from its famous neutron star counterpart, providing us with an unequivocal diagnostic tool to distinguish boson stars from ordinary compact stars or other celestial bodies in GW observations. Such universal (i.e. model-independent) relations also provide a useful tool to probe the strong gravity regime of general relativity and to constrain the equation of state of matter inside compact stars., Comment: 12 pages, 20 figures. In this current version, we have changed the format and the Appendix

Old age represents a social group that is undergoing continuous expansion. The aging population will be prone to chronic diseases and falls, which is a marker of frailty and a public health problem. This study aims to examine the relationship between living conditions and the prevalence of the risk of falls in older adults within the community. As an observational cross-sectional study, intentional sampling was carried out on residents of the metropolitan area over 75 years of age. The socio-demographic data of the subjects and their history of falls were collected. Additionally, the subjects were evaluated on the risk of falling, basic activities of daily living, such as walking and balance, fragility, and their fear of falling. The statistical analyses used were based on the Shapiro–Wilk test for normality, statistics of central tendency with description, mean (M) and dispersion, standard deviation (SD), bivariate contingency tables for studying the relationships between the variables, and the analysis of Pearson’s relational statistics (χ2). The comparisons of means were resolved by parametric or non-parametric routes. We obtained the following results: 1. The socio-demographic profile of our sample consisted of adults over 75 years of age, the majority of whom were overweight or obese women living in an urban area, specifically in an apartment, and receiving care; 2. Older people in the studied community had mild dependency and frailty, and were also at severe risk of falls; 3. The prevalence of falls was higher in women than in men in this study. Through these results, we confirmed the relationship between living conditions and the prevalence of risk of falls in older adults within the community.

The canonical quantization method for collective coordinates in crystalline configurations of the generalized Skyrme model is applied in order to find the quantum ground state of Skyrmion crystals and study the quantum corrections to the binding energy resulting from the isospin degrees of freedom. This leads to a consistent description of asymmetric nuclear matter within the Skyrme framework and allows us to compute the symmetry energy of the Skyrmionic crystal as a function of the baryon density, and to compare with recent observational constraints., Comment: V3: 16 pages, 4 figures. Some typos corrected. Minor changes in figures 1, 3 and 4. References added

Chemical reaction engineering is interested in elucidating the reaction kinetics through the determination of the fundamental influencing variables. The understanding of enzyme kinetics is needed to implement the potential of enzymes to satisfy determined production targets and for the design of the reactor. The quantification of the enzyme kinetics is implemented by the elucidation and building of the kinetic model (it includes one or more kinetic equations). In the context of process development, the kinetic model is not only useful to identify feasibility and for optimizing reaction conditions but also, at an early stage of development it is very useful to anticipate implementation bottlenecks, and so guide reactor setup. In this chapter we describe theoretical and practical considerations to illustrate the methodological framework of kinetic analysis. We take as study cases four archetypal kinetic cases by using as example the hydrolysis of cellobiose catalyzed by a beta-glucosidase. We show the different experimental data that can be obtained by the monitoring of enzymatic reactions in different configuration of free enzyme homogeneous ideal reactors; we show step-by-step the visualization, treatment, and analysis of data to elucidate kinetic models and the procedure for the quantification of kinetic constants. Finally, the performance of different reactors is compared in the interplay with the enzyme kinetics. This book chapter aims at being useful for a broad multidisciplinary audience and different levels of academic development.

Skyrmion crystals are the field configurations which minimize the energy per baryon in the infinitely large topological charge sector of the Skyrme model, at least for sufficiently high density. They are, therefore, an important tool to describe the ground state of cold, symmetric nuclear matter at high density regimes. In this work, we analyze different crystalline phases and the existence of phase transitions between them within the generalized Skyrme model, with the ultimate goal of describing symmetric nuclear matter in a wide regime of densities. Furthermore, we propose a new energy-minimizing phase for densities lower than the nuclear saturation point ($n_0$) which also presents a good qualitative behavior in the zero density limit, thereby improving the description of strongly interacting matter in the region $n, Comment: 16 pages, 10 figures; v2: presentation slightly improved, some references added

Enzymatic synthesis of penicillin V (penV) by acylation of 6-aminopenicillanic acid (6-APA) was carried out using methyl phenoxyacetate (MPOA) as activated acyl donor and soluble penicillin acylase...

The estimation of forest residual biomass is of interest to assess the availability of green energy resources. This study relates the Pinus halepensis Miller forest residual biomass (FRB), estimate...

First proposed in 2013 by Yagi and Yunes, the quasi-universal \emph{I-Love-Q relations} consist of a set of relations between the moment of inertia, the spin-induced quadrupole moment and the electric quadrupolar tidal deformability of neutron stars which are independent of the Equation of State (EoS) within an accuracy of $\sim1\%$. In this work, we show that these relations hold for different Skyrme-based nuclear matter EoS and also for the star-like solutions of different Einstein-BPS-Skyrme-models, some of which do not even present a barotropic equation of state. Further, other quasi-universal relations are analyzed, and together with recent GW observations, we use them to select the generalized Skyrme model that better reproduces observations. Our results reaffirm both the universality of the \emph{I-Love-Q} relations and the suitability of generalized Skyrme models to describe nuclear matter inside neutron stars., Comment: 23 pages, 11 figures

Fuel type is one of the key factors for analyzing the potential of fire ignition and propagation in agricultural and forest environments. The increase of three-dimensional datasets provided by active sensors, such as LiDAR (Light Detection and Ranging), has improved the classification of fuel types through empirical modelling. Empirical methods are site and sensor specific while Radiative Transfer Models (RTM) approaches provide broader universality. The aim of this work is to analyze the suitability of Discrete Anisotropic Radiative Transfer (DART) model to replicate low density small-footprint Airborne Laser Scanning (ALS) measurements and subsequent fuel type classification. Field data measured in 104 plots are used as ground truth to simulate LiDAR response based on the sensor and flight characteristics of low-density ALS data captured by the Spanish National Plan for Aerial Orthophotography (PNOA) in two different dates (2011 and 2016). The accuracy assessment of the DART simulations is performed using Spearman rank correlation coefficients between the simulated metrics and the ALS-PNOA ones. The results show that 32% of the computed metrics overpassed a correlation value of 0.80 between simulated and ALS-PNOA metrics in 2011 and 28% in 2016. The highest correlations were related to high height percentiles, canopy variability metrics as for example standard deviation and Rumple diversity index, reaching correlation values over 0.94. Two metric selection approaches and Support Vector Machine classification method with variants were compared to classify fuel types. The best-fitted classification model, trained with the DART simulated sample and validated with ALS-PNOA data, was obtained using Support Vector Machine method with radial kernel. The overall accuracy of the classification after validation was 88% and 91% for the 2011 and 2016 years, respectively. The use of DART demonstrates its value for simulating generalizable 3D data for fuel type classification providing relevant information for forest managers in fire prevention and extinction.

Made available in DSpace on 2020-12-12T01:34:32Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-11-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Fire severity assessment is crucial for predicting ecosystem response and prioritizing post-fire forest management strategies. Although a variety of remote sensing approaches have been developed, more research is still needed to improve the accuracy and effectiveness of fire severity mapping. This study proposes a unitemporal simulation approach based on the generation of synthetic spectral databases from linear spectral mixing. To fully exploit the potential of these training databases, the Random Forest (RF) machine learning algorithm was applied to build a classifier and regression model. The predictive models parameterized with the synthetic datasets were applied in a case study, the Sierra de Luna wildfire in Spain. Single date Landsat-8 and Sentinel-2A imagery of the immediate post-fire environment were used to develop the validation spectral datasets and a Pléiades orthoimage, providing the ground truth data. The four defined severity categories – unburned (UB), partial canopy unburned (PCU), canopy scorched (CS), and canopy consumed (CC) – demonstrated high accuracy in the bootstrapped (about 95%) and real validation sets (about 90%), with a slightly better performance observed when the Sentinel-2A dataset was used. Abundance of four ground covers (green vegetation, non-photosynthetic vegetation, soil, and ash) was also quantified with moderate (~45% for NPV) or high accuracy (higher than 75% for the remaining covers). No specific pattern in the comparison of sensors was observed. Variable importance analysis highlighted the complementary behavior of the spectral bands, although the contrast between the near and shortwave infrared regions stood out above the rest. Comparison of procedures reinforced the usefulness of the approach, as RF image-derived models and the multiple endmember spectral unmixing technique (MESMA) showed lower accuracy. The capabilities for detailed mapping are reflected in the development of different types of cartography (classification maps and fraction cover maps). The approach holds great potential for fire severity assessment, and future research needs to extend the predictive modeling to other burned areas – also in different ecosystems – and analyze its competence and the possible adaptations needed. Department of Geography and Spatial Management University of Zaragoza, C/Pedro Cerbuna 12 GEOFOREST-IUCA research group Environmental Sciences Institute (IUCA) University of Zaragoza, C/Pedro Cerbuna 12 Lab of Vegetation Ecology Instituto de Biociências Universidade Estadual Paulista (UNESP), Avenida 24-A 1515 Centro Universitario de la Defensa de Zaragoza Academia General Militar, Ctra. Huesca s/n Lab of Vegetation Ecology Instituto de Biociências Universidade Estadual Paulista (UNESP), Avenida 24-A 1515 FAPESP: 2019/07357-8

Pectin oligosaccharides, which can be obtained from fruit wastes, have proven their potential as plant immune-system elicitors. Although the precise size of active species is still under investigation, medium size oligosaccharides have been reported as the most active. Three defined oligogalacturonic acid (OGAs) mixtures were produced from commercial pectin, orange peel and apple pomace residues. The methodology developed involves two sequential acid treatments followed by stepwise ethanol precipitation. Without the need of chromatographic separations, three different fractions were obtained. The fractions were analyzed by high performance anion exchange chromatography (HPAEC) and were completely characterized by mass spectrometry, showing that the small size, medium size and large size fractions contained OGAs of degree of polymerization 3 to 9, 6 to 18, and 16 to 55, respectively.

27 pags., 2 figs., Quantum field theory in curved spacetimes suffers in general from an infinite ambiguity in the choice of Fock representation and associated vacuum. In cosmological backgrounds, the requirement of a unitary implementation of the field dynamics in the physical Hilbert space of the theory is a good criterion to ameliorate such ambiguity. In- deed, this criterion, together with a unitary implementation of the symmetries of the equations of motion, leads to an equivalence class of unitarily equivalent quantizations that, even though it is still formed by an infinite number of Fock representations, is unique. In this work, we apply the procedure developed for fields in cosmological settings to analyze the quantization of a scalar field in the presence of an external electromagnetic classical field in a flat background. We find a natural Fock representation that admits a unitary implementation of the quantum field dynamics. It automatically allows to define a particle number density at all times in the evolution with the correct asymptotic behavior, when the electric field vanishes. Moreover we show the unitary equivalence of all the quantizations that fulfill our criteria, so that they form a unique equivalence class. Although we perform the field quantization in a specific gauge, we also show the equivalence between the procedures taken in different gauges., Financial support was provided by the Spanish Government through the projects FIS2017- 86497-C2-2-P (with FEDER contribution) and FIS2016-78859-P (AEI/FEDER,UE). AGMC acknowledges financial support from the Ministry of Education, Culture, and Sports, Spain (Grant No. FPA2017-83814-P), the Xunta de Galicia (Grant No. INCITE09.296.035PR and Conselleria de Educacion), the Spanish Consolider-Ingenio 2010 Programme CPAN (CSD2007-00042), Maria de Maetzu Unit of Excellence MDM2016-0692, and FEDER.

The development of biorefinery processes to platform chemicals for most lignocellulosic substrates, results in side processes to intermediates such as oligosaccharides. Agrofood wastes are most amenable to produce such intermediates, in particular, cellooligo-saccharides (COS), pectooligosaccharides (POS), xylooligosaccharides (XOS) and other less abundant oligomers containing mannose, arabinose, galactose and several sugar acids. These compounds show a remarkable bioactivity as prebiotics, elicitors in plants, food complements, healthy coadyuvants in certain therapies and more. They are medium to high added-value compounds with an increasing impact in the pharmaceutical, nutraceutical, cosmetic and food industries. This review is focused on the main production processes: autohydrolysis, acid and basic catalysis and enzymatic saccharification. Autohydrolysis of food residues at 160−190 °C leads to oligomer yields in the 0.06−0.3 g/g dry solid range, while acid hydrolysis of pectin (80−120 °C) or cellulose (45−180 °C) yields up to 0.7 g/g dry polymer. Enzymatic hydrolysis at 40−50 °C of pure polysaccharides results in 0.06−0.35 g/g dry solid (DS), with values in the range 0.08−0.2 g/g DS for original food residues.

The aim of study is to map the carbon dioxide (CO2) emission of the aboveground tree biomass (AGB) in case of a fire event. The suitability of low point density, discrete, multiple-return, Airborne...

Mediterranean forests are recurrently affected by fire. The recurrence of fire in such environments and the number and severity of previous fire events are directly related to fire risk. Fuel type classification is crucial for estimating ignition and fire propagation for sustainable forest management of these wildfire prone environments. The aim of this study is to classify fuel types according to Prometheus classification using low-density Airborne Laser Scanner (ALS) data, Sentinel 2 data, and 136 field plots used as ground-truth. The study encompassed three different Mediterranean forests dominated by pines (Pinus halepensis, P. pinaster y P. nigra), oaks (Quercus ilex) and quercus (Q. faginea) in areas affected by wildfires in 1994 and their surroundings. Two metric selection approaches and two non-parametric classification methods with variants were compared to classify fuel types. The best-fitted classification model was obtained using Support Vector Machine method with radial kernel. The model includes three ALS and one Sentinel-2 metrics: the 25th percentile of returns height, the percentage of all returns above mean, rumple structural diversity index and NDVI. The overall accuracy of the model after validation was 59%. The combination of data from active and passive remote sensing sensors as well as the use of adapted structural diversity indices derived from ALS data improved accuracy classification. This approach demonstrates its value for mapping fuel type spatial patterns at a regional scale under different heterogeneous and topographically complex Mediterranean forests.

Fire severity can be assessed by identifying and quantifying the fractional abundance of post-fire ground cover types, an approach with great capacity to predict ecosystem response. Focused on shrubland formations of Mediterranean-type ecosystems, three burned areas (Ibieca and Zuera wildfires and Penaflor experimental fire) were sampled in the summers of 2006 and 2007. Two different ground measurements were made for each of the 356 plots: (i) 3-band high spatial resolution photography (HSRP) and (ii) the hemispherical-conical reflectance factor (HCRF) in the visible to near-infrared spectral range (VNIR, 400–900 nm). Stepwise multiple lineal regression (SMLR) models were fitted to spectral variables (HCRF, first derivative spectra or FDS, and four absorption indices) to estimate the fractional cover of seven post-fire ground cover types (vegetation and soil – unburned and charred components – and ash – char and ash, individually and as a combined category). Models were developed and validated at the Penaflor site (training, n = 217; validation, n = 88) and applied to the samples from the Ibieca and Zuera sites (n = 51). The best results were observed for the abundance estimations of green vegetation ( R adj . 2 0.70–0.90), unburned soil ( R adj . 2 0.40–0.75), and the combination of ashes ( R adj . 2 0.65–0.80). In comparison of spectral data, FDS outperforms reflectance or absorption data because of its higher accuracy levels and, importantly, its greater capacity to yield generalizable models. Future efforts should be made to improve the estimation of intermediate severity levels and upscaling the developed models. In the context of fire severity assessment, our study demonstrates the potential of hyperspectral data to estimate in a quick and objective manner post-fire ground cover fractions and thus provide valuable information to guide management responses.

The aim of this study is the estimation of forest residual biomass (FRB) in Mediterranean Pinus halepensis Miller forests. Two regression methods were compared in order to relate the FRB estimated in 45 field plots, to several independent variables derived from low-density Airborne Laser Scanning (ALS) data. The leave-one-out cross-validation technique demonstrated that Support Vector Machine (SVM) model with radial kernel, including the 40th percentile of the return heights, the variance and the percentage of first returns above mean as the selected variables, provided the most accurate result (root mean square error of 2.73 tons/ha). This study confirms the usefulness of low-density ALS data to accurately estimate FRB to better assess the availability of organic renewable fuel energy in the Mediterranean environment.

The account of total biomass can assist with the evaluation of climate regulation policies from local to global scales. This study estimates total biomass (TB), including tree and shrub biomass fractions, in Pinus halepensis Miller forest stands located in the Aragon Region (Spain) using Airborne Laser Scanning (ALS) data and fieldwork. A comparison of five selection methods and five regression models was performed to relate the TB, estimated in 83 field plots through allometric equations, to several independent variables extracted from ALS point cloud. A height threshold was used to include returns above 0.2 m when calculating ALS variables. The sample was divided into training and test sets composed of 62 and 21 plots, respectively. The model with the lower root mean square error (15.14 tons/ha) after validation was the multiple linear regression model including three ALS variables: the 25th percentile of the return heights, the variance, and the percentage of first returns above the mean. This study confirms the usefulness of low-density ALS data to accurately estimate total biomass, and thus better assess the availability of biomass and carbon content in a Mediterranean Aleppo pine forest.

El aumento de la mortalidad y la pérdida de vitalidad de los bosques son procesos que afectan a diversas especies de árboles y biomas. Estos fenómenos de decaimiento forestal se han asociado a la interacción de múltiples factores relacionados con el cambio global, como el aumento de las sequías o la mayor incidencia de factores bióticos que generan estrés. Sin embargo, el papel que juegan estos organismos en el decaimiento del bosque en relación a la sequía no está del todo comprendido. En este trabajo caracterizamos retrospectivamente los efectos que ejercen dichos organismos sobre el crecimiento radial de árboles estresados por sequía utilizando dendrocronología. El efecto combinado del muérdago y las sequías reducen el crecimiento del pino silvestre y aumentan la defoliación, siendo estos efectos más evidentes en el ápice del árbol. La procesionaria induce pérdidas de crecimiento en pino laricio, pero en general se observa una recuperación dos años después. No encontramos un papel determinante de los hongos patógenos de raíz en el decaimiento del abeto. Finalmente, pinos carrasco muertos que presentaban ataques de insectos perforadores mostraron patrones de crecimiento similares a pinos vivos. Este estudio nos permite caracterizar algunos de los complejos y múltiples efectos de diferentes factores bióticos de estrés sobre el crecimiento y la vulnerabilidad de los bosques susceptibles a los efectos negativos de las sequías. Increasing mortality and decline of forests are affecting several tree species and biomes. These events of forest decline have been related to the interaction of several global change elements, like the increase in drought stress or a higher incidence of biotic stress factors. However, the roles played by biotic stressors have been scarcely explored as related to drought stress. In this paper we retrospectively characterize the effects of these organisms on radial growth of trees already stressed by drought using dendrochronology. The combined effect of mistletoe and droughts reduce tree growth in Scots pine and increase defoliation, being these effects more noticeable in the apex. The pine processionary moth induces growth losses in the affected stands of black pine, but they usually recover two years after the outbreak. We did not find a determinate role of root pathogens on silver fir dieback. Finally, dead Aleppo pine trees infested by bark beetles showed similar growth patterns than living pine trees. This study allows characterizing some of the complex and multiple effects of different biotic stressors on growth and vulnerability of forests subjected to the negative effects exerted by droughts. Este estudio ha sido financiado por los proyectos CGL-2007-6066-C04-02/BOS, CGL2008-04847-C02-01, CGL2011-26654 (MINECO), 387/2011 (Organismo Autónomo de Parques Naciona-les) y PROCLIM (La Caixa-Gobierno de Aragón).

Atmospheric correction of optical satellite imagery is an essential pre-processing for modelling biophysical variables, multi-temporal analysis, and digital classification processes. Sentinel-2 products available for users are distributed by the European Space Agency (ESA) as Top Of Atmosphere reflectance values in cartographic geometry (Level-1C product). In order to obtain Bottom Of Atmosphere reflectance images (Level-2A product) derived from this Level-1C products, ESA provides the SEN2COR module, which is implemented in the Sentinel Application Platform. Alternatively, ESA recently distributes Level-2A products processed by SEN2COR with a default configuration. On the other hand, the conversion from Level-1C to Level-2A product can be generated using alternative atmospheric correction methods, such as MAJA, 6S, or iCOR. In this context, this paper aims to evaluate the quality of Level-2A products obtained through different methods in Mediterranean shrub and grasslands by comparing data obtained from Sentinel-2 imagery with field spectrometry data. For that purpose, six plots with different land covers (asphalt, grass, shrub, pasture, and bare soil) were analyzed, by using synchronous imagery to fieldwork (from July to September 2016). The results suggest the suitability of the applied atmospheric corrections, with coefficients of determination higher than 0.90 and root mean square error lower than 0.04 achieving a relative error in bottom of atmosphere reflectance of only 2–3%. Nevertheless, minor differences were observed between the four tested methods, with slightly varying results depending on the spectral band and land cover. This work has been supported by the SynerTGE project (CGL2015-69095-R), funded by the Spanish Ministry of Economy and Competitiveness; the HyZCP project (2015-17), funded by the Centro Universitario de la Defensa de Zaragoza, and projects CGL2016-75217-R (MINECO/FEDER, EU) and PyrenEOS EFA 048/15, which has been 65% cofinanced by the European Regional Development through the Interreg V-A Spain-France-Andorra programme (POCTEFA 2014-2020).

Application of polyester-degrading enzymes should be considered as an eco-friendly alternative to chemical recycling due to the huge plastic waste disposal nowadays. Many hydrolases from several fungi and bacteria have been discovered and successfully evaluated for their activity towards different aliphatic polyesters (PHA, PBS, PBSA, PCL, PLA), aromatic polyesters (PET, PBT, PMT) as well as their co-polyesters (PBST, PBAT, PBSTIL). This revision gives an up-to-date overview on the main biochemical features and biotechnological applications of those reported enzymes which are able to degrade polyester-based plastics, including different microbial polyester depolymerases, esterases, cutinase-like enzymes and lipases. Summarized information includes available protein sequences with the corresponding accession numbers deposited in NCBI server, 3D resolved structures, and data about optimal conditions for enzymatic activity and stability of many of these microbial enzymes that would be helpful for researchers in this topic. Although screening and identification of new native polyester hydrolases from microbial sources is undeniable according to literature, we briefly highlight the importance of the design of improved enzymes towards recalcitrant aromatic polyesters through different approaches that include site-directed mutagenesis and surface protein engineering.

Ticks are sensitive to changes in relative humidity and saturation deficit at the microclimate scale. Trends and changes in rainfall are commonly used as descriptors of field observations of tick populations, to capture the climate niche of ticks or to predict the climate suitability for ticks under future climate scenarios. We evaluated daily and monthly relationships between rainfall, relative humidity and saturation deficit over different ecosystems in Europe using daily climate values from 177 stations over a period of 10 years. We demonstrate that rainfall is poorly correlated with both relative humidity and saturation deficit in any of the ecological domains studied. We conclude that the amount of rainfall recorded in 1 day does not correlate with the values of humidity or saturation deficit recorded 24 h later: rainfall is not an adequate surrogate for evaluating the physiological processes of ticks at regional scales. We compared the Normalized Difference Vegetation Index (NDVI), a descriptor of photosynthetic activity, at a spatial resolution of 0.05°, with monthly averages of relative humidity and saturation deficit and also determined a lack of significant correlation. With the limitations of spatial scale and habitat coverage of this study, we suggest that the rainfall or NDVI cannot replace relative humidity or saturation deficit as descriptors of tick processes.

Outbreak insects are among the major biotic disturbances affecting Mediterranean forests by reducing their growth and vitality through severe defoliations. Despite their relevance for the management of these drought-prone areas, we lack information on the relationships between growth, canopy cover and insect defoliations at ample spatial and temporal scales. Here, we combine remote sensing (vegetation indexes) and dendrochronological information (basal area increment, BAI) to assess the effects of pine processionary moth (Thaumetopoea pityocampa, PPM) defoliations on growth and cover changes of several pine species, mainly focusing on Pinus nigra. We compared both datasets with a long-term field survey of PPM stand defoliations carried out in eastern Spain during the period 1970-2012. Lastly, we fitted linear-mixed models of BAI using as predictors climatic variables and a multi-scalar drought index to distinguish drought-related growth reductions from those attributed to PPM outbreaks. PPM severe defoliations mainly affected edge or low-density stands. Several vegetation indices (Moisture Stress Index, Normalized Burn Ratio and Normalized Difference Infrared Index) reflected the cover decrease corresponding to severe PPM defoliations. We found that trees affected by various severe PPM defoliations took at least 2. years to recover BAI values similar to those observed before the outbreak. The combined use of remote sensing and dendrochronology allows monitoring the impact of PPM defoliations from stands to trees, and these are valuable approaches to forecast outbreaks and their effects on Mediterranean forest. We consider both powerful tools to further assess the interacting effects of climate warming and PPM dynamics on drought-prone forests. © 2014 Elsevier B.V., This study was supported by projects PROCLIM and CGL2011-26654 financed by La Caixa-Aragón Government and the Spanish Commission of Science and Technology-FEDER, respectively. JJC acknowledges support by ARAID. We sincerely thank J. D. Galván, A.Q. Alla and the personal from the Forest Health Laboratory (Mora de Rubielos, Gobierno de Aragón) for the help in the field works. The authors acknowledge the Landsat scenes provided by the European Space Agency (ESA) in the context of the project C1P.15257.

Vegetation is one of the components of the ecosystems more affected by the fire and its destruction is, indeed, the most immediate and visible of its effects. In the same way, vegetal regeneration processes are a clear sign of the ecosystems recovery. These vegetal regeneration processes depends on the interaction of multiple factors (biotic and abiotic) which take place in different intensity/severity fire scenes. In this context, this work has as objective to study vegetal regeneration processes in two climatically contrasted environments (semiarid and subatlantic). It presents a methodology based on the controlled burning of experimental plots and on the capture of spectral information by means of a field spectro-radiometer during a period of two years. Collected data have shown important differences in the behavior of the two experimental stations, differences which can be summarize in the presence of quick vegetal regeneration processes in the subatlantic environment and their absence in the semiarid one. The low influence shown by some analyzed variables such as prefire vegetation level or immediate severity level allows concluding that, in these vegetal communities, the climatic variable is the one which influences vegetal regeneration processes.

Se analiza el impacto y la evolucion de la estructura del paisaje en un incendio del Prepirineo aragones utilizando 6 imagenes Landsat 5 TM, identificando relaciones entre valores de heterogeneidad espacial y comunidades vegetales. Se extraen los NDVI (Normalized Difference Vegetation Index), se calcula el indice de Shannon y se cartografia para cada ano. Se definen tres componentes representativos de los valores de diversidad (preincendio, a corto plazo y a medio plazo). Se concluye que a nivel de comunidad vegetal el incendio ha supuesto un incremento de la diversidad interna en algunas comunidades. No obstante, a medio plazo y a escala global el fuego provoca la homogeneizacion del paisaje, tal y como describen otros autores utilizando fuentes de informacion similar.

Remote sensing has been shown to be an efficient tool in the study of forest-fire processes. However, a lack of information on the amount of biomass burnt reduces the accuracy of fire severity and emission models. In this study, we use imagery from the Landsat Thematic Mapper to map crown biomass and burn severity for a large Mediterranean area. Considering the specific characteristics of the Mediterranean environment, two methods to extract useful remote sensing data were employed; both sought to analyze relationships between crown biomass and spectral information. As a result, a crown biomass map of Pinus spp. was created for the entire study area, applying nonlinear regression using the variable MID57 (TM5 + TM7) (R2 = 0.651). Considering only P. halepensis pixels that were burnt in the selected fire scar, the relationships between crown biomass and burn severity were found to be high and significant, yielding an R2 value of 0.516. Finally, a logistic regression model was constructed to map the presence or otherwise of high burn severity levels using crown biomass as the independent variable, yielding in the confusion matrix an overall percentage of data points correctly classified of 77% and a Kappa statistic in the validation sample of 0.554.

Fire severity can be considered one of the most influential factors in the postfire development of burnt areas. Ground level studies documenting changes in reflectance values improve the discrimination of spatial fire severity across large areas. The objective of this study was to determine those spectral regions most sensitive to fire severity levels by investigating the relationship between postfire surface materials and reflectance values. A total of 34 field plots were analyzed immediately following two natural fires in Spain. To obtain postfire data, we used a portable structure to obtain vertical photographs and reflectance values in the visible-near-infrared (VIS-NIR) range. Spectral reflectance and convolved Landsat Thematic Mapper (TM) bands were statistically correlated with individual postfire materials and with a fire severity index, the Combustion Products Index (CPI), derived from them. The wavelengths most sensitive to postfire materials were 450.6, 758.2, and 797 nm for ash, black carbon, and vegetation, respectively, although these wavelengths were included within ranges of comparable behavior. Finally, stepwise multiple regression models (SMLRs) were applied to the data. SMLR predicted ash, black carbon, and vegetation levels from reflectance data with accuracies higher than 75%. Estimation from Landsat-TM bands yielded slightly lower accuracies. The CPI severity index was also well estimated using either reflectance data or TM bands (r 2 = 0.925 and r 2 = 0.840 , respectively).