DpgC-Catalyzed Peroxidation of 3,5-Dihydroxyphenylacetyl-CoA (DPA-CoA): Insights into the Spin-Forbidden Transition and Charge Transfer Mechanisms

[EN]Despite being a very strong oxidizing agent, most organic molecules are not oxidized in the presence of O2 at room temperature because O2 is a diradical whereas most organic molecules are closed-shell. Oxidation then requires a change in the spin state of the system, which is forbidden according to non-relativistic quantum theory. To overcome this limitation, oxygenases usually rely on metal or redox cofactors to catalyze the incorporation of, at least, one oxygen atom into an organic substrate. However, some oxygenases do not require any cofactor, and the detailed mechanism followed by these enzymes remains elusive. To fill this gap, here the mechanism for the enzymatic cofactor-independent oxidation of 3,5-dihydroxyphenylacetyl-CoA (DPA-CoA) is studied by combining multireference calculations on a model system with QM/MM calculations. Our results reveal that intersystem crossing takes place without requiring the previous protonation of molecular oxygen. The characterization of the electronic states reveals that electron transfer is concomitant with the triplet–singlet transition. The enzyme plays a passive role in promoting the intersystem crossing, although spontaneous reorganization of the water wire connecting the active site with the bulk presets the substrate for subsequent chemical transformations. The results show that the stabilization of the singlet radical-pair between dioxygen and enolate is enough to promote spin-forbidden reaction without the need for neither metal cofactors nor basic residues in the active site

Estimation of potato yield using satellite data at a municipal level: A machine learning approach

Producción CientíficaCrop growth modeling and yield forecasting are essential to improve food security policies worldwide. To estimate potato (Solanum tubersum L.) yield over Mexico at a municipal level, we used meteorological data provided by the ERA5 (ECMWF Re-Analysis) dataset developed by the Copernicus Climate Change Service, satellite imagery from the TERRA platform, and field information. Five different machine learning algorithms were used to build the models: random forest (rf), support vector machine linear (svmL), support vector machine polynomial (svmP), support vector machine radial (svmR), and general linear model (glm). The optimized models were tested using independent data (2017 and 2018) not used in the training and optimization phase (2004–2016). In terms of percent root mean squared error (%RMSE), the best results were obtained by the rf algorithm in the winter cycle using variables from the first three months of the cycle (R2 = 0.757 and %RMSE = 18.9). For the summer cycle, the best performing model was the svmP which used the first five months of the cycle as variables (R2 = 0.858 and %RMSE = 14.9). Our results indicated that adding predictor variables of the last two months before the harvest did not significantly improved model performances. These results demonstrate that our models can predict potato yield by analyzing the yield of the previous year, the general conditions of NDVI, meteorology, and information related to the irrigation system at a municipal level

Potato yield prediction using machine learning techniques and Sentinel 2 data

Producción CientíficaTraditional potato growth models evidence certain limitations, such as the cost of obtaining the input data required to run the models, the lack of spatial information in some instances, or the actual quality of input data. In order to address these issues, we develop a model to predict potato yield using satellite remote sensing. In an effort to offer a good predictive model that improves the state of the art on potato precision agriculture, we use images from the twin Sentinel 2 satellites (European Space Agency—Copernicus Programme) over three growing seasons, applying different machine learning models. First, we fitted nine machine learning algorithms with various pre-processing scenarios using variables from July, August and September based on the red, red-edge and infra-red bands of the spectrum. Second, we selected the best performing models and evaluated them against independent test data. Finally, we repeated the previous two steps using only variables corresponding to July and August. Our results showed that the feature selection step proved vital during data pre-processing in order to reduce multicollinearity among predictors. The Regression Quantile Lasso model (11.67% Root Mean Square Error, RMSE; R2 = 0.88 and 9.18% Mean Absolute Error, MAE) and Leap Backwards model (10.94% RMSE, R2 = 0.89 and 8.95% MAE) performed better when predictors with a correlation coefficient > 0.5 were removed from the dataset. In contrast, the Support Vector Machine Radial (svmRadial) performed better with no feature selection method (11.7% RMSE, R2 = 0.93 and 8.64% MAE). In addition, we used a random forest model to predict potato yields in Castilla y León (Spain) 1–2 months prior to harvest, and obtained satisfactory results (11.16% RMSE, R2 = 0.89 and 8.71% MAE). These results demonstrate the suitability of our models to predict potato yields in the region studied

Analysis of spatial and temporal variability in Libya-4 with Landsat 8 and Sentinel-2 data for optimized ground target location

Producción CientíficaPseudo-Invariant Calibration Sites (PICS) have been widely used by the remote sensing community in recent decades for post-launch absolute calibration, cross-calibration, and the monitoring of radiometric stability. The Committee on Earth Observation Satellites (CEOS) has established several official PICS for these purposes. Of these, Libya-4 is the most commonly used, due to its high uniformity and stability. The site was chosen as a large-area site for medium resolution sensors, and with high-resolution sensors now common, smaller sites are being identified. This work has identified an improved area of interest (AOI) within Libya-4 by using combined Landsat 8 and Sentinel 2 data. The Optimized Ground Target (OGT) was determined by calculating the coefficient of variation along with the use of a quasi-Newton optimization algorithm combined with the Basin–Hopping global optimization technique to constrain a search area small enough to perform a final brute-force refinement. The Coefficient of Variation CV of the proposed OGT is significantly lower than that in the original CEOS area, with differences between the CV of both zones in the order of 1% in the visible near-infrared (VNIR) bands. This new AOI has the potential to improve the cross-calibration between high-resolution sensors using the PICS methodology through an OGT with more homogeneous and stable characteristics

An empirical radiometric intercomparison methodology based on global simultaneous nadir overpasses applied to Landsat 8 and Sentinel-2

Producción CientíficaThe Simultaneous Nadir Overpass (SNO) method was developed by the NOAA/NESDIS to improve the consistency and quality of climate data acquired by different meteorological satellites. Taking advantage of the reduced impact induced by the Bidirectional Reflectance Distribution Function (BRDF), atmospheric effects, illumination and viewing geometries during an SNO, we created a sensor comparison methodology for all spectral targets. The method is illustrated by applying it to the assessment of data acquired by the Landsat 8 (L8), Sentinel-2A (S2A), and Sentinel-2B (S2B) optical sensors. Multiple SNOs were identified and selected without the need for orbit propagators. Then, by locating spatially homogeneous areas, it was possible to assess, for a wide range of Top-of-Atmosphere reflectance values, the relationship between the L8 bands and the corresponding ones of S2A and S2B. The results yield high coefficients of determination for S2 A/B with respect to L8. All are higher than 0.980 for S2A and 0.984 for S2B. If the S2 band 8 (wide near-infrared, NIR) is excluded then the lowest coefficients of determination become 0.997 and 0.999 from S2A and S2B, respectively. This methodology can be complementary to those based on Pseudo-Invariant Calibration Sites (PICS) due to its simplicity, highly correlated results and the wide range of compared reflectances and spectral targets

Pericarditis purulenta por Staphylococcus aureus sin foco en paciente con neoplasia pancreática

We present the case of a patient with a short-term fever and dyspnea, diagnosed with purulent pericarditis with bacteremia associated with Staphylococcus aureus, and no primary infectious focus found. As a casual finding, a pancreatic neoplasm was detected. After undergoing surgery, he developed hemodynamic instability and poor evolution despite intensive measures and died during the postoperative period.Presentamos el caso de un varón con cuadro febril y disnea de corta duración, diagnosticado de pericarditis purulenta con bacteriemia asociada por Staphylococcus aureus, en el que no se encontró foco primario infeccioso. Como hallazgo casual, se le diagnosticó de neoplasia pancreática. Tras someterse a intervención quirúrgica, comenzó con inestabilidad hemodinámica y mala evolución a pesar de las medidas intensivas, y falleció durante el posoperatorio

Model-Assisted Bird Monitoring Based on Remotely Sensed Ecosystem Functioning and Atlas Data

Urgent action needs to be taken to halt global biodiversity crisis. To be effective in the implementation of such action, managers and policy-makers need updated information on the status and trends of biodiversity. Here, we test the ability of remotely sensed ecosystem functioning attributes (EFAs) to predict the distribution of 73 bird species with different life-history traits. We run ensemble species distribution models (SDMs) trained with bird atlas data and 12 EFAs describing different dimensions of carbon cycle and surface energy balance. Our ensemble SDMs—exclusively based on EFAs—hold a high predictive capacity across 71 target species (up to 0.94 and 0.79 of Area Under the ROC curve and true skill statistic (TSS)). Our results showed the life-history traits did not significantly affect SDM performance. Overall, minimum Enhanced Vegetation Index (EVI) and maximum Albedo values (descriptors of primary productivity and energy balance) were the most important predictors across our bird community. Our approach leverages the existing atlas data and provides an alternative method to monitor inter-annual bird habitat dynamics from space in the absence of long-term biodiversity monitoring schemes. This study illustrates the great potential that satellite remote sensing can contribute to the Aichi Biodiversity Targets and to the Essential Biodiversity Variables framework (EBV class “Species distribution”)Fieldwork campaigns were carried out within the project “Estudios sobre a biodiversidade do Macizo Central Galego. Lugar de Importancia Comunitaria” (PGIDT99PXI20002B) and “Caracterización de los vertebrados del LIC Macizo Central e Bidueiral de Montederramo”, code: 2008-CE227”, funded by SAYFOR S.L. This work also received funding from Xunta de Galicia through the grant to structure and consolidate competitive research groups of Galicia (ED431B 2018/36). A.R. was funded by the Xunta de Galicia, Spain (post-doctoral fellowship ED481B2016/084-0). S.A.-C. was financially supported by PORBIOTA—E-Infraestrutura Portuguesa de Informação e Investigação em Biodiversidade (POCI-01-0145-FEDER-022127)S

The use of clinical guidelines for referral of patients with lesions suspicious for oral cancer may ease early diagnosis and improve education of healthcare professionals

Early diagnosis and referral of oral cancer is essential. Successful implementation of clinical guidelines must include current practitioners and students. Objective: To evaluate the diagnostic accuracy of students at oral cancer screening and to assess the effectiveness of clinical referral guidelines. Study Design: Fifth year dental students were randomly allocated to either control (n=19) or experimental groups (n=18). Both received the customary training in oral diagnosis. The experimental group underwent a 2 hour workshop where the guidelines for the referral of suspicious lesions were discussed. Three months later, a set of 51 clinical cases including benign, malignant, and precancerous conditions/lesions were used to assess the screening ability of each subject. Results: All 37 students entered the study. Sensitivity (control group) ranged from 16.7% to 66.7%; the experimental group scored from 16.7% to 83.3%. Fifty percent of the experimental students reached sensitivity values ?62.5% (p=0.01). Diagnostic specificity (control group) spanned from 80% to 93.3% (median=50%); amongst experimental group it ranged from 82.2% to 97.8% (median=92.8%); (p=0.003). Concordance -control group- was X=82.5 (SD=3.2), and X=88.2 (SD=4.3) for the experimental, (p>0.001). Cohen's kappa test was poor (K<0.40) for the controls and moderate for the experimental group. The experimental group referred more oral cancers urgently (p=0.002) and left less unreferred cancers (0.04). This group also referred more precancerous lesions/conditions urgently (p=0.02). Conclusions: The implementation of a clinical referral guideline at undergraduate level has proved valuable, under experimental conditions, to significantly increase diagnostic abilities of the examiners and thus to improve screening for oral cancer. © Medicina Oral S. L