Geographic range estimates and environmental requirements for the harpy eagle derived from spatial models of current and past distributio

Abstract Understanding species–environment relationships is key to defining the spatial structure of species distributions and develop effective conservation plans. However, for many species, this baseline information does not exist. With reliable presence data, spatial models that predict geographic ranges and identify environmental processes regulating distribution are a cost‐effective and rapid method to achieve this. Yet these spatial models are lacking for many rare and threatened species, particularly in tropical regions. The harpy eagle (Harpia harpyja) is a Neotropical forest raptor of conservation concern with a continental distribution across lowland tropical forests in Central and South America. Currently, the harpy eagle faces threats from habitat loss and persecution and is categorized as Near‐Threatened by the International Union for the Conservation of Nature (IUCN). Within a point process modeling (PPM) framework, we use presence‐only occurrences with climatic and topographical predictors to estimate current and past distributions and define environmental requirements using Ecological Niche Factor Analysis. The current PPM prediction had high calibration accuracy (Continuous Boyce Index = 0.838) and was robust to null expectations (pROC ratio = 1.407). Three predictors contributed 96% to the PPM prediction, with Climatic Moisture Index the most important (72.1%), followed by minimum temperature of the warmest month (15.6%) and Terrain Roughness Index (8.3%). Assessing distribution in environmental space confirmed the same predictors explaining distribution, along with precipitation in the wettest month. Our reclassified binary model estimated a current range size 11% smaller than the current IUCN range polygon. Paleoclimatic projections combined with the current model predicted stable climatic refugia in the central Amazon, Guyana, eastern Colombia, and Panama. We propose a data‐driven geographic range to complement the current IUCN range estimate and that despite its continental distribution, this tropical forest raptor is highly specialized to specific environmental requirements

Brachiaria brizantha cv. Marandu em sistema silvipastoril.

bitstream/CNPAB-2010/36212/1/bot033.pd

New FOCUS results on charm mixing and CP violation

We present a summary of recent results on CP violation and mixing in the charm quark sector based on a high statistics sample collected by photoproduction experiment FOCUS (E831 at Fermilab). We have measured the difference in lifetimes for the $D^0$ decays: $D^0 \to K^-\pi^+$ and $D^0 \to K^-K^+$. This translates into a measurement of the $y_{CP}$ mixing parameter in the \d0d0 system, under the assumptions that $K^-K^+$ is an equal mixture of CP odd and CP even eigenstates, and CP violation is negligible in the neutral charm meson system. We verified the latter assumption by searching for a CP violating asymmetry in the Cabibbo suppressed decay modes $D^+ \to K^-K^+\pi^+$, $D^0 \to K^-K^+$ and $D^0 \to \pi^-\pi^+$. We show preliminary results on a measurement of the branching ratio $\Gamma(D^{*+}\to \pi^+ (K^+\pi^-))/\Gamma(D^{*+}\to \pi^+ (K^-\pi^+))$.Comment: 9 pages, 6 figures, requires espcrc2.sty. Presented by S.Bianco at CPConf2000, September 2000, Ferrara (Italy). In this revision, fixed several stylistic flaws, add two significant references, fixed a typo in Tab.

Genome of the Avirulent Human-Infective Trypanosome—Trypanosoma rangeli

Background: Trypanosoma rangeli is a hemoflagellate protozoan parasite infecting humans and other wild and domestic mammals across Central and South America. It does not cause human disease, but it can be mistaken for the etiologic agent of Chagas disease, Trypanosoma cruzi. We have sequenced the T. rangeli genome to provide new tools for elucidating the distinct and intriguing biology of this species and the key pathways related to interaction with its arthropod and mammalian hosts.  Methodology/Principal Findings: The T. rangeli haploid genome is ,24 Mb in length, and is the smallest and least repetitive trypanosomatid genome sequenced thus far. This parasite genome has shorter subtelomeric sequences compared to those of T. cruzi and T. brucei; displays intraspecific karyotype variability and lacks minichromosomes. Of the predicted 7,613 protein coding sequences, functional annotations could be determined for 2,415, while 5,043 are hypothetical proteins, some with evidence of protein expression. 7,101 genes (93%) are shared with other trypanosomatids that infect humans. An ortholog of the dcl2 gene involved in the T. brucei RNAi pathway was found in T. rangeli, but the RNAi machinery is non-functional since the other genes in this pathway are pseudogenized. T. rangeli is highly susceptible to oxidative stress, a phenotype that may be explained by a smaller number of anti-oxidant defense enzymes and heatshock proteins.  Conclusions/Significance: Phylogenetic comparison of nuclear and mitochondrial genes indicates that T. rangeli and T. cruzi are equidistant from T. brucei. In addition to revealing new aspects of trypanosome co-evolution within the vertebrate and invertebrate hosts, comparative genomic analysis with pathogenic trypanosomatids provides valuable new information that can be further explored with the aim of developing better diagnostic tools and/or therapeutic targets

Evaluation of classification algorithms in the Google Earth Engine platform for the identification and change detection of rural and periurban buildings from very high-resolution images

[EN] Building change detection based on remote sensing imagery is a key task for land management and planning e.g., detection of illegal settlements, updating land records and disaster response. Under the post- classification comparison approach, this research aimed to evaluate the feasibility of several classification algorithms to identify and capture buildings and their change between two time steps using very-high resolution images (<1 m/pixel) across rural areas and urban/rural perimeter boundaries. Through an App implemented on the Google Earth Engine (GEE) platform, we selected two study areas in Colombia with different images and input data. In total, eight traditional classification algorithms, three unsupervised (K-means, X-Means y Cascade K-Means) and five supervised (Random Forest, Support Vector Machine, Naive Bayes, GMO maximum Entropy and Minimum distance) available at GEE were trained. Additionally, a deep neural network named Feature Pyramid Networks (FPN) was added and trained using a pre-trained model, EfficientNetB3 model. Three evaluation zones per study area were proposed to quantify the performance of the algorithms through the Intersection over Union (IoU) metric. This metric, with a range between 0 and 1, represents the degree of overlapping between two regions, where the higher agreement the higher IoU values. The results indicate that the models configured with the FPN network have the best performance followed by the traditional supervised algorithms. The performance differences were specific to the study area. For the rural area, the best FPN configuration obtained an IoU averaged for both time steps of 0.4, being this four times higher than the best supervised model, Support Vector Machines using a linear kernel with an average IoU of 0.1. Regarding the setting of urban/rural perimeter boundaries, this difference was less marked, having an average IoU of 0.53 in comparison to 0.38 obtained by the best supervised classification model, in this case Random Forest. The results are relevant for institutions tracking the dynamics of building areas from cloud computing platfo future assessments of classifiers in likewise platforms in other contexts.[ES] La detección de cambios de áreas construidas basada en datos de teledetección es una importante herramienta para el ordenamiento y la administración del territorio p.e.: la identificación de construcciones ilegales, la actualización de registros catastrales y la atención de desastres. Bajo el enfoque de comparación post-clasificación, la presente investigación tuvo como objetivo evaluar la funcionalidad de varios algoritmos de clasificación para identificar y capturar las construcciones y su cambio entre dos fechas de análisis usando imágenes de alta resolución (<1 m/píxel) en ámbitos rurales y límites del perímetro urbano municipal. La anterior evaluación fue llevada a cabo a través de una aplicación desarrollada mediante la plataforma Google Earth Engine (GEE), donde se alojaron y analizaron diferentes imágenes y datos de entrada sobre dos áreas de estudio en Colombia. En total, ocho algoritmos de clasificación tradicional, tres no supervisados (K-means, X-Means y Cascade K-Means) y cinco supervisados (Random Forest, Support Vector Machine, Naive Bayes, GMO maximum Entropy y Minimum distance) fueron entrenados empleando GEE. Adicionalmente, se entrenó una red neuronal profunda denominada Feature Pyramid Networks (FPN) sobre la cual se aplicó la estrategia de modelos preentrenados, usando pesos del modelo EfficientNetB3. Por cada una de las dos áreas de estudio, tres zonas de evaluación fueron propuestas para cuantificar la funcionalidad de los algoritmos mediante la métrica Intersection over Union (IoU). Esta métrica representa la evaluación de la superposición de dos regiones y tiene un rango de valores de 0 a 1, donde a mayor coincidencia de las imágenes mayor es el valor de IoU. Los resultados indican que los modelos configurados con la red FPN tienen la mejor funcionalidad, seguido de los algoritmos tradicionales supervisados. Las diferencias de la funcionalidad fueron específicas por área de estudio. Para el ámbito rural, la mejor configuración de FPN obtuvo un IoU promedio entre ambas fechas de 0,4, es decir, cuatro veces el mejor modelo supervisado, correspondiente al Support Vector Machine de kernel Lineal con un IoU de 0,1. Respecto al área de límites del perímetro urbano municipal, esta diferencia fue menos marcada, con un IoU promedio de 0,53 en comparación con el 0,38 derivado del mejor modelo de clasificación supervisada, que en este caso fue Random Forest. Los resultados de esta investigación son relevantes para entidades responsables del seguimiento de las dinámicas de las áreas construidas a partir de plataformas de procesamiento en la nube como GEE, estableciendo una línea base para futuros estudios evaluando la funcionalidad de los clasificadores disponibles en otros contextos.Los autores agradecen a las Subdirecciones de Catastro, y Geografía y Cartografía del IGAC. Esta investigación hace parte de la licencia del programa GEO-GEE administrada por la Subdirección de Geografía y Cartografía. Se agradece igualmente al equipo de EODataScience por su soporte constante en los desarrollos técnicos de esta investigación.Coca-Castro, A.; Zaraza-Aguilera, MA.; Benavides-Miranda, YT.; Montilla-Montilla, YM.; Posada-Fandiño, HB.; Avendaño-Gomez, AL.; Hernández-Hamon, HA.... (2021). Evaluación de algoritmos de clasificación en la plataforma Google Earth Engine para la identificación y detección de cambios de construcciones rurales y periurbanas a partir de imágenes de alta resolución. Revista de Teledetección. 0(58):71-88. http://hdl.handle.net/10251/169765OJS718805

Varespladib and cardiovascular events in patients with an acute coronary syndrome: the VISTA-16 randomized clinical trial

IMPORTANCE: Secretory phospholipase A2(sPLA2) generates bioactive phospholipid products implicated in atherosclerosis. The sPLA2inhibitor varespladib has favorable effects on lipid and inflammatory markers; however, its effect on cardiovascular outcomes is unknown. OBJECTIVE: To determine the effects of sPLA2inhibition with varespladib on cardiovascular outcomes. DESIGN, SETTING, AND PARTICIPANTS: A double-blind, randomized, multicenter trial at 362 academic and community hospitals in Europe, Australia, New Zealand, India, and North America of 5145 patients randomized within 96 hours of presentation of an acute coronary syndrome (ACS) to either varespladib (n = 2572) or placebo (n = 2573) with enrollment between June 1, 2010, and March 7, 2012 (study termination on March 9, 2012). INTERVENTIONS: Participants were randomized to receive varespladib (500 mg) or placebo daily for 16 weeks, in addition to atorvastatin and other established therapies. MAIN OUTCOMES AND MEASURES: The primary efficacy measurewas a composite of cardiovascular mortality, nonfatal myocardial infarction (MI), nonfatal stroke, or unstable angina with evidence of ischemia requiring hospitalization at 16 weeks. Six-month survival status was also evaluated. RESULTS: At a prespecified interim analysis, including 212 primary end point events, the independent data and safety monitoring board recommended termination of the trial for futility and possible harm. The primary end point occurred in 136 patients (6.1%) treated with varespladib compared with 109 patients (5.1%) treated with placebo (hazard ratio [HR], 1.25; 95%CI, 0.97-1.61; log-rank P = .08). Varespladib was associated with a greater risk of MI (78 [3.4%] vs 47 [2.2%]; HR, 1.66; 95%CI, 1.16-2.39; log-rank P = .005). The composite secondary end point of cardiovascular mortality, MI, and stroke was observed in 107 patients (4.6%) in the varespladib group and 79 patients (3.8%) in the placebo group (HR, 1.36; 95% CI, 1.02-1.82; P = .04). CONCLUSIONS AND RELEVANCE: In patients with recent ACS, varespladib did not reduce the risk of recurrent cardiovascular events and significantly increased the risk of MI. The sPLA2inhibition with varespladib may be harmful and is not a useful strategy to reduce adverse cardiovascular outcomes after ACS. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01130246. Copyright 2014 American Medical Association. All rights reserved

Observation of an Excited Bc+ State

Using pp collision data corresponding to an integrated luminosity of 8.5 fb-1 recorded by the LHCb experiment at center-of-mass energies of s=7, 8, and 13 TeV, the observation of an excited Bc+ state in the Bc+π+π- invariant-mass spectrum is reported. The observed peak has a mass of 6841.2±0.6(stat)±0.1(syst)±0.8(Bc+) MeV/c2, where the last uncertainty is due to the limited knowledge of the Bc+ mass. It is consistent with expectations of the Bc∗(2S31)+ state reconstructed without the low-energy photon from the Bc∗(1S31)+→Bc+γ decay following Bc∗(2S31)+→Bc∗(1S31)+π+π-. A second state is seen with a global (local) statistical significance of 2.2σ (3.2σ) and a mass of 6872.1±1.3(stat)±0.1(syst)±0.8(Bc+) MeV/c2, and is consistent with the Bc(2S10)+ state. These mass measurements are the most precise to date

Bose-Einstein correlations of same-sign charged pions in the forward region in pp collisions at √s=7 TeV

Bose-Einstein correlations of same-sign charged pions, produced in protonproton collisions at a 7 TeV centre-of-mass energy, are studied using a data sample collected by the LHCb experiment. The signature for Bose-Einstein correlations is observed in the form of an enhancement of pairs of like-sign charged pions with small four-momentum difference squared. The charged-particle multiplicity dependence of the Bose-Einstein correlation parameters describing the correlation strength and the size of the emitting source is investigated, determining both the correlation radius and the chaoticity parameter. The measured correlation radius is found to increase as a function of increasing charged-particle multiplicity, while the chaoticity parameter is seen to decreas