Prevalencia e incidencia de cáncer en habitantes de Sibaté, y georeferenciación de casos 2010-2014

Introducción: El Cáncer es prevenible en algunos casos, si se evita la exposición a sustancias cancerígenas en el medio ambiente. En Colombia, Cundinamarca es uno de los departamentos con mayores incrementos en la tasa de mortalidad y en el municipio de Sibaté, habitantes han manifestado preocupación por el incremento de la enfermedad. En el campo de la salud ambiental mundial, la georreferenciación aplicada al estudio de fenómenos en salud, ha tenido éxito con resultados válidos. El estudio propuso usar herramientas de información geográfica, para generar análisis de tiempo y espacio que hicieran visible el comportamiento del cáncer en Sibaté y sustentaran hipótesis de influencias ambientales sobre concentraciones de casos. Objetivo: Obtener incidencia y prevalencia de casos de cáncer en habitantes de Sibaté y georreferenciar los casos en un periodo de 5 años, con base en indagación de registros. Metodología: Estudio exploratorio descriptivo de corte transversal,sobre todos los diagnósticos de cáncer entre los años 2010 a 2014, encontrados en los archivos de la Secretaria de Salud municipal. Se incluyeron unicamente quienes tuvieron residencia permanente en el municipio y fueron diagnosticados con cáncer entre los años de 2010 a 2104. Sobre cada caso se obtuvo género, edad, estrato socioeconómico, nivel académico, ocupación y estado civil. Para el análisis de tiempo se usó la fecha de diagnóstico y para el análisis de espacio, la dirección de residencia, tipo de cáncer y coordenada geográfica. Se generaron coordenadas geográficas con un equipo GPS Garmin y se crearon mapas con los puntos de la ubicación de las viviendas de los pacientes. Se proceso la información, con Epi Info 7 Resultados: Se encontraron 107 casos de cáncer registrados en la Secretaria de Salud de Sibaté, 66 mujeres, 41 hombres. Sin división de género, el 30.93% de la población presento cáncer del sistema reproductor, el 18,56% digestivo y el 17,53% tegumentario. Se presentaron 2 grandes casos de agrupaciones espaciales en el territorio estudiado, una en el Barrio Pablo Neruda con 12 (21,05%) casos y en el casco Urbano de Sibaté con 38 (66,67%) casos. Conclusión: Se corroboro que el análisis geográfico con variables espacio temporales y de exposición, puede ser la herramienta para generar hipótesis sobre asociaciones de casos de cáncer con factores ambientales.

Bilinear models for spatio-temporal point distribution analysis: application to extrapolation of left ventricular, biventricular and whole heart cardiac dynamics

In this work we describe the usage of bilinear statistical models as a means of factoring the shape variability into two components attributed to inter-subject variation and to the intrinsic dynamics of the human heart. We show that it is feasible to reconstruct the shape of the heart at discrete points in the cardiac cycle. Provided we are given a small number of shape instances representing the same heart atdifferent points in the same cycle, we can use the bilinearmodel to establish this. Using a temporal and a spatial alignment step in the preprocessing of the shapes, around half of the reconstruction errors were on the order of the axial image resolution of 2 mm, and over 90% was within 3.5 mm. From this, weconclude that the dynamics were indeed separated from theinter-subject variability in our dataset.

SESAM - a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages

Two different approaches currently prevail for predicting spatial patterns of species assemblages. The first approach (macroecological modelling, MEM) focuses directly on realised properties of species assemblages, whereas the second approach (stacked species distribution modelling, S-SDM) starts with constituent species to approximate assemblage properties. Here, we propose to unify the two approaches in a single 'spatially-explicit species assemblage modelling' (SESAM) framework. This framework uses relevant species source pool designations, macroecological factors, and ecological assembly rules to constrain predictions of the richness and composition of species assemblages obtained by stacking predictions of individual species distributions. We believe that such a framework could prove useful in many theoretical and applied disciplines of ecology and evolution, both for improving our basic understanding of species assembly across spatio-temporal scales and for anticipating expected consequences of local, regional or global environmental changes. In this paper, we propose such a framework and call for further developments and testing across a broad range of community types in a variety of environments.

Impact of model complexity on cross-temporal transferability in Maxent species distribution models: An assessment using paleobotanical data

Maximum entropy modeling (Maxent) is a widely used algorithm for predicting species distributions across space and time. Properly assessing the uncertainty in such predictions is non-trivial and requires validation with independent datasets. Notably, model complexity (number of model parameters) remains a major concern in relation to overfitting and, hence, transferability of Maxent models. An emerging approach is to validate the cross-temporal transferability of model predictions using paleoecological data. In this study, we assess the effect of model complexity on the performance of Maxent projections across time using two European plant species (Alnus giutinosa (L.) Gaertn. and Corylus avellana L) with an extensive late Quaternary fossil record in Spain as a study case. We fit 110 models with different levels of complexity under present time and tested model performance using AUC (area under the receiver operating characteristic curve) and AlCc (corrected Akaike Information Criterion) through the standard procedure of randomly partitioning current occurrence data. We then compared these results to an independent validation by projecting the models to mid-Holocene (6000 years before present) climatic conditions in Spain to assess their ability to predict fossil pollen presence-absence and abundance. We find that calibrating Maxent models with default settings result in the generation of overly complex models. While model performance increased with model complexity when predicting current distributions, it was higher with intermediate complexity when predicting mid-Holocene distributions. Hence, models of intermediate complexity resulted in the best trade-off to predict species distributions across time. Reliable temporal model transferability is especially relevant for forecasting species distributions under future climate change. Consequently, species-specific model tuning should be used to find the best modeling settings to control for complexity, notably with paleoecological data to independently validate model projections. For cross-temporal projections of species distributions for which paleoecological data is not available, models of intermediate complexity should be selected.

Modeling data with structural and temporal correlation using lower level and higher level multilevel models

Novel imaging techniques are playing an increasingly important role in drug development, providing insight into the mechanism of action of new chemical entities. The data sets obtained by these methods can be large with complex inter-relationships, but the most appropriate statistical analysis for handling this data is often uncertain - precisely because of the exploratory nature of the way the data are collected. We present an example from a clinical trial using magnetic resonance imaging to assess changes in atherosclerotic plaques following treatment with a tool compound with established clinical benefit. We compared two specific approaches to handle the correlations due to physical location and repeated measurements: two-level and four-level multilevel models. The two methods identified similar structural variables, but higher level multilevel models had the advantage of explaining a greater proportion of variation, and the modeling assumptions appeared to be better satisfied.

Cannabidiol exerts anti-convulsant effects in animal models of temporal lobe and partial seizures

Cannabis sativa has been associated with contradictory effects upon seizure states despite its medicinal use by numerous people with epilepsy. We have recently shown that the phytocannabinoid cannabidiol (CBD) reduces seizure severity and lethality in the well-established in vivo model of pentylenetetrazoleinduced generalised seizures, suggesting that earlier, small-scale clinical trials examining CBD effects in people with epilepsy warrant renewed attention. Here, we report the effects of pure CBD (1, 10 and 100 mg/kg) in two other established rodent seizure models, the acute pilocarpine model of temporal lobe seizure and the penicillin model of partial seizure. Seizure activity was video recorded and scored offline using model-specific seizure severity scales. In the pilocarpine model CBD (all doses) significantly reduced the percentage of animals experiencing the most severe seizures. In the penicillin model, CBD (�10 mg/kg) significantly decreased the percentage mortality as a result of seizures; CBD (all doses) also decreased the percentage of animals experiencing the most severe tonic–clonic seizures. These results extend the anticonvulsant profile of CBD; when combined with a reported absence of psychoactive effects, this evidence strongly supports CBD as a therapeutic candidate for a diverse range of human epilepsies.

Two fast radiative transfer methods to improve the temporal sampling of clouds in numerical weather prediction and climate models

The high computational cost of calculating the radiative heating rates in numerical weather prediction (NWP) and climate models requires that calculations are made infrequently, leading to poor sampling of the fast-changing cloud field and a poor representation of the feedback that would occur. This paper presents two related schemes for improving the temporal sampling of the cloud field. Firstly, the ‘split time-stepping’ scheme takes advantage of the independent nature of the monochromatic calculations of the ‘correlated-k’ method to split the calculation into gaseous absorption terms that are highly dependent on changes in cloud (the optically thin terms) and those that are not (optically thick). The small number of optically thin terms can then be calculated more often to capture changes in the grey absorption and scattering associated with cloud droplets and ice crystals. Secondly, the ‘incremental time-stepping’ scheme uses a simple radiative transfer calculation using only one or two monochromatic calculations representing the optically thin part of the atmospheric spectrum. These are found to be sufficient to represent the heating rate increments caused by changes in the cloud field, which can then be added to the last full calculation of the radiation code. We test these schemes in an operational forecast model configuration and find a significant improvement is achieved, for a small computational cost, over the current scheme employed at the Met Office. The ‘incremental time-stepping’ scheme is recommended for operational use, along with a new scheme to correct the surface fluxes for the change in solar zenith angle between radiation calculations.

Short-term temporal changes of bare soil CO2 fluxes after tillage described by first-order decay models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Optical flow estimation with consistent spatio-temporal coherence models

[EN] In this work we propose a new variational model for the consistent estimation of motion fields. The aim of this work is to develop appropriate spatio-temporal coherence models. In this sense, we propose two main contributions: a nonlinear flow constancy assumption, similar in spirit to the nonlinear brightness constancy assumption, which conveniently relates flow fields at different time instants; and a nonlinear temporal regularization scheme, which complements the spatial regularization and can cope with piecewise continuous motion fields. These contributions pose a congruent variational model since all the energy terms, except the spatial regularization, are based on nonlinear warpings of the flow field. This model is more general than its spatial counterpart, provides more accurate solutions and preserves the continuity of optical flows in time. In the experimental results, we show that the method attains better results and, in particular, it considerably improves the accuracy in the presence of large displacements.

Spatial and temporal characteristics of summer precipitation over Central Europe in a suite of high-resolution climate models

High-impact, localized intense rainfall episodes represent a major socio-economic problem for societies worldwide, and at the same time these events are notoriously difficult to simulate properly in climate models. Here, the authors investigate how horizontal resolution and model formulation influence this issue by applying the HARMONIE regional climate model (HCLIM) with three different setups; two using convection parameterization at 15 and 6.25 km horizontal resolution (the latter within the “grey-zone” scale), with lateral boundary conditions provided by ERA-Interim reanalysis and integrated over a pan-European domain, and one with explicit convection at 2 km resolution (HCLIM2) over the Alpine region driven by the 15 km model. Seven summer seasons were sampled and validated against two high-resolution observational data sets. All HCLIM versions underestimate the number of dry days and hours by 20-40%, and overestimate precipitation over the Alpine ridge. Also, only modest added value were found of “grey-zone” resolution. However, the single most important outcome is the substantial added value in HCLIM2 compared to the coarser model versions at sub-daily time scales. It better captures the local-to-regional spatial patterns of precipitation reflecting a more realistic representation of the local and meso-scale dynamics. Further, the duration and spatial frequency of precipitation events, as well as extremes, are closer to observations. These characteristics are key ingredients in heavy rainfall events and associated flash floods, and the outstanding results using HCLIM in convection-permitting setting are convincing and encourage further use of the model to study changes in such events in changing climates.

Improved models for estimating temporal changes in carbon sequestration in above-ground biomass of mixed-species environmental plantings

Plantings of mixed native species (termed 'environmental plantings') are increasingly being established for carbon sequestration whilst providing additional environmental benefits such as biodiversity and water quality. In Australia, they are currently one of the most common forms of reforestation. Investment in establishing and maintaining such plantings relies on having a cost-effective modelling approach to providing unbiased estimates of biomass production and carbon sequestration rates. In Australia, the Full Carbon Accounting Model (FullCAM) is used for both national greenhouse gas accounting and project-scale sequestration activities. Prior to undertaking the work presented here, the FullCAM tree growth curve was not calibrated specifically for environmental plantings and generally under-estimated their biomass. Here we collected and analysed above-ground biomass data from 605 mixed-species environmental plantings, and tested the effects of several planting characteristics on growth rates. Plantings were then categorised based on significant differences in growth rates. Growth of plantings differed between temperate and tropical regions. Tropical plantings were relatively uniform in terms of planting methods and their growth was largely related to stand age, consistent with the un-calibrated growth curve. However, in temperate regions where plantings were more variable, key factors influencing growth were planting width, stand density and species-mix (proportion of individuals that were trees). These categories provided the basis for FullCAM calibration. Although the overall model efficiency was only 39-46%, there was nonetheless no significant bias when the model was applied to the various planting categories. Thus, modelled estimates of biomass accumulation will be reliable on average, but estimates at any particular location will be uncertain, with either under- or over-prediction possible. When compared with the un-calibrated yield curves, predictions using the new calibrations show that early growth is likely to be more rapid and total above-ground biomass may be higher for many plantings at maturity. This study has considerably improved understanding of the patterns of growth in different types of environmental plantings, and in modelling biomass accumulation in young (<25. years old) plantings. However, significant challenges remain to understand longer-term stand dynamics, particularly with temporal changes in stand density and species composition. © 2014.

Deficit of quantal release of GABA in experimental models of temporal lobe epilepsy

Because GABA (gamma-aminobutyric acid) receptor-mediated inhibition controls the excitability of principal neurons in the brain, deficits in GABAergic inhibition have long been favored to explain seizures. In an experimental model of temporal lobe epilepsy, we have identified a deficit of inhibition in presynaptic GABAergic terminals characterized by decreased GABA quantal activity associated with reduced synaptic vesicle density. This decrease in vesicle number primarily seems to affect the reserve pool, rather than the docked or the readily releasable pool.