Thymic stromal lymphopoietin plays divergent roles in murine models of atopic and nonatopic airway inflammation.

BACKGROUND: Thymic stromal lymphopoietin (TSLP) is a cytokine primarily produced by epithelial cells, which has been shown to be a potent inducer of T-helper 2 (Th2)-type responses. However, TSLP has pleiotropic effects upon immune cells, and although extensively studied in the context of atopic asthma, its relevance as a therapeutic target and its role in the pathogenesis of nonatopic asthma remains unknown. We sought to investigate the role of TSLP in atopic, nonatopic and viral-induced exacerbations of pulmonary inflammation. METHODS: Using stringently defined murine models of atopic, nonatopic and virally exacerbated forms of pulmonary inflammation, we compared inflammatory responses of C57BL/6 wild-type (WT) and TSLP receptor-deficient (TSLPR KO) mice. RESULTS: Thymic stromal lymphopoietin receptor (TSLPR) signaling was crucial for the development of atopic asthma. Specifically, TSLPR signaling to lung recruited CD4+ T cells enhanced eosinophilia, goblet cell hyperplasia, and overall inflammation within the airways. In contrast, the absence of TSLPR signaling was associated with strikingly exaggerated pulmonary neutrophilic inflammation in a nonatopic model of airway inflammation. The inflammation was associated with excessive levels of interleukin (IL)-17A in the lungs, indicating that TSLP negatively regulates IL-17A. In addition, in a model of influenza-induced exacerbation of atopic airway inflammation, the absence of TSLPR signaling also led to exaggerated neutrophilic inflammation. CONCLUSION: Thymic stromal lymphopoietin plays divergent roles in the pathogenesis of atopic and nonatopic asthma phenotypes by either enhancing Th2 responses or curtailing T-helper 17 responses. These findings raise important caveats for the design of therapeutic interventions targeting TSLP in asthma.

Development and evaluation of neural network models to estimate daily solar radiation at Córdoba, Argentina

The objective of this work was to develop neural network models of backpropagation type to estimate solar radiation based on extraterrestrial radiation data, daily temperature range, precipitation, cloudiness and relative sunshine duration. Data from Córdoba, Argentina, were used for development and validation. The behaviour and adjustment between values observed and estimates obtained by neural networks for different combinations of input were assessed. These estimations showed root mean square error between 3.15 and 3.88 MJ m-2 d-1 . The latter corresponds to the model that calculates radiation using only precipitation and daily temperature range. In all models, results show good adjustment to seasonal solar radiation. These results allow inferring the adequate performance and pertinence of this methodology to estimate complex phenomena, such as solar radiation.

Equacions diferencials i models

En tot cas, jo voldria que aquesta conferència fos això que he dit: una breu lliçó sobre la importància de les equacions diferencials. Parlaré d'elles des de el punt de vista del models, és a dir, dels fenòmens que modelitzeu. I intentaré explicar que malgrat el seu origen antic, totes elles segueixen presentant avui en dia problemes nous i interessants, tant des de el punt de vista teòric com pràctic.

Application of theoretical models to financial innovation

The present paper is aimed at providing a general strategic overview of the existing theoretical models that have applications in the field of financial innovation. Whereas most financialdevelopments have relied upon traditional economic tools, a new stream of research is defining a novel paradigm in which mathematical models from diverse scientific disciplines are being applied to conceptualize and explain economic and financial behavior. Indeed, terms such as ‘econophysics’ or ‘quantum finance’ have recently appeared to embrace efforts in this direction. As a first contact with such research, the project will present a brief description of some of the main theoretical models that have applications in finance and economics, and will try to present, if possible, potential new applications to particular areas in financial analysis, or new applicable models. As a result, emphasiswill be put on the implications of this research for the financial sector and its future dynamics.

Structure of intracellular mature vaccinia virus observed by cryoelectron microscopy.

Intracellular mature vaccinia virus, also called intracellular naked virus, and its core envelope have been observed in their native, unfixed, unstained, hydrated states by cryoelectron microscopy of vitrified samples. The virion appears as a smooth rounded rectangle of ca. 350 by 270 nm. The core seems homogeneous and is surrounded by a 30-nm-thick surface domain delimited by membranes. We show that surface tubules and most likely also the characteristic dumbbell-shaped core with the lateral bodies which are generally observed in negatively stained or conventionally embedded samples are preparation artifacts.

Role of dendritic cells in the lung: in vitro models, animal models and human studies

Dendritic cells (DCs) are the most potent antigen-presenting cells in the human lung and are now recognized as crucial initiators of immune responses in general. They are arranged as sentinels in a dense surveillance network inside and below the epithelium of the airways and alveoli, where thet are ideally situated to sample inhaled antigen. DCs are known to play a pivotal role in maintaining the balance between tolerance and active immune response in the respiratory system. It is no surprise that the lungs became a main focus of DC-related investigations as this organ provides a large interface for interactions of inhaled antigens with the human body. During recent years there has been a constantly growing body of lung DC-related publications that draw their data from in vitro models, animal models and human studies. This review focuses on the biology and functions of different DC populations in the lung and highlights the advantages and drawbacks of different models with which to study the role of lung DCs. Furthermore, we present a number of up-to-date visualization techniques to characterize DC-related cell interactions in vitro and/or in vivo.

Dynamic models of energy allocation and investment

In dynamic models of energy allocation, assimilated energy is allocated to reproduction, somatic growth, maintenance or storage, and the allocation pattern can change with age. The expected evolutionary outcome is an optimal allocation pattern, but this depends on the environment experienced during the evolutionary process and on the fitness costs and benefits incurred by allocating resources in different ways. Here we review existing treatments which encompass some of the possibilities as regards constant or variable environments and their predictability or unpredictability, and the ways in which production rates and mortality rates depend on body size and composition and age and on the pattern of energy allocation. The optimal policy is to allocate resources where selection pressures are highest, and simultaneous allocation to several body subsystems and reproduction can be optimal if these pressures are equal. This may explain balanced growth commonly observed during ontogeny. Growth ceases at maturity in many models; factors favouring growth after maturity include non-linear trade-offs, variable season length, and production and mortality rates both increasing (or decreasing) functions of body size. We cannot yet say whether these are sufficient to account for the many known cases of growth after maturity and not all reasonable models have yet been explored. Factors favouring storage are also reviewed.

Climate change and plant distribution: local models predict high-elevation persistence

Mountain ecosystems will likely be affected by global warming during the 21st century, with substantial biodiversity loss predicted by species distribution models (SDMs). Depending on the geographic extent, elevation range and spatial resolution of data used in making these models, different rates of habitat loss have been predicted, with associated risk of species extinction. Few coordinated across-scale comparisons have been made using data of different resolution and geographic extent. Here, we assess whether climate-change induced habitat losses predicted at the European scale (10x10' grid cells) are also predicted from local scale data and modeling (25x25m grid cells) in two regions of the Swiss Alps. We show that local-scale models predict persistence of suitable habitats in up to 100% of species that were predicted by a European-scale model to lose all their suitable habitats in the area. Proportion of habitat loss depends on climate change scenario and study area. We find good agreement between the mismatch in predictions between scales and the fine-grain elevation range within 10x10' cells. The greatest prediction discrepancy for alpine species occurs in the area with the largest nival zone. Our results suggest elevation range as the main driver for the observed prediction discrepancies. Local scale projections may better reflect the possibility for species to track their climatic requirement toward higher elevations.

Introduction of snow and geomorphic disturbance variables into predictive models of alpine plant distribution in the Western Swiss Alps

Indirect topographic variables have been used successfully as surrogates for disturbance processes in plant species distribution models (SDM) in mountain environments. However, no SDM studies have directly tested the performance of disturbance variables. In this study, we developed two disturbance variables: a geomorphic index (GEO) and an index of snow redistribution by wind (SNOW). These were developed in order to assess how they improved both the fit and predictive power of presenceabsence SDM based on commonly used topoclimatic (TC) variables for 91 plants in the Western Swiss Alps. The individual contribution of the disturbance variables was compared to TC variables. Maps of models were prepared to spatially test the effect of disturbance variables. On average, disturbance variables significantly improved the fit but not the predictive power of the TC models and their individual contribution was weak (5.6% for GEO and 3.3% for SNOW). However their maximum individual contribution was important (24.7% and 20.7%). Finally, maps including disturbance variables (i) were significantly divergent from TC models in terms of predicted suitable surfaces and connectivity between potential habitats, and (ii) were interpreted as more ecologically relevant. Disturbance variables did not improve the transferability of models at the local scale in a complex mountain system, and the performance and contribution of these variables were highly species-specific. However, improved spatial projections and change in connectivity are important issues when preparing projections under climate change because the future range size of the species will determine the sensitivity to changing conditions.

Implementation Manual — 3D Engineered Models for Highway Construction: The Iowa Experience; RB33-014, 2015

3D engineered modeling is a relatively new and developing technology that can provide numerous benefits to owners, engineers, contractors, and the general public. This manual is for highway agencies that are considering or are in the process of switching from 2D plan sets to 3D engineered models in their highway construction projects. It will discuss some of the benefits, applications, limitations, and implementation considerations for 3D engineered models used for survey, design, and construction. Note that is not intended to cover all eventualities in all states regarding the deployment of 3D engineered models for highway construction. Rather, it describes how one state—Iowa—uses 3D engineered models for construction of highway projects, from planning and surveying through design and construction.

Do stacked species distribution models reflect altitudinal diversity patterns?

The objective of this study was to evaluate the performance of stacked species distribution models in predicting the alpha and gamma species diversity patterns of two important plant clades along elevation in the Andes. We modelled the distribution of the species in the Anthurium genus (53 species) and the Bromeliaceae family (89 species) using six modelling techniques. We combined all of the predictions for the same species in ensemble models based on two different criteria: the average of the rescaled predictions by all techniques and the average of the best techniques. The rescaled predictions were then reclassified into binary predictions (presence/absence). By stacking either the original predictions or binary predictions for both ensemble procedures, we obtained four different species richness models per taxa. The gamma and alpha diversity per elevation band (500 m) was also computed. To evaluate the prediction abilities for the four predictions of species richness and gamma diversity, the models were compared with the real data along an elevation gradient that was independently compiled by specialists. Finally, we also tested whether our richness models performed better than a null model of altitudinal changes of diversity based on the literature. Stacking of the ensemble prediction of the individual species models generated richness models that proved to be well correlated with the observed alpha diversity richness patterns along elevation and with the gamma diversity derived from the literature. Overall, these models tend to overpredict species richness. The use of the ensemble predictions from the species models built with different techniques seems very promising for modelling of species assemblages. Stacking of the binary models reduced the over-prediction, although more research is needed. The randomisation test proved to be a promising method for testing the performance of the stacked models, but other implementations may still be developed.

Models for vertical wood density of Scots pine, Norway spruce and birch stems, and their application to determine average wood density

Abstract

A critical evaluation of secondary cancer risk models applied to Monte Carlo dose distributions of 2-dimensional, 3-dimensional conformal and hybrid intensity-modulated radiation therapy for breast cancer.

The comparison of radiotherapy techniques regarding secondary cancer risk has yielded contradictory results possibly stemming from the many different approaches used to estimate risk. The purpose of this study was to make a comprehensive evaluation of different available risk models applied to detailed whole-body dose distributions computed by Monte Carlo for various breast radiotherapy techniques including conventional open tangents, 3D conformal wedged tangents and hybrid intensity modulated radiation therapy (IMRT). First, organ-specific linear risk models developed by the International Commission on Radiological Protection (ICRP) and the Biological Effects of Ionizing Radiation (BEIR) VII committee were applied to mean doses for remote organs only and all solid organs. Then, different general non-linear risk models were applied to the whole body dose distribution. Finally, organ-specific non-linear risk models for the lung and breast were used to assess the secondary cancer risk for these two specific organs. A total of 32 different calculated absolute risks resulted in a broad range of values (between 0.1% and 48.5%) underlying the large uncertainties in absolute risk calculation. The ratio of risk between two techniques has often been proposed as a more robust assessment of risk than the absolute risk. We found that the ratio of risk between two techniques could also vary substantially considering the different approaches to risk estimation. Sometimes the ratio of risk between two techniques would range between values smaller and larger than one, which then translates into inconsistent results on the potential higher risk of one technique compared to another. We found however that the hybrid IMRT technique resulted in a systematic reduction of risk compared to the other techniques investigated even though the magnitude of this reduction varied substantially with the different approaches investigated. Based on the epidemiological data available, a reasonable approach to risk estimation would be to use organ-specific non-linear risk models applied to the dose distributions of organs within or near the treatment fields (lungs and contralateral breast in the case of breast radiotherapy) as the majority of radiation-induced secondary cancers are found in the beam-bordering regions.

In vivo brain macromolecule signals in healthy and glioblastoma mouse models: (1) H magnetic resonance spectroscopy, post-processing and metabolite quantification at 14.1 T.

In (1) H magnetic resonance spectroscopy, macromolecule signals underlay metabolite signals, and knowing their contribution is necessary for reliable metabolite quantification. When macromolecule signals are measured using an inversion-recovery pulse sequence, special care needs to be taken to correctly remove residual metabolite signals to obtain a pure macromolecule spectrum. Furthermore, since a single spectrum is commonly used for quantification in multiple experiments, the impact of potential macromolecule signal variability, because of regional differences or pathologies, on metabolite quantification has to be assessed. In this study, we introduced a novel method to post-process measured macromolecule signals that offers a flexible and robust way of removing residual metabolite signals. This method was applied to investigate regional differences in the mouse brain macromolecule signals that may affect metabolite quantification when not taken into account. However, since no significant differences in metabolite quantification were detected, it was concluded that a single macromolecule spectrum can be generally used for the quantification of healthy mouse brain spectra. Alternatively, the study of a mouse model of human glioma showed several alterations of the macromolecule spectrum, including, but not limited to, increased mobile lipid signals, which had to be taken into account to avoid significant metabolite quantification errors.