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Coarse spatial resolution of Thermal Infrared (TIR) satellites hampers measuring the temperature and emissivity of scene elements from space that improves our understanding of land surfaces' thermal behavior. The stringent conditions of current TIR unmixing methods hinder the production of extensive component temperature and emissivity products. To address this, we designed a gradient-based multi-pixel physical model, TRUST-DART, to derive the temperature and emissivity of urban features from non-isothermal mixed pixels of satellite images using the DART 3D radiative transfer model. Unlike traditional TIR unmixing methods, TRUSTDART is not constrained by issues related to spatial, spectral, temporal resolution, angular, scene, field measurement requirements, or manual operations. Its inputs include an at-surface radiance image, downwelling sky irradiance, a 3D urban mock-up with feature information, and DART input parameters such as spatial resolution. It generates maps of emissivity and temperature per urban feature. Its accuracy is validated for two vegetation and urban scenes and two types of images (DART simulated pseudo satellite and ASTER observed images). The accuracy of the TRUST-DART depends heavily on the fraction of components. TRUST-DART proves robust for high-fraction components. However, its accuracy decreases with decreasing fractions. TRUST-DART is distributed with DART and is available for education and research via Toulouse III University (https://dart.omp.eu).

Abstract Introduction The transition to metabolically unhealthy obesity (MUO) is driven by the limited expandability of adipose tissue (AT). Familial Partial Lipodystrophy type 2 (FPLD2) is an alternative model for AT dysfunction that is suitable for comparison with obesity. While MUO is associated with low-grade systemic inflammation, studies of inflammation in FPLD2 have yielded inconsistent results. Consequently, comparison of inflammation markers between FPLD2 and obesity is of great interest to better understand the pathophysiological defects of FPLD2. Objective To compare the levels of inflammatory biomarkers between a population of patients with FPLD2 due to the same ‘Reunionese’ LMNA variant and a population of patients with obesity (OB group). Methods Adiponectin, leptin, IL-6, TNF-α and MCP-1 plasma levels were measured by enzyme-linked immuno assays for 60 subjects with FPLD2 and for 60 subjects with obesity. The populations were closely matched for age, sex, and diabetic status. Results Metabolic outcomes were similar between the two populations. Adiponectinemia and leptinemia were lower in the FPLD2 group than in the OB group (p

Remote sensing (RS) of solar-induced chlorophyll fluorescence (SIF) has a great potential for monitoring plant photosynthetic activity. Radiative transfer models (RTM) are essential to better interpret and extract information from SIF signals. DART is one of the most comprehensive and accurate 3D RTMs. Its standard mode DART-FT simulates SIF using a discrete ordinates method but is not adapted to large landscapes due to computational constraints. DART-Lux, the new mode based on a bi-directional path tracing algorithm, greatly improves DART computational efficiency for simulating images. This paper presents the theory of a novel SIF modelling algorithm in DART-Lux. We verified its accuracy with DART-FT and the SCOPE model for three types of canopies: turbid medium, maize field and forest. DART-Lux closely matches DART-FT (relative difference

Discrete anisotropic radiative transfer (DART) calibration is an iterative inversion method that is applied to shortwave (SW) satellite images to get maps of spectral signatures (SS) of city materials at the satellite spatial resolution. Therefore, it is potentially a handy spectral unmixing tool. However, up to now, it has only been validated by comparing the time series of SW radiative budget Q*SW from a flux tower in Basel to DART simulated Q*SW using maps of SS derived from satellite images. This article thoroughly assesses the DART calibration accuracy with two synthetic case studies, called “ideal” and “nonideal,” for short wavelengths. In both cases, the satellite image is a DART simulated image of an urban scene with ground, buildings with various structures, water, and shrubs. In the ideal case, SS maps are the only unknowns in the inversion process. The mean absolute value of the relative errors over all bands for ground, roof, water, tree, and shrub maps were 0.013, 0.005, 0.027, 0.297, and 0.250. In the nonideal case, we considered an uncertainty on parameters assumed to be known in the ideal case: solar zenith angle (SZA); satellite image spatial resolution; pixel-shift; inaccuracy of landscape modeling; and modulation transfer function (MTF). It led to larger errors: for ground, roof, water, tree, and shrubs, the mean absolute value of the relative error was 0.233, 0.507, 3.088, 0.834, and 1.256, respectively. By descending order of importance, the parameters that most affect the accuracy of the retrieved SS of urban material were SZA, satellite image spatial resolution, pixel-shift, inaccuracy of three-dimensional urban scene modeling, and MTF.

Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) for validation. The first one is derived from observations of MODerate resolution Imaging Spectroradiometer (MODIS) from 16 April 2013, to 21 October 2020, in the Apiacás area. Its corresponding VIs are calculated from a combination of Sentinel-2 and Landsat-8 surface reflectance products. The second one is a global LAI dataset with VIs calculated from Landsat-5 surface reflectance products. A linear regression model is applied to both datasets to evaluate four VIs that are commonly used to estimate LAI: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed SAVI (TSAVI), and enhanced vegetation index (EVI). The optimal soil adjustment factor of SAVI for LAI estimation is determined using an exhaustive search. The Dickey-Fuller test indicates that the time series of LAI data are stable with a confidence level of 99%. The linear regression results stress significant saturation effects in all VIs. Finally, the exhaustive searching results show that a negative soil adjustment factor of SAVI can mitigate the SAVIs’ saturation in the Apiacás area (i.e., X = −0.148 for mean LAI = 5.35), and more generally in areas with large LAI values (e.g., X = −0.183 for mean LAI = 6.72). Our study further confirms that the lower boundary of the soil adjustment factor can be negative and that using a negative soil adjustment factor improves the computation of time series of LAI.

Satellite and airborne optical sensors are increasingly used by scientists, and policy makers, and managers for studying and managing forests, agriculture crops, and urban areas. Their data acquired with given instrumental specifications (spectral resolution, viewing direction, sensor field-of-view, etc.) and for a specific experimental configuration (surface and atmosphere conditions, sun direction, etc.) are commonly translated into qualitative and quantitative Earth surface parameters. However, atmosphere properties and Earth surface 3D architecture often confound their interpretation. Radiative transfer models capable of simulating the Earth and atmosphere complexity are, therefore, ideal tools for linking remotely sensed data to the surface parameters. Still, many existing models are oversimplifying the Earth-atmosphere system interactions and their parameterization of sensor specifications is often neglected or poorly considered. The Discrete Anisotropic Radiative Transfer (DART) model is one of the most comprehensive physically based 3D models simulating the Earth-atmosphere radiation interaction from visible to thermal infrared wavelengths. It has been developed since 1992. It models optical signals at the entrance of imaging radiometers and laser scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental configuration and instrumental specification. It is freely distributed for research and teaching activities. This paper presents DART physical bases and its latest functionality for simulating imaging spectroscopy of natural and urban landscapes with atmosphere, including the perspective projection of airborne acquisitions and LIght Detection And Ranging (LIDAR) waveform and photon counting signals.

This paper follows on from our previous text, “Language(s) and Utopia. I”. Its aim is to present what is known from the Aliens’ languages, from the constructed languages in Ancient Greece, and especially from the utopian languages. First, the paper points out how difficult it is to study such a topic, because of the huge gap in the sources and of the burdensome literary conventions, because Greek people have had an uninterested (if not contemptuous) attitude towards the Aliens and their languages, which is reflected in the ambiguous lexicon for multilingualism and for oral translation. One must nevertheless emphasize the sophists’ open-mindedness in the respect of linguistic concerns. Secondly, the tradition has provided us with few testimonies, confirming only that literary jargons did exist (those of the onomatotherai, gathered by Atheneus) as well as a universal language based on gests (in the Roman world at least). On the other hand, facts are more tricky to be established as far as prophetic or tragic delusion and the “language of gods” are concerned. Lastly, regarding the utopian fictions, one must stress how scarce the mentions related to Aliens’ languages are. Some involve Egypt and its sacred writing system, that play a crucial role in the narrative framework introducing the Atlantis myth by Plato or in the inscription topos by Euhemerus. A single fiction, The Fortunate Island by Iambulos, includes significant linguistic developments, which is not surprising from a utopia inspired by stoicism.

The aim of this paper is to give an overview on the relations the language and languages bear to utopia. Thus, on the one hand, the paper explores the range of constructed languages (conlangs), whether they are created consciously (jargons, slangs, pidgins, as well as auxiliary international languages [auxlangs] and pasigraphies) or not (such as religious and non-religious glossolalies). This investigation is led in order to show how these languages work and what is at stake in them. On the other hand, natural languages (natlangs) are sometimes involved in utopian processes, when linguistic policies implement drastic reforms (Attaturk’s linguistic reform, the “rebirth” of Hebrew) or when totalitarian regimes prevail (Hitlerian German). Even if imaginary languages are found in each literary genre, most of them appear in fictional utopias, where they have various functions. In the voyages of standard utopias, imaginary languages are a mere ornament or they serve as a means to authenticate the utopian civilisation. In the dystopias, however, the linguistic manipulations are the very symptom of the distorted societies. At last, forging an imaginary language can give its creator the opportunity of developing metalinguistic considerations mirroring the scholarly concerns of his time.

Concerns about body shape and weight are core diagnostic criteria for eating disorders although intensity varies between patients. Few studies have focused on the clinical differences relative to the intensity of these concerns. Nonetheless, they might have a prognostic value. This study was aimed at identifying the characteristics associated with marked body shape concerns in patients with an eating disorder. Data was collected from a systematic and standardized clinical assessment of outpatients seeking treatment in our department for eating disorders. Only female patients, suffering from anorexia nervosa or bulimia nervosa, and with "no / mild" or "marked" body shape concerns according to the Body Shape Questionnaire, were included for the present study. We focused on sociodemographic characteristics, eating disorder characteristics, axis 1 disorders, types of attachment, self-esteem and dissociation. A multiple logistic regression was performed to identify factors related to "marked" body shape concerns. In our sample (123 participants, with a mean age of 24.3 years [range 16-61]), 56.9% had marked concerns with body shape. Marked body shape concerns were associated with a major depressive episode (OR = 100.3), the use of laxatives (OR = 49.8), a high score on the item "body dissatisfaction" of the Eating Disorders Inventory scale (OR = 1.7), a higher minimum body mass index (OR = 1.73), and a high score on the item "loss of control over behavior, thoughts and emotions" from the dissociation questionnaire (OR = 10.74). These results are consistent with previous studies, and highlight the importance of denial.

Patient-reported outcomes (PRO) have gained importance in clinical and epidemiological research and aim at assessing quality of life, anxiety or fatigue for instance. Item Response Theory (IRT) models are increasingly used to validate and analyse PRO. Such models relate observed variables to a latent variable (unobservable variable) which is commonly assumed to be normally distributed. A priori sample size determination is important to obtain adequately powered studies to determine clinically important changes in PRO. In previous developments, the Raschpower method has been proposed for the determination of the power of the test of group effect for the comparison of PRO in cross-sectional studies with an IRT model, the Rasch model. The objective of this work was to evaluate the robustness of this method (which assumes a normal distribution for the latent variable) to violations of distributional assumption. The statistical power of the test of group effect was estimated by the empirical rejection rate in data sets simulated using a non-normally distributed latent variable. It was compared to the power obtained with the Raschpower method. In both cases, the data were analyzed using a latent regression Rasch model including a binary covariate for group effect. For all situations, both methods gave comparable results whatever the deviations from the model assumptions. Given the results, the Raschpower method seems to be robust to the non-normality of the latent trait for determining the power of the test of group effect.