dataesr/scientific-paragraphs-categorization huggingface dataset:
This is probably linked to the larger model errors simulated for this class not correctly simulated by the SETHYS model, as previously mentioned. Indeed, a poor model spread was obtained for the prescribed range of variation of the parameters for the water class (not shown here). In other words, the solution space, being outside of the model simulated space, cannot be explored by the GPS and the selected particles remain far from the optimal solution. Nevertheless, on the whole, the results appear quite satisfactory and show the significant contribution of the Meteosat LST data combined with the LSM to estimate relevant subscale temperatures. Concerning the impact of the assimilation window length, our results show better performances for the shortest time period (one day); this was not expected initially.
Some large errors seen on some small agricultural fields give the suspicion of land cover map errors or irrigation practices which were not accounted for in the modeling, the atmospheric forcing data (and the water input) being unique for each land cover type considered. m spatial resolution (original resolution of Landsat 7 LST data). The same statistics (RMSE, efficiency, and bias) have been calculated for the same downscaling experiments, over our set of seven Landsat images and by land cover class. It should be noted that at the Landsat 7 scale, each pixel is considered homogeneous. As an example, Figure  3  presents the results obtained for DOY 240 at 60 m resolution, on the same extracted zone than previously (similarly to Figure  2 ). We should point out that the GPS downscaled LST image (at 10:15 UTC) and the Meteosat one present a slight delay of 5 min compared to the Landsat 7 image acquired at 10:20 UTC, which is still acceptable given the temporal dynamics of LST.
Some large errors seen on some small agricultural fields give the suspicion of land cover map errors or irrigation practices which were not accounted for in the modeling, the atmospheric forcing data (and the water input) being unique for each land cover type considered. Table  5  presents the overall statistics on the whole set of Landsat images for all the downscaling experiments. The best results are obtained for the Experiment 1 (1-day assimilation window, noised forcing data) like for the MODIS comparison. A systematic positive bias ranging between 1. 3 and 1. 7 K appears for all the downscaling experiments, which was not present in the prior estimations. The Table  5  presents the overall statistics on the whole set of Landsat images for all the downscaling experiments. The best results are obtained for the Experiment 1 (1-day assimilation window, noised forcing data) like for the MODIS comparison.
Conclusions The methodology presented here allows downscaling of Meteosat high-frequency LST time series at higher spatial resolution. The approach is based on the assimilation of low spatial resolution (LSR) observations in a LSM able to simulate the temperature of the various land cover types (components) inside the Meteosat mixed pixel and the composite thermal emission. The method assumes that LST is stationary for each land cover class inside the Meteosat 3 km × 5 km pixel. It requires the availability of a high-resolution land cover map providing the fractions of each land cover type, as well as atmospheric and surface forcing data to simulate prior estimations of the component temperatures. The method was applied over a 4539 km 2 area of Southeastern France during a seven-month period in 2009 to downscale Meteosat temperatures to 1 km scale as a first step, using MODIS LST for the validation and, at 60 m scale, to match Landsat 7 data.
The various downscaling experiments performed demonstrate the benefits of the GPS approach to improve the prior model simulations and the increased performance when short time assimilation period (one day) is prescribed and The same comparisons performed on each of the Landsat images showed that these deviations are not consistent from one day to the next, but also depend on the landscape, and on the land cover. For example, forested areas appear to present larger warmer biases than bare soils. The inconsistent character of these discrepancies, depending on time of day, season, land cover, etc. , and the limited dataset did not allow us to conclude about the respective role of directional effects (that are known to be significant at high spatial resolutions (  [42] ), atmospheric corrections, or sensor inter-calibration issues.
Conclusions The methodology presented here allows downscaling of Meteosat high-frequency LST time series at higher spatial resolution. The approach is based on the assimilation of low spatial resolution (LSR) observations in a LSM able to simulate the temperature of the various land cover types (components) inside the Meteosat mixed pixel and the composite thermal emission. The method assumes that LST is stationary for each land cover class inside the Meteosat 3 km ˆ5 km pixel. It requires the availability of a high-resolution land cover map providing the fractions of each land cover type, as well as atmospheric and surface forcing data to simulate prior estimations of the component temperatures. The method was applied over a 4539 km 2 area of Southeastern France during a seven-month period in 2009 to downscale Meteosat temperatures to 1 km scale as a first step, using MODIS LST for the validation and, at 60 m scale, to match Landsat 7 data.
The method requires the implementation of a LSM at finer scales and the various input datasets needed to run the model. Given that, it allows dynamic monitoring of the subpixel LSTs at high spatio-temporal resolutions. As a result, the method not only provides downscaled maps at the time of observation, but at the model time step (in our case, each half-hour) and, therefore, even in cloudy conditions. These results also show the potentialities of the GPS for the assimilation of remote sensing data over mixed pixels. If the dynamic model is able to simulate the composite signal, the GPS can be implemented efficiently to optimize subpixel model parameters. The added value compared to other DA approaches is the easy development and parallel implementation of the downscaling process that could present an interesting framework for an automatic real-time implementation.
Experimental procedures The drilling tests were carried out on a five-axis CNC machining centre, with two different twist drills: drill 1 Ø12. 7 mm and drill 2 Ø15. 9 mm, with two lips and a drill point thinning. Internal minimal quantity lubrication was used. Cutting forces were measured using a Kistler 9257B dynamometer. Aluminum alloy 7010 was drilled at several feeds, from 0. 025 mm. rev -1 to 1 mm. rev -1 , and with a cutting speed of 75 m. min -  in traditional drilling. For feed higher than 0. 3 mm. rev -1 , only the tip of the tool penetrated the material to avoid unnecessary tool wear or tool breakage. The simulation obtained through traditional drilling data is verified with vibration assisted drilling tests for two values of feed (0. 1 mm. rev -1 ; 0. 2 mm. rev -1 ) and several amplitudes of vibrations a from 0.
0, 200 mM calcium acetate, 0. 143 mM ammonium sulfate. An aliquot (200 nl) of PTPN4-PDZ⅐p38␥-KETPL complex solution (concentration of the PDZ domain at 5 mg/ml) in the same buffer with 200 nl of well precipitant solution containing 1. 2 M sodium dihydrogen phosphate, 0. 8 M dipotassium hydrogen phosphate, 0. 1 M CAPS, pH 10. 5, 0. 2 M lithium sulfate, 0. 67 M non-detergent sulfobetaine (NDSB) 201. Crystals were then flash-cooled in liquid nitrogen using Paratone-paraffin (50%/50%) oil as the cryoprotectant. X-ray diffraction data were collected on beamline PROXIMA-1 at Synchrotron SOLEIL (St. Aubin, France). The data were processed with XDS  (21) , SCALA  (22) , and other programs from the CCP4 suite  (23). The structures were solved by molecular replacement with PHASER (24) using the search atomic model of PTPN4-PDZ (PDB access code 3NFK).
The locations of the bound peptides were determined from a F o Ϫ F c difference electron density map. Models were rebuilt using COOT  (25) , and refinement was performed using BUSTER  (26). The overall assessment of model quality was performed using MolProbity  (27). The crystal parameters, data collection statistics, and final refinement statistics are shown in Table  1. All structural figures were generated with the PyMOL Molecular Graphics System, version 1. 5. 0. 4 (Schrödinger). The asymmetric unit of PTPN4-PDZ⅐Cyto8-RETEV and PTPN4-PDZ⅐p38␥-KETPL crystal structures contains four subunits of protein⅐peptide complex. Each subunit corresponds to the complex formed by PTPN4-PDZ protein (chains A, B, C, and D) with the peptides (chains E, F, G, and H), respectively, A-E, B-F, C-G, and D-H.
Three hours post treatment, cells were assayed for peptide entry (FITC-positive cells), and cell death (propidium iodide-positive cells) was by flow cytometry (FACSCalibur, BD Biosciences) using the Cell Quest Pro software as described  (15). NMR Experiments-15 N-Labeled NMR samples of PTPN4-PDZ and of p38␥ were prepared at concentrations of 220 mM in 250 ml of a buffer containing 50 mM Tris-HCl, pH 7. 5, 150 mM NaCl, 0. 5 mM TCEP, protease inhibitor mixture (Roche Applied Science), and 12% D 2 O. Stock solutions of unlabeled PTPN4-PDZ and p38␥ were prepared at concentrations of 540 M in the same buffer. The 15 N-PTPN4-PDZ⅐unlabeled-p38␥ complex was formed at 140:225 M (1:1. 6 molar ratio), and the 15 N- p38␥⅐unlabeled-PTPN4-PDZ complex was formed at 150:175 M (1:1. 2 molar ratio). All the 1 H, 15 N HSQC spectra recorded on these samples to map the PTPN4-PDZ⅐p38␥ interaction were acquired at 25 °C on a Varian Inova 600-MHz spectrometer equipped with a cryoprobe, with 128 (60 ms) and 1024 (95 ms) complex points (acquisition times) in 15 N and 1 H, respectively.
35 Å and 2. 09 Å resolutions, respectively (Table  1 ). The structure factors and coordinates for the structures PTPN4/Cyto8-RETEV and PTPN4/p38␥-KETPL have been deposited in the Protein data Bank under accession codes 5EYZ and 5EZ0, respectively. The structures of PTPN4-PDZ⅐Cyto8-RETEV and PTPN4-PDZ⅐p38␥-KETPL are very similar with a root mean square deviation of 0. 22 Å. PTPN4-PDZ adopts the typical PDZ fold comprising five ␤-strands and two ␣-helices (Fig. 2A ) and possesses the interaction network specific to class I PDZ domains that recognize the consensus peptide sequence X(S/T) X⌽ COOH , where X is any residue, and ⌽ is a hydrophobic residue. The last six C-terminal residues encompassing the PBM of Cyto8-RETEV (-GRETEV -COOH ) and p38␥-KETPL (-SKETPL -COOH ) are well ordered in the crystal.
The peptides were inserted into the PDZ-binding pocket in the conventional mode (Fig. 2 ). The interactions of the residues at the positions 0, Ϫ2, and Ϫ3 of both peptides with PTPN4-PDZ are quite similar to the bonding patterns already observed in the complex between PTPN4-PDZ and the viral peptide Cyto13-att (-GETRL -COOH ) (x-ray structure PDB code 3NFK; Ref. 15). The peptide Cyto13-att corresponds to the last 13 residues of the G envelope protein from an attenuated RABV strain. Starting from the Cyto13-att sequence that binds PTPN4-PDZ with a K d of 157 M, the Cyto8-RETEV sequence has been optimized focusing on the residues at positions Ϫ1 (Arg 3 Glu) and Ϫ4 (Gly 3 Arg). These positions are not involved in the network of interactions between the viral sequence and PTPN4-PDZ  (15). Here, we confirm our previous postulate that Glu at position Ϫ1 and Arg at position Ϫ4 in Cyto8-RETEV likely induces crucial polar contacts with the surrounding residues of PTPN4-PDZ and consequently improve the affinity for PTPN4-PDZ.
3C ). We compared 1 H, 15 N HSQC spectra recorded on 15 N-labeled full-length p38␥ in its free form and in the presence of unlabeled PTPN4-PDZ to map the residues of the kinase perturbed by the interaction with PTPN4-PDZ (Fig. 4 ). Backbone assignment of p38␥ was performed on a 13 C, 2 H, 15 N-labeled sample using a set of three-dimensional heteronuclear NMR experiments recorded at 950 MHz. Over the 349 expected nonproline residues, 273 amide signals were visible in the HSQC spectrum and only 154 could be assigned (44. 1%) (Fig. 4 ) (BMRB (Biological Magnetic Resonance Bank) accession number 26758). Note that similar results were previously obtained for the homologous MAP kinase p38␣  (28)  for which 64% of amide signals were initially assigned using a set of selectively 15 N-labeled samples and distance information from a paramagnetic adenosine derivate.
Acknowledgments-We thank M. Lafage (Viral Neuro-Immunology Unit,  Institut Pasteur), P. England, S. Hoos (Proteopole, Institut Pasteur) ,  C. Malosse  ( Structural Mass Spectrometry and Proteomics Unit ), and  C. Simenel  ( NMR unit, Institut Pasteur ) for technical expertise. We thank  E. Lescop  for NMR technical expertise and helpful discussions (ICSN,  Gif/ Yvette , France). We thank the staff from the  Protein Crystallization platform at the Institut Pasteur  and the staff of the PROXIMA-1 beamline at the SOLEIL synchrotron ( St Aubin , France).
* This work was supported by  TGIR-RMN-THC Fr3050 CNRS. The authors declare that they have no conflicts of interest with the contents of this article. The atomic coordinates and structure factors (codes  5EYZ  and  5EZ0 ) have been deposited in the Protein Data Bank (1 Supported by grants from the  Ministère de l'Enseignement Supérieur et de la Recherche  and the  Fondation pour la Recherche Médicale   FDT20130927999. 2 Recipient of a  Bourse Roux postdoctoral fellowship  from the  Pasteur Institute. 3Supported by a student mobility grant through the  Erasmusϩ Programme.
Such features also facilitate robust and powerful studies to dissect the quantitative genetic basis of complex traits. The standardized recording of both ecologically and production-relevant phenotypes and cataloguing of functional and phenotypic responses, e. g. within the Gene Ontology framework is also high priority. Standardised phenotypic assays will also help interpret the molecular basis of phenotypic variation observed in the numerous wild populations gained by long-term data series, e. g. [66]. Macqueen et al. Functional Analysis of All Salmonid Genomes (FAASG).
Engaging the research community The FAASG initiative will promote inclusiveness among all stakeholders and draw in expertise in aquaculture, bioinformatics/biostatistics, genetics, molecular biology, functional genomics, physiology, ecology and conservation, ensuring quality at all levels. A website (http://www. faasg. org/) will report progress, including experimental and computational protocols, publications and datasets, along with contact information for researchers or funders wishing to contribute to efforts. The initiative is being advertised at several scientific conferences (see http://www. faasg. org/) to promote wider awareness.
Need for better understanding and more accurate estimation of radiative fluxes in urban environments, specifically urban surface albedo and exitance, motivates development of new remote sensing and three-dimensional (3D) radiative transfer (RT) modeling methods. The discrete anisotropic radiative transfer (DART) model, one of the most comprehensive physically based 3D models simulating Earth/atmosphere radiation interactions, was used in combination with satellite data (e. g. , Landsat-8 observations) to better parameterize the radiative budget components of cities, such as Basel in Switzerland. After presenting DART and its recent RT modeling functions, we present a methodological concept for estimating urban fluxes using any satellite image data.
In other words, its goal is to estimate the impact of human urban activities on the energy budget of city using satellite images. Important parts of the surface energy balance computation are the 3D radiative budget components specifically as urban surface albedo including thermal exitance. However, no remote sensing model able to simulate accurately spatial distribution of urban spectral albedo and exitance has been previously available. Three conditions must be fulfilled in order to achieve an acceptable solution of an urban radiative budget simulation: -The model must consider explicitly the 3D architecture of urban environments and simulate radiance images and radiative budget of urban environment. Hence, apart from physical modeling considerations, the model must be able to work with urban databases, including spatial information on vegetation and digital elevation model.
Urban objects (houses, roads, etc. ) contain solid walls and roofs built from triangular facets. Finally, water bodies (rivers, lakes, etc. ) are simulated as facets of appropriate optical properties. DART can use external libraries to import and to some extent also edit landscape elements, digital elevation models (DEMs), and digital surface models (DSM) produced by other software or measured in field (e. g. , translation, homothetic and rotational transformations). Most importantly, the imported and DART-created landscape objects can be combined into virtual Earth scenes of user-defined complexity. This allows importation of whole cities from urban databases provided by city councils and urban planners. The optical properties of landscape elements and their geometry, as well as and properties of atmosphere, are specified and stored in adjacent SQL databases.
Atmospheric cells are used to simulate attenuation effects of satellite at-sensor radiance and also to model influence of the atmosphere composition on radiative budget of Earth surfaces. The atmosphere can be treated just as an interface above the simulated Earth scene or as a lightpropagating medium above and also within the simulated Earth scene, with cell sizes inversely proportional to the particle density. These cells are characterized by their gas and aerosol contents and spectral properties (i. e. , phase functions, vertical profiles, extinction coefficients, spherical albedo). These quantities can be defined manually or obtained automatically from an atmospheric database. DART contains a database that stores the properties of major atmospheric gases and aerosol parameters for wavelengths between 0.
3 and 50 µm. In addition, external databases can be imported, for instance, from the AErosol RObotic NETwork (AERONET; http://aeronet. gsfc. nasa. gov/) or from the European Centre for Medium-Range Weather Forecasts (ECMWF; http://ecmwf. int/). Atmospheric RT modeling includes the Earth/ atmosphere radiative coupling (i. e. , radiation that is emitted and/or scattered by the Earth and backscattered by the atmosphere towards the Earth). It can be simulated for any spectral band within the optical domain from the ultraviolet up to the thermal infrared part of the electromagnetic spectrum. The Earth/atmosphere coupling was cross-compared and successfully validated  [18, 19]  with simulations of the MODTRAN atmosphere RT model  [20].
Work preparation The urban information used for the URBANFLUXES project was provided in the form of urban databases (for an example, see Figure  7 ). The two following adaptations had to be introduced to interface the databases with DART: (1) the format of 3D objects (i. e. , triangular facets) stored in a common file format "*. obj", and (2) the information about the vegetation, which was simulated as a turbid medium. Turbid vegetation was created from a set of characteristics, which for each tree in the urban scene provided geographical coordinates, physical dimensions, and optical properties. Those characteristics were obtained partially from external sources or partially from databases already exiting in DART (e. g. , from database of optical properties for urban elements and vegetation).
Atmosphere database In relation to the in situ cameras implementation, the DART atmosphere database and its management in the model were improved to offer higher flexibility of DART when dealing with different atmospheric conditions, especially in case of available in situ and/or satellite measurements of atmosphere. This atmosphere database was originally derived from simulations of the MODTRAN atmosphere radiative transfer model. Its use with DART was already validated with MODTRAN simulations. However, the simulation of aerosols was not as accurate as for gases. The DART atmosphere database was therefore completed using transmittance spectra for scattering and absorption mechanisms, derived from the atmospheric model MODTRAN. An interesting feature of this improvement is a new possibility to specify gas and aerosol amounts within the urban scene, independently of the atmosphere characteristics above the considered environment.
Computation of urban albedo: remote sensing calibration of DART Bottom of atmosphere irradiance and exitance are essential terms in the 3D radiative budget of urban and natural landscapes. In the short-wavelength spectral domain, the ratio of exitance and irradiance is simply the albedo. DART can compute these terms for any time, date, and spectral band Δλ, by using urban database inputs and actual atmosphere conditions that can be derived from satellite images or data from meteorological centers (e. g. , ECMWF) or in situ measurements (e. g. , AERONET network). The urban database, presented to DART in a *. obj file format, must contain all buildings of the considered area, as well as the relevant information about urban vegetation in that area (i. e. , geographic locations and physical dimensions of trees).
It also requires distinction between roofs, walls, and streets, which means each facet in the database has to be registered in one of these groups. DART uses this registration to assign optical properties of the different scene elements. An example of a 3D urban model in nadir and an oblique view, which was built based on the urban database of the city of Basel, is shown in Figure  7. Roofs and walls appear with different colors. These colors depend on the roof and wall optical properties. A major problem is that it is practically impossible to know the spatial distribution of these properties accurately beforehand, due to their spatial variability. In addition, they vary also in time. In consequence, a major objective of the URBANFLUXES project is to derive maps of optical properties per type of Earth surface element.
Direct method The direct method consists of the following five steps: Step 1. Reflectance images corresponding to the satellite bands used for calibration are simulated. For these simulations, we set the optical properties to a realistic, but not exact value, as this piece of information is unavailable. The spatial resolution of images is equivalent to the size (x DART , y DART ) of the DART voxels (in this example to 2. 5 m). • The DART reflectance image of interest ρ DART ,Δλ (x DART , y DART , Ω sat ) is simulated in the viewing direction of the available satellite image. It is important to note that this approach can also work with an image which has not been atmospherically corrected. In this case, the simulated image corresponds to a "top of atmosphere" (TOA) data in a "direct-direct" configuration, for which the incident and exiting radiative fluxes are following a single direction (ρ dd,TOA ).
Albedo derived from climatological data Alternative methodology to derive urban albedo images has been adapted for a case when no satellite data of the area of interest are available. DART images are, in this case, simulated with optical properties of surface elements calibrated from the last available satellite image and with available atmospheric illumination conditions of direct sun irradiance E S ,BOA,Δλ and diffuse sky irradiance E atm,Δλ. This information can be obtained from several sources including ECMWF (www. ecmwf. int), Meteo France (www. meteofrance. com), and other in situ meteorological sensor networks. In this alternative approach, DART calculates the so-called white sky and black sky albedos. The white sky albedo A DART ,white sky, Δλ (x sat , y sat , E S ,BOA (Ω S ) = 0, L atm = E atm π , t sat ) is computed for an irradiance coming solely from the atmosphere, that is, without any direct sunlight contribution.