dataesr/scientific-paragraphs-categorization huggingface dataset:
SNPs and Indels were called by FreeBayes (v1. 0. 1-2) (Garrison and Marth 2012)  (option: -p 1) to assess the performance of Pilon correction and estimate the remaining error rate in our final assemblies. The raw SNP and Indel calls were further filtered by the vcffilter tool from vcflib (https://github. com/vcflib/vcflib) with the filter expression: "QUAL > 30 & QUAL / AO > 10 & SAF > 0 & SAR > 0 & RPR > 1 & RPL > 1".
, which covered 15 centromeres. All these S. cerevisiae and S. paradoxus centromere queries were searched against our PacBio assemblies by Exonerate (v2. 2. 0) (Slater and Birney 2005)  (options: --showvulgar no --showcigar no --showalignment no --showtargetgff yes --bestn 1). The chrVIII centromere in S. paradoxus was not annotated in any of those previous studies. For this centromere, we used Dr. github. com/dbensasson/bensasson11) to perform de novo annotation based on consensus sequence profile of yeast centromeres. All annotated centromeres were further verified based on their flanking genes. Several chromosomes from the strain UWOPS03-461. 4, UFRJ50816 and UWOPS91-917. 1 are involved in large-scale interchromosomal rearrangements. These rearranged chromosomes were numbered based on the identity of the centromeres that they are containing.
Acknowledgements We thank  Guenola Drillon  for the help with using the program CHROnicle. We thank  Olivier Croce  and  Roberto Marangoni  for the help with maintaining the computing server and various bioinformatics tools. We thank  Liti lab technician Agnès Llored  for preparing some of strain and DNA samples. This work was supported by  ATIP-Avenir (CNRS/INSERM) ,  Fondation ARC pour la Recherche sur le Cancer  (grant number  PJA20151203273 ),  European Commission  ( Marie Curie Career Integration Grants  ( FP7-PEOPLE-2012-CIG ) grant number  322035 ),  Agence Nationale de la Recherche  (grant number  ANR-13-BSV6-0006-01  and  Labex   SIGNALIFE ANR-11-LABX-0028-01 ),  Cancéropôle PACA  (AAP émergence 2015) and  DuPont Young Professor Award  to GL,  Wellcome Trust  (grant number  WT098051 ) to RD, and  Vetenskapsrådet  (The  Swedish Research Council)  (grant number  325-2014-4605 ) to JW.
JXY is supported by a postdoctoral fellowship from  Fondation ARC pour la Recherche sur le Cancer  (grant number  n°PDF20150602803 ). JL is supported by a postdoctoral fellowship from  Fondation ARC pour la Recherche sur le Cancer  (grant number  n°PDF20140601375 ). JH is supported by the  Labex   SIGNALIFE program  from  Agence Nationale de la Recherche  (grant number  ANR-11-LABX-0028-01 ).
Land surface temperature (LST) is an important variable involved in the Earth's surface energy and water budgets and a key component in many aspects of environmental research. The Landsat program, jointly carried out by NASA and the USGS, has been recording thermal infrared data for the past 40 years. Nevertheless, LST data products for Landsat remain unavailable. The atmospheric correction (AC) method commonly used for mono-window Landsat thermal data requires detailed information concerning the vertical structure (temperature, pressure) and the composition (water vapor, ozone) of the atmosphere. For a given coordinate, this information is generally obtained through either radio-sounding or atmospheric model simulations and is passed to the radiative transfer model (RTM) to estimate the local atmospheric correction parameters.
Although this approach yields accurate LST data, results are relevant only near this given coordinate. To meet the scientific community's demand for high-resolution LST maps, we developed a new software tool dedicated to processing Landsat thermal data. The proposed tool improves on the commonly-used AC algorithm by incorporating spatial variations occurring in the Earth's atmosphere composition. The ERA-Interim dataset (ECMWFmeteorological organization) was used to retrieve vertical atmospheric conditions, which are available at a global scale with a resolution of 0. 125 degrees and a temporal resolution of 6 h. A temporal and spatial linear interpolation of meteorological variables was performed to match the acquisition dates and coordinates of the Landsat images. The atmospheric correction parameters were then estimated on the basis of this reconstructed atmospheric grid using the commercial RTMsoftware MODTRAN.
The needed surface emissivity was derived from the common vegetation index NDVI, obtained from the red and near-infrared (NIR) bands of the same Landsat image. This permitted an estimation of LST for the entire image without degradation in resolution. The software tool, named LANDARTs, which stands for Landsat automatic retrieval of surface temperatures, is fully automatic and coded in the programming language Python. In the present paper, LANDARTs was used for the local and spatial validation of surface temperature obtained from a Landsat dataset covering two climatically contrasting zones: southwestern France and central Tunisia. Long-term datasets of in situ surface temperature measurements for both locations were compared to corresponding Landsat LST data. This temporal comparison yielded RMSE values ranging from 1.
Introduction Land surface temperature (LST) and land surface emissivity (LSE) are two key parameters used in many environmental studies because they are closely connected to the Earth's surface energy balance. At a regional scale, land and sea surface temperatures (LST and SST, respectively) can be estimated with the help of thermal infrared (TIR) measurements recorded by remote sensors onboard spaceborne platforms. These estimates are used for hydrological and meteorological purposes. In particular, they provide valuable information to monitor evapotranspiration and determine the soil water status on agricultural lands  [1] [2] [3]. To study the oceans, SST data provide information on sea warming, potential evaporation and water circulation  [4]. However, atmospheric effects of absorption and emission affecting the thermal spectrum must first be removed in order to use thermal imagery in absolute temperature research application  [5].
Landsat satellites have been continuously recording multispectral images of Earth since the early 1970s. The U. S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center provides these data free of charge. In fact, from the first Landsat 1 (launched in 1972) to the current Landsat 8 (launched in 2013), the project has made available more than 40 years of Earth land surface data to study land processes. In total, this represents more than one million scenes, each one offering at least one thermal band. Landsat 5 and 7 provide one thermal channel; Landsat 8 provides two, although it is recommended to use only one band  [11, 12]. In this sense, the Landsat program appears to be an interesting source of LST records. However, land and sea surface temperatures have not been made available in a free-to-use format.
Nevertheless, several methods have been proposed to estimate LST from Landsat's single TIR channel  [13] [14] [15]  by using atmospheric profiles obtained from databases or through radio sounding. Several studies  [16, 17]  have validated the application of such methods. For example, Barsi  [18]  proposed a web-based correction tool that used modeled atmospheric global profiles obtained from the National Center for Environmental Prediction (NCEP) in conjunction with the commercially available radiative transfer model MODTRAN  [19]. This process allowed the authors to obtain the necessary atmospheric transmission, upwelling, and downwelling radiance values to convert sensor radiance data into surface brightness temperatures  (Tb). A subsequent study  [20]  used the same process to successfully reproduce the results with the North American Regional Reanalysis (NARR) database by using atmospheric profiles as inputs.
[23]. The present study expands on Barsi's approach and proposes an independent and open source software tool that uses the European ERA-Interim global atmospheric reanalysis database in conjunction with the MODTRAN software package. The proposed software tool, named LANDARTs, which stands for Landsat automatic retrieval of surface temperature, is able to automatically estimate surface brightness temperatures and LST for an entire Landsat thermal image with a coherent spatial correction. Estimating LST through TIR radiance measurements obtained with remote sensors usually requires four steps of correction  [5] : (1) converting spectral radiances into at-sensor brightness temperatures, which is typically done by inverting Planck's law adapted to the spectral sensor window as described by Equation (  4 ); (2) correcting for atmospheric absorption and re-emission, which requires knowledge of the atmospheric profile composition, especially concerning the water content and temperature; (3) correcting for surface emissivity (estimated if unknown); and (4) correcting for surface roughness or topography.
This last correction is rarely taken into account at resolutions of 100 m and is neglected at the nadir view angle. Neglecting atmospheric correction results in systematic errors that are proportional to the target temperature for a given atmosphere. Various approaches have been developed to solve or simplify the atmospheric correction process for TIR data dedicated to single-channel sensors. The available methods can be classified into four categories: the mono-window algorithm, which requires air temperature data only  [17] ; the single-channel algorithm, which requires total atmospheric water content values only  [13] ; the look-up tables approach, which supposes an exhaustive learning database  [14] ; and the physical approach, which requires the calculation of atmospheric parameters with a radiative transfer model and demands detailed knowledge of the atmospheric profile  [15, 18].
The main goal of our paper is to present and evaluate an automatic Python tool that relies on the MODTRAN model and on ERA-Interim data. The tool was designed to perform atmospheric and surface correction of Landsat (TM and TIRS) thermal data sensors and to retrieve LST and LSE values at a resolution of 30 m (the resolution of radiances provided by the USGS). First, we present the theory of atmospheric correction and the applicable processing algorithm; We then describe the process of comparing the LST reference product from ASTER with in situ temperature measurements to validate the tool's performance; Finally, we discuss the sensitivity of the results to spatial variations in the atmospheric correction parameters.
Atmospheric Profiles Construction The ECMWF (European Centre for Medium-Range Weather Forecasts) provides full access to several atmospheric reanalysis datasets. In accordance with our requirements, we selected the ERA-Interim global atmospheric reanalysis  [27] , which provides a variety of vertical and surface atmospheric fields from 1979 to the present (accessible with a 2-month delay) in uniform latitude/longitude grids (0. 125 • , 0. 25 • , 0. 75 • , 1 • , 1. 125 • , 1. 5 • , 2 • , 2. 5 • and 3 • ). The parameters are interpolated from the original N128 reduced Gaussian grid using bilinear methods. LANDARTs integrates the Python API provided by ECMWF to process automatic requests sent to the database  [28]. For the Landsat LST data atmospheric correction process, each request sent to the ERA-Interim dataset is defined to cover a footprint that is larger than the image.
The two datasets were merged to create a consistent meteorological vertical altitude profile extending from the Earth's surface to the top of the atmosphere. In practice, for surface field pressures higher than or equal to 1000 hPa (first vertical level), surface fields were added at the first level of the vertical profile; for surface field pressures less than 1000 hPa (occurring mainly because of topographic features), surface fields were added to vertical profiles according to the pressure value, and all field profiles below these levels were cut off. For each case, pressure and altitude values were verified to ensure a strictly decreasing order of the vertical profile. However, the values of the vertical profile were not smoothed through interpolation. In fact, failure to respect this decreasing order created a conflict in the MODTRAN execution.
NDV I v is the maximum NDV I for full vegetation (set to 0. 99) and NDV I s is the minimum NDV I for bare soil (set to 0. 17). The soil emissivity was not set because its value depends on the soil's composition and roughness. In our case, the soil emissivity was set to 0. 96 in agreement with the mean emissivity value extracted from the JPL library database  [38]  corresponding to the soil composition of our selected sites. The k parameter was fixed arbitrary to 2, but note that  [39]  proposed explore values between 2 and 3. This parameter can be adjusted in the future version. Another difficulty in determining soil emissivity is the non-homogeneity of the soil at the pixel scale. However, for acquisitions at a resolution of 100 m, Zhang et al. [40]  found that the pixels can be considered homogeneous.
The Tunisian site is part of the SICMED program (http://www. sicmed. net/). The measurements considered in the present study were acquired on non-irrigated olive tree fields composed of trees spaced at regular intervals of 20 m and with crown diameters of about 5 m. The ground was a sandy bare soil. The French site was composed of two cultivated plots located at a distance of 12 km from each other and with different soils and crop rotations. Aurade is a crop rotation of wheat/rapeseed and sunflower each 5 years; Lamasquere is a rotation of wheat and silage maize. The measurement methodology for both sites follows ICOS (integrated carbon observation system) recommendations. A complete description of the agricultural practices and field instrumentation for the two French plots are available in  [41, 42].
The validation of our atmospheric correction tool considered two main areas corresponding to the ground dataset measurements (Section 2. 5). The first area was located in south-western France (near Toulouse) at the intersection of two tiles: 199/030 for the western portion, and 198/030 for the eastern portion, in accordance with USGS WRS-2 nomenclature. The second area was located in Tunisia (near Kairouan) on tile 191/035. The downloaded Landsat images for France were acquired between 1 January 2011 and 31 December 2013. For the Tunisia site the images were acquired between 5 April 2012 and 31 December 2014.
ASTER Data ASTER thermal channels constitute a valuable dataset for our investigation because both channel 13 and 14 (Table  2 ) are similar to the single TIR channel of L5 and L7 (Table  1 ). The L8 channel 10 also covers a large part of ASTER channel 13. It is therefore possible to compare Landsat and ASTER products for certain ranges of wavelengths. The ASTER database provides several data products. However, our study only had access to one higher-level product: the surface kinetic temperature called AST_08, which is the result of the TES algorithm applied to the five thermal channels at a resolution of 90 m  [44]. All cloudy images were discarded to prevent problems with automatic cloud detection. A comparison between the ASTER dataset covering southwestern France for the period from 2000-2006 and the USGS database revealed an acquisition time overlap of only a few dates.
All dates after 2003 were discarded, to avoid large bands of missing data on the L7 images due to SLC failure and unfortunately corresponding to the ASTER position in the Landsat images. The ASTER dataset for Tunisia consisted of only two images, one of which was cloudy. Incidentally, an L8 image was available for the only cloud-free ASTER image date. The data selected and used in the present study are presented in Section 4. 2. It was our aim to implement identical procedures to evaluate the performance of the proposed tool for both sites; we consequently considered only the surface kinetic temperature product (the only product available for the Tunisian site).
Main Algorithm LANDARTs is a Python tool that works (currently) only with Landsat archives in the new metadata file format (data acquired after 2012) that can be downloaded from the USGS website. Although the tool accepts a custom installation of MODTRAN, we tested the following three versions only: 4. 1 on Unix, 4. 3 on Windows, and 5. 2 on Unix and Windows operating systems. A flowchart for the proposed tool is presented in Figure  2. The implementation process consists of three steps to produce LST maps at a resolution of 30 m using Landsat 5, 7 or 8 images. Step 1 consists of calculating the three atmospheric correction parameters. The details of the parameter calculations are described below: 1. Acquisition dates and times are read automatically according to the metadata file provided by the USGS data.
The four corners of the Landsat footprint are also extracted from GeoTiff metadata. 2. The information obtained in (1) are used to create four spatial queries to the ECMWF server: for each two time samples bounding the time input, two queries are done, one for the surface and one for the pressure level dataset. The query zone is defined to be larger than the Landsat footprint to allow interpolations at the edges. Data are in the uniform latitude/longitude grids in one of the resolution proposed by ERA-Interim and selected by user. 3. As explained in the ECMWF Section 2. 3, the two time profiles constructed with surface and pressure levels for the height from the Earth's surface to an elevation of 100 km are linearly interpolated between the two times samples to give acquisition time  [18].
Results and Discussion We performed a local as well as a spatial validation of the LST estimation results obtained with the LANDARTs. For the local validation, we used a large set of 140 images acquired by L7 and L8 and obtained from the USGS database archive (Section 2. 6) for the two considered regions-southwestern France (southwest of Toulouse) and central Tunisia (west of Kairouan). Atmospheric and surface corrections were performed with the LANDARTs. The obtained Landsat LST were filtered for clouds and compared with in situ surface temperature measurements for the local validation (Section 2. 5). We took advantage of two ASTER data from archive (Section 2. 7) acquired in quite same time than an L7 (SLC-on) and an L8 image to evaluate the performance of LANDARTs correction capabilities at the spatial scale.
Spatial Validation First, estimated surface temperatures were corrected for spatial atmospheric and surface emissivity with the LANDARTs. Two ASTER surface kinetic temperature products (AST08 from Level 1B), whose acquisitions matched the two Landsat acquisitions, were selected to evaluate the spatial coherence of the resulting estimates (Table  3 ). Images for the comparison were selected according to the following criteria: availability of the ASTER dataset in the archive, no clouds on the scene, identical dates, and availability of images from both currently active Landsat sensors, L7 and L8. For the sake of simplicity, we excluded SLC-failure data for L7. In fact, the thermal and emissivity data products proposed through ASTER's TES algorithm  [48]  can be considered a reference. We compared the two surface temperature products after resampling the data at a resolution of 270 m, that is a multiple of 30 m (Landsat) and 90 m (ASTER), to avoid alignment problems and to cover more than four pixels.
Indeed, ASTER acquisitions are 14 min and 30 min later than Landsat for the Tunisian and the French location respectively. Unfortunately, no in-field measurements were available on 2003 for the French site to discuss this offset. Nevertheless, on the Tunisian site, according to the thermo-radiometer temperature measurement, we observed an increase of between 1 • C (on tree canopy) to 1. 5 • C (on bare soil) between 10:00 and 10:30. As a result, we can estimate that 0. 5 • C-0. 7 • C of the offset observed in Figure  5  can be associated to the difference due to acquisition time for the Tunisian dataset. An offset of the same order of magnitude must be associated with the French datasets, whether between one quarter and one third of the mean bias. For their comparison between ASTER, MODIS, Landsat 7, and ground measurements, Srivastava et al.
[47]  established a similar mean error with a value of 2. 3 • C. They also determined an RMSE of around 3 • C for the differences between Landsat and the two other datasets. Zhou et al. [46]  compared ground measurements with data from ASTER bands 13 and 14, which were corrected separately using several algorithms. For the present study, we used the atmospheric correction algorithm (AC) with estimated emissivity and modeled atmospheric parameters. Zhou et al. [46]  determined an RMSE of around 2. 2 • C for their comparison of atmospherically corrected ASTER band 13 and in situ temperature measurements. disparity maps for the ASTER surface kinetic temperature product and the surface temperatures obtained with LANDARTs. The Tunisian maps show considerable homogeneity; most variations occurred within a ±2.
The Importance of Spatial Corrections The following section examines the importance of using a set of spatialized atmospheric parameters for the atmospheric correction process. As illustrated by the example in Figure  11A ,B, the three atmospheric correction parameters (τ, L u , L d ) may vary for any particular Landsat image acquired on a single date with one atmospheric profile. The LST variation depicted in Figure  11D , which is representative of most of the observed cases, shows the possible amplitude of atmospheric variability for a complete image. The same dataset can therefore be used for the in situ validation to retrieve the variability of the atmospheric parameters over time. To demonstrate the spatial impact of the atmosphere at the image level, we compared four correction approach.
As shown in Figure  12 , this variation can attain amplitudes of transmittance up to 0. 2 between the minimum and maximum, which has considerable impact on the LST. A mean difference of around 0. 04 for the transmittance and 0. 31 for the upwelling radiance was found between the center scene and the spatialized parameters extracted from the Aurade site. These mean values were calculated for the dataset shown in Figures  12  and 13. A mean difference of 0. 11 for the transmittance and 0. 83 for the upwelling radiance was found between the minimum and the maximum of the Landsat footprint for this dataset. Qin et al. [17]  have shown that an error of 0. 05 on the transmittance value leads to an error of 1-4 • C on the LST value. Consequently, performing atmospheric correction with local parameters induces certain errors in the final outcome and decreases the accuracy of the LST map for a large satellite image.
Conclusions We propose an automatic correction tool for atmospheric and surface emissivity data provided by Landsat remote sensors (L5, L7 and L8). The LANDARTs tool, which stands for Landsat automatic retrieval of surface temperatures, permits the conversion of Landsat mono-window radiances in the infrared band into land surface temperatures (LST) for an entire image, retaining the USGS spatial resolution (30 m for USGS resampled data). LANDARTs was developed in the programming language Python. The tool was built around the radiative transfer model MODTRAN and exploits the model's outputs in the specific window of Landsat's thermal spectrum. MODTRAN runs were used to determine the three parameters involved in the radiative transfer Equation (3): the atmospheric transmittance, the upwelling radiance and the downwelling radiance of the atmosphere in the spectral window and for a given vertical atmospheric profile prescribed as the input.