dataesr/scientific-paragraphs-categorization huggingface dataset:
Each Landsat scene is available in an atmospherically corrected format called the LSR product and freely available from the USGS. For the Landsat TM, ETM and ETM+, the specialized Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software  [29]  and the specialized L8SR software  [30]  are used to retrieve the SR for Landsat TM, ETM and ETM+ and Landsat-8 OLI, respectively.
Satellite Rainfall Estimates Two reanalysis rainfall products named PERSIANN-CDR  [31]  and MSWEP  [32]  were considered to cover the whole 1990-2016 period. PERSIANN-CDR was released in 2014 by the National Climatic Data Center (NCDC) Climate Data Record (CDR) program of the National Oceanic and Atmospheric Administration (NOAA). PERSIANN-CDR covers the 1983 to 2015 period providing daily rainfall estimate on a 0. 25 • spatial resolution. MSWEP was released in 2016  [32]  and covers the 1979-2015 period with daily rainfall data on a 0. 25 • spatial resolution. MSWEP estimates are based on the Climate Hazards Group Precipitation Climatology (CHPclim) dataset (0. 05 • )  [33]. Additionally, precipitation anomalies from gauge observation, satellite rainfall estimates and atmospheric model reanalysis are used to fix MSWEP estimates temporal variability.
MOD16 Global Evapotranspiration MOD16 global evapotranspiration is a product of NASA. Estimates are made according to an algorithm based on Penman-Monteith equation  [34, 35]. NASA's MERRA GMAO (GEOS-5) daily meteorological reanalysis data are used to retrieve radiation, air pressure, temperature and humidity. MODIS products are used to retrieve complementary data on an 8 days temporal scale. MOD12Q1 collection 4 is used for the land cover classification  [36] , MOD15A2 collection 5 to retrieve Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR)  [37]  and MCD43B2/B3 Collection 5 are necessary to get albedo estimates  [38]. Four products are available: Potential Evapotranspiration (ETp), Real Evapotranspiration (ETr), Latent Heat Flux (LE) and Potential Latent Heat Flux (PLE).
L8SR and FLAASH Assessment At each GCPs location, field spectral measurement, FLAASH, LSR and Landsat SR value for Blue, Green, Red and NIR were extracted to build the database. Comparisons between field spectral measurement and FLAASH, LSR and Landsat SR were made considering all single band values (red, blue, green and near infra-red) and possible band ratios (green/red, blue/green, red/blue, blue/near infra-red, green/near infra-red, and red/near infra-red) (Table  2 ). Landsat reflectance value was considered to observe the enhancements of the SR estimations gained through the L8SR and FLAASH processes. The band ratios are considered to observe the relative errors between bands. Indeed, low error in band ratio implies that the trend between the concerned bands is well represented. Considering all GCPs, the Mean Error (ME), Root Mean Square Error (RMSE) and Correlation Coefficient (CC) were computed for all single and band ratios.
Rain versus Superficial Lake Area From January 1990 to December 2015, the annual highest and lowest lake extents were derived for each year from Landsat imagery by using the most efficient previously identified atmospheric correction processes and SDI. Attention was paid to only select almost cloud free Landsat TM, ETM or OLI scenes. Overall, 48 Landsat scenes were selected and used. From 2012, only Landsat ETM+ scenes are available. Over Poopó Lake, the Landsat ETM+ scenes are strongly impacted by the SLC failure that occurred on 31 May 2003. Thus, these data are not suitable for use in the study. Therefore 2 MODIS scenes were used to fill the gap. Using the MODIS scene to derive the extent of Poopó Lake is acceptable because a strong correlation was previously found between the extents of Poopó Lake derived from MODIS and Landsat data  [14].
Effects of Atmospheric Correction Using a single band approach and for all considered bands, the Landsat SR estimate was generally closer to the field SR measurements than the LSR and FLAASH SR with %ME and %RMSE close to 0. FLAASH and LSR resulted in underestimations of SR with negative bias values. When considering the band ratio, the FLAASH and LSR SR estimations were closer to the field SR than the Landsat SR estimates with %ME closer to 0 and lower RMSE value. The corrected LSR and FLAASH data tended to result in more homogeneous relative trends between the bands than the uncorrected data. Indeed, for all band ratios, the error distribution was closer to 0 for the LSR and FLAASH scenes than for the Landsat scene, which presented greater error distributions. The NIR band is the most affected by atmospheric absorption and scattering.
Using the single band approach, atmospheric correction degraded the SR estimation because lower %ME and %RMSE values were found before atmospheric correction was applied. However, the opposite trend was observed when considering the band ratios. FLAASH Atmospheric correction enhanced the relative error between the bands, with %RMSE reduction by a factor of 3, higher CCs and lower %MEs for all of the considered band ratios. Thus, over Poopó Lake, FLAASH correction is more suitable than the L8SR algorithm. SDIs are based on band combination and thus their efficiency highly relies on relative band error. As the study aimed to use SDI to retrieve lake extent, we used the FLAASH correction to build the Landsat scene database for the 1990-2015 period.
Conclusions In this study, a guideline to monitor Poopó Lake extent variation in time from remote sensing data is presented to understand its recent disappearance. Landsat imagery was used to retrieve Poopó Lake extent and PERSIANN-CDR, MSWEP and MOD16 data were used to understand Poopó Lake extent variation in time at seasonal and annual scales in relation to climate variability and agricultural activities. The first step consisted of a quick assessment of atmospheric correction, SDIs to retrieve Poopó Lake extent, reanalysis satellite rainfall products (PERSIANN-CDR and MSWEP) and MOD16 ET products at a monthly scale. Despite the scarcity of the ground reference, some consistent features emerged from the analysis: (1) More accurate SR values are obtained after the FLAASH correction was applied on the Landsat scene than from the already atmospheric corrected LSR scene.
Acknowledgments: This work was supported by the  Centre National d'Etudes Spatiales (CNES)  in the framework of the  HASM  project (Hydrology of Altiplano: from Spatial to Modeling). The first author is grateful to the  IRD (Institut de Recherche pour le Développement)  and  CAPES (Coordenação de Aperfeiçoamentode Pessoal de Nível Superior) Brazil  for their financial support.
Acknowledgements The authors acknowledge the Laharisk Project ( French Research Funding  ( ANR) -Coord. JC. Thouret, Clermont-Ferrand University ) for the financial support necessary for the implementation of the survey. The authors acknowledge  Prof. M. Willinger ,  Dr. M. A. Bchir  and  Prof. G. Enjolras  for their collaboration during the design of the surveys' protocol and during the implementation of the surveys in Arequipa. The authors acknowledge  Elio Orlando Bazan Huamaní ,  José Delfín Cáceres Martínez ,  José Carlos Quispe Barreto ,  Pablo Ernesto Peña Meza ,  Sofia Vargas Romero  and  Noe Felipe Coaguila  for their collaboration during the implementation of the surveys in Arequipa.
Bioinformatic analyses Raw reads were de-multiplexed based on individual barcode information, end-clipped to 95 pb and quality filtered using the program process_radtags from the stacks pipeline with default settings  (Catchen et al. , 2013). Retained read were then aligned to a draft assembly of the S. senegalensis genome (98,590 scaffolds, total length 740 Mb, N50 contig length 10,767 bp,  Manchado et al. , 2016)  using bowtie 2 v. 2. 1. 0 (Langmead and Salzberg, 2012)  with the -very-sensitive option. In order to take into account both the level of divergence among species and the possibility of introgression, we used a subset of individuals to empirically determine the optimal maximum number of mismatches allowed between aligned reads and the reference genome (m = 7). We then used pstacks to call variable positions under the bounded SNP model, setting the upper sequencing error rate to 2.
5% and a minimum sequencing depth to 5X per stack. Homologous loci across samples were merged based on their genomic position within scaffolds using cstacks to construct a catalogue of loci. Individual stacks were then matched against the catalogue of loci with sstacks to determine genotypes. The module rxstacks was ran with the --prune_haplo option to exclude poor quality loci using a loglikelihood threshold of -300 (determined empirically). Individual genotypes were finally exported in VCF format using the module populations. We then applied population-specific filters with VCFtools  (Danecek et al. , 2011)  to remove SNPs showing significant deviation to Hardy-Weinberg equilibrium within S. senegalensis and S. aegyptiaca samples located outside the hybrid zone, using a P-value threshold of 0.
01. Next, we excluded loci displaying more than 20% of missing genotypes. Over the 116,385 remaining SNPs, we randomly selected one single SNP for each pair of RAD loci associated with the same restriction site, in order to limit the impact of linkage disequilibrium. Finally, we only retained loci with available sequence data in the outgroup species S. solea, which had to be fixed for the polymorphic site found in S. senegalensis and S. aegyptiaca. This resulted in a final dataset containing 10,758 SNPs.
Genetic structure and hybridisation Genetic variation within and among S. senegalensis and S. aegyptiaca was characterized with a principal component analysis (PCA) performed with the R package Adegenet  (Jombart, 2008; Jombart and Ahmed, 2011). We then estimated individual admixture proportions from two differentiated genetic clusters using fastStructure  (Raj et al. , 2014)  with 10 8 iterations, in order to distinguish parental and admixed genotypes.
The best-fit SC2m model estimated that only 5% of the loci can still introgress between species with effective migration rates m 1-2 and m 2-1 , whereas the remaining 95% of loci experience a highly reduced introgression rate with effective migration rates m' 1-2 and m' 2-1. We used estimated model parameters to perform coalescent simulations with MSMS  (Ewing and Hermisson, 2010)  under the secondary contact model (assuming theta=747. 202; N 1 =0,818; N 2 =1,136; T S =4,803; T SC =0,081) using two different conditions for gene flow (assuming either m 1-2 =4,607 and m 2- 1 =0,381, or m' 1-2 =0,057 and m' 2-1 =0,153) to generate a free introgression and a reduced introgression rate dataset. One thousand JAFS with 40 sampled chromosomes per species were produced under each model. We then averaged the number of derived alleles within entries across replicates to obtain a single JAFS for both the free introgression ( ) (Fig.
3c ) and the reduced introgression ( ) (Fig. 3d ) model. Finally, we used the average number of SNPs per entry (i,j) in each JAFS to estimate the probability that a SNP with a derived allele count i in species 1 and j in species 2 can be obtained under the free introgression model: ( ) ⁄ (1) Where and are the average number of SNP predicted in the JAFS entry (i,j) under the free introgression and reduced introgression model, respectively. Every SNP from the real dataset was finally associated to the introgression probability given by equation (1) based on its corresponding entry in the JAFS.
Geographic Clines The geographic cline analysis was carried out in order to link allele frequencies at individual loci with geographic position along a transect spanning the hybrid zone. We used the R package HZAR  (Derryberry et al. , 2014)  that fits allele frequency data to classic equilibrium models of geographic clines  (Szymura and Barton, 1986)  using the MCMC algorithm. We used a model that fits cline center, width, and independent introgression tails using estimated values for minimum (p min ) and maximum (p max ) allele frequencies.
Links between different approaches Correlation tests were used to investigate whether different approaches that use different aspects of the data tend to produce similar results. First, we evaluated the extent to which an excess of ancestry from a given species relates to a spatial shift of cline center into the other species range. The joint distribution of the genomic cline parameter α and geographic cline center c (Fig. 5 ), showed a significant positive correlation on both sides of the hybrid zone. The correlation was however much stronger in the S. aegyptiaca side (R² c-α = 0. 274, P < 10 -10 ) compared to the S. senegalensis side (R² c-α = 0. 023, P < 10 -10 ). We then tested whether the loci exhibiting the most abrupt transition in ancestry between the two species (i. e. a decreased introgression rate) also tended to display the steepest geographic clines.
Acknowledgments This work was conducted during the PhD of  A. Souissi  with the financial support of programme  PHC  Maghreb No. XE27961  from the  French Ministère des Affaires Etrangères to F. Bonhomme and Ministère de l"Enseignement Supérieur et de la Recherche  to  L. B. Sfar. The authors are indebted to  M. -T. Augé  for skillful technical assistance.
(1998). Automated DNA sequencing was performed by Genoscreen (Lille, France). Sequences were analyzed using BioEdit software to obtain consensus sequences from forward and reverse sequences. They were subsequently aligned with available cox2 rDNA or ITS sequences for members of Anisakidae and Raphidascarididae by GenBank Blast software (Basic Local Alignment Search Tool) and Clustal X  (Altschul et al. 1997).
Statistical analysis All statistical analyses were performed using R software (R Core Team 2014) and packages 'vegan'  (Oksanen et al. 2016) , 'car'  (Fox and Weisberg 2011) , 'RVAideMemoire'  (Hervé 2016) and 'lsmeans' (Lenth 2016). Mean number of parasite taxa was compared between (i) all individuals, (ii) immatures and (iii) adults of the two host species using likelihood ratio tests applied on Generalized Linear Models (family: Poisson, link: log) (GLMs). The same procedure was used separately for each host species to test for an effect of developmental stage (immature vs adult), sex of adults (female vs male), salinity (river vs coastal/estuarine water) and site (with factor site nested into factor salinity). In subsequent quantitative analyses, parasite taxa with low prevalence (< 6%) in both fish species  [Parahemiurus merus (Linton, 1910) , Pomphorhynchus laevis Müller, 1776, Anilocra physodes (Linnaeus, 1758), Argulus foliaceus (Linnaeus, 1758) and Petromyzon marinus Linnaeus, 1758] were not considered in analyses performed separately for each parasite species because of their rarity and probable limited impact on host populations.
Acknowledgments. This study was funded by the  French Ministry of Ecology and Sustainable Development  (' Programme de connaissances Natura2000 amphihalins en mer '). Authors warmly thank the numerous fishermen and local angling federation for providing the fish. We gratefully acknowledge  Valérie Briand  ( UMR ECOBIO 6553 ) for her bibliographical support and the colleagues ( E. Chavance ,  C. Oudard ,  E. Sola , and T. Lavigne) who collaborated to the study.
Flies were then kept at 18 °C and 60% humidity. After 1 h, flies were transferred to a 2 ml microtube and frozen in liquid nitrogen. Groups of five flies were deposited in a 1. 5 ml microtube and ground in 200 ml PBS. Microtubes were then centrifuged at 12,000 r. p. m. for 7 min. The supernatants were collected and centrifuged again at 12,000 r. p. m. for 7 min. Then, 100 ml of each supernatant was aliquoted to 96-well plate, and absorbance was measured at 570 nm. As a blank absorbance control, naive flies placed on a 5% sucrose solution without sulforhodamine B were treated in parallel. The mean absorbance of these controls was subtracted from the absorbance of the other samples. A single experiment typically included 3-4 groups of five flies per condition. The complete dataset from a given associative protocol was normalized to the control unpaired protocol.
Female flies were preferred because their bigger size makes surgery easier, as in all previous imaging work from our lab. Other transgenes that were used in addition to VT30559 and UAS-Pyronic are indicated in the figure panels. Crosses for imaging experiments were raised at 25 °C to increase the expression level of genetically-encoded sensors through the UAS/GAL4 system. However, flies were raised at 18 °C for the experiments involving GAL80 ts (Fig. 4d, e ), in order to avoid any leak of GAL4 activity. The two-day induction period at 30 °C drove sufficient expression of Pyronic to perform imaging. For experiments on conditioned flies, data were collected indiscriminately from 30 min to 3 h after training. Flies were prepared for in vivo imaging, as described previously  30. The proboscis was not glued, and no agarose was applied on the brain.
Being a weak base, 50 mM azide had no detectable effect on the pH of the solution (pH ¼ 7. 42 ± 0. 03 measured on three different solutions). Image analysis was performed using a custom-written Matlab script. Regions of interest (ROI) were delimited by hand around each visible vertical lobe, and the average intensity of both mTFP and Venus channels over each ROI were calculated over time after background subtraction. The Pyronic sensor was designed so that FRET from mTFP to Venus decreases when pyruvate concentration increases  35  ). To obtain a signal that positively correlates with pyruvate concentration, the inverse FRET ratio was computed, that is, mTFP intensity divided by Venus intensity. This ratio was normalized by a baseline value calculated over the 30 s preceding azide injection to give a normalized 'Pyronic ratio'.
The increase in the Pyronic ratio typically had a bilevel waveform, with a linear increase from B10 to B70% of the plateau. The risetime (from t ¼ 0 until reaching 70% of the plateau) and the slope (between 10 and 70% of the plateau) were determined automatically using the statelevels, risetime and slewrate functions in the Matlab signal processing toolbox. Traces from all hemispheres were pooled. The indicated 'n' is the number of animals that were assayed for each condition. All acquisition and analysis parameters were determined during preliminary experiments that are not included in the study. For experiments with the FLII  12  Pglu-700md6 glucose sensor, the modifications to the protocol followed with Pyronic were the following: the images were acquired at 850 nm. The FRET ratio (YFP/CFP) was computed to obtain a signal positively correlated to glucose concentration.
An application of cytochalasin B was made 20 s before azide application. For cytochalasin B application, cytochalasin B (Sigma Aldrich) was aliquoted in DMSO at a concentration of 2 mM. After a ten times dilution in physiological solution, 10 ml were injected into the 90 ml droplet bathing the brain to reach a final concentration of 20 mM. For unexplained reasons, the application of cytochalasin B produced a transient but marked drop of fluorescence in both channels. This caused artifactual twitches in the FRET ratio signals, which were masked on Fig. 2e. Glycolytic rates were measured as the slope of a linear fit to the data 12 s before azide injection for intrinsic glycolysis, and 20 s after for azideevoked glycolysis. Statistical analyses. All data are presented as mean±s. e. m. The different experimental groups are determined by their genotypes, so no randomized allocation was performed, and the investigators were not blinded to the group allocation during experiments.
70. ANOVA results are given as the value of the Fisher distribution F (x,y) obtained from the data, where x is the number of degrees of freedom between groups and y is the total number of degrees of freedom of the distribution. Statistical tests were performed using the GraphPad Prism 5 software. In the figures, asterisks illustrate the significance level of the t-test, or of the least significant pairwise comparison following an ANOVA, with the following nomenclature: *Po0. 05; ** Po0. 01; *** Po0. 001; NS: not significant, P40. 05).
Acknowledgements We thank  Honorine Lucchi  and  Aurelie Lampin-Saint-Amaux  for assistance in behavioral experiments and molecular biology. We thank  Gilles Bonvento  and  L. Felipe Barros  for sharing the Pyronic and FLII 12 Pglu-700md6 plasmids, and  Franc¸ois Bolduc  for providing the Feb170 GAL4 line. Research in the T. P. laboratory was funded by the  Agence Nationale pour la Recherche  and the  Labex MemoLife. E. D. T. was funded by a doctoral fellowship from the  French Ministry of Research. L. S. was funded by a postdoctoral fellowship from the  Deutsche ForschungsGemeinschaft  ( SCHE 1884/1-1 ).
The Meteosat Second Generation (MSG) geostationary platform equipped with the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument provides observations of the Earth every 15 min since 2004. Based on those measurements, we present a new method called North African Sandstorm Survey (NASCube) to: (i) generate day/night remote sensing images in order to detect sandstorms over the Sahara and Saudi Arabia; and (ii) estimate day and night aerosol optical depth (AOD). This paper presents a method to create true color day and night images from the SEVIRI instrument level 1. 5 products and the complete operational data processing system to detect sandstorms and quantify the AOD over the desert areas of North Africa and Saudi Arabia. The designed retrieval algorithms are essentially based on the use of artificial neural networks (ANN), which seems to be well suited to this issue.
Our methods are validated against two different datasets, namely the Deep Blue NASA moderate-resolution imaging spectroradiometer (MODIS) product and AErosol RObotic NETwork (AERONET) acquisitions located in desert areas. It is shown that NASCube products deliver better estimations for high AOD (>0. 2) over land areas than Deep Blue products. The open-public web platform will help researchers to identify, quantify and retrieve the impact of sandstorms over desert regions.
NASCube system is divided in three levels: The first level is devoted to the data acquisition and is divided in two segments, one receiving the close near real time data using an antenna for the EUMETCast distribution and the other processing the EUMETSAT archive query system to go backward in time (one year of results needs to process 4. 5 terabytes of data). The second level computes the different masks of clouds and glitters, creates the color composite images and computes the AOD fields. Some steps use artificial neural networks (ANN). The third one creates all the subsets needed by the web distribution and the user specific tasks. The flowchart displayed in Figure  2  is useful to guide the reader through the various stages of the NASCube algorithm. The first step (Section 2. 1) aims at generating in daytime (Section 2.
NASCube system is divided in three levels: The first level is devoted to the data acquisition and is divided in two segments, one receiving the close near real time data using an antenna for the EUMETCast distribution and the other processing the EUMETSAT archive query system to go backward in time (one year of results needs to process 4. 5 terabytes of data). The second level computes the different masks of clouds and glitters, creates the color composite images and computes the AOD fields. Some steps use artificial neural networks (ANN). The third one creates all the subsets needed by the web distribution and the user specific tasks.