dataesr/scientific-paragraphs-categorization huggingface dataset:
Residues located at mutated positions are displayed as sticks. Ligand 438 (HETATM code) is displayed in stick form with its Cα atoms colored yellow. These figures were generated using PyMOL [61]  and the script_pymol. pml generated during the step 5 output. A red triangle was added to easily locate the mutated positions. The presented p53 DBD corresponds to the first protein ID in the MSA file. Region R1 P53 is located in the N-terminal region, regions R2 P53 , R4 P53 , R5 P53 , R6 P53 , R7 P53 are located in loops, R3 P53 is located in a β-strand, and R8 P53 is located in an α-helix. (C) Structural description of the human p53 DBD domain complexed to DNA (PDB ID: 1TSR). The protein is displayed as a cartoon and is colored according to its secondary structures: α-helices in purple, β-strands (named S1 to S10) in yellow, turns in cyan and loops in gray.
In this figure, we added blue rectangles to localize the 8 variable regions highlighted during the PR1 set analysis: R1 PR1-NMR (positions 3-15), R2 PR1-NMR (positions 34-44), R3 PR1-NMR (positions 46-49), R4 PR1-NMR (positions 57-61), R5 PR1-NMR (positions 63-67), R6 PR1-NMR (positions 88-93) and R7 PR1 (positions 96-98). (TIFF) S9 Fig. Sequence and structural analysis of the P53-NMR set. Representation of the neq AA (bottom graph) and neq SL (top graph) values along the 241 MSA positions in the P53-NMR dataset (Step 4 output: Neq_graph. pdf). Bars presenting neq SL values are colored according to their secondary structure status: red presents the positions in which all chains have an α-helix conformation, magenta presents the positions in which all chains have a β-strand conformation, gray presents the aligned positions in which all chains have a loop conformation, and.
Acknowledgments This work was supported by an  ANRS  Grant, an  USPC grant  ( SA-Flex)  and a  ANR grant  ( ANR-10-BINF-0003 ) (BIP:BIP). DT is supported by an  ANRS  fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors thank  Geraldine Caumes  and  Anne Badel  for helpful discussions. We also thank  Elodie Duprat  for her test of the SA-conf tool.
This work was supported by an  ANRS  Grant to LR, DF and  ACC. DT is supported by an  ANRS  fellowship. This work was supported by a  USPC grant  ( SA-Flex)  and an  ANR grant  ( ANR-10-BINF-0003 ) (BIP:BIP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
, p. 160). Noussios et al. (2016)  also considered that photographic measurements of the stapes footplate in the oval window niche may entail errors regarding the projection of such a small surface into a screen (parallax error). Since several studies now consider in situ micro-ct measurements of the middle ear as more accurate and efficient than measurements taken on photographs (this point will be discussed in more details below), we prioritize micro-cts to measure OWA in fossil hominins and extant catarrhines  (Braga et al. , 2013 (Braga et al. , , 2015)). We first visualized (by using the Avizo solftware. https://www. fei. com/software/amira-avizo/) the oval window niche in 3D after extracting an isosurface from the micro-ct data set, as shown in Fig. 2a  with the case of SK 879. We then defined an oblique slice that best-fitted the complete outline of the oval window at the location of the annular stapedial ligament, a ring of fibrous tissue that connects the oval window to the stapes footplate.
The OWA was then measured from its segmentation on this oblique slice. Since we used high resolution micro-ct data (Table  1 ), the outline of the insertion area of the annular stapedial ligament could be visualized accurately. This allowed us to reduce to a minimum (less than 5%) the interobserver variability between two operators (J. B. and J. R. D. ) in setting an oblique slice in the plane of the OWA.
The phylogenetic signal We first investigate whether the cochlear data observed for extant species derive from a nonrandom, gene-based tree-like descent process. The basic principle is to determine whether a given tree derived from genetic distances better fits a set of cochlear data (as represented by species mean values) at its tips, when compared with the fit obtained when the cochlear data have been randomly permuted across the tips of the tree, thus destroying any phylogenetic signal that may have existed. We investigate the phylogenetic signal in this dataset before and after controlling for body size. We use distinct R packages to compare two estimates of the phylogenetic signal for RECL and OWA considered separately: the of Pagel (1999) the K of Blomberg  (Blomberg et al. , 2003). Pagel's (1999)  is a parameter multiplied to each of the off-diagonal elements of the variance-covariance matrix (see  Nunn, 2011 , for more details).
When = 0, the tree has a single polytomy at the basal node for all species, whereas when = 1 the original candidate tree is recovered. Statistical tests for phylogenetic signal are performed under the null hypothesis that = 0. Tests for less signal than the candidate tree are performed under the null hypothesis that = 1. We compute Pagel's with the R package "Geiger" (its function "fitContinuous") under the assumption of a Brownian model of evolution (see below). We also compute Pagel's with the R package "Caper" (its function "pgls") with no prior assumption about any given model of evolution. In all instances, we test our null hypotheses with the likelihood ratio (LR) test (twice the log-likelihood difference between the two models is expected to fit a Chi 2 distribution with P degrees of freedom)  (Baum and Smith, 2013; Nunn, 2011).
We compute Blomberg's K statistic  (Blomberg et al. , 2003)  with the R "Picante" and "Ape" packages in order to test also whether the observed distributions for RECL and OWA across catarrhine species exhibit more or less divergence than expected when evolving under Brownian motion. Values of K range from 0 to infinity, with K = 1 indicating Brownian motion evolution. K > 1 indicates that close relatives are more similar than expected, and K < 1 indicates more divergence between taxa than expected under a Brownian model. In order to decide whether the calculated K value corresponds to a Brownian model, we test if it is significantly higher than the mean K value obtained after simulating 1000 datasets (i. e. trees) with random permutations of the tips. We also use phylogenetic generalized least squares (PGLS) (with the "pgls" function of the R package "Caper") to assess whether either RECL or OWA considered separately may have undergone correlated evolution with body mass (BM).
Estimates of ancestral values We estimate RECL and OWA ancestral values (using the preferred evolutionary model chosen in the previous step) at internal nodes of a phylogeny reconstructed with morphological and molecular data by grafting a tree for three extinct hominin species of known cochlear values (A. africanus, P. robustus and H. erectus) onto the gene-based phylogram representing contemporaneous catarrhine species (Fig. 1 ). The extinct hominin phylogram (i. e. branch lengths proportional to the amount of evolutionary change) is obtained from  Organ et al. (2011)  and was inferred using 109 morphological characters from  Strait and Grine (2004)  in a Bayesian framework. We use the R package "Phytools" (and its "fastAnc" function)  (Revell, 2012)  to compute ancestral values and 95% CIs (to reflect uncertainty) using ML (i.
Discussion In order to better interpret our fossil cochlear parameters, we aimed to use our dataset representing contemporaneous catarrhine species to (i) explore further the phylogenetic signal before and after controlling for body size, (ii) investigate whether the Brownian model is appropriate when modeling cochlear evolution and (iii) estimate some fossil hominin ancestral conditions of RECL and OWA from a phylogram combining extant and fossil data. Here, we discuss only the taxonomic and phylogenetic aspects of our results since direct transcriptions of fossil hominin RECL and OWA differences into hearing capabilities and communication signals would be premature for the following reasons. Indeed, previous studies in living great apes have demonstrated the complexity of calling behaviors on primate sound and vocal repertoires as, for instance, within highly variable call types (e.
Acknowledgments We take this opportunity to share our memories of Laurent Puymerail who was a magnificent and resourceful young colleague. Laurent's contributions and personality will remain with us for a long time to come. We thank  Roberto Macchiarelli  and  Clément Zanolli  for their invitation to contribute to this thematic issue in honor of Laurent Puymerail's work. This work was supported by the the  French Ministry of Foreign Affairs , the " Centre national de la recherche scientifique" and its "Projet exploratoire pluridisciplinaire inter-instituts "  PEP II-AUDEVO in France , the  South African National Research Foundation. We thank  Emmanuel Gilissen  from the  "Musée royal de l'Afrique centrale" (Tervuren, Belgium) ,  Bernhard Zipfel  and the  Fossil Access Committee , from the  University of the Witwatersrand (Johannesburg, South Africa ) for giving us access to the specimens newly investigated in this study.
Third, we explain how to model individual heterogeneity in capture-recapture models and provide the code to fit these models (https://github. com/oliviergimenez/indhet_in_CRmodels). The distinction is made between situations in which heterogeneity is actually measured and situations in which part of the heterogeneity remains unobserved. Regarding the latter, we outline recent developments of random-effect models and finite-mixture models. Finally, we discuss several avenues for future research.
In the context of closed populations (i. e. assuming a population with no immigration, no emigration, no recruitment and no mortality), data are collected several times during the period when assumptions characterizing closed populations hold. CR models are restricted to the estimation of population size and can account for individual heterogeneity in the probability of being detected (e. g. Burnham and Overton 1979 , Pollock 1980 , Pollock et al. 1990 , Link 2004 , Pledger 2005 , Farcomeni and Tardella 2010). In such a situation, "Individuals with high detection probabilities would tend to appear in the encountered sample in greater proportion than they occur in the population"  (White and Cooch 2017). In CR models, the probability of detection can be assumed to vary between individuals in relation to, e.
Why individual heterogeneity in a CR context? Individual heterogeneity seen as a nuisance Because analyzing most CR datasets requires using models that include detection probability, the existence of individual heterogeneity in this parameter has stimulated a large number of works in the early CR literature (e. g. Otis et al. 1978). As  Eberhardt (1969)  pointed out, "various sets of data indicate that the equal-probability-of capture assumption is not fulfilled. " Unequal detection rates may lead to biases in abundance estimate, estimated survival probability or population growth in the context of open populations (e. g. Carothers 1973 , Schwarz 2001). Heterogeneity in detection probability is still receiving attention in contemporary studies  (Crespin et al. 2008 , Cubaynes et al. 2010 , Pradel et al.
2016). This phenomenon has long been acknowledged in wildlife studies for life stages other than senescence (e. g. nest mortality;  Green 1977 and Johnson et al. 1986 , and Burnham and Rexstad 1993  in the context of CR studies). The consequences of ignoring individual heterogeneity in survival probability have also been investigated in CR studies using special datasets with perfect detection of individuals (e. g. Cam et al. 2002a , 2013 , Wintrebert et al. 2005 , Fox et al. 2006 , Aubry et al. 2011 , Knape et al. 2011). The requirement of accounting for heterogeneity in survival studies was raised by human demographers very early (which distribution to use to account for individual heterogeneity in mortality risk,  Manton et al. 1986 ) and has become a key topic in ecology. Some demographers argued that observable criteria might not account for individual heterogeneity in a satisfactory manner, and developed mixed models or mixture models for time to event data  (Kannisto 1991, Abbring and Van Den Berg 2007).
How to infer individual heterogeneity in CR models In this section, we provide details about the CR models used in the case studies reviewed above. Specifically, we focus on multistate, random-effect and finite-mixture CR models possibly including individual covariates because these are currently the most commonly used tools to incorporate individual heterogeneity and deal with detectability less than one. We Below we present results from the frequentist approach only. The code to simulate data and fit CR models is available in the Supplementary materials and from GitHub (https://github. com/oliviergimenez/indhet_in_CRmodels).
Acknowledgements -EC was supported by the  Laboratoire d'Excellence LabEx TULIP  ( ANR-10-LABX-41 ). OG was supported by  ANR-16-CE02-0007. We are grateful to  Jim Nichols ,  Doug Johnson , and  Ken Burnham  for sharing ideas and material on their early work focusing on the consequences of individual heterogeneity in survival on population dynamics, and the development of CR models accounting for heterogeneity in survival and recapture probability. Evan Cooch  provided material on ongoing analyses completing this early work with alternative methods, and  Jim Nichols  provided helpful comments on an earlier version of this paper. We also thank the three referees for insightful suggestions that improved the paper.
The study focuses on the effect of the leaf area index (LAI) spatial and temporal variability on these fluxes. We compare the use of the LAI climatology from ECOCLIMAP-II, used by default in SURFEX-ISBA, and time series of LAI derived from the high-resolution Formosat-2 satellite data (8 m). The study area is an agricultural zone in southwestern France covering 576 km 2 (24 km × 24 km). An innovative plot-scale approach is used, in which each computational unit has a homogeneous vegetation type. Evaluation of the simulations quality is done by comparing model outputs with in situ eddy covariance measurements of latent heat flux (LE). Our results show that the use of LAI derived from high-resolution remote sensing significantly improves simulated evapotranspiration with respect to ECOCLIMAP-II, especially when the surface is covered with summer crops.
The comparison with in situ measurements shows an improvement of roughly 0. 3 in the correlation coefficient and a decrease of around 30 % of the root mean square error (RMSE) in the simulated evapotranspiration. This finding is attributable to a better description of LAI evolution processes with Formosat-2 data, which further modify soil water content and drainage of soil reservoirs. Effects on annual drainage patterns remain small but significant, i. e. , an increase roughly equivalent to 4 % of annual precipitation levels with simulations using Formosat-2 data in comparison to the reference simulation values. This study illustrates the potential for the Sentinel-2 mission to better represent effects of crop management on water budgeting for large, anthropized river basins.
, 2014 (Ferrant et al. , , 2016)). In this study, we used the Interactions between Surface Biosphere Atmosphere (ISBA) LSM as part of the EXternalized SURFace (SURFEX) modeling platform  (Masson et al. , 2013). SURFEX was developed by the French National Center for Meteorological Research (CNRM) to represent and interface the surface processes for atmospheric and hydrological models. This platform is also used for research purposes in the fields of climatology, meteorology, and hydrology. Within SURFEX, ISBA is the submodel in charge of simulating variables over natural emerged areas. It uses the ECOCLIMAP-II database to determine vegetation types and associated parameters (e. g. , temporal LAI, fractional vegetation cover, albedo) at a spatial resolution of 1 km  (Masson et al. , 2003; Faroux et al.
, 2013). Each ECOCLIMAP-II grid cell is composed of up to 12 vegetation types or plant functional types (PFTs). Each PFT LAI forcing in ECOCLIMAP-II database has a temporal resolution of 10 days. It is a climatology derived from MODIS satellite observations collected between 1999 and 2005. The LAI of each PFT is determined by unmixing the LAI of the MODIS grid cell using neighboring, unmixed pixels. The method, detailed in  Faroux et al. (2013) , consists of using the LAI of the nearest MODIS pixels with a pure PFT to unmix the effect of each PFT in the LAI signature. Our study aims at evaluating the impact of introducing high-resolution information on vegetation type and LAI from Sentinel-2-like observations instead of the low-resolution climatology of ECOCLIMAP-II in ISBA-SURFEX simulations.
A single surface temperature is used to compute the different energy fluxes. The energy budget calculation method is detailed by  Noihlan and Planton (1989). The meteorological forcing for the study area is drawn from the Système d'Analyse Fournissant des Renseignements Atmosphériques à la Neige (SAFRAN;  Durand et al. , 1993)  reanalysis data  (Quintana-Segui et al. , 2007) , which provides precipitation data (in solid and liquid form), air temperature and specific humidity data at 2 m, air pressure data, and wind and solar radiation data at hourly time intervals at 8 km resolution. The SAFRAN data were spatially linearly interpolated to match plot centroids. Regarding soil parameters, by default, ISBA uses the Harmonized World Soil Database (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2012), which gives the percentage of clay and sand at a 30 arcsec (∼ 1 km) resolution.
Soil parameters were then computed using empirical pedotransfer functions  (Noilhan and Lacarrère, 1995). The ECOCLIMAP-II database  (Faroux et al. , 2013 ) is used to describe land cover at a 1 km resolution, thus corresponding to the resolution of the SURFEX-ISBA simulation grid. This land cover database is divided in 273 ecosystems. These ecosystems were determined by crossing a land cover classification derived from the SPOT/VEGETATION normalized difference vegetation index (NDVI) series and pre-existing land cover maps, as described by  Faroux et al. (2013). Twelve different plant functional types (PFT, also referred to as patches) are considered to describe these ecosystems. Each ecosystem is described by a correspondent composition of these 12 patches. Each grid cell in ISBA belongs to a unique ecosystem and thus is described as a com-bination of these patches.
Vegetation parameters are thus determined for each patch of this ecosystem. Some of the parameters are fixed such as root depth and minimal stomatal resistance, and some are temporally variable such as albedo and LAI. In particular, the LAI follows a cycle determined from MODIS LAI analysis data for 2000 to 2005 averaged for each vegetation type of each ecosystem with a temporal resolution of 10 days. Because of the low spatial resolution of MODIS, the LAI signatures of several vegetation types are often mixed in a pixel. An unmixing method is then used by  Faroux et al. (2013). It uses the nearest unmixed pixels of each PFT present in the MODIS pixel considered to assess the contribution of each PFT in the LAI climatology. ISBA then uses these data to separately simulate all variables for each vegetation type present in the pixels, and then based on the fraction of each type as a weighting coefficient, it calculates global pixel fluxes.
Formosat-2 leaf area index Formosat-2 is an NSPO (Taiwan) satellite that can generate daily multispectral images of the Earth's surface at 8 m resolution and with a swath of 24 km. It functions on tasking mode; i. e. , it does not acquire data systematically like Sentinel-2 but rather must be programmed for a target area. Its sensor detects radiation within four frequency bands of blue, green, red, and near-infrared. After geometric, atmospheric, and radiometric corrections were made and clouds are detected  (Hagolle et al. , 2008 (Hagolle et al. , , 2010)) , measured reflectances were entered into the neural network BV-NET, which inverts the PROSAIL radiative transfer model  (Claverie, 2012). This neural network deduces a set of vegetation parameters (among them the LAI and fraction of vegetation cover -FCOVER) for each pixel.
Formosat-2 land cover maps Annual land cover maps were generated using the previously described Formosat-2 image time series (Fig. 2 ,  Ducrot, 2005; Ducrot et al. , 2007 Ducrot et al. , , 2009)). Then, a supervised classification algorithm based on the iterative conditional method (ICM) was applied to determine the vegetation type of each plot  (Ducrot et al. , 1998; Masse et al. , 2011). This algorithm uses a learning sample composed of selected plots where the vegetation type is known. These plots are extracted from the Politique Agricole Commune database, made of farmers' official land use declarations. The algorithm then uses the annual NDVI profiles of these plots to separate all the pixels into 34 classes with similar NDVI profiles.
Comparison methods First, we did a local comparison with in situ measurements. We extracted the outputs of both simulations from the Auradé and Lamasquère station plots. We then calculated correlation coefficient (R 2 ) and root mean square error (RMSE) values between monthly cumulated measured and simulated evapotranspiration (ET) fluxes. Then, we did a spatialized comparison over the entire study area. We analyzed differences between both simulations for the entire study area by calculating correlation coefficients between the monthly simulated evapotranspiration time series for each plot. These correlation maps allow one to identify plots where effects of using Formosat-2 data on the temporal evolution of evapotranspiration are more pronounced. Eventually, we aggregated the simulated LAI, evapotranspiration, drainage, runoff, and soil water index (SWI, Eq.
e. , winter crops, where the improvement on R 2 rarely exceeds 0. 1, the decrease in RMSE being around 20 %. To understand why these differences appear, we compared the time series of measured and simulated LAI and evapotranspiration for both sites in 2006 (Figs. 3  and 4 ). Silage maize was grown in Lamasquère in 2006. This crop is harvested before plant senescence. Hence, the observed cycle is shorter than a typical maize cycle. Figure  3  shows that the LAI is more realistic when using Formosat-2 data rather than   This better description of the LAI based on Formosat-2 data leads to a better simulation of evapotranspiration timing (Fig. 4 ). In particular, the evapotranspiration peak is delayed by 1 month for summer crops (i. e. , on Lamasquère). It thus fits the measurements better. However, the FORMOSAT experiment results do not match the actual amplitude of the measured evapotranspiration peak.
Uncertainties with remote sensing data Thanks to the high spatial resolution of Formosat-2, the plotscale modeling approach could be applied at regional scale. It allows one to distinguish the effects on specific vegetation types in the context of LSM studies. The revisit frequency is also sufficient to clearly monitor the phenological cycle and its critical stages at the plot scale. The local comparison between model and measurements (Sect. 4. 1) clearly shows that the use of Formosat-2 (Sentinel-2 like) data allows the model to capture the seeding and harvest dates (Figs. 3  and 5 ) unlike with the ECOCLIMAP-II forcing. Note that the quality of the Formosat-2 acquisition is quite similar to that of Sentinel-2  (Koetz et al. , 2017). In particular, the geolocation uncertainty is smaller than the size of a pixel, which allows precise extraction of LAI values from plot geometries.