dataesr/scientific-paragraphs-categorization huggingface dataset:
(2009a)  and is used for many real-time and climatological applications in the French mountain areas (e. g. Verfaillie et al. , 2018). The period from 1981 to 2010 was set as a reference period to estimate standardized anomalies. For each MODIS pixel, we extracted all the required meteorological data to compute a Heat Wave Magnitude index, hereafter HWI, and a Reference Crop Evapotranspiration, hereafter ET 0 as described below. We simplified the methodology proposed by  Russo et al. (2014)  to calculate a HWI that accounts for both magnitude and duration of heat waves. First, we identified days exhibiting a maximum temperature above a threshold value. For each day of year (DOY) i, we assembled a dataset (DS i ) comprising the daily Tmax values of the n days before and after day i for the 30 yearlong reference period  according to Eq.
For example, the combined use of high-resolution satellite imagery in combination with a regional-scale database of vegetation could provide new insights into this line of enquiry. Our comparative analysis of mountain grassland phenology over the last two decades provides a knowledge base to help anticipate the long-term response of grasslands under future climate conditions. Regional climate models used to compute climate projections are operating at coarse resolutions (10-50 km)  (Jacob et al. , 2014)  and are insufficient to provide ecologicallyrelevant surface parameters in mountainous regions  (Ford et al. , 2013). Although downscaling methods can refine projections of meteorological variables and snow cover dynamics  (Verfaillie et al. , 2018) , further work is needed to translate these model outputs into bioclimatic indicators and assess to what extent they are driving canopy greenness.
Acknowledgements Thanks are due to  Brad Carlson  for helpful discussions and to  Deborah Verfaillie ,  Matthieu Lafaysse  and  Matthieu Vernay  ( CNRM/CEN ) for maintenance and provision of the SAFRAN reanalysis used in this study. Comments and suggestions from the two reviewers have greatly improved the manuscript. This research was conducted within the  Long-Term Socio-Ecological Research (LTSER) Zone Atelier Alpes , a member of the eLTER-Europe network. It received funding from the  LTSER Zone Atelier Alpes  and from a scholarship of  CONACYT. LECA and CNRM/CEN are part of  Labex OSUG@2020  ( ANR10 LABX56 ).
Results We propose a new method to correct short reads using de Bruijn graphs, and implement it as a tool called Bcool. As a first step, Bcool constructs a compacted de Bruijn graph from the reads. This graph is filtered on the basis of k-mer abundance then of unitig abundance, thereby removing from most sequencing errors. The cleaned graph is then used as a reference on which the reads are mapped to correct them. We show that this approach yields more accurate reads than k-mer-spectrum correctors while being scalable to human-size genomic datasets and beyond.
Other read correction techniques Other correction techniques rely either on suffix arrays (allowing the use of substrings of various sizes instead of only fixed k-length words)  [17]  or on multiple-read alignments  [18]. Despite their methodological appeal, these techniques are resource-expensive and do not scale well on large data sets. Moreover, benchmarks suggest that they perform significantly worse than state-of-the-art k-mer-spectrum correctors  [19]. Yet another approach for correcting reads, pioneered by LoRDEC  [20]  then by LoRMA  [21] , is to use de Bruijn graphs instead of strings as a basis for correction. In the LoRDEC approach, this de Bruijn graph is built from highly accurate short reads and used to correct long reads. In LoRMA, the de Bruijn graph is built from the very same long reads that the program is attempting to correct.
The unitig threshold can be user-specified or can be inferred by looking at the unitig abundance distribution and choosing the first local minimum. These cleaning steps are applied several time in an iterative manner for handling complex scenarios The blue boxes are FASTA files and the grey boxes represent the tools that process or generate them. Ntcard  [23]  is used to select the best-suited k-mer size. A compacted DBG is then constructed using Bcalm2  [24]. The Btrim  [25]  module cleans the graph, and the reads are finally mapped back on the de Bruijn graph using Bgreat2  [26]. where error patterns are nested. Together, those two approaches allow us to address the issues depicted in Figure  1. 1 and Figure  1. 2. Compared with a strategy based only on k-mer abundance, our approach keeps more low-abundance genomic k-mers while removing more erroneous k-mers.
Real data sets In this section we evaluate the impact of the correction on assembly continuity using several real data sets: a C. elegans Illumina HiSeq 2500 run with 79. 8 millions reads of length 150 bp amounting to 12 Gbp (DRR050374); and a A. thaliana Illumina MiSeq run with 33. 6 millions reads of length 250 bp amounting to 4. 4 Gbp (ERR2173372). For this benchmark we used the string graph assembler fermi  [31]  given its efficiency and robustness. Assembly reports provided by Quast  [32]  are presented in Tables 2 and 3 using contigs larger than 1000 nucleotides. We see that on both datasets that Bcool obtains the most contiguous assembly in view of both N50 and N75 statistics. Corrector.
DBG construction strategies In this section we evaluate diverse DBG construction strategies. We performed tests on a simulated C. elegans 50X data set of 150-bp reads with a 1% error rate. Results are presented in Table  9. We show only graph-cleaning results obtained with low k-mer abundance thresholds (2 and 3) as higher values would not make sense for our approach. Results using higher k-mer abundance thresholds are showed here for KAF only, as it corresponds to a classical k-mer spectrum approach. k-mers present in the DBG and v2 is the number of genomic k-mers missing in the DBG. In this experiment (and by default in Bcool) the unitig filtering threshold was set to five. The different rows represent the efficiency of the different strategies tested: k-mer abundance filter alone (KAF), tip removal after k-mer filter (KAF+TIP), unitig abundance filtering after k-mer filtering (KAF+UAF), and the combination of the three strategies (KAF+TIP+UAF).
We thank  Romain Feron  and  Camille Marchet  for their support and implication in this project. Antoine Limasset 's postdoctoral position is funded by the  Fédération Wallonie-Bruxelles  via a " Action de Recherche Concertée" (ARC)  grant to  Jean-François Flot. Computational resources were provided by the  Consortium des Équipements de Calcul Intensif (CÉCI) , funded by the  Fonds de la Recherche Scientifique de Belgique (F. R. S. -FNRS)  under Grant No. 2. 5020. 11 ,as well as by GenOuest platform (genouest. org).
Acknowledgements The work was financed from the Research Subvention from the  Polish Ministry of Science and Higher Education for the AGH University of Science and Technology in  Krakow No. 16. 16. 210. 476. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creat iveco mmons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Landscape characterization The land use of the study area was surveyed twice a year from 2009 to 2013 corresponding to the periods for early harvesting and late sowing of crops. All data were geo-referenced and mapped into a GIS geodatabase. Spatial data were processed using Quantum GIS version 1. 7. 3  (Development Team 2002 -2010). We identified 37 land use types based on the field survey (33 agricultural, 3 urban and 1 forest). These land uses were regrouped into 11 categories: alfalfa, grassland, ryegrass, sunflower, spring crops of pea-flax-field beans, rapeseed, cereals, maize, other crops (e. g. mustard, sorghum, millet and tobacco, representing less than 2% of crops), urban, and forest. This clustering was based on expert opinion to allow simplifying the analysis and obtaining a more functional classification according to species preferences.
A negative value indicates an evenly spaced distribution, showing that there are significant differences in environmental tolerances between species. These three indices (coherence, spatial species turnover and boundary clumping) were then compared against the values expected for purely random variations to assess their significance (see details of the null model below). All the analyses were calculated using the Matlab "metacommunity" function developed by Presley et al. [5]  (available from http://faculty. tarleton. edu/higgins/metacommunity-structure. html). The function can be used to test several null models. Here we used a null model where the species richness per site was fixed (equal to the observed richness), but the probability that a particular species occurred at that site varied according to the occurrence of that species (i.
Both year and session were included as temporal predictors. Environmental and spatial variables were selected independently for each scale using a forward selection procedure to reduce the number of variables while keeping the variation explained by these variables to a maximum. This analysis used the "forward. sel" function of "packfor" R package (R. 3. 1. 0, 2014) (available from R-forge: http://r-forge. r-project. org/R/?group_id=195), and the "varpart" function of the "vegan" package for the variation partitioning. This analysis was carried out for each environmental scale from 200 to 1400, in 200m intervals. Step 3: Breaking down diversity patterns into landscape and species classes. In the above stages, the dataset was analyzed as if it were homogeneous. However, there were considerable differences in both landscapes and bird communities in the study area.
For instance, landscapes ranged from highly intensive open fields without any trees, to mosaics of plots delimited by hedges. In the same way, the data set includes many species representing a wide panel of habitat preferences. To refine our findings, we explored whether results remained valid with habitat and species classes based upon major shared attributes. Environmental and spatial variables were selected independently for each habitat or species group and each spatial scales. Species classes were based on co-occurrence patterns in the data set. To build the classes, the "dudi. coa" function of the "ade4" package (R. 3. 1. 0, 2014) was used to perform a correspondence analysis (as described above). The species distribution (mean and standard deviation) was then ordinated along the first axis of the correspondence analysis which explained most of the variation (17.
The values of the traits correspond to a mean value per species, defined according to the literature. Sources: 1. Guide ornitho, Lars Svensson, Peter J. Grant, Killian Mullarney, Dan Zetterström, edition 2011 2. Website: Oiseaux. net. (DOCX) S8 Appendix. Database. The database contains all the data from the point counts and GIS data. This includes the ID of the point ("IdPoint"), the year and the session (2 sessions per year from 2009 to 2013). The abundance of each species is reported on the table. The first column ("Gradient_HedgerowsForest200m") corresponds to the gradient of wooded components used to do the partitioning per landscape. This variable was computed by summing the area of hedgerows (considering a buffer of 100m around this linear component) and woodland. The data base also included the areas of the 11 categories of land use: alfalfa, grassland, ryegrass, sunflower, spring crops of pea-flax-field beans, rapeseed, cereals, maize, other crops (e.
g. mustard, sorghum, millet and tobacco, representing less than 2% of crops), urban, and forest and two linear components (roads and hedgerows). For each point count station, we recorded the area occupied by each land use category as well as the length of hedgerows and roads or paths within a buffer area from 200m to 1400m. Finally, the database contains the 9 spatial functions (third degree polynomial of the geographic coordinates of the point counts) used in the variation partitioning analysis ("Spatial1" to "Spatial9"). (XLS).
The prior information used for Level 2 (L2) retrievals in the thermal infrared can influence the quality of the retrievals themselves and, therefore, their further assimilation in atmospheric composition models. In this study we evaluate the differences between assimilating L2 ozone profiles and Level 1 (L1) radiances from the Infrared Atmospheric Sounding Interferometer (IASI). We minimized potential differences between the two approaches by employing the same radiative transfer code (RTTOV) and a very similar setup for both the L2 retrievals (1D-Var) and the L1 assimilation (3D-Var). We computed hourly 3D-Var reanalyses assimilating respectively L1 and L2 data in the chemical transport model MOCAGE and compared the resulting O 3 fields among each other and against ozonesondes. We also evaluated the joint assimilation of limb measurements from the Microwave Limb Sounder (MLS) on top of IASI to assess the impact of stratospheric O 3 on tropospheric reanalyses.
Since the sensitivity of IASI TIR measurements to O 3 is not uniform along the atmospheric column, we also investigate the impact of assimilating more accurate stratospheric profiles from the Microwave Limb Sounder (MLS) in combination with IASI radiances. This might reveal possible synergies when assimilating multiple instruments that sense different layers of the atmospheres. The paper is organized as follows. The satellite measurements, the Level 2 retrievals and the validation measurements used for this study are described in Sec. 2, as well as main steps concerning the preprocessing for some of the datasets. The chemical transport model, the radiative transfer model, the assimilation algorithm and the setup of the experiments are described in Sec. 3. The assimilation of IASI retrievals and radiances is compared in Sec.
L1 radiances IASI L1c data contain calibrated and geolocalized spectra at 0. 5 cm 1 spectral resolution (after apodization), i. e. 8700 radiance values for each ground-pixel, with a footprint of 12 km for nadir observations. For this study, historical L1c data granules have been downloaded from the EUMETSAT Earth Observation data portal (https://eoportal. eumetsat. int) in NETCDF format. Data files contain also the observation geometry (sun and satellite angles) for each ground-pixel and the collocated land mask and cloud fraction values, obtained from the Advanced Very High Resolution Radiometer (AVHRR) measurements, also onboard MetOP.
SOFRID L2 retrievals The Software for a Fast Retrieval of IASI Data (SOFRID) was developed at the Laboratoire d'Aérologie to retrieve O 3  (Barret et al. , 2011)  and  CO (De Wachter et al. , 2012)  profiles from IASI in near-real time. It is based on the Radiative Transfer for TOVS (RTTOV) RTM  (Saunders et al. , 1999)  and the 1D-VAR scheme developed within the Numerical Weather Prediction Satellite Application Facilities (NWP-SAF) program. SOFRID retrieves the O 3 profile in volume mixing ratio (vmr) units at 43 pressure levels between the surface and 0. 1 hPa using 469 spectral channels within the main IASI O 3 window (980 -1100 cm 1 ). The choices that are made in SOFRID and are relevant for this study are summarized in Tab. 1. Note that a single a-priori profile and error covariance matrix are used globally and that the Surface Skin Temperature (SST) is estimated within the retrieval.
The number of DOF of the SOFRID retrieval has been evaluated between 2 and 3 for the full atmospheric column, with about one DOF for the tropospheric column  (Dufour et al. , 2012). The accuracy of the retrieved O 3 depends on the latitude and the vertical level, but sits generally within 10-20 % of the corresponding radiosoundings values, once the averaging kernels are applied. However, increased biases are found in the troposphere with SOFRID (+10%) and positive biases of about 15% are found in the Upper Troposphere -Lower Stratosphere (UTLS) region with all current IASI O 3 products  (Dufour et al. , 2012). The reasons for such biases are not yet fully understood and can impact negatively data assimilation  (Emili et al. , 2014)  or trends analysis  (Gaudel et al. , 2018). This study will provide further insights about the impact of the constant a-priori on IASI retrievals.
, 2010; Miyazaki et al. , 2012; Inness et al. , 2015). Similarly to previous studies  (Emili et al. , 2014) , we retain only the most accurate data using MLS, i. e. above 170 hPa. The MLS V4. 2 product used in this study has been downloaded from the Goddard Earth Sciences Data and Information Services Center (GES DISC) web portal (https://disc. gsfc. nasa. gov).
Data preprocessing Some further preprocessing has been applied to the original L1c and SOFRID datasets to ease the interpretation of the assimilation experiments presented later in Sec. 4. The objective was to ensure that exactly the same spectra are used for both L1 and L2 assimilation. Only the spectral channels that are used in SOFRID are extracted from IASI L1c granules, i. e. channel n. 1350 (980 cm 1 ) to 1818 (1100 cm 1 ). Some further screening is applied to remove channels that are affected by strong H 2 O absorption, as also done in SOFRID. The spatial resolution of the CTM (2 x2 degrees, Sec. 3. 1) is much coarser than IASI pixels size. Since it is preferable to avoid all kind of spatial averaging of the observations, a significant reduction of ground-pixels is needed. In return, we employ strict selection criteria to avoid as much as possible contamination from clouds and bright surfaces, which reduce the RT accuracy and increase retrieval or assimilation errors.
The data selection is performed as follows. First, only L1 pixels with both IASI and AVHRR highest quality flags are kept. Then, ground-pixels from IASI L1 and SOFRID products are filtered using their respective cloud masks (Sec. 2. 1. 1 and 2. 1. 2) and keeping only pixels with cloud fraction less or equal to 1%. SOFRID pixels with a sand signature greater than 0. 5 and with a number of retrieved levels lower than 35 (mountains) are also filtered out. Resulting datasets are then matched, i. e. only common ground-pixels that remained available after the previous L1 and SOFRID independent selections are kept. Finally, data thinning is performed to retain a maximum of about two pixels for each model grid point. After the completion of the data selection procedure the final number of retained ground-pixels for L1 and SOFRID is about 3300 per day, compared to about 10 5 when only the cloud screening is applied.
Assimilating directly L1 radiances represents a viable alternative to this. We could imagine extending this analysis to other chemical species (e. g. CO) and spectral regions (e. g. UV) that show a similar behavior to O 3 in the TIR spectrum in terms of information content of the measurements. Finally, a positive synergy has been found when assimilating simultaneously MLS profiles and IASI (either L1 or L2), which corrected stratospheric biases due to IASI assimilation alone. The addition of MLS was found to influence the results of IASI assimilation also in the free troposphere (500 hPa), with L1 assimilation providing in general better results than L2 in the tropics and in the SH. This suggests that using L1 data might also be beneficial in a context of assimilating multiple instruments with different vertical sensitivities at the same time.
Acknowledgements. We acknowledge  EUMETSAT  for providing IASI L1C data, WOUDC for providing ozonesondes data and the  NASA Jet Propulsion Laboratory  for the availability of Aura MLS Level 2 O3. We also thanks the  MOCAGE team at Météo-France  for providing the chemical transport model, the RTTOV team for the radiative transfer model,  Andrea Piacentini  and  Gabriel Jonville  for the help on technical developments of the assimilation code. This work has been possible thanks to the financial support from the  Région Midi-Pyrénées ,.
To do so, participants completed a comprehensive battery of neuropsychological, functional and symptom assessments in the lab, and then completed one week of EMA measures of functional participation and location, as well as GPS sensing. With respect to concurrent validity, we hypothesize that GPS metrics of mobility will be associated with EMA-sampled indicators of time spent in and out of the home. We also hypothesize that participants with schizophrenia will have lower mobility than comparators, and that within the schizophrenia sample that GPS data will correspond to in-lab measures of community functioning. Finally, we expect, based on prior work on mobility in schizophrenia, that GPS mobility indicators will be associated with more severe symptoms, worse neurocognition, and reduced community function.
GPS values and EMA data The average adherence to EMA (number of surveys answered divided by total number possible) was high and did not differ between groups (patient mean = 86. 4%, SD = 0. 15, healthy comparator mean = 85. 5%, SD = 0. 15, T(140) = 0. 3, p = 0. 748). The total number of accurate GPS samples was higher in the patient group (mean = 1320. 4, SD = 451. 5) vs. the healthy comparators (mean = 1160. 8, SD = 480. 5; T(140) = 2. 1, p = 0. 047). Further analyses indicated that the number of accurate samples was associated with the ratio of samples at-home (rho = 0. 492, p < 0. 001) and distance traveled from home (rho = -0. 205, p = 0. 014). Therefore, we included the number of GPS samples as a covariate in group comparisons.
By integrating graphical information systems, variability in the kinds of locations that people travel to and from, modes of travel, as well as the influence of the individual's neighborhood and surroundings on mobility could be examined  26. Temporal aspects of data can also be investigated, with instability or stability in mobility and "bouts" of home time being possible to assess. Thus, the present findings may constitute a starting point for a deeper understanding of GPS data on mobility in schizophrenia. Among the limitations of this study, the sample was comprised of a convenience sample of comparators and outpatients willing to consent to GPS data collection and participation in EMA research. Although the sample composition was similar to that of other research studies, the results may not generalize to more severely ill participants or comparisons with healthy comparators who were working full time on average.
People with schizophrenia were recruited from flyers posted in board-and-cares/assisted living residences, mental health clinics and clubhouses in the UC San Diego Health, San Diego County Mental Health and Veterans Affairs San Diego Healthcare Systems. Healthy Comparators (N = 61) were recruited using advertisements in free periodicals and flyers posted in hospital clinic (e. g. , primary care) settings. We attempted to match age and gender of healthy comparators to that of the sample with schizophrenia. Data collection occurred between 2014 and 2016. The study was approved by VA San Diego Institutional Review Board, and participants were informed about the collection of GPS data concurrent to GPS sampling and provided written informed consent.