dataesr/scientific-paragraphs-categorization huggingface dataset:
The coordinate re-projection and aggregation of the LC_CCI data uses slightly different resampling algorithms depending on whether the tool is used on the land cover or seasonality products. The tool converts the original LC_CCI geotiff file to target files produced in NetCDF-4 format and following CF (Climate and Forecast) conventions, more commonly used in numerical modeling. The open-source BEAM tool (source code at https://github. com/bcdev) can be run independently using either Windows or Unix-based operating systems and the compiled operational tool can be downloaded from http: //maps. elie. ucl. ac. be/CCI/viewer/download. php.
Re-sampling algorithm for LC_CCI land cover For the land cover classes, the resampling algorithm produces an aggregated LC_CCI data set that in addition to the fractional area of each PFT, also includes the fractional area of each LC_CCI UNLCCS class, the majority (dominant) LC_CCI UNLCCS class and the overall accuracy of the aggregated classification. The majority class n is defined as the LC_CCI class which has the rank n for the sorted list of LC_CCI classes by fractional area in the target cell (see Fig. 1 ). The number of majority classes computed is a parameter, which can be defined by the user, so that the full number of LCCS classes can be reduced to a user-defined subset, i. e. , the top 3. Each original valid land, water, snow or ice pixel contributes to the final target cell according to its area percentage contribution.
Extension to specific model needs The LC_CCI tool provides users with a zero-order classification; that is, the PFT classes are defined as broadly as possible so that users have the advantage to continue to aggregate to the requirements of their model (Fig. 2 ). For example, models that do not include shrub PFTs can merge shrub and tree categories together to create a single woody PFT category. Modeling groups that require climatic distinctions for PFTs; for example, temperate versus tropical versus boreal types can use their own climate or biome data sets, such as Köppen-Geiger or Trewartha ecological zones  (Baker et al. , 2010; Kottek et al. , 2006; Peel et al. , 2007) , and define classification rules based on temperature thresholds, for example  (Poulter et al. , 2011). Most models also require a distinction between the C3 and C4 photosynthetic pathways for different grass species, where C4 is more common in warm and dry climates  (Edwards et al.
Analysis and comparison to PFT maps For analysis and demonstration of the tool, we compare the LC_CCI PFTs with the original PFTs used by the land surface model (LSM) components of the ESMs from the three modeling centers developing ORCHIDEE at LSCE  (Krinner et al. , 2005) , JULES at MOHC  (Clark et al. , 2011; Cox et al. , 2000; Pacifico et al. , 2011) , and JSBACH at MPI  (Knorr, 2000; Pongratz et al. , 2009; Reick et al. , 2013). The original ORCHIDEE PFT map, based on 12 PFTs plus bare soil, has its origins in the Olson land cover data set from the 1980s  (Olson et al. , 1983)  and the International Geosphere Biosphere Program (IGBP) DISCover data set for the period 1992-1993  (Loveland and Belward, 1997). This was implemented within ORCHIDEE using a look-up table approach to estimate PFT fractions  (Verant et al.
, 2004). The JULES model also uses PFT distributions derived from the IGBP DISCover data set to estimate fractional coverage of five PFTs and four non-vegetated surfaces (water, urban, snow/ice and bare soil). JSBACH uses original data from  Wilson and Henderson-Sellers (1985)  and continuous tree fractions from  DeFries et al. (1999)  to represent the distribution and abundance of 12 PFTs. The LC_CCI Epoch 2010 was converted to 0. 5 degree resolution using the LC_CCI user tool and compared with the individual default model PFT maps to illustrate regional differences and biases between products and to provide a baseline of how the LC_CCI products may improve LSM performance.
This also partly explains why the JSBACH C4 pasture PFT decreases exactly in the same areas where the C4 crops increase due to the use of the LC_CCI data. In JULES, the C4 types over Sahel shift to bare soil. In the tropics, reductions in broadleaf tropical tree cover were largely consistent across all three ESMs, although increases in broadleaf forest area were found for some parts of the African Congo Basin for JULES (Fig. 6 ). Needleleaf forest area increased compared to the reference data set for both JULES and JSBACH for boreal Europe and Australia (shrub- land PFTs). The increase in needleleaf PFTs in boreal Europe was partially associated with a decrease in broadleaves (Fig. 6a  and b ) for all three models, but also a decrease in natural grassland cover.
Advantages of the LC_CCI for ESM modeling The LC_CCI approach provides the ESM modeling community with a flexible tool for using up-to-date land cover information consistently provided over time. Following the requests of the user survey, the land cover data set is available across multiple spatial domains, conforms to standard file formats used in numerical models, and includes information on classification confidence levels for the land cover classes and resulting PFT fractions. The standardized conversion tool provides users with a consistent documented approach for aggregating land cover classes and thus overcomes limitations associated with consensus approaches (e. g. ,  Tuanmu and Jetz 2014). Of particular importance is that the multitemporal LC_CCI mapping approach facilitated more accurate mapping leading to improved remote sensing observations of deforested areas in the tropics, the tree line-tundra boundary in the high latitudes, and better distinctions between managed and non-managed grasslands in Africa.
Earth system modeling challenges Updating PFT data sets used in ESMs will clearly lead to improvements in the realism of the patterns of biogeography and have important feedbacks on simulating ecosystem processes and interactions with the atmosphere. Available PFT data sets used in ESMs remain outdated, using land cover information from the 1980s mainly because of a lack of tools available for cross-walking land cover to PFTs. The LC_CCI scheme and tool fills a critical data need for improving the representation of carbon, water and energy cycles be-ing developed by the modeling community; however, extensive model benchmarking and calibration activities may now be necessary before the new PFT data sets result in model improvement. For example, model processes may be calibrated to some extent to produce performance metrics under outdated land cover information, and thus a range of benchmarks should be considered when transitioning to new PFT information.
Summary The LC_CCI has made significant progress in responding to the ESM community data needs  (Tsendbazar et al. , 2014). These include -new land cover classifications for three Epochs using consistent algorithms and based on the UNLCCS system; -a user-friendly tool for mapping the UNLCCS classes into user-defined PFT classes and at most grid resolutions used by the ESM community; -seasonality products describing average weekly conditions for burned area, NDVI and snow cover; -confidence information for each of the UNLCCS classes and a median estimate for the converted PFT legend. The UNLCCS-PFT conversion tool and the land cover products will continue to be improved during phase 2 of the LC_CCI with updates made periodically and described at http://www. esa-landcover-cci. org.
Method assessment To assess for the correctness and precision of the IEC method, different concentrations of each type of samples were required. Hence, the low heat skim milk powder was dedicatedly reconstituted at 5, 10 and 20% (w/ w) as described above. AMU was used pure and diluted 1:2 and 1:1 v/ v. The three cheeses were considered together as a set of solid dairy samples with different phos phorus concentrations. Assessment of the IE C method was pe rformed using the INRA's application of the French standard NF V03-110 by  Feinberg (2010)  for the assessment of exactness of analytical methods. This application can be found in the form of an Open Office-c ompatible Excel sheet (Microsoft) that can be downloaded free of charge at http://www6. jouy. inra. fr/metarisk/Produc tions/Software-programs/Excel-templates/NF-V03-1 10 (last accessed April 2015).
Here, we reanalyse the available data for V. dabneyi, and provide new information on germination rate and population genetic structure and diversity. We also analyse species distribution in order to understand what ecological factors might be constraining it. Due to the small size of the populations, and to the degree of isolation and fragmentation, we expect to find reduced levels of seed set, comparably low levels of genetic diversity and some degree of differentiation between the studied populations. Because it is rare, we expect the species to have a relatively restricted ecological niche and high levels of ecological specialization.
Sampling Veronica dabneyi populations. Veronica dabneyi was searched in areas with potentially suitable habitat on the islands of Corvo, Flores and Faial (Fig. 1 ), between the years 2000 and 2014. The search was not successful on Faial but on Flores and Corvo a total of seven (sub)populations were found. All cited locations here are recorded in the Atlantis database of the 'Azorean Biodiversity Portal'  (Borges et al. 2010) , including data from  Pereira et al. (2002)  and  Schaefer (2003). Demographic data. Three populations were studied in detail: Miradouro Craveiro Lopes and Tapada da Forcada (Flores) and Madeira Seca (Corvo). Variables measured included: individual number and size, stem length, number of inflorescences and inflorescence height (measured in loco), number of fruits per inflorescence, number of seeds per fruit, seed diameter and weight, using a digital calliper and an electronic scale (measured/counted at the laboratory).
Fruits were sampled in July 2008 from Miradouro Craveiro Lopes, and in June 2010 form Madeira Seca and Tapada da Forcada. Associated flora and invasive species. Characterization of the associated flora was based on the work by  Pereira et al. (2002)  and on data collected by the authors in 2010, using a 1 m 2 plot centred on V. dabneyi individuals or groups of individuals. The presence of plant invaders (see  Silva et al. 2008)  was recorded from the immediate surroundings of V. dabneyi populations. The presence of cattle, goats and rabbits was confirmed through direct visual observation of the animals or by the presence of characteristic faeces. Plant material for genetic analysis. In 2008 and 2010, a collection of leaf material was carried out to complement the samples already available at the DNA bank collection of the AZB herbarium (Biology Department, Azores University).
1. 97 Demo version (Softgenetics). Analysis of genetic data. Population structure was analysed with GenAlEx 6. 5  (Peakall and Smouse 2012) , to obtain mean values per population of the total number of alleles, the number of alleles with a minimum allele frequency of 5 %, the number of effective alleles, the Shannon's Information Index, the number of private alleles, the expected heterozygosity, R st and the estimation of gene flow. A principal coordinates analysis (PCoA) and an analysis of molecular variance (AMOVA) were also performed. Furthermore, we used a Bayesian approach to estimate the number of genetic clusters present in the whole sample. This model-based analysis was run with the software STRUCTURE version 2. 3. 3  (Pritchard et al. 2000) , using a batch-oriented web programme package for construction of super matrices ready for phylogenomic analyses  (Kumar et al.
Acknowledgements We are grateful to  Hugo Costa  for help in managing ecogeographical variables, to  Selı ´cia Cordeiro  for help in the lab and to  Raquel Alcaria ,  Graciete Maciel ,  JoseMartins ,  Orlanda Moreira ,  Mark Carine  and  Fred Rumsey  for help in field work. We thank two anonymous reviewers who helped to improve the manuscript quality.
The same data were used to run the model in Biomapper. An R script was used to calculate the Boyce index and the Boyce curve, as well as to compare the habitat suitability maps obtained in the two methods, using the McNemar test (for further details on model validation and comparison, seeCosta et al. 2012, 2013a, and  references within). The best model was then changed to include one or two variables describing the distance to different types of land use. The best model was selected and compared as previously. The projection of the potential distribution of V. dabneyi in Faial Island was obtained using Maxent. Primer Motif Forward Reverse Vd2B04 AC(9) GTTTAGTGACGAGGACATTGATTG GGAAACAGCTATGACCATCCTTCTAACATCGCAAACTG Vd2B09 AC(10) GTTTGCACACTGAAGGGTATCAAC GGAAACAGCTATGACCATAAATCGGTGAATGTTTGATC Vd3A01 AAG(9) GTTTGTGTTCAGCTTGGAATTGAG GGAAACAGCTATGACCATCTCTTCGACCAAATTCTTG Vd3A03 ATC(18) GGAAACAGCTATGACCATAAGTTCTTGCTCTGCTTGTC GTTTCTTGTAGCCCAGATTGAAAC Vd3A05 ATC(14) GGAAACAGCTATGACCATCTAAACTCCCTTTCACTGG GTTTGCGTCGAAGTACAAGAACAG Vd3B07 AAG(15) GTTTAGCTCGGAAACTTTGTAATG GGAAACAGCTATGACCATGCAATAAAGTGATTAAGTGG Vd4A01 AATG(6) GTTTCCCACTATCCAACCATAATC GGAAACAGCTATGACCATAACTCAGCTCAGCGTGAC Vd4B01 AAAC(11) GGAAACAGCTATGACCATAACCACATCACTCCAAACAG GTTTGACTGGGCTAGAGTTGTC Vd4B04 ACAT(20) GTTTAATCCATTGTGTGCAGTCTC GGAAACAGCTATGACCATCACCTCCCACACTTAATC Vd4A03 AAAC(6) GGAAACAGCTATGACCATGCTTTAATTTGTGCGTATC GTTTCCTATCCCTTAACCTTTCTTC The topographic and land cover EGVs were acquired from the supporting data available in the CIELO model database, which matches the same spatial resolution of 100 m.
To this natural distribution of homologues, one must add the actual sampling of species, which typically induces a bias toward model species and their relatives. This variability of gene representation in homologous sequence databases is also found at the intra-genic level. For a given family of homologous sequences, some positions are highly represented in the collection of sequences and present in most species. Other positions are found in only one or a few species. In the resulting sequence alignment, such positions would include gaps ('-') or unresolved characters (e. g. , 'N', 'X' or '?') depending on the cause of their absence. This variability of site-specific sequence coverage (further simply referred to as coverage) is a typical missing data problem. Depending on the downstream analyses, sites with insufficient coverage are not analyzable, or lead to inaccurate estimates.
Sequence clustering tree Any a priori hierarchical clustering of sequences, represented as a tree, can be used as an input to the method. The phylogeny of the underlying sequences, if known or inferable, is a natural choice. Yet for the purpose of optimizing the number of alignment columns without gaps, it is efficient to cluster the sequences according to their overlap in the alignment. For that purpose, we define the overlap distance between two sequences S 1 and S 2 from a multiple alignment with n columns as d overlap S 1 , S 2 = n- n i=1 S 1 i = NA ∧ S 2 i = NA. ( 3 ) The sum represents the number of positions in the pairwise alignment for which both sequences do not have missing data (gap or generic character). All pairwise distances from the sequence alignment are computed using the overlap distance.
Example data sets ORTHOMAM database We used the ORTHOMAM database  [12]  version 8  [13] , which gathers mammalian orthologous coding sequences based on ENSEMBL annotations. Nucleotide sequences for aligned orthologous gene coding sequences were retrieved with no filtering. Only alignments having at least 30 mammalian species out of the 40 available were kept, resulting in a total of 11,305 gene families. The phylogeny of the 40 species was downloaded from the ORTHOMAM website, and the corresponding phylogeny for each family was extracted by removing missing species. In addition, approximate maximum likelihood trees were inferred for each family using the Fasttree program  [11]. For comparison, we applied our method on a random guide tree generated for each gene family using the rtree command from the R package ape  [14].
Each species was searched in Pubmed to retrieve the number of publications with a title including its name (Additional file 1). This number of publications reflects the extent to which the species is studied and is typically high for model species such as Homo sapiens and Mus musculus. Correlation of the number of species with the frequency of sequence removal was performed using a Kendall correlation test as implemented in the R software. To correct for ties, input values were randomized 10 times using the jitter function. We report the minimum correlation value as well as the maximum p-value obtained over these 10 randomizations.
PFAM database We retrieved all PFAM families with a number of sequences between 1,000 and 5,000  [15]. This selection comprises 2,785 protein families and includes species from bacteria, archaea and eukaryotes. The original PFAM alignments were used in all subsequent analyses. An approximate maximum likelihood phylogenetic tree was reconstructed for each family using the Fasttree program  [11]  as well as a random tree using the rtree command from the R package ape  [14]. As opposed to the ORTHOMAM benchmark data set, families in the PFAM benchmark might contain paralogous sequences.
Results We applied the new optimization algorithm to two contrasting data sets to cover a wide range of potential applications. First, we analyzed 11,305 mammalian gene families from the ORTHOMAM database  [13]. Each family contained one orthologous sequence for at least 30 species of mammals. Second, we studied 2,785 protein families, each having between 1,000 and 5,000 sequences, from the PFAM database. In both cases, we aimed to maximize the number of sites with a given minimum coverage: we allowed a maximum of 5 % and 30 % sequence gaps and unresolved characters for ORTHOMAM and PFAM, respectively. The two data sets have contrasting dimensions: the ORTHOMAM data set contains a large number of sites but few sequences, while the PFAM families contain a large number of sequences but typically very few sites.
The stopping condition for the algorithm was to keep a minimum of 80 % of the original sequences. We showed that all species were excluded in at least one family (Fig. 4 ). The frequency of removal reflects the quality of genome annotation: there is a significant negative correlation between the frequency of removal of species and the number of articles in Pubmed containing the species name in their title (Kendall's tau = -0. 264, p-value = 0. 0185). Model organisms that are studied more and therefore benefit from manual annotation of genes were less frequently removed than species for which gene prediction relies mostly on de novo and homology-based predictions. The trade-off curves obtained from the PFAM data set exhibited a different shape, with the site gain linearly increasing as more sequences were removed (Fig.
5 ). As for the ORTHOMAM benchmark, the overlap distance clustering led to a slightly better performance than the fasttree method, while no difference was observed on average between clustering methods (Additional file 3: Figure  S2 ). As opposed to the ORTHOMAM benchmark, the PFAM data set demonstrated the effect of the choice of a relevant guide tree for data sets with greater sequence divergences: the performance of the algorithm dropped to nearly zero site gain when a random tree was used as a guide (Fig. 5 ). As the PFAM alignments were much larger and had a higher underlying sequence diversity, we assessed the effect of the minimum coverage set for defining a site as included in the analysis. We used the output of fasttree to generate a guide tree. The corresponding trade-off curves showed that the relative site gain increased with the minimum coverage, but this effect was largely due to the number of sites initially fitting the given criterion being very low (Fig.
Conversely, rate estimation methods are more robust in the presence of unresolved characters, and a more permissive threshold can be used. It is noteworthy that another criterion for removing sequences from an alignment while maintaining most of its biological signal is to remove identical or highly similar sequences, for instance using the CD-HIT software  [9]. Such similarity-based approaches are complementary to the missing data reduction method described here because they exploit distinct properties of the data set. Because sequence similarity measures are affected by the occurrence of missing data and because our algorithm is independent of the similarity of input sequences, we recommend using our method prior to similarity reduction filters such as CD-HIT. Because similar sequences tend to have missing data at similar positions (gaps in particular), the sequential removal of sequences tend to remove more distantly related sequences and reduce the global diversity of the input alignment.
To illustrate this aspect, we computed the Shannon entropy for each site and computed the average over all sites as an estimate of the sequence diversity in an alignment. Using the PFAM data set as an example, we plotted this measure of diversity along the trade-off curve (Fig. 7 ). We report a decrease of diversity as more sequences are removed, and this decrease is faster for large sequence removal (more than 60 % sequences removed). Conversely, the random removal of sequences keeps diversity constant but does not improve coverage. The relative amount of sequence diversity in the optimized data set can be used as a stopping condition for the optimization algorithm.
Aneurysm recurrence was modeled and receiver operating characteristic (ROC) curves were constructed both for each of the 4 raters individually and by using a consensus rating for each scale. Statistical analyses for ICC were performed in SAS (Version 9. 3; SAS, Cary, North Carolina). All other analyses were performed in R (Version 3. 1. 1; http://www. r-project. org/). Penalized logistic regression was performed by using the logistf package (version 1. 21; https://cran. r-project. org/web/packages/logistf/ logistf. pdf). ROC, area under the curve, and confidence interval estimates were obtained by using the pROC package (Version 1. 7. 3; http://www. rdocumentation. org/packages/pROC/functions/ pROC-package).
Patient Cohort This study was conducted at the Seattle Children's Research Institute (SCRI) and the University of Florence Meyer's Children's Hospital. Patients at both centers were enrolled in the developmental brain disorders research program. Patients included in this study were children less than 18 years of age with polymicrogyria identified by brain imaging, with or without brain overgrowth (or megalencephaly). Patients with inadequate imaging and/or clinical data were excluded from this study. Informed written consent was obtained from all of the patients' legal guardians to share clinical, neuroimaging and electroencephalographic (EEG) data, as well as provide key research samples including blood, saliva, and skin, when available. Clinical and neuroimaging studies were reviewed by the investigators.
7 These 38 patients had BPP in association with either a normal head size (N = 6) or large head size (N = 32). An additional patient with features of the megalencephalypolymicrogyria-polydactyly-hydrocephalus syndrome (MPPH) underwent clinical trio-based WES independently. WES analysis-Library preparation, exome enrichment and WES were performed at the French National Centre for Genotyping (CNG, Evry, France). Libraries were prepared from 3 μg genomic DNA extracted from whole blood using an optimized SureSelect Human Exome kit (Agilent). Captured, purified and clonally amplified libraries targeting the exome were then sequenced on a HiSeq 2000 (Illumina). Sequence reads were aligned to the human genome (hg19 assembly) using BWA software. Downstream processing was carried out with the Genome analysis toolkit (GATK), SAMtools and Picard Tools.