dataesr/scientific-paragraphs-categorization huggingface dataset:
Estimates of diversity within populations The ML frequency distributions of NAT2 haplotypes in the six Sahelian population samples and published frequency distributions from other African samples were used to estimate molecular diversity indices (gene and nucleotide diversity), and to test for possible deviation from Hardy Weinberg equilibrium and selective neutrality (the latter through the use of three tests, Ewens-Watterson homozygosity test, Tajima's D test and Fu's F s test) with the Arlequin software. For all tests that revealed at least one significant departure from the null hypothesis in one population, we used R  [100]  to apply a correction method to control for type I error rate (either Bonferroni, or the less conservative Benjamini and Hochberg FDR when the number of tests exceeded ten), so as to obtain adjusted P-values.
Estimates of differentiation between populations The Arlequin software was also used to estimate population pairwise Φ ST values (using the observed number of pairwise differences between haplotypes as a measure of molecular distance, with a 4:1 transition:transversion ratio), test their statistical significance (10,000 permutations) and compute Reynold's genetic distances, as well as to perform AMOVA analyses to test the significance of population groups' structures (here with100,000 permutations) according to geographic location, linguistic affiliation, subsistence mode or climatic zone and biome (i. e. , ecoregion, following  [99] ). Under this last classification criterion, all populations living in the Sahel or in areas defined by a tropical with dry seasons climate were grouped into the "dry savanna" biome, whereas those living to the south and west of it, namely in the tropical humid and equatorial zones, were grouped into the "humid" or "desert" biome (Table  1 ).
Acknowledgements The project was supported by the  Sciex-NMS. chprogramme of the Rector's Conference of the Swiss Universities  (Project  12. 377  to ESP) and by the  Grant Agency of the Czech Republic  (grant  13-37998S-P505  to VC). We wish to express our gratitude to all donors for providing the samples for this study, to  Pavel Podgorný  for invaluable programing support in data analyses, and to  Sotiria Boukouvala  for her dedicated assistance in data submission to the official NAT2 nomenclature committee. We would also like to thank two anonymous reviewers for their valuable comments and suggestions that helped to improve an earlier version of this manuscript.
Some teams derived their candidate models from a parent model  (Finlay et al. 2015; Gillet et al. 2015; Hamilton et al. 2015; Sabaka et al. 2015). The parent model parameterizations are more complex in space and in time than IGRF models. They are primarily derived for scientific purposes and include co-estimation of various internal and external source fields. The candidate models to IGRF were subsequently estimated from the parent models by selecting the internal field contribution at the epoch and to a spatial resolution requested by the call for IGRF-12. Other teams focused their effort on deriving directly a model closely meeting the IGRF specifications  (Alken et al. 2015 , Lesur et al. 2015; Saturnino et al. 2015; Vigneron et al. 2015). They relied on data selected within time windows centered on the epochs of interest.
This sometimes involved drastic data selection and preprocessing to separate empirically the various source fields. In general, all models relied on statistical weighting schemes to down-weight measurements poorly fit by the model and were directly expanded in spherical harmonics, with the exception of the candidate models derived by the IZMIRAN team (Petrov et al. unpublished) which relied on a principal component analysis of the data that was then converted into spherical harmonics. IGRF-12 also contains a predictive estimate for the secular variation that covers the epochs 2015-2020. Teams submitted prediction candidates derived using both "physical" and "mathematical" approaches. The "mathematical" models were built by teams relying on analytical extrapolation and who assumed that the magnetic field will evolve linearly over the next 5 years (e.
Future retrospective analyses and improvements of these models might be achieved by data reprocessing, of course. For instance,  Stockman et al. (2015)  propose a new calibration of the POGO satellite measurements for the late 1960s. However, the systematic revision of the IGRF models for epochs earlier than 2010. 0 will be more likely conceivable when our understanding about the physics of Earth's magnetic field is sufficient to include more realistic prior information in geomagnetic field models. Each candidate model to the IGRF model was evaluated in order to inform the construction of the IGRF-12 model. The variety of techniques applied amongst the candidate models often complicated the work of the evaluators but, after some debate, all submitted models were included in the derivation of the final IGRF-12 via a robust weighting scheme applied in space.
The grids were incubated for 10 seconds on ammonium molybdate 1%, dried on blotting paper and then observed with a Morgani 268D transmission electron microscope (Philips/FEI, Hillsboro, OR, USA) at an operating voltage of 60 kV. MALDI-TOF protein analysis was carried out as previously described  [12, 19]  using a Microflex spectrometer (Bruker Daltonics, Leipzig, Germany). Twelve individual colonies were deposited on a MTP 96 MALDI-TOF target plate (Bruker). The 12 spectra were imported into the MALDI BioTyper software (version 2. 0, Bruker) and analysed by standard pattern matching (with default parameter settings) against the main spectra of 6. 252 bacteria, including 199 spectra from 104 validly named Bacillus species used as reference data in the BioTyper database. A score enabled the presumptive identification and discrimination of the tested species from those in a database: a score of >2 with a validated species enabled the identification at the species level, and a score of <1.
7 did not enable any identification. No significant score was obtained for strain SIT3 T , thus suggesting that our isolate was not a member of any known species. The reference spectrum for strain SIT3 T (Fig. 4 ) was incremented in our database and then compared to other known species of the Bacillus genus. The differences are shown in Fig. 5. Growth conditions and genomic DNA preparation B. niameyensis strain SIT3 T (= CSUR P1266 = DSM 29725) was grown on 5% sheep's blood-enriched Columbia agar (bio-Mérieux) at 37°C in aerobic atmosphere. Bacteria grown on three petri dishes were collected and resuspended in 4 × 100 μL of Tris-EDTA (TE) buffer. Then 200 μL of this suspension was diluted in 1 mL TE buffer for lysis treatment that included a 30-minute incubation with 2. 5 μg/μL lysozyme at 37°C, followed by an overnight incubation with 20 μg/μL proteinase K at 37°C.
Genome annotation and genome analysis Open reading frames (ORFs) were predicted using Prodigal with default parameters, but the predicted ORFs were excluded if they were spanning a sequencing gap region (containing N). The predicted bacterial protein sequences were searched against the Clusters of Orthologous Groups (COGs) database using BLASTP (E value 1e -03 , coverage 0. 7 and identity percentage 30%). If no hit was found, it was searched against the NR database using BLASTP with an E value of 1e -03 , coverage 0. 7 and identity percentage 30%. If the sequence length was smaller than 80 aa, we used an E value of 1e -05. The tRNAS-canSE tool  [20]  was used to find tRNA genes, whereas ribosomal RNAs were found by using RNAmmer  [21]. Lipoprotein signal peptides and the number of transmembrane helices were predicted using Phobius  [22].
ORFans were identified if all the BLASTP analyses performed did not provide positive results (E value smaller than 1e -03 for ORFs with sequence size larger than 80 aa or E value smaller than 1e -05 for ORFs with sequence length smaller than 80 aa). Such parameter thresholds have already been used in previous studies to define ORFans. Genomes were automatically retrieved from the 16S RNA tree using Xegen software (Phylopattern)  [23]. For each selected genome, complete genome sequence, proteome genome sequence and Orfeome genome sequence were retrieved via File Transfer Protocol (FTP) from the National Center for Biotechnology Information. All proteomes were analysed with proteinOrtho  [24]. Then for each couple of genomes, a similarity score was computed. This score is the mean value of nucleotide similarity between all couple of orthologues between the two genomes studied (average genomic identity of orthologous gene sequences, AGIOS)  [25].
The G + C content of the genome is 37. 40%. The 16S rRNA gene sequence and whole-genome shotgun sequence of B. niameyensis strain SIT3 T are deposited in GenBank under accession numbers LK955389 and CTDY01000000, respectively. The type strain SIT3 T (= CSUR P1266 = DSM 29725) was isolated from the stool of a child living in Niamey, Niger, with kwashiorkor.
76 Amino acid transport and metabolism F 86 2. 08 Nucleotide transport and metabolism H 97 2. 35 Coenzyme transport and metabolism I 85 2. 06 Lipid transport and metabolism P 218 5. 28 Inorganic ion transport and metabolism Q 73 1. 77 Secondary metabolites biosynthesis, transport and catabolism R 520 12. 59 General function prediction only S 326 7. 89 Function unknown - 374 8. 91 Not in COGs COGs, Clusters of Orthologous Groups database. a Total is based on either size of genome (bp) or total number of protein coding genes in annotated genome.
The project aims at enhancing the wood supply chain within the Alpine Space (Alps core area and surrounding foothills/lowlands) to improve forest timber evaluation and mobilization using new remote sensing technologies such as airborne laser scanning (ALS), also referred to as Lidar. When processing ALS remote sensing data for forest applications, area-based approaches  [8] [9] [10]  as well as single-tree based methods  [11] [12] [13]  can be found in the literature. Area-based methods provide statistically calibrated maps of forest stand parameters such as growing stock, stem density, and stand height, which are useful for large-area forest inventory and long term management planning. They can also be sufficient for harvesting planning in the case of simple forests such as plantations. Meanwhile, in complex alpine forests single tree information is highly valuable.
To gain deeper knowledge about the performance of different single tree detection methods, an international benchmark was carried out from 2005 to 2008 by the European Spatial Data Research Organization (EuroSDR), the International Society for Photogrammetry and Remote Sensing (ISPRS), and the Finnish Geodetic Institute (FGI). The study was published by Kaartinen et al. [24, 25]. The benchmark was carried out using homogeneous ALS data and FI data from two study areas in southern Finland. A different benchmark based on ALS data from different types of forest was carried out in 2012 by Vauhkonen et al. [26]  to test six different algorithms under different forest conditions. The investigated study areas are located in Norway, Sweden, Germany, and Brazil. Both studies had a great influence on understanding the performance of single tree detection based on ALS data.
While the benchmark of Kaartinen et al. [24]  focused on the performance of different methods using quite homogeneous dataset from one region, the benchmark of Vauhkonen et al. [26]  focused on the effect of different study areas and forest types on the detection results of different algorithms. Vauhkonen et al. [26]  used a very heterogeneous dataset which spans from a plantation of monospecies forest in Brazil to natural mixed forests in Europe. For the Alps, the previously published benchmark results are only partly applicable as forests in Central Europe are different from forests in Northern Europe or Brazil. For this reason, the present study focuses on testing ALS-based single tree detection methods established in the Alpine Space. Based on a unique dataset covering different study areas, forest types, and structures from different regions in the Alpine Space, different methods are tested and analyzed in a clear and reproducible way.
The focus is on the performance of the methods. Investigations on the effect of the heterogeneity of the ALS data (i. e. , different point density) on the detection results are not in the scope of this study. To the authors' best knowledge, this is the first benchmark ever performed for different forests within the Alps. This study is based on the single tree detection benchmark carried out within the project NEWFOR  [7]. Detailed results are published in the project's final report  [27]. The presented study summarizes the findings of the report and provides a discussion of the results. The dataset used in this study is presented in Section 2. Section 3 provides a brief description of the tested methods as well as a detailed description of the matching procedure and evaluation of the results.
Data and Materials In total eight study areas in five alpine countries were investigated, representing different types of forest (Figure  1 ). For each study area, ALS data as well as detailed FI data were available. The ALS data and FI data used in the presented study were made public. The data are published on the NEWFOR website  [28]. The dataset was somehow representative of the currently available operational data within the Alpine Space. This means that the data are heterogeneous as they originate from different sources who acquired the data for different purposes. For example, the ALS data in Austria were collected with a focus on a nationwide terrain modeling campaign while the data in Slovenia were acquired with a focus on forestry applications. Detailed information about forest parameters is given in Table  1.
FI Data For all study areas, fully calipered plots are available. Parameters such as, for example, stem locations, diameter at breast height (DBH), and tree heights as well as information about species composition and number of layers are provided for all plots (Table  2 ). Since the dataset originates from different institutions and inventory layouts, the size and shape of the inventory plots as well as the acquisition dates are different. Statistical measures are presented in Table  2 , while the inventory dates of the FI data are presented in Table  1.
Methods of Participants In total eight methods were applied to the benchmark dataset (Table  3 ). The tested methods were chosen as they are common in the Alpine Space and originate from different countries. Most methods rely on local maxima (LM) detection in a rasterized CHM, but also one point cloud-driven method was applied. The method  [30, 31]   The remaining maxima are the final tree top candidates. Corresponding coordinates are the pixel centers and heights, which are extracted from the CHM. Algorithm parameters (raster resolution, Gaussian kernel, and LM filtering size) were chosen in the framework of a previous study  [31] , by using an automatic training process designed to minimize the trade-off between omission and commission errors.
Method #3 (LM + Multi CHM) The method is based on iterative CHM generation and LM detection within a moving window of 3 × 3 pixels for various CHMs. The method is fully automated and processes the data in two general steps, which are (A) sequential identification of potential trees and (B) filtering of the extracted potential trees. Step (A): The ALS point cloud is normalized to local heights by removing the terrain trend using a DTM. From the normalized point cloud, an initial CHM is created by assigning the 95th height percentile within each raster cell. Based on this CHM, LM are detected and the found positions and heights are stored in a database. For the next iteration, points in the uppermost layer of the normalized ALS data are eliminated. The "eliminating" layer is defined as a band of 0.
5 m below the current CHM. Based on the filtered data, a new CHM is created, LM are extracted, and the LM parameters are added to the database. This procedure is carried out sequentially until all points are removed from the normalized point cloud. Step (B): All detected LM in the database are sorted by decreasing heights. The highest LM is considered a detected tree. For each following LM, the LM is considered a detected tree if there is no detected tree within a 2D distance of 2 m as well as a 3D distance of 5 m.
Method #4 (LM + Watershed) The presented method  [34]  is based on the method published in Koch et al. [35]. In a first step, a rasterized CHM with a spatial resolution of 0. 5 × 0. 5 m 2 is generated from the ALS data. The next processing step consists of a CHM surface smoothing using a Gaussian kernel filter. Focal statistics with a LM detection algorithm are used to extract potential tree tops from the smoothed CHM. The identified trees are then analyzed through a conditional script that considers a minimum distance and height difference from the nearest trees in order to identify and delete potential false positives. The coordinates of the found tree tops are then used as seed points in a watershed algorithm run on the inverse CHM in order to delineate single tree crowns and to generate polygon features with associated information on the canopy area.
Method #5 (Segmentation + Clustering) In the presented method  [37] , the delineation is done by segmentation of a correlation surface model followed by ellipsoidal tree model clustering of the ALS data in 3D. The aim of the segmentation is to establish one segment for each tree in the topmost canopy layer. The segmentation method is based on geometric tree crown models and raster maps with 0. 25 m cells. For each raster cell, an ellipsoid surface is calculated from different generalized ellipsoids. Correlation coefficients are calculated between the height of the ellipsoid surfaces and the height of the ALS data within the horizontal model radius. The correlation surface (CS) is defined as the highest correlation coefficient for each raster cell. The CS is smoothed and delineated with watershed segmentation.
Method #6 (LM 3 × 3) The method is published in Eysn et al. [38]. First, a DSM is processed based on the ALS point cloud using a land-cover-dependent derivation approach  [39]. This approach makes use of the strengths of different algorithms for generating the final DSM by using surface roughness information to combine two DSMs, which are calculated based on (i) the highest echo within a raster cell, and (ii) moving planes interpolation. Second, a CHM with a spatial resolution of 1 × 1 m 2 is derived by subtracting the DTM from the DSM. The base products are derived consistently for all study areas using the OPALS  [40]  software. Finally, the positions and heights of single trees are determined from the CHM using an LM filter based on a moving window (MW) approach. If the center pixel of the MW is an LM, a potential tree is detected.
[24]. Additionally, the ΔD2D criterion considers positional errors from the FI survey as well as inaccuracies originating from the tree detection. The height accuracy of trees measured in a terrestrial forest inventory is believed to be decreasing with increasing tree height. Therefore, when comparing a terrestrially measured tree height to an automatically detected one, this effect should be considered. This is accounted for by increasing the ΔH value with increasing tree heights (Table  4 ). The thresholds presented in Table  4  were empirically found by testing different settings on a subset of the dataset while visually interpreting the quality of the matching results. The resulting values are applied to all datasets within this benchmark.
e. , tilted trees, complex crowns); and (C) errors originating from the detection algorithm. To minimize error source A, forest inventory data is ideally acquired by using a survey grade GNSS system or a total station. A positional check by manually co-registering the inventory data to the remote sensing data in a post-processing step is necessary in most cases. Automated co-registration methods  [44] [45] [46]  can help to support the co-registration process. The reported performance of automated methods varies from 68% up to 92% and depends mainly on the input data and the variability of the forest stands  [45]. In the presented study, error A was minimized by using manually co-registered datasets, with an estimated planar accuracy of ±2. 0 m. and ±1. 0 m for the vertical accuracy. The errors B and C are handled within the matching procedure by allowing flexible matching connections with a search radius depending on tree heights.
The used FI data are heterogeneous due to different acquisition guidelines and methods. Therefore the data can be considered as imperfect compared to a local standardized FI. However, imperfect data should not have disadvantaged one of the detection methods as all participants faced the same conditions. The presented matching procedure enables interpreter-independent and reproducible results in a short amount of time. The automatic matching took a few minutes while the manual interpretation within the validation process of the subset took several hours. The Overall Accuracy of 97% indicates that the matching procedure worked sufficiently for the presented dataset.
Regarding the detection of small trees, it can be assumed that small trees in subdominant layers theoretically get mapped more efficiently at a higher ALS point density. This can be linked to a potentially higher canopy penetration rate. Kaartinen and Hyyppä  [25]  and Reitberger et al. [49]  conclude that the laser point density has less impact on the individual tree detection. In contrast Wallace, Lucieer, and Watson  [43]  conclude that high point densities are more significant for single tree detection than the method used. Wallace et al. studied a very young, planted stand of Eucalyptus trees using high-resolution UAV Lidar data, which is not comparable to the dataset of the Alpine Space. Therefore, it is assumed that the different point densities given in the presented study should not significantly influence the detection results.
Matching Results per Method The best ratio between a high matching rate and a low commission rate was found for Method #1, which consists of a local maximum search in a canopy height model using a moving window approach. The fact that the algorithm parameters were automatically optimized for this purpose on an independent dataset seems to be an advantage compared to methods where parameters are set according to the user appreciation. In the lower height layers up to 10 m tree height, only up to 3% of the extracted trees could be correctly matched. Since the method relies on a rasterized canopy height model and filtering of trees smaller than 7. 5 m, this rather low value was expected. For the spatial accuracy, the obtained values for the location are comparable to the results of the other tested methods.
In order to improve the detection algorithm available for forest practitioners, it seems important to A) have datasets that allow us to test the robustness of algorithms on a wide range of forest structures, and B) design algorithms able to optimize their setting, either based on internal (Lidar itself) or external (tree allometry) data. For the comparison of results, an automated matching procedure like that presented in this paper is of high relevance. Moreover, the choice of a trade-off criterion between the omission and commission errors would make comparisons easier, but it has to be application-oriented.