dataesr/scientific-paragraphs-categorization huggingface dataset:
These additional localities have coordinates with very low accuracy and the corresponding environmental variables extracted from a 1 × 1 km resolution grid (30 arc-seconds; see below) therefore have high degrees of uncertainty. When examined, the majority of values fell within the total range for the respective environmental variable (the two exceptions are mentioned in the Results section), we consequently decided to keep them for our analysis. We reduced the final locality dataset to one record per km 2 (the resolution of the environmental input data, see below) to avoid pseudoreplication using the R package spThin  79. We then tested whether the remaining presence points are randomly dispersed or clustered using the Nearest Neighbor Index NNI in the R packages sp 80 and spatialEco  81. NNI was 1.
4 for A. guineensis and 1. 2 for A. boueti, indicating that the filtered point dataset consists of randomly distributed points which are not spatially autocorrelated. As environmental parameters we downloaded spatial layers of the Terrestrial Ecoregions of the World (TEOW)  82  , global bioclimate and elevation layers  39  , data on potential evapotranspiration  83  and global aridity index  84  with a spatial resolution of 30 arc-seconds (~ 1 km 2 near the equator). Although the climate datasets are interpolated from data from weather stations much farther apart from one another and are consequently not measurements of actual environmental conditions, they were rigorously cross-validated with observed data (including satellite data) during the development of the dataset and generally showed high correspondence with observations (especially temperature variables)  39.
Since for many applications such as our species distribution models data at high spatial resolution (i. e. , 1 × 1 km) are preferable over lower resolution to capture variation for example across steep climate gradients in mountains  39  we chose the 30 arc-seconds dataset over the 5 arc-minutes dataset (~ 9 × 9 km near the equator) for the ecoregions and climate datasets. In addition, we downloaded spatial layers with forest and grassland/scrub/woodland cover from the harmonized soil database (only available in 5 arc-minutes spatial resolution  85  ). We downscaled the forest and grassland/scrub/woodland dataset to 30 arcseconds resolution despite the resulting slight inaccuracy, which we kept in mind during interpretation but did not consider relevant for the vegetation datasets. In order to compare the prevailing climate within the distribution range of A.
erythrurus and changed the respective records in our database accordingly. For species that did not have records published on GBIF we added further locality data from the literature (Supplementary Table  S6  online). The final database contained 4286 records from all 44 currently recognized species of Acanthodactylus. We acknowledge that our database is by no means complete, however, we wanted to follow the most conservative approach and use only records that are supported by a voucher specimen and can be traced back using published databases. We trust that our record list covers a nearly complete representation of the ecological conditions inhabited by all currently accepted Acanthodactylus species. Using the bioclim dataset  39  we plotted annual mean temperature (bio1) and annual precipitation (bio12) for all locality records in our Acanthodactylus species database and compared the respective data for A.
guineensis and A. boueti with its congeners. We developed species distribution models using Maxent 3. 4. 1  46  for A. guineensis and A. boueti. Maxent applies the maximum entropy principle  86  for model fitting under the basic premise that the estimated species distribution deviates from a uniform distribution as minimally as required to explain the observations  46. We used the cleaned datasets of A. guineensis and A. boueti with only one record per km 2 as input presence locality data. We extracted environmental data for all presence sites from the 19 bioclim variables (bio1-19), elevation (alt), aridity index (AI), percentage cover of forest (forest) and grassland, scrub and woodland (grass) per grid cell, as well as four parameters comprising potential evapotranspiration (PET): PET of the wettest, driest, warmest and coldest quarter of the year (PETwet, PETdry, PETwarm, PETcold).
To avoid issues resulting from correlated parameters we reduced the number of environmental predictors by performing multicollinearity analyses using the Variance Inflation Factor (VIF) with a threshold < 10 for non-collinearity  87  in the R package usdm  88. The VIF, one of the most commonly used factors in collinearity analyses, is the result from regressing the predictor variable against all other predictor variables. The VIF measures how strongly each predictor can be explained by the rest of predictors  89. After removing all variables with collinearity problems our final environmental input variable dataset comprised bio3, bio11, bio13, bio14, bio18, bio19, PETcold, PETdry, PETwet, forest, grass. We ran the Maxent algorithm with the following settings: we chose 10,000 random background points from a rectangular area extending an additional 500 km in every compass direction from the outermost presence sites of A.
guineensis for both species  90  , allowed only linear and quadratic features and chose the output format cloglog. For A. guineensis we had 38 presence sites, of which we used 70% for model training and 30% for testing with 25 model replicates of type "subsampling" and a regularization multiplier of 2. 5  91. For A. boueti, due to low number of presence sites (N = 11), we did not use a training dataset but ran the model with eleven replicates and bootstrapping (following Morales et al. 91  ) and a regularization multiplier of 1. The predictive model performance was evaluated using the area under the receiver operating curve (AUC) which measures the ability of predictions to discriminate between presences and absences, irrespective of the absolute value of the predictions  92. Finally, we plotted the median of all models to predict the potential range of each species and determined the most important environmental predictors.
Acknowledgements We are grateful to the following museum curators and collection managers for loans of specimens, tissue, or providing crucial information:  Patrick Campbell  (NHM),  Andreas Schmitz  (MHNG),  Nicolas Vidal  and  Annemarie Ohler  (both MNHN),  Wolfgang Böhme ,  Ursula Bott ,  Dennis Rödder ,  Morris Flecks  (all ZFMK),  Sharon Birks ,  Peter Miller  (both UWBM),  Alexander Kupfer  (SMNS),  Frank Tillack  (MfN). We would like to thank the  Center for Genomics and Systems Biology at New York University Abu Dhabi , and specifically  Marc Arnoux  and  Nizar Drou , for their help and use of their facilities. The work of M. V. and J. M. was supported by the  Deutsche Forschungsgemeinschaft  (project  VE 247/11-1/MU 1760/9-1 ). M. L. L. thanks  São Paulo Research Foundation  for fellowship (FAPESP # 2017/26162-8 ).
Additional financial contribution to the project was provided by the  MfN Taxonomy Fund. The molecular analyses were in part funded by  NYUAD  Grant  AD180  to SB. The  NYUAD Sequencing Core  is supported by  NYUAD Research Institute  grant  G1205A  to the  NYUAD Center for Genomics and Systems Biology. Parts of the analyses have been conducted using the High Performance Computing resources at  New York University Abu Dhabi. Thanks a lot to  Karin Tamar  and  Salvador Carranza  for their input during the preparation of the study. Theodore Papenfuss provided important literature, thank you very much. Furthermore, we would like to thank  Adam Leaché ,  Laurent Chirio ,  Johannes Penner ,  Lotanna Micah Nneji , and  Gilbert Adum  for information and helpful comments.
Here, we used VSG-seq to analyze VSGs expressed in the blood of 12 patients infected with T. b. gambiense. The number of VSGs identified per patient ranged from one to 14; notably, two VSGs were shared by more than one patient. Analysis of expressed VSG N-terminal domain types revealed that 82% of expressed VSGs encoded a type B N-terminus, a bias not seen in datasets from other T. brucei subspecies. C-terminal types in T. b. gambiense infection were also restricted. These results demonstrate a bias either in the underlying VSG repertoire of T. b. gambiense or in this subspecies' expression of VSGs during infection. This work demonstrates the feasibility of using VSG-seq to study antigenic variation in human infections and highlights the importance of understanding VSG repertoires in the field.
Individuals in whom trypanosomes were observed underwent lumbar puncture. The cerebrospinal fluid was examined for white blood cell count and the presence of trypanosomes to determine the disease stage and select the appropriate treatment. Patients were questioned about their place of residence. The geographic coordinates of their corresponding villages were obtained from the Atlas of HAT  [39]  and plotted on a map of the DR Congo using ArcGIS® software by Esri. Distances were determined and a distance matrix generated (see Supplemental Table  2 ).
VSG-seq analysis of T. b. gambiense and T. b. rhodesiense sequencing libraries To analyze both T. b. gambiense (VSG-seq preparations) and T. b. rhodesiense (traditional mRNA sequencing library preparations; sequences were obtained from ENA, accession numbers PRJEB27207 and PRJEB18523), we processed raw reads using the VSG-seq pipeline available at https://github. com/mugnierlab/VSGSeqPipeline. Briefly, VSG transcripts were assembled de novo from quality-and adapter-trimmed reads for each sample (patient or patient replicate) from raw reads using Trinity (version 5. 26. 2)  [43]. Contigs containing open reading frames (ORFs) were identified as previously described  [30]. ORF-containing contigs were compared to Lister 427 and EATRO1125 VSGs as well as a collection of known contaminating non-VSG sequences.
VSG reference databases for each patient are available at https://github. com/mugnierlab/Tbgambiense2021/. For T. b. gambiense, we then aligned reads from each patient replicate to that patient's consolidated reference genome using Bowtie with the parameters -v 2 -m 1 -S (version 1. 2. 3)  [45]. For T. b. rhodesiense, we aligned each patient's data to its own VSG ORF assembly. RPKM values for each VSG in each sample were generated using MULTo (version 1. 0)  [46] , and the percentage of parasites in each population expressing a VSG was calculated as described previously  [30]. For T. b. gambiense samples, we included only VSGs with an expression measurement above 1% in two or more patient replicates in our analysis. For T. b. rhodesiense samples, we included only VSGs with expression >0. 01%.
To compare VSG expression between patients, despite the different reference genomes used for each patient, we used CD-HIT to cluster VSG sequences with greater than 98% similarity among patients, using the same parameters used to consolidate reference VSG databases before alignment. We gave each unique VSG cluster a numerical ID (e. g. , Gambiense #) and chose the longest sequence within each group to represent the cluster. Before analysis, we manually removed clusters representing TgsGP and SRA from the expressed VSG sets. UpSet plots were made with the UpSetR package  [47]. The R code used to analyze resulting data and generate figures is available at https://github. com/mugnierlab/Tbgambiense2021/.
Genomic VSG sequences The VSG repertoires of T. b. brucei Lister 427 ("Lister427_2018" assembly), T. b. brucei TREU927/4 and T. b. gambiense DAL972 were taken from TriTrypDB (v50), while the T. b. brucei EATRO 1125 VSGnome was used for analysis of the EATRO1125 VSG repertoire (vsgs_tb1125_nodups_atleast250aas_pro. txt, available at https://tryps. rockefeller. edu/Sequences. html or GenBank accession KX698609. 1 -KX701858. 1). VSG sequences from other strains (except those generated by VSG-seq) were taken from the analysis in Cross, et al. [15]. Likely VSG N-termini were identified as predicted proteins with significant similarity (e-value ≤ 10 -5 ) to hidden Markov models (HMMs) of aligned type A and B VSG N-termini taken from  [15].
N-terminal domain phylogenies Phylogenies of VSG N-termini based on unaligned sequence similarities were constructed using the method described in  [48]  and used previously to classify VSG sequence  [15]. We extracted predicted N-terminal domain protein sequences by using the largest bounding envelope coordinates of a match to either type A or type B HMM. A matrix of similarities between all sequences was constructed from normalized transformed BLASTp scores as in Wickstead, et al. [48]  and used to infer a neighborjoining tree using QuickTree v1. 1  [49]. Trees were annotated and visualized in R with the package APE v5. 2  [50].
HMM For N-terminal typing by HMM, we used a python analysis pipeline available at (https://github. com/mugnierlab/find_VSG_Ndomains). The pipeline first identifies the boundaries of the VSG N-terminal domain using the type A and type B HMM profiles generated by Cross et al. which includes 735 previously-typed VSG N-terminal domain sequences  [15]. N-terminal domains are defined by the largest envelope domain coordinate that meets e-value threshold (1x10 -5 , --domE 0. 00001). In cases where no N-terminal domain is identified using these profiles, the pipeline executes a "rescue" domain search in which the VSG is searched against a 'pan-VSG' N-terminus profile we generated using 763 previously-typed VSG N-terminal domain sequences. This set of VSGs includes several T. brucei strains and/or subspecies: Tb427 (559), TREU927 (138), T.
Alignment output files in STOCKHOLM format were used to generate distinct HMM profiles for type A1, A2, A3, B1, and B2 VSGs using the pre-determined subtype classifications of the 763 VSGs using HMMer version 3. 1b2  [52]. The number of sequences used to create each subtype profile ranged from 75 to 211. The most probable subtype is determined by the pipeline based on the highest scoring sequence alignment against the subtype HMM profile database when HMMscan is run under default alignment parameters. The pipeline generates a FASTA file containing the amino acid sequence of each VSG N-terminus and a CSV with descriptions of the N-terminal domain including its type and subtype. Network graph N-terminal network graphs were made using VSG N-terminal domains from the TriTrypDB Lister427_2018 and T.
b. gambiense DAL972 (v50) VSG sets described above, and the T. b. gambiense and T. b. rhodesiense patient VSG N-termini which met our expression thresholds. Identified N-terminal domains were then subjected to an allversus-all BLASTp. A pairwise table was created that includes each query-subject pair, the corresponding alignment E-value, and N-terminal domain type of the query sequence if previously typed in Cross, et al. [15]. Pseudogenes and fragments were excluded from the Lister427_2018 reference prior to plotting by filtering for VSG genes annotated as pseudogenes and any less than 400 amino acids in length, as the remaining sequences are most likely to be full length VSG. Network graphs were generated with the igraph R package  [53]  using undirected and unweighted clustering of nodes after applying link cutoffs based on E-value < 10 -2.
Comparison of gHAT patient VSGs to the DAL972 reference The nucleotide sequences of expressed T. b. gambiense patient VSGs were compared to the complete T. b. gambiense DAL972 reference genome sequence (v54) from TriTrypDB using BLASTn. To find evidence of VSG diversification by mosaicism or segmental gene conversion, permissive parameters were used so short regions of homology would be reported (blastn -word_size 11 -evalue 0. 1). Both query and subject alignment coordinates were recorded in the output for downstream analysis, along with E-value and percent identity. Alignment ranges were plotted with the IRanges R 384 package  [55].
We evaluated two approaches. The first approach was to create a bioinformatic pipeline to determine each N-terminal domain subtype, using HMM profiles we created for each subtype from sets of N-terminal domains previously typed by Cross et al. [15]. The second approach was to create a BLASTp network graph based on a published method  [58]  where the N-terminal subtype of a VSG is determined by the set of VSGs it clusters with, and clusters are identified using the leading eigenvector method  [54]. We used each approach to determine the N-terminal subtype of each expressed VSG in our patient sample dataset, along with 863 VSG N-termini from the Lister 427 genome. We compared these results to either existing N-terminal classification (for Lister 427 VSGs) or classification based on position in a newly-generated BLASTp-tree  [15]  (for T.
A better understanding of such differences in T. brucei, if they exist, could lead to development of more sensitive HAT diagnostics. To better understand the VSG proteins expressed in natural infections, we developed an HMM VSG typing pipeline that revealed an intriguing bias in T. b. gambiense infection towards type B VSGs, which appears to be specific to T. b. gambiense patient samples. Small sample sizes and important differences between each T. brucei subspecies' dataset limit the conclusions that can be drawn. However, comparisons between these sets suggest that the genomic VSG repertoire determines the distribution of VSG N-terminal types expressed during T. brucei infection and may account for the bias we have observed in T. b. gambiense patients. Thus, the T. b. gambiense VSG repertoire may contain a larger proportion of type B VSGs than its more virulent counterparts.
In addition to examining N-terminal types in our T. b. gambiense dataset, we also examined expressed VSG C-termini. Previous studies defined six C-terminal types, although resolving these types in larger VSG sets has been difficult due to the extensive similarity among VSG C-termini [15, 58]. In line with previous observations, a BLASTptree analysis of assembled T. b. gambiense C-terminal domains revealed frequent sequence similarity between expressed C-terminal types but did not provide sufficient resolution to confidently assign types (Fig6A).
Acknowledgments We are very grateful to the patients without whom this work would not have been possible. We thank  George Cross  and  Danae Schulz  for comments on the manuscript, and  Mary Gebhardt  for help with GIS. The Atlas of HAT is an initiative of the  World Health Organization (WHO) , jointly implemented with the  Food and Agriculture Organization of the United Nations (FAO)  in the framework of the  Programme Against African Trypanosomiasis (PAAT). Field work and specimen collection in DR Congo were funded through the  Wellcome Trust  (study number  099310/Z/12/Z ) awarded to the  TrypanoGEN Consortium  (www. trypanogen. net),members of H3Africa (h3africa. org). Sample work-up was supported by the  Research Foundation Flanders (FWO  grant  1501413N ). Work by BW was supported by  University of Nottingham/Wellcome Trust Institutional Strategic Support Fund  award  204843/Z/16/Z.
terminal domains show a bias for type 2 domains 2 In addition to examining N-terminal types in our T. b. gambiense dataset, we also examined expressed VSG C-termini. Previous studies defined six C-terminal types, although resolving these types in larger VSG sets has been difficult due to the extensive similarity among VSG C-termini [15, 58]. In line with previous observations, a BLASTptree analysis of assembled T. b. gambiense C-terminal domains revealed frequent sequence similarity between expressed C-terminal types but did not provide sufficient resolution to confidently assign types (Fig6A).
We use a newly developed ImageJ macro to process the image data, identify IR-RF emitting grains and obtain single-grain IR-RF signal curves. For subsequent analysis, we apply the statistical programming environment R and the package Luminescence. In the second part of the article, we test SR IR-RF on two K-feldspar samples. One sample was irradiated artificially; the other sample received a natural dose. The artificially irradiated sample renders results indistinguishable from conventional IR-RF measurements with the photomultiplier tube. The natural sample seems to overestimate the expected dose by ca. 50 % on average. However, it also shows a lower dose component, resulting in ages consistent with the same sample's quartz fraction. Our experi-ments also revealed an unstable signal background due to our cameras' degenerated cooling system.
Images were analysed using the software AgesGalore  (Greilich et al. , 2006). When Matthias Krbetschek joined Freiberg Instruments GmbH, the capability of performing SR IR-RF measurements became part of the design of the commercial available lexsyg research reader  (Richter et al. , 2013). Contrary to the original design by  Krbetschek and Degering (2005) , in this system, the 90 Sr / 90 Y source is placed above the sample position. A circular opening in the middle of the source module enables luminescence detection  (Richter et al. , 2012). A sketch of this lexsyg research RF imaging module is shown in Fig. 1. With the early death of M. Krbetschek in 2012, the progression in the SR IR-RF technique's development came to a temporary halt.
Software Our software toolchain consisted of three different tools: LexStudio 2 for measurement sequence control, Im-ageJ for image processing and the R function library Luminescence for data analysis. ImageJ and the Luminescence package are open-source (GPL-3 licence) and freely available for all major platforms (Windows, Linux, macOS). However, our software toolchain was tested so far just on Windows 10 and macOS (v10-v11). Detailed installation guides and additional download links to the SR IR-RF-specific software modules can be found at https:// luminescence. de/ (last access: 28 March 2021).
Image acquisition with LexStudio 2 We used the software LexStudio 2 (version 2. 5. 0, 2019-11-01) shipped with the measurement system for sequence writing, camera parameterisation and image acquisition. For the presented work, Freiberg Instruments updated LexStudio 2 in 2018/2019 with a new module to control the camera settings relevant for luminescence imaging (Fig. 2a ). The new module also enables sequence-synchronous camera control and data handling. Thus, sequence writing does not differ from routine luminescence measurements with a PMT. The new LexStudio 2 camera module uses the 32-bit PVCAM drivers by Princeton Instruments and maintains the compatibility to the camera control software WinView shipped with the ProEM camera. Unfortunately, this enhanced version of LexStudio 2 is currently bound to 32-bit Microsoft Windows platforms.
Image processing with ImageJ For processing the image data, we used the open-source software ImageJ (version: 1. 52p)  (Schneider et al. , 2012). We developed a macro called SR-RF (file SR-RF. ijm, see the Supplement) to automatise the workflow. The SR-RF macro is a plain ASCII file and written in the JavaScript-like Im-ageJ macro language. It provides a graphical user interface (Fig. 2b ) to simplify user interactions. The output is an ASCII text file with the file-ending *. rf. The file contains the single-grain IR-RF curves, the size and spatial location of the associated regions of interests (ROIs) and further imageprocessing information. We used the enhanced ImageJ distribution Fiji (https://fiji. sc, last access: 28 March 2021) (ver-sion: 2. 0. 0-rc-69) for most of our analyses. A cross-platform version of ImageJ and the SR-RF macro and all necessary plug-ins pre-installed can be downloaded from https: //luminescence.