dataesr/scientific-paragraphs-categorization huggingface dataset:
While the total number of SNP loci identified per individual and throughout the population varied depending on the stringency of the filter, the allele frequency spectrum remained constant (data not shown). For the analysis presented in the manuscript, our SNP-calling parameters corresponded with a relatively low stringency filter, which was considered capable of detecting a very high proportion of variant sites while ablating the effects of sporadic read errors. Thus, the filter was designed to eliminate artefactual variants in the first instance, although a relatively low number of variant sites may remained undetected due to the necessity for every sample to pass QC at a given locus. The entire SNP dataset is deposited at the TritrypDB pathogen database, which is freely accessible at http://www.
Panels of SNPs for population analysis A subset of SNP loci was selected where a high-confidence genotype could be identified for every sample in the dataset (Figure  1 -source data 1). This totalled 230,891 bi-allelic markers, which were used as the basis for the population genomic analyses presented in this study. The number of SNP loci was calculated for each sub-species using two methods: (1) in comparison to the Tb927 reference genome; and (2) in comparison to other members of that sub-species (Figure  1 -source data 1). A total of 130,180 SNP loci were identified among the seventy-five Group 1 isolates in comparison to the reference T. b. brucei genome although only 11,398 of these showed polymorphism within Group 1 itself. In order to facilitate different types of analysis, a series of panels of a sub-set of SNP loci were defined (Figure  1 -source data 4).
Phylogenetic networks and trees Phylogenetic networks were constructed using the Split Decomposition method of SplitsTree4  (Huson and Bryant, 2006) : Figure  1A  shows the reconstruction using all the isolates sequenced in this study (n=85) and the full panel of SNPs; Figure  1B  shows relationships among the Group 1 isolates (n=75) using the SNP panel corresponding to derived alleles in non-LOH areas (Figure  1 source data 4). The virtually non-reticulated topology of the network presented in Figure  1B  supports our finding of strict asexuality in the Group 1 population and it is therefore appropriate to utilise a classical phylogenetic tree approach for the analysis of this sub-species. Maximum Likelihood phylogenetic trees were constructed using RAxML (Stamatakis, 2014) using a generalised timereversible model of sequence evolution.
Linkage disequilibrium and Hardy-Weinberg equilibrium The Haploview software package  (Barrett, 2009)  was used to investigate LD across each chromosome using unphased data and to calculate the normalised measure of allelic association, D'  (Daly et al. , 2001). Linkage blocks were defined using the method of  Gabriel et al. (2002)  with a block being created if 95% of informative comparisons between SNP loci were found to possess 'strong LD'. Strong LD was defined as D'=1 and LOD score 2. Haploview was also used to calculate the probability that any observed deviation from HWE could be explained by chance using a c 2 test. This was performed for SNP loci across the T. b. gambiense Group 1 genome utilising the set of 'Tbg1 ancient' SNP loci (Figure  1 -source data 4). Statistically significant loci at P<0.
gambiense Group 1 genome, again utilising the set of 'ancient' SNP loci (Figure  1 -figure supplement 2). A median figure of -1 was calculated for the entire set of T. b. gambiense Group 1 isolates and for each of the Bonon, Boffa and Dubreka sub-populations, indicating strict asexuality. This was supported by permutation testing (n iterations = 30,000), which indicated that F IS was lower than expected (Figure  1 -source data 3). Similarly, using the Bonon, Boffa and Dubreka sub-populations, Weir & Cockerham's f is was also calculated using Fstat. This had a uni-modal distribution with a median of -1 (f is = -1 at 91. 4% of loci), indicating strict asexuality.
'Loss of heterozygosity' analysis To assess the distribution of heterozygous sites across the genome, the density of these sites was calculated in 10 kb segments for every isolate. These density figures were used to determine whether each 10 kb segment could be considered a candidate area for long-range LOH. LOH blocks were defined using a custom Perl script to perform Interval Analysis using the following criteria: max number of heterozygous sites allowed per block = 0, minimum number of contiguous blocks = 6, maximum gap size in a contiguous block = 2, max number of heterozygous sites allowed within gap = 2. LOH block data was converted for viewing in the Integrative Genome Viewer (IGV)  (Thorvaldsdottir et al. , 2013). In order to determine whether genomic structural variation could explain observed LOH, we performed a systematic copy number variation (CNV) analysis across the genome using CNVnator  (Mills et al.
Since the divergence of CP_1_2_KIVI with LISA, at this locus 15 mutations arose in the former isolate and 9 mutations in the latter and this is illustrated for LISA in Figure  3. Again, because of the existence of only two haplotypes, more distant isolates could also be phased in this manner, which allowed intermediate haplotypes to be inferred and the accumulating sequence of Meselson mutations to be determined. To permit a genome-wide analysis, computational phasing of the diploid genotypic data was performed using the segmented haplotype estimation and imputation tool SHAPEIT2  (Delaneau et al. , 2012; Delaneau et al. , 2013). The default parameters were used together with an adjusted window size of 0. 5 Mb and a recombination rate of 0. 0003  (MacLeod et al. , 2005). The accuracy of the computational phasing for each isolate was assessed in comparison to a large LOH region on one isolate (DEOLA at the 650 kb Locus 1 on Chromosome 10).
To assess whether the trees were more similar than would be expected by chance, 1,000 simulations were carried out using each of: (a) a random tip-mapping method; and (b) a random tree method (beta = -1). For every chromosome, A and B haplotype trees were significantly similar to each other (Figure  4 -source data 1, P < 0. 001). In order to illustrate this similarity between A and B haplotype trees, a set of 27 isolates was selected which could be resolved with 100% bootstrap support from a phylogenetic tree constructed using the whole-genome dataset (Figure  4 -figure supplement 3 ). The three chromosomes with the largest number of SNPs among T. b. gambiense Group 1 (8, 10 and 11) were then selected, as these were the most informative. Isolates which could be resolved by the most highly supported nodes in trees representing the A and B haplotypes were subsequently selected and this corresponded to 16, 18 and 13 isolates for chromosomes 8, 10 and 11 respectively.
The number of SNP loci where derived alleles were present in the genome was counted. 388 such loci (+/-12) were identified on both homologues over 8. 47 Mb of the non-LOH portion of the genome. The second involved focusing on Chromosome 9, where a large region of LOH is found over much of the chromosome in every Group 1 isolate. Any mutations occurring since the existence of the most recent common ancestor of all the isolates analysed can easily be identified as they manifest themselves as heterozygous loci; this avoids the issue of identifying fixed heterozygous loci, which represent uninformative pre-existing mutations rather than accumulated mutations. Similar results were achieved with both methods, providing us with estimates of the time to most recent common ancestor (TMRCA) of approximately 1,000 to 1,500 years before present (Figure  3 -source data 1).
Funding Funder Grant reference number Author Wellcome Trust 095201/Z/10/Z  William Weir Paul Capewell Caroline Clucas Andrew Pountain Pieter Steketee Nicola Veitch Anneli Cooper Annette MacLeod Wellcome Trust 085349 William Weir Paul Capewell Caroline Clucas Andrew Pountain Pieter Steketee Nicola Veitch Anneli Cooper Annette MacLeod Wellcome Trust 098051 Bernardo Foth Matt Berriman The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Role of the funding source The trial was supported by an unrestricted educational grant and free supply of TMZ by Merck & Co (formerly Schering-Plough). The drug manufacturer was neither involved in the trial design nor analysis. Translational research was funded in part by the Swiss Cancer League and the Swiss Bridge Award (to MEH). The corresponding author, MEH and RS had full access to all of the data and the final responsibility to submit for publication. The study was registered at EudraCT#2004-002714-11 and ClinicalTrials. gov NCT#00182819, and was performed according to the declaration of Helsinki. The trial completed its enrollment, patients continue to be followed as per protocol.
8, 41 Only very long-term follow-up will determine the role of single modality chemotherapy in patients with chemotherapysensitive disease. Consort statement. The numbers of patients registered, eligibility and allocation per treatment arms, including reasons for exclusion per treatment arm. At the date of database lock, 51 registered patients did not meet the criteria to be eligible for randomization. Progression-free survival per treatment group. The difference between per log rank test was not significant (HR: 1. 16; 95 percent confidence interval, 0. 9 to 1. 5; P=0. 22). • WHO Performance Status: a performance status of 0 denotes asymptomatic, 1 symptomatic and fully ambulatory, and 2 symptomatic and in bed less than 50 % of the day. • RTOG NFS: a neurologic functions status of 0 denotes asymptomatic, a status of 1 minor neurologic symptoms, 2 moderate symptomatic, ambulatory with assistance, 3 moderate symptomatic; not fully ambulatory and requires assistance, 4 severely symptomatic; complete assistance at home or in institution-unable to work.
Acknowledgments Supported in part by an unrestricted educational grant and drug by  MSD-Merck&Co. Molecular analyses were supported by the  Swiss-Bridge  Award 2011 and the  Swiss Cancer League  ( KFS-2949-02-2012 , MEH, SK). The EORTC is supported by  NCI  grants  5U10 CA011488-35  through  2U10 CA011488-41. The UK participation was supported by the  National Institute for Health Research through the National Cancer Research Network , and a grant to the  MRC   Clinical Trials Unit  at  UCL  from  Cancer Research UK  (trial reference  CRUK/07/032 ). The NCIC CTG participation has been supported by the  Canadian Cancer Society Research Institute  (grant # 015469  and # 021039 ). The TROG participation has been supported by a  National Health and Medical Research Council (NHMRC)  project grant ( ID 509094 ).
). Coverslips were washed three times with PBS between incubations and all steps were performed at room temperature. Coverslips were then mounted on Fluoromount-G (SouthernBiotech) and sealed with nail polish. Analyses of LC3 distribution were performed with a Bio-Rad Radiance 2000 confocal imaging system (Bio-Rad Laboratories) installed on an Eclipse E800 microscope, using an argon/krypton laser at 488 nm with a Plan Apo Nikon 60X (NA 1. 4) oil immersion lens for LC3 fluorescence and a 638 nm diode laser at 650 nm long-pass with a Plan Apo Nikon 60x (NA 1. 4) oil immersion lens for DRAQ5 fluorescence. Images were acquired in the normal scanning mode with a Kalman filter of 3 to 6 using the LaserSharp software. Analyses for Beclin, gp91 phox and VAMP8 were performed with a Plan APOCHROMAT 63x oil-immersion DIC 1.
ROS measurement Adherent BMM were infected with L. major NIH S promastigotes for 30 min at 37°C in the presence of 5 μM CellROX Deep Red Reagent (Thermo Fisher Scientific). Subsequently, after two washes with PBS, cells were collected by scraping in PBS, fixed for 10 min in 4% PFA and washed twice with PBS. Samples were acquired in the APC channel on a LSRFortessa SORP cytometer with the BD FACSDiva6. 2 software (BD Biosciences). Fluorescence intensity values were analyzed using the FlowJo software (version 10; TreeStar).
Field Sampling and Contextual Data Seawater samples were collected from coastal surface waters at Palmer Station, on the west coast of the Antarctic Peninsula at one to 2 week intervals beginning in the austral mid-winter (July 2013) and ending in late summer (March 2014). Samples were drawn directly from a seawater intake located at a depth of 6 m, 16 m from the station. Triplicate 20-L samples were collected in acid-washed Nalgene carboys and immediately transferred to the laboratory for processing. All processing took place in a 0 • C cold room to maintain initial water temperature. Each 20-L carboy was sub-sampled for dissolved nutrients (phosphate, silicate, and nitrate), particulate organic carbon and nitrogen (POC and PON), chlorophyll a (chl a), and bacterial abundance and bacterial production measurements.
Samples were collected and processed according to Palmer LTER standard protocols 1. Briefly, nutrient samples were filtered through combusted 0. 7-µm glass fiber filters (Whatman, GE Healthcare Life Sciences, Piscataway, NJ, USA) and frozen at -80 • C until analysis on a SEAL AutoAnalyzer 3 (data available at doi:10. 6073/pasta/e893d71c5586769731875d49fde21b1d;  Ducklow, 2016). POC and PON samples were collected on combusted 0. 7-µm glass fiber filters from 1 to 3 L of seawater and were frozen at -80 • C until analysis via combustion using a Perkin Elmer 2400 Series II CHNS/O Analyzer. Chl a samples, as with POC and PON, were collected on 0. 7-µm glass fiber filters from 1 to 3 L of seawater and were assayed fluorometrically using acetone extracts (data available at doi: 10. 6073/pasta/012de0cf7d1f00951b7289037a3a4c19;  Schofield and Vernet, 2016).
Bacterial abundance samples were analyzed by flow cytometry following the protocol of  Gasol and Del Giorgio (2000) , with SYBR R Green I nucleic acid staining (Invitrogen, Carlsbad, CA, USA) on an Accuri C6 flow cytometer (BD Biosciences, San Jose, CA) (data available at doi:10. 6073/pasta/012de0cf7d1f00951b7289037a3a4c19;  Ducklow et al. , 2016). Bacterial production rates were derived from rates of 3 H-leucine incorporation  (Ducklow et al. , 2012). Samples for bacterial community composition were collected via gentle vacuum filtration (-0. 5 bar vacuum) of approximately 2-6 L of seawater through successive 3. 0-µm polycarbonate (EMD Millipore, Billerica, MA, USA) and 0. 22µm polyethersulfone (EMD Millipore, Billerica, MA, USA) 1 http://oceaninformatics. ucsd. edu/datazoo/data/pallter/datasets filters.
, 2014). The beta diversity between samples was visualized with nonmetric multidimensional scaling (NMDS) based on Bray-Curtis similarity using the metaMDS function in the vegan R package  (Oksanen et al. , 2016). NMDS was also used to assess several normalization methods for un-rarefied libraries: converting read numbers to relative abundances, as well as Relative Log Expression (RLE) and Trimmed Mean of M-Values (TMM) normalizations as implemented in edgeR  (McMurdie and Holmes, 2013). To identify OTUs with non-random temporal patterns, we first selected OTUs with a relative abundance ≥0. 01 in at least three samples. We assessed the seasonality of this subset through local polynomial regression (LOESS) with serial day as the independent variable and relative abundance as the dependent variable, using r 2 > 0.
8 as a threshold to identify OTUs with nonrandom temporal patterns. We used these OTUs to generate a co-occurrence network based on Pearson's correlation (r > 0. 5) using the CoNet plugin in Cytoscape  (Shannon et al. , 2003; Faust et al. , 2012). We extracted non-overlapping clusters from this network through k-means clustering using the Cytoscape clusterMaker2 plugin after first selecting a reasonable value for k through the evaluation of a scree plot of within-clusters sum of squares (Supplementary Figure  S1 ;  Morris et al. , 2011). The resulting network clusters were visualized in Cytoscape. This helped us to identify groups of OTUs with similar temporal trends. In order to model the relationship between environmental drivers and potentially delayed bacterial responses, we used linear interpolation as implemented in the R package zoo to predict values between sampling dates and create regular time series  (Zeileis and Grothendieck, 2005).
We then performed stepwise linear regression with forward-backward variable selection as implemented in the R packages dynlm and MASS, minimizing Akaike Information Criterion  (Venables and Ripley, 2002; Zeileis, 2016). Regression models were tested for multicollinearity among predictor variables and models with variance inflation factors (VIF) greater than 5 discarded. The variables tested included temperature, inorganic nutrient levels, POC, and PON. To account for delayed bacterial responses, we built distributed lag models, testing 0-, 10-, and 20-day time lags  (Jorgenson, 1966). Additional details are given in Supplementary Text S1 and Supplementary Table  S1. The R code used for all data analysis and figure production can be accessed at: https://github. com/cmluria/Palmer-Station-Bacterial-Succession.
Silicate drawdown was less than nitrate during the summer bloom, suggesting that other phytoplankton groups in addition to diatoms contributed to the bloom. Bacterial production was very low but still detectable from July to October (Figure  1B ). By January bacterial production increased substantially, peaking about 3 weeks after the peak in chl a (Figures  1A, B ). Our bacterial production rates measured from the seawater intake were similar to those measured at a near shore station (Supplementary Figure  S3 ; data available at doi: 10. 6073/pasta/814628d18d4e23753d1164f0bd81095e). Stepwise linear regression revealed that chl a with time lags of 0, 10, and 20 days was a good predictor of both bacterial production (r 2 = 0. 93) and abundance (r 2 = 0. 62; Supplementary Figure  S4 ; Supplementary Table  S1 ).
001, r 2 = 0. 4) and estimated richness (p < 0. 001, r 2 = 0. 35) significantly correlated with library size (Supplementary Figure  S5 ). This may indicate that the species abundance distributions vary widely among our datasets  (Gwinn et al. , 2016). Likewise, NMDS based on un-rarefied sequence libraries showed the influence of initial library size even after relative abundance, RLE, and TMM normalizations (Supplementary Figure  S6 ). For these reasons, all subsequent analyses relied on libraries rarefied to the smallest library size of 43,308 sequences. Observed richness of the free-living bacterial community varied significantly between midwinter and midsummer (p < 0. 0001), ranging from a maximum of approximately 1800 OTUs in July to a minimum of approximately 700 OTUs in January (Figure  1 ).
ACKNOWLEDGMENTS We acknowledge the help of the following people for providing field and laboratory assistance at  Palmer Station  as part of this project:  Jamie Collins ,  Sharon Grim ,  Sean O'Neill ,  Monica Stegman ,  Sebastian Vivancos , and  Madelyne Willis , as well as the staff at  Palmer Station. We thank  Annaliese Jones ,  Leslie Murphy , and  Naomi Shelton  for conducting laboratory analyses. We thank  Casey Dunn  for his support. We thank the two reviewers for helping improve the manuscript.
FUNDING CL was partially funded by the  Graduate School  and the  Department of Ecology and Evolutionary Biology at Brown University  and the  Brown University-Marine Biological Laboratory   Joint Graduate Program. This material is based upon work supported by the  National Science Foundation  under Grant Nos. ANT-1142114  to  LA-Z ,  OPP-0823101  and  PLR-1440435  to HD, and  ANT-1141993  to JR.
One set of declination and inclination observations was performed with the Zeiss theodolite by an experienced observer. The absolute measurements were carried out on 2003 August 24 between 11:44  and 12:16 UT. Data processing follows the standard geomagnetic observatory method (e. g. Jankowski & Sucksdorff 1996). The declination, inclination and intensity results reported in Table  1  are reduced to 11:57 UT on 2003 August 24 using variation recordings of the Adolf-Schmidt-Observatory for Geomagnetism in Niemegk of Helmholtz-Center Potsdam, German Research Center for Geosciences at about 200 km distance. Magnetic conditions were quiet during this and the following days, the variations from August 24th to August 30th did not exceed 27 arcmin in declination, 8 arcmin in inclination and 60 nT in intensity.
Intensity obtained from Thellier experiments Evaluation of five Thellier experiments was done with the Thellier-Tool4. 2 software  (Leonhardt et al. 2004 ) using the default values for classification. Only one specimen passed the automatic routine (Figs  9a-d ) and was classified as B because of a large cumulative pTRM check difference δ(pal) and a tail check slightly above the limit of 3 per cent. At 350 • C δ(pal) has a large negative value indicating chemical alteration (Fig. 9d , lower left hand). Nevertheless data points in the Arai-diagram as well as in the Zijderveld-diagram (Figs  9a  and b ) form straight lines. Using all data points would not change the obtained palaeointensity significantly. This specimen unblocks over a wide range of temperatures and has an almost linear decay of NRM until 480 • C (Fig.
Intensity obtained from multiple-specimen experiments 48 specimens prepared from 5 different samples (cf. Table  4 ) have been subjected to the MSP-DSC palaeointensity experiments. As delineated above the MSP-DSC technique could also be interpreted as a multi-specimen palaeointensity experiment with the  Dekkers & Böhnel (2006)  technique. Here, only the results with NRM fractions between 40 and 60 per cent have been used because the temperature in the DB-steps was not adjusted close to the expected MDT value. This was evaluated by using the TH-step. In order to avoid alteration (see below) lower temperatures have been preferred and thus only six specimens met the criterion. The result is shown in Fig. 10(a). The data points obviously form a straight line and this result looks almost ideal compared to many other published   (d, h ) shows further information as, e.
g. Skype 24  ). Generally, the greater the degree of centralisation, the higher will be the degree of control given to the administrator over the process and output of production. Architectures with a centralised system of governance facilitate the coordination of individuals contributing to collaborative work and are generally easier to implement technically than the decentralised alternatives (for propositions to resolve these difficulties, see Antoniadis and Le Grand 2009). Centralised platforms based on proprietary software and single points of control at the technical level are, however, more likely to facilitate privatization and concentration (with the possible facilitation of abuse of dominant positions that this entails). At the other end of the spectrum, technical architecture can also be distributed among peers  (Musiani 2015).
This will move towards more distribution of power if users have the capacity to propose new features to be integrated into the service. It will go further if they can develop such features themselves, in the form of plug-ins or add-ons. If the technology is based on Free/Libre Open Source Software (FLOSS), this feature will reach the fullest level of decentralisation and enable exit without penalty, by means of forking to create a new, parallel project duplicating previous contributions and starting afresh. Decentralised governance is here supported by, and embedded in, the openness of the technical design based on the software and data licensing, empowering peers to modify the platform, adapt it to their needs and transfer the datasets. Inversely, if the technology is closed, a central point of control will be re-introduced in order to validate the new feature (e.