dataesr/scientific-paragraphs-categorization huggingface dataset:
M€ uller's index decreased with acting plant's nectar splitting level and with nectar tube length for all pollinators groups except for flies. For bees and beetles the effect of tube length was most accentuated when splitting was low. Dots represent partial residuals (i. e. residuals after removing the variation explained by other variables). Regression line is presented in red. Graphs showing the results obtained with the full dataset (without considering the effect of nectar sugar content) are presented in Fig. S8. nested pattern characteristic of pollinator networks  (Joppa et al. 2010 ): more rewarding plants are more likely to be visited by rarer (and less connected) pollinators as well as by the most common pollinators, hence sharing pollinators with less abundant (and less connected) plant species.
STATEMENT OF AUTHORSHIP LGC conceived the project, gathered datasets, analysed the data and wrote the manuscript. WEK and JCB helped conceive the project and develop hypotheses. GB, JF and LGC performed analyses on simulated networks to evaluate potential bias. MS provided information on flower shape. GB, MS, IB, MB, CKB and LGC provided data on nectar tube length. MB and CKB provided information on nectar sugar content. SG and VM calculated phylogenetic distances. LGC, GB, IB, JF, CKB and all authors from KB to MV provided empirical network data. All authors discussed the results and contributed during manuscript writing.
Figure4Effect of nectar tube depth and level of nectar splitting within floral unit on the influence that a plant species has on another via shared pollinators (M€ uller's index, Gaussian model, TableS4), after removing the effect of nectar sugar content. M€ uller's index decreased with acting plant's nectar splitting level and with nectar tube length for all pollinators groups except for flies. For bees and beetles the effect of tube length was most accentuated when splitting was low. Dots represent partial residuals (i. e. residuals after removing the variation explained by other variables). Regression line is presented in red. Graphs showing the results obtained with the full dataset (without considering the effect of nectar sugar content) are presented in Fig. S8.
ACKNOWLEDGEMENTS This project was funded by the  EU   FP7 projects "Status and Trends of European Pollinators " ( 244 090 , www. STEP-project. net) and " Securing the Conservation of biodiversity across Administrative Levels and spatial, temporal and Ecological Scales " (226852, www. scales-project. net);and by the  UK Insect Pollinator Initiative AgriLand project  ( BB/H014934/1 ). MV and AM-C acknowledge funding from the  Spanish Severo Ochoa Program  ( SEV-2012-0262 ) and FLORMAS (CGL 2012-33801). HGS and RB were funded by the  Swedish research council FORMAS. TK acknowledges DAAD and AG Leventis foundation  for funding. AH, CFD and TT acknowledge funding by the  Helmholtz Association  ( VH-NG 247 ). CKB was funded by the  German Research Foundation  ( KA 3349/2-1 ). IN and ATDB were funded by a  UK NERC  studentship and fieldwork scholarship.
Positions that showed a difference in the PL score of less than 30 between homozygote reference (0/0) and homozygote alternative (1/1) were called an 'N' in that sample, as were positions where heterozygote calls (0/1) either had a higher PL score than the most likely homozygous genotype, or differed by less than 30 from the most likely homozygous genotype. Note that a PL score of 30 between genotype 0/0 and 1/1 means that the former is 1,000 times more likely than the latter, for example, the genotype-calling error rate is expected to be less than 1 in 1,000. Sites where more than two bases were called (that is, multi-allelic sites) were also removed. For the mtDNA-enriched data, reads were mapped to the revised mtDNA reference sequence (GenBank number: NC_012920) using the software MIA  [28].
For each haplotype with missing data for that site, the missing base was imputed as the allele present in the reference haplotype that had the fewest differences (based on the sites with no missing data). After imputation was finished for that site, it was added to the reference set, and the procedure continued for the next site with the smallest amount of missing data. As a check on the accuracy of the imputation, we randomly deleted 2. 54% of the known alleles, following the distribution of missing alleles in the full dataset, thereby creating an artificial dataset with a similar distribution of missing alleles as in the observed dataset. We then imputed the missing data according to the above procedure and compared the imputed alleles to the true alleles; this procedure was carried out 1,000 times.
Recurrent NRY mutations We expect the majority of the NRY SNPs to have mutated only once, as recurrent mutations in the known NRY phylogeny are quite rare  [31, 32]. Therefore, as a further quality control measure, we investigated the NRY data for recurrent mutations by constructing a maximum parsimony tree for the 2,276 SNPs using programs in PHYLIP. We then estimated the number of mutations at each SNP, and removed 48 SNPs that had mutated more than twice, and only in terminal branches, as these are likely to reflect sequencing errors. The final dataset contains 2,228 SNPs.
Data analysis Basic summary statistics (haplotype diversity, mean number of pairwise differences, nucleotide diversity, Tajima's D value and theta(S)) were calculated using Arlequin v3. 5. 1. 3 [33]. Arlequin was further used to estimate pairwise Φ ST values and for Analysis of Molecular Variance (AMOVA). The observed ratio of the mean pairwise differences (mpd) for the NRY versus mtDNA was calculated as mpd NRY / mpd mt. In order to detect group-specific deviations from the mean distribution of the mpd ratio in the dataset, we carried out a resampling approach. For each group sample size (N group ) we chose randomly N group individuals (out of 623) and calculated the mpd ratio using the dist. dna command from the APE package  [34]  in R. This was repeated 10,000 times for each N group sample size to obtain the distribution of resampled mpd ratios.
We first simulated the combined mtDNA and NRY sequences with the fastsimcoal software  [48]  and used approximate Bayesian computation (ABC)  [49]  to estimate divergence times based on the combined dataset, with the same mtDNA mutation rate used in the BEAST analysis and an average of the fast and slow NRY mutation rates. We simulated 5,808,805 observations, which were log transformed via ABC linear regression  [49]  using the following statistics: polymorphic sites (S), pairwise differences (Pi), Tajima's D, pairwise Φ ST , and the variance components for an AMOVA based on two groups, Africa versus non-Africa (the latter consisting of the pooled data from the five non-African regional groups). We assumed a single out-of-Africa migration and further population divergence events (see text for further details).
More details about the results from each individual, including mtDNA and NRY haplogroups, are provided in Additional file 1: Table  S1. The mtDNA sequences have been deposited in Genbank with accession numbers KF450814-KF451871. A datafile with the alleles at each of the NRY SNPs in each sample has been provided to the CEPH-HGDP and additionally is available from the authors. The NRY raw sequencing data are in the European Nucleotide Archive with the study accession number PRJEB4417 (sample accession numbers ERS333252-ERS333873). Basic summary statistics for the mtDNA and NRY diversity in each population are provided in Additional file 3: Table  S3. As the sample sizes for many of the individual populations are quite small, for most subsequent analyses we grouped the populations into the following regions (based on analyses of genome-wide SNP data  [43, 47] ): Africa, America, Central Asia, East Asia, Europe, Middle East/North Africa (ME/NA), and Oceania (the regional affiliation for each population is in Additional file 1: Table  S1 ).
NRY and mtDNA population differentiation An outstanding question is whether or not there are differences in the relative amounts of between-population versus within-population diversity for mtDNA versus the NRY, as some studies have found much larger betweenpopulation differences for the NRY than for mtDNA  [6]  while others have not  [7]. To address this question, we carried out an AMOVA; the results (Figure  2B ) show that in the entire worldwide dataset, the between-population differences are indeed bigger for the NRY (approximately 36% of the variance) than for mtDNA (approximately 25% of the variance). However, there are substantial differences among the regional groups. The ME/NA, East Asia, and Europe regional groups follow the worldwide pattern in having bigger between-population differences for the NRY than for mtDNA.
The excess of N f versus N m become even more pronounced in recent times due to higher rates of growth in N f than in N m (Figures 4, 5, and 6); these results are in line with previous studies of smaller datasets that used different methods  [4, 80]. These results suggest, in turn, that sex-specific processes that reduce N m , such as polygyny and/or sex-specific migration  [2] , have characterized humans over most of our prehistory. However, there are several reasons why this conclusion should be viewed as tentative. First, the sample sizes of some of the regional groups in the HGDP are quite low, precluding confident estimates of effective population sizes. Moreover, there are some surprising features of our results, such as the much larger effective size estimates for East and Central Asians than for Europeans.
Figure2Diversity and AMOVA results. (A) Mean number of pairwise differences (and SE bars) for the NRY and mtDNA sequences from each regional group. (B) AMOVA results for the entire worldwide dataset, and for each regional group of populations. Two comparisons are shown for the entire dataset; the left comparison includes regional groups as an additional hierarchical level, while the right one does not. * indicates that the among-population component of diversity does not differ significantly from zero (after Bonferroni adjustment of the P value for multiple comparisons).
Acknowledgments We thank:  Maanasa Raghavan  and  Martina Codina  for technical assistance; the  MPI-EVA Evolutionary Genetics Sequencing Group and the Bioinformatics Group  for carrying out the sequencing and the initial processing of the sequence data; and  Chiara Barbieri ,  Peter de Knijff ,  Brigitte Pakendorf ,  Kay Prüfer , and  Udo Stenzel  for helpful discussion. This research was supported by the  Max Planck Society ; we also acknowledge financial support from the  Biotechnology and Biological Sciences Research Council  (to Francois Balloux). We dedicate this paper to the memory of  Howard Cann , in recognition of his instrumental role in establishing and guiding the HGDP.
In parallel, cells were treated with increasing doses of recombinant 6-His-sAPPa purified from yeast or IGF1 as positive control activating cell survival. Cell viability was measured photometrically in a bioluminescence assay by quantifying ATP levels. Serum deprived SH-SY5Y wt (d) or APP-KD (e) cells were treated with increasing doses of 6-His-sAPPa or recombinant E1. Cell death was assessed microscopically by counting PI-stained (dead) cells in three random visual fields (4150 cells) and calculated as a percentage of the total number of visualized cells (Hoechst staining). Data are means from four to ten cultures ± S. E. M. Statistical significance: *Po0. 05 compared with controls ( þ FCS); # Po0. 05 compared with serum withdrawal in the absence of sAPPa/E1/IGF1; NS ¼ not significant medium still present in purified sAPPa and E1, we also tested heat-inactivated fractions, which did not show any rescuing effects (Supplementary Figure  1A ).
The asparagine residue at position 467 of APP695 (glycosylation site) was replaced with serine by site-directed mutagenesis. The yeast was cultured in buffered glycerol complex medium with peptone. Addition of methanol induced the expression of 6-His-tagged sAPPa/E1 and secretion into the surrounding medium. The proteins were isolated with Nisepharose beads or Ni-affinity chromatography (HPLC) from the medium and eluted with imidazole. Their biologic, neuroprotective activity was verified in several control experiments with different cell lines under proteasomal stress or serum deprivation (data not shown). Cell death and viability assays. Cell death was quantified based on plasma membrane permeabilization. To this aim, cells or tissue cultures were cultured under normal serum conditions (control) and serum/trophic factor deprivation ( À FCS/ À B27/ À HS) or double deprivation with additional removal of glucose ( À FCS/ À Gluc) for 24-48 h.
8 mg/ml PI for 20 min at 37 1C. When applying the ADAM10 (a-secretase) inhibitor GI254023X (5 mM), slices were cultured in serum-/glucose-free medium for 48 h containing the inhibitor or its respective carrier (DMSO) as control. Round circles of identical size (Ø 500 mm) were positioned in equivalent locations within the CA1 region of each hippocampus image and all PI-stained cells were counted using ImageJ software (NIH, Bethesda, MD, USA). Cell viability assays were performed with a commercial kit (CellTiter-Glo Luminescence Assay; Promega, Mannheim, Germany) according to the manufacturer's instructions. The assay quantitates ATP levels, an indicator of metabolically active cells, photometrically with a fluorescence plate reader. Additionally, we applied the live-dead cell staining kit II from PromoKine (Heidelberg, Germany) according to the manual.
On-cell-Western assays were performed to evaluate cell surface expression of APP. The experiment was performed by adding primary antibody (22C11, 1:180) directly into the medium of live, unpermeabilized cells for 4 h. After three washing steps with PBS, secondary antibody (IRDye goat anti-mouse from LI-COR; 1:1000) was incubated for 1 h. Following washing steps, fluorescence signals were measured using the LI-COR Odyssey Infrared Imager and Image Studio 3. 1 software (LI-COR Odyssey, Bad Homburg, Germany). Values are arbitrary fluorescence units calculated from trim signals (means of n ¼ 3 experiments ±-S. E. M. ) normalized to background staining controls. Statistical analyses and microscopy. Results are expressed as mean ± S. E. M. All experiments were repeated at least three times yielding similar results.
Statistical analyses were performed using SPSS (IBM, Armonk, NY, USA) with one-way ANOVA followed by Tukey's HSD post hoc comparisons. P-values o0. 05 were considered as statistically significant. Western blot quantifications were performed with LI-COR Odyssey Image Studio 3. 1 software to guarantee analysis of fluorescence signals in a linear range or ImageJ software for ECL-developed blots. Values were normalized to serum/glucose-treated controls (dashed line in figures). Band intensities in blots with whole-cell lysates were normalized to their corresponding loading control (GAPDH) and plotted as pGSK3b/GAPDH ratios. All graphs show means of n ¼ 3 blot quantifications±S. E. M. For microscopy, we used the Nikon Eclipse TE2000-S fluorescence microscope with Plan Fluor Â 4, Â 10 or Â 20 dry objectives, a 100 W mercury lamp and FITC (green; ex: 465-495 nm; dichroic mirror: 505 nm; em: 515-555 nm) or Texas Red (red; ex: 540-580 nm; dichroic mirror: 595 nm; em: 600-660 nm) excitation filters.
5%, 97. 5%) quantile interval of the infection efficacy over genotypes and time (hereafter referred to as the 'efficacy range') on the susceptible host of each genetic cluster. e e and the efficacy range describing the genetic clusters in terms of specialisation level (due to the trade-off function, the highest e e the more specialised the genetic cluster is) and homogeneity (the highest the efficacy range the less homogeneous the genetic cluster is), respectively. The effects of the input factors on the descriptors of the pathogen evolutionary trajectory were assessed by fitting a multinomial logistic regression model (R package nnet  [35] ), for the number of genetic clusters, and penalized Generalised Additive Models with tensor product smooths (R package mgcv  [36] ), for e e and the efficacy range, respectively.
Statistical analysis All data were entered in Microsoft Excel and subsequently analysed using Stata V12 and R 3. 0. 1  [27, 28]. To assess the relative sensitivity of the parasitological tests, we compared the number of positives for each test in the whole study population using McNemar's test  [29]. We presumed that there were no false positive results and consequently the difference in the number of test positives reflects the differences in sensitivities. Similarly the number of PCR positives was compared between samples collected on filter paper and samples stabilised in GE-buffer. In this analysis, we used the first reading of the filter paper and storage buffer samples, analysed at the ITM. As false positives cannot be excluded for PCR tests (through contamination), we performed a similar analysis as on the mAECT on the PCR test formats, but restricting the analysis to the parasitologically positive cases only.
To this end, we analyzed H2B ubiquitination in yeast strains expressing hH2B and after further deletion of UBP8. H2Bub levels were increased by about threefold to fivefold in an ubp8D strain when compared with the parental strain, suggesting that SAGA has a global DUB activity on the yeast genome (Fig. 3B ; Supplemental Fig. S5A ). We next performed H2Bub ChIP-seq experiments in the same strains. To get an accurate normalization of the two data sets, we quantified H2Bub levels on a number of selected genes and intergenic regions by ChIP combined with quantitative PCR (ChIP-qPCR) (Fig. 3D ). A mean 2. 5-fold increase of H2Bub levels in gene bodies upon UBP8 deletion was calculated from three independent ChIP-qPCR experiments and was applied to normalize our two ChIP-seq data sets. On a representative region, the H2Bub levels were found increased in all gene bodies upon UBP8 deletion, in good agreement with our observation in human cells (Fig.
4A ). To determine the genome-wide distribution of GCN5containing complexes, we profiled and quantified the distribution of H3K9ac by ChIP-seq in cells expressing either control or ADA3 shRNA (Supplemental Fig. S6A, B ). In control HeLa cells, H3K9ac peaks were detected exclusively at the promoters of expressed genes together with Pol II and H3K4me3 but were absent from silent genes (Fig. 4B ). The control and ADA3 knockdown data sets were normalized by using intergenic regions where only background levels of H3K9ac could be detected (Supplemental Material). In ADA3-depleted cells, the overall profile of H3K9ac was conserved, but the peak heights were significantly reduced (Fig. 4B ). As a measure of GCN5 HAT activity, we calculated the ratio of H3K9ac densities obtained in ADA3-expressing versus control shRNA-expressing cells both in intergenic regions and at the promoters of all expressed genes.
Yeast strains and protein extraction Different yeast strains used in this study were derived from the previously described FY406  (Hirschhorn et al. 1995). Deletion mutants were generated as described previously  (Janke et al. 2004)  or by transformation with PCR products amplified from genomic DNA of the corresponding strain obtained from the Saccharomyces Genome Deletion (SGD) strain database. The plasmid expressing the humanized version of yeast HTB1 was generated by site-directed mutagenesis of pRS413-Flag-HTB1-HTA1. The genotypes of the strains used in this study are listed in Supplemental Table  S3. Yeast whole-cell extracts were prepared as described  (Gardner et al. 2005 ) with minor changes. Cells (3 3 10 8 ) from log-phase yeast cultures were harvested by centrifugation and lysed in 400 mL of SUME buffer (8 M urea, 1% SDS, 10 mM MOPS at pH 6.
Acknowledgments We thank  F. Winston  for the gift of material. We thank  B. Jost  and the  Institut de G en etique et de Biologie Mol eculaire et Cellulaire microarray  and deep sequencing platform for high-throughput sequencing, and  Y. Tao  and  S. Le Gras  for their help with bioinformatics analyses. We thank the  Institut Clinique de la Souris  for the generation of the Atxn7L3 knockout mouse line. We are grateful to  J. M€ uller ,  M. Timmers ,  R. Schneider , and  D. Umlauf  for critically reading the manuscript. This study was supported by funds from the  Agence Nationale de la Recherche  ( ANR-10-INTB-1201 ) to D. D. and the  NSC  ( 100-2911-I-001-510  and  100-2923-B-001-001-MY3 ) to C. F. K. This study was also supported by grant  ANR-10-LABX-0030-INRT  under the  frame program  labeled  ANR-10-IDEX-0002-02.
X-ray data collection and processing Preliminary diffraction data were collected at 100 K at the Institut Pasteur Montevideo, Uruguay on a MicroMax-007 HF rotating-anode diffractometer (Rigaku, The Woodlands, Texas, USA) equipped with a MAR345 image-plate detector (Rayonix, Evanston, Illinois, USA). The final data sets were collected at 100 K on the PROXIMA1 protein crystallography beamline at SOLEIL, France using a PILATUS 6M detector (Dectris, Baden, Switzerland) and on the X6A protein crystallography beamline at the National Synchrotron Light Source (NSLS), USA equipped with an ADSC Quantum 270 CCD detector (Poway, California, USA). Single crystals were used for the collection of each data set. X-ray diffraction data were processed with the programs MOSFLM  (Leslie & Powell, 2007) , SCALA  (Evans, 2006)  and XDS  (Kabsch, 2010) , with the latter running in a semi-automated approach using the xdsme package (http:// code.
Structure resolution, model building and refinement All structures were solved by means of the molecularreplacement method in the resolution range 15-4 A ˚with AMoRe  (Navaza, 1994). RS-RBF was the first structure to be solved, using the coordinates of the trimeric RS from E. coli (PDB entry 1i8d;  Liao et al. , 2001)  as a search model. The other structures were then solved using RS-RBF as a search   et al. , 2011) , with the application of noncrystallographic symmetry (NCS) restraints under the local structure similarity restraints (LSSR) approach  (Smart et al. , 2012)  to take into account the observed differences between individual chains by removing these from the NCS relation. Model building was performed with Coot  (Emsley et al. , 2010). In the last steps of the refinement, water and ligand molecules were added to the corresponding models.
The coordinates for riboflavin  (Gerhardt, Haase et al. , 2002) , roseoflavin  (Serganov et al. , 2009)  and NRP  (Klinke et al. , 2007)  were obtained from published structures, and their CIF restraint libraries were obtained from the CCP4 database  (Winn et al. , 2011). Detailed statistics of the refinement process can be found in Table  1.
Validation and analysis of the models The four crystallographic structures were subjected to validation with MolProbity  (Chen et al. , 2010)  as well as with the validation module implemented in Coot. The following web servers at the European Bioinformatics Institute (EBI) were used: PDBePISA (study of interface areas; http:// www. ebi. ac. uk/msd-srv/prot_int/; Krissinel & Henrick, 2007) , PDBeFold (superpositions and r. m. s. d. calculations; http:// www. ebi. ac. uk/msd-srv/ssm/; Krissinel & Henrick, 2004 ) and ClustalW2 (sequence alignments; http://www. ebi. ac. uk/Tools/ msa/clustalw2/; Larkin et al. , 2007).
Riboflavin dissociation constant and binding stoichiometry The dissociation constant for riboflavin (K d ) and its binding stoichiometry were determined by fluorescence titration on a Jasco FP-6500 spectrofluorometer (Easton, Maryland, USA) according to published procedures  (Fischer et al. , 2002). Briefly, a 45 mM ligand solution was titrated by the sequential addition of 5 mM RS aliquots (subunits) in 50 mM potassium phosphate buffer pH 7. 0 at 20 C. Fluorescence emission was measured after each aliquot with excitation and emission wavelengths of 470 and 535 nm, respectively. Control experiments were performed using BSA. Data analysis was performed using GraphPad Prism 5 (GraphPad Software, La Jolla, California, USA).