dataesr/scientific-paragraphs-categorization huggingface dataset:
Regions differentially enriched in histone modifications associated with P. falciparum infection We find a considerable overlap in histone modification profiles between infected and uninfected mosquitoes, with more than 30,000 common ChIP-seq peaks between the two conditions. However, a portion of the peaks appears to be condition specific (8234 in infected and 14,138 in the uninfected) (Additional file 4: Figure  S2C ). Based on this observation, we used the diffReps software  [33]  to further investigate localized changes in hPTMs enrichment that might occur in response to P. falciparum infection. We identified 15,916 regions containing significantly different levels of ChIP-seq signal (P value < 10E-5) for all four histone modifications. The number of diffReps regions was similar for H3K9ac (2396) and H3K4me3 (2837), whereas H3K27ac and H3K9me3 displayed a larger number of differential regions (4810 and 5873, respectively) (Additional file 5: Table  S4 ).
There was also a considerable proportion of regions that switched between the depleted and H3K9me3-enriched states (Fig. 2c ). The Gene Ontology (GO) analysis shows that diffRepsannotated genes appeared significantly enriched in GO terms related to development, transcription regulation and metabolism (Additional file 5: Table  S4 ). In addition to this analysis, we also looked for coincidences between the diffReps-annotated genes and genes that have been reported to be involved in the immune response to infection  [34, 35]. Among diffReps-annotated genes, we found 133 genes encoding for proteins involved in the immune response (26 considering the high-confidence set of dif-fReps regions). Genes differing between conditions in their histone modification profiles encode proteins involved in apoptosis (IAP3 and IAP7), Clip-domain serine proteases and serine protease inhibitors (CLIPC, CLIPE, SRPN10 and SRPN4), C-type lectins (CTLs) (Fig.
3c , Additional file 4: Figure  S4B , Additional file 6: Table  S5 ). In addition to the analysis of the diffReps-annotated genes, we also examined differentially expressed genes and looked for differential chromatin marks between conditions. The DESeq2 analysis on the RNA-seq data revealed 713 significant differentially expressed transcripts (P value < 0. 05, 184 up-regulated genes and 529 down-regulated genes in the infected vs. the control condition) (Additional file 7: Table  S6 ). We found 105 differential expressed genes that contain a diffReps region annotated to the promoter or the gene body. Of those, there were 72 in which the direction of the change (active or repressive histone marks) agreed with the functional prediction (up or down-regulation) (Additional file 7: Table  S6 ).
Chromatin immunoprecipitation Chromatin immunoprecipitation in mosquito tissues was performed as previously described  [26]. Antibodies to histone modifications used in this study were anti-H3K9ac (Millipore #07-352), anti-H3K4me3 (Abcam ab8580), anti-H3K27ac (Abcam ab4729) and anti-H3K9me3 (Abcam ab8898). ChIP-seq libraries were prepared following the procedure described by Bowman et al. [64]  and using the HiFi Kapa Sybr library preparation kit (KapaBiosystems). To obtain the quantity needed to perform ChIP-seq, the two samples for which we have RNA-seq expression data were pooled, resulting in one biological replicate for infected mosquito midguts and one biological replicate of uninfected tissues. ChIP-seq libraries were sequenced at the HudsonAlpha Institute for Biotechnology using an Illumina HiSeq2000 sequencer.
ChIP-seq data analysis We performed quality control of Illumina reads using QualiMap v. 2. 2. 1  [59]  (Additional file 2: Table  S2 ). Correlation analysis was performed using deepTools2 (v. 2. 5. 0)  [65]. We reported Spearman rho correlation coefficients between each pair of histone modification datasets. We mapped reads for various histone modifications and the input to A. gambiae PEST strain genome version 4. 3  [56]  using Bowtie v. 2. 2. 9  [66]  with default parameters except for -no-mixed. Reads were trimmed five bases from each read 5′ end (-trim5 5). Mapped reads were then sorted and deduplicated using SAMtools v. 1. 6. We applied a quality threshold of 10 in MAPQ score. All libraries were downsampled (SAMtools) to the same number of reads (9 M) for further downstream analysis. To calculate the enrichment, we used the BEDtools software suite (v.
2. 25. 0) [67]  to obtain the number of reads overlapping regions of interest. Resulting read counts were normalized (RPKM), input-corrected and log2transformed using R. We conducted peak calling using the MACS2 (v. 2. 1. 1) [30]  "callpeak" module with -t and -c options and default parameters, except for -g 2. 73E8 -keep-dup all -B -SPMR -q 0. 01 -nomodel. We further assessed statistical significance of MACS2 peaks using the RECAP software  [68]  to recalibrate peak calling P-values. Over 99% of MACS2 peaks remained significant according to a recalibrated P-value threshold of 0. 05 (Additional file 3: Table  S3 ). For visualization in IGV  [69, 70] , tracks of input-corrected ChIP-seq signal were computed using the MACS2 "bdgcmp" module (-m ppois) on each pair of fragment pileup and control lambda bed-Graph files from peak calling analysis  [71].
To quantitatively compare histone modification profiles between infected and control mosquito tissues, we used the diffReps software  [33]. This method uses a sliding window approach to identify regions that show significant changes in ChIP-seq signal, without constraining regions to compare by peak calling. ChIP-seq data for infected and control and the corresponding inputs were provided with -tr, -co, -btr and -bco options. Due to the lack of biological replicates, the statistical test used was G-test (-me gt). The threshold P-value was set to 10E-5. Other parameters were set as default except for -window 1000, as recommended for the scanning of histone modification peaks. We performed annotation of MACS2 peaks and diffReps regions to genomic features (TSSs, exons, introns and intergenic regions) using the anno-tatePeaks.
pl module in HOMER (v. 3. 12) [72]. Based on the density distribution of the distances from upstream MACS2 peaks and diffReps regions to the nearest ATG site, we considered 2 Kb upstream from the translation start site ATG as the putative promoter region. We used the chromatin state segmentation software ChromHMM  [31]  to compute genome-wide chromatin state predictions in each condition based on relative enrichment levels of histone modifications. For the binarization of the genome, we used default parameters except for -b 200. We chose a four-states model assuming chromatin states with high levels of enrichment of each histone modification. The chromatin states we found were: depleted (low levels of enrichment for all histone modifications), high levels of enrichment for H3K9me3, high levels of enrichment for H3K9me3 and H3K4me3 (bivalent state) and high levels of enrichment for H3K9ac, H3K27ac and H3K4me3.
Regions that fall in the lower quantiles were discarded (Additional file 5: Table  S4 ). We performed Gene Ontology (GO) terms overrepresentation tests for the sets of genes of interest annotated to diffReps regions using PANTHER Overrepresentation Test  [73, 74]. We chose Fisher's exact with FDR multiple tests correction and applied a threshold of FDR < 0. 05. Sets of differentially expressed genes between conditions, infected and control, were annotated based on the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) using DAVID. We used ChromHMM segmentation and plotEnrichment function in chromstaR R package  [75]  to assess enrichment of predicted chromatin states in certain features of interest, e. g. , the subset of genes encoding for immune response factors. Average profile plots and heatmaps representing enrichment of histone modifications (RPKM normalized, inputcorrected and centered on gene coordinates) were built using ngs.
For selecting the best model and testing the linear model fit of different combinations of histone modifications, we used the MuMIn R package (dredge function)  [80]  and kept the model with higher likelihood and R2 and delta AICc < 2. Multicollinearity was assessed using the R package car  [81]. VIF values for all the covariates were below 3. For assessing the relative importance of the covariates as predictors, we used the R2 decomposition method implemented in the calc. relimp function of the relaimpo R package  [82]. To connect differential enrichment of histone modifications with regulation of gene expression, we filtered those genes containing high-confidence diffReps regions in promoters and gene bodies and performed a soft clustering approach using the Mfuzz R package  [37]  over the ratio of histone modifications between conditions (ratio of enrichment at infected to uninfected samples).
Based on the soft clustering of each region and each gene mRNA level, we then isolated those cases where differential histone enrichment profiles, a gain/loss in active hPTMs or gain/loss in the repressive H3K9me3 modification, coincide with the expected functional output: up-or down-regulation of the gene. We also performed a soft clustering analysis with the same parameters as above computing histone modification enrichment ratios at promoters and gene bodies of significant differentially expressed genes according to DESeq2 (P value < 0. 05), with similar results (Additional file 4: Figures  S7, S8 , Additional file 8: Table  S7 ). Heatmaps showing histone modification enrichment and mRNA levels were built using the iheatmapr R package  [83]. Bar and violin plots were produced using the ggplot2 R package  [84].
For comparative and visualization purposes, histone enrichment signals and mRNA levels were log2-transformed. When computing histone modifications enrichments and ratios, a pseudocount (0. 1) was added to obtain finite values (avoid dividing by 0 in ratios) when input-correcting or converting the signal to log2 scale. When categorizing values in quantile groups according to their means, high, medium and low groups, we used the cut2 function in the Hmisc R package  [85]. We identified potential enhancer-like regions in infected and control conditions by taking distal (> 2 Kb upstream from the ATG) H3K27ac MACS2 peaks overlapping with active chromatin state regions according to the ChromHMM genome segmentation analysis. From these active regions, we subset only those overlapping with H3K27ac MACS2 peaks of enrichment but that appeared depleted in all other histone modifications.
Motif analysis We conducted de novo motif analysis using HOMER software (v. 3. 12) [72]  on the set of MACS2 peaks that intersect with high-confidence diffReps regions and annotate to genes (promoters and gene bodies). For this analysis, we considered the center of the ChIP-seq peak region and slopped 100 bp in each direction. We limited the number of background sequences to double the number of ChIP-seq target sequences for each histone modification. Only motifs enriched in more than 5% of the target sequences and below a threshold P-value of 10E-15 were considered, and results corresponding to low complexity motifs and offsets or degenerate versions of highly enriched motifs were avoided. The purpose of this analysis was to identify enrichment in particular sequence footprints associated with changes in histone modifications occupancy that are associated with P.
Acknowledgements We would like to thank all children and their parents who participated in this study, as well as the local authorities in  Burkina Faso  for their support. We are very grateful to  B. Yameogo ,  S. Tamboula ,  R. Hien ,  J. Bazié. , B. Dabiré. ,  F. Da ,  A. Diasso  and  F. Yao in Burkina Faso  for technical assistance. We thank The  Genomic Services Lab at the HudsonAlpha Institute for Biotechnology , specially  Braden Boone ,  Angela Jones  and  Terri Pointer , for their help in preparing RNA-seq libraries and performing Illumina sequencing of RNA-seq and ChIP-seq samples.
Digitization Methodology Models of lithic cores were generated using the technique of close range photogrammetry, also known as structure from motion. Our setup and workflow are described in greater detail in  Porter et al. 2016. Initially, photographs were taken with a 12. 1 megapixel Canon Powershot sx260 compact digital camera. Roughly midway through data collection, we changed our camera to a 20. 2 megapixel Sony RX100i. Since the analyses in this study focus on gross measures of shape as opposed to the quantification of fine morphological details, we think it is unlikely this change in image resolution had an impact on our results. All cores were photographed using manual camera settings. ISO was set as low as possible (100 or 80) and the aperture was set to maximize the depth of field of view while minimizing distortion (f.
Methodology Traditionally, angles on lithic artifacts have been measured using a goniometer or have been calculated from caliper measurements. These observations can be difficult to make and can be prone to inter-observer error, particularly when the angle being measured is more acute  (Dibble and Bernard 1980). We chose to measure the angle between core surfaces digitally using the software application Geomagic Design X (3D Systems 2015). First, we used the paintbrush selection tool to select polygons corresponding to the area of the platform surface from which there was evidence for blanks having been struck. We used this selection to create a best-fit plane. Next, we looked for the most recent complete flake scar on the face of the core, made a selection of the corresponding polyfaces, and used this selection to create a second best fit plane.
g. cortical surfaces) as opposed to intentional shaping. We captured a screenshot of the cross section curve and vector as viewed from above orthogonal to the platform best-fit surface. We also took a screenshot of the entire artifact mesh, which we used in the following analysis (see section 4. 3). Next, we imported these screenshots into Adobe Photoshop. We filled in the area bounded by the curve and the vector between the limits of the worked area with black, and added a white background. We then used the layer straightening tool to make the straight portion of each cross section parallel to the bottom of the image. All cross section images used in our study are shown in Figure  7. These data were analyzed using the software package SHAPE  (Iwata and Ukai 2002). First, a chain code was generated using the application ChainCoder.
Methodology This analysis utilizes the same black and white platform cross section images obtained for the previous analysis (section 4. 2). Additionally, we used a similar Geomagic-to-Photoshop workflow to obtain cross section images of the entire core as viewed from above. We captured these full core screenshots simultaneously with the platform data to ensure both images were taken from the same perspective and at the same scale. Next, we brought both images into ImageJ  (Schneider et al. 2012). We used a macro to draw a vertical line through the center of the platform cross section. This macro works by splitting a selection into equal portions by area  (Vischer 2011). We then used the built-in ImageJ macro DrawEllipse to overlay a best-fit ellipse and the major and semi-major axes of this ellipse over the image of the full core.
We hope that a trend towards openly publishing 3D artifact models, as we are doing with this paper, will make it easier for researchers in the future to access larger and more diverse datasets. Next, we would advocate more methodological experimentation and the publication of results, especially those that are negative or ambiguous. New ideas only become established methodologies through testing, adaptation, and iteration. These processes are most successful when undertaken as a community. We emphasize that although this research was quantitative in nature, we are not advocating the point of view that quantitative analyses are inherently superior to qualitative observations, although we do recognize certain quantitative variables are more straightforward to collect in an objective manner. Both types of analysis can inform one another.
Acknowledgements We thank  Glen Favoretto ,  Steffen Schatz ,  Dana Winkler ,  Kieran McNulty , and  Gilbert Tostevin  for their contributions to this project. We are grateful to the staff at the  Musée National de Préhistoire  for access to the  Roc de Combe collections  and support throughout the data collection process. The Les Cottés excavation project has been funded by the  Service Régional de l'Archéologie (France)  and by the  Department of Human Evolution of the Max Planck Institute for Evolutionary Anthropology. We thank the site owner, J. B. , for welcoming our research on his property. This research was made possible by a grant from the  Leakey Foundation.
Genomic Data Collection and Analysis Genomic DNA was extracted from whole embryos using a Qiagen DNEasy kit following the manufacturer's guidelines. Sample quality and concentration were evaluated using a 1% agarose gel and a Qubit fluorometer using the dsDNA Broad Range Assay Kit (v1. 0, Invitrogen/Life Sciences, Norwalk, CT). Double-digest RAD (ddRAD) libraries were built following a protocol modified from  Peterson et al. (2012). First, we digested 100 ng of DNA for each sample using SbfI-HF and MspI restriction enzymes. We then ligated barcoded P1 adapters to SbfI cut sites and a P2 adapter to MspI sites before pooling 32 samples for subsequent amplification and sequencing. DNA underwent purification and size selection using 1. 5× volume Sera-Mag SpeedBeads magnetic particles at multiple points following digestion, ligation, and amplification.
In total, 3 ddRAD libraries with 32 individuals each were sequenced at the Duke Center for Genomic and Computational Biology (Durham, NC) using an Illumina HiSeq2000 to collect 50 bp single end reads. Genotyping was performed using Stacks v1. 32  (Catchen et al. 2011). We used the process_radtags module to demultiplex reads using their barcodes, remove reads below the default quality threshold of Phred score <10  (Catchen et al. 2011) , and removed reads without the SbfI cut site. Next, we used the denovo_map. pl script to run the Stacks pipeline, including removing highly repetitive RAD tags before loading the resulting catalog into a MySQL database with the load_radtags. pl script. We performed a sensitivity analysis on the Stacks assembly parameters that dictate the number of identical, raw reads required to create a stack (m), the number of mismatches allowed between loci when processing a single individual (M), and the number of mismatches allowed between loci when building the catalog (n;  Paris et al.
Neutral Genetic Structure and Genetic Diversity We used discriminant analysis of principal components (DAPC;  Jombart et al. 2010 ) executed in the adegenet R package  (Jombart 2008)  to characterize the overall genetic differentiation among the study populations. DAPC summarizes genetic data using a PCA before conducting a discriminant analysis on retained principal components to create K genetic clusters that minimize variation within groups and maximize it among groups. This approach provides a means of evaluating whether our sampling design achieved our goal of more similarity in evolutionary history within replicates than among them. For instance, we expected to find each replicate population pairing placed into its own cluster that is unique from the other clusters in a K = 4 scenario and clear population-level differentiation using a K = 8 scenario.
We also used DAPC to estimate individual reassignment probabilities as an indicator of our ability to differentiate among various grouping levels (environment type, population, and replicate). In addition to DAPC, we examined overall genetic differentiation among populations using pairwise F ST values  (Nei and Chesser 1983 ) calculated with the "genetic_diff" function in the R package vcfR  (Knaus and Grünwald 2017). Differences in genetic diversity between urban and rural sites were quantified based on observed heterozygosities and nucleotide diversities after removing 37 loci with evidence of selection (see below). We constructed empirical cumulative distribution functions for each genetic diversity measure for each SNP. These distributions were compared between the rural and urban populations using the nonparametric Kolmogorov-Smirnov test  (Massey 1951) , which provides a test statistic D that quantifies the maximum absolute difference between each empirical cumulative distribution function, thereby providing a measure of effect size that is bound from 0 to 1.
2008)  when only 2 populations are sampled. They noted that F ST values are constrained by the value of H T (i. e. , global heterozygosity;  Wright 1943) , which is inherently limited when biallelic loci are analyzed in only 2 populations  (Jakobsson et al. 2013). This can lead to an inaccurate estimation of confidence intervals and erroneous identification of outlier loci. OutFLANK  (Whitlock and Lotterhos 2015) , an approach that uses trimmed F ST distributions to identify outlier loci, is inappropriate for 2 population scenarios owing to a lack of power to detect signals of selection. Replicated sampling can be problematic in other software such as PCAdapt  (Luu et al. 2017) , an outlier analysis that is commonly used for detecting evidence of selection and that uses an approach that assumes loci most strongly related to population structure are associated with local adaptation.
Acknowledgments We are grateful to  Tess Howard  and the  Of Pools and People research team  for field, laboratory, and conceptual contributions. The  University of Maine's Advanced Computing Group  provided technical support and resources. Seth Smith  assisted with code for performing alignments to the bullfrog genome. Andrew Whiteley ,  Tim Waring , and  Malcolm Hunter  provided helpful feedback on earlier drafts. The article benefited greatly from the suggestions of 3 anonymous reviewers. This research was conducted with approval of the  University of Maine Institutional Animal Care and Use Committee  for protocol number  A2014-03-05. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Acknowledgements C. S. and S. J. Z. contributed equally to this work. C. S. thanks the  EPSRC  ( Nano-Doctoral Training Centre )a, and the  Cambridge Trust. S. J. Z. acknowledges the support within the Etiuda 5 scholarship from the  National Science Centre Poland  (No. 2017/24/T/ST3/00257 ). S. D. S acknowledges the  Royal Society and Tata Group  ( UF150033 ). The work was supported by a  Royal Society   International Exchanges Cost Share award  ( IEC\R2\170108 ). K. F. thanks the  EPSRC, Cambridge Trust , the  Winton Sustainability Program  and the  George and Lilian Schiff Foundation. This work was supported in part by  BLAPHENE  and STRABOT projects, which received funding from the  IDEX Toulouse, Emergence program , \Programme des Investissements d'Avenir under the program   ANR-11-IDEX-0002-02 , reference  ANR-10-LABX-0037-NEXT.
Ectopic expression of c-SRC completely abolished SMURF2 auto-ubiquitination, a mark of SMURF2 ligase activity (Fig. 4a ). In addition to the C2 and HECT domains, SMURF2 contains three WW domains, which recognise PPXY (PY) motifs on SMURF2 target proteins or adapters, such as SMAD7. Further investigations have shown that the PY motif of SMAD7 directly interacts with the WW3 (297-330aa) domain of SMURF2  47, 48. As c-SRC phosphorylates SMURF2 at Tyr314 in the WW3 domain, we sought to explore if phosphorylation of Tyr314 altered SMAD7 binding. The structure of WW3 domain of SMURF2 complexed with a SMAD7 peptide incorporating the PY motif (PDB ID: 2LTZ) provides a model to investigate this effect. We generated an electrostatic potential surface of WW3 domain, which, reveals that the PY motif of SMAD7 tracks along a hydrophobic region of the domain, where Tyr314 resides (Fig.