dataesr/scientific-paragraphs-categorization huggingface dataset:
The modulus of the Burgers vector was found to be compatible with |c|, and higher values such as |B| = 2|c| could be excluded. These experiments illustrate the importance of spatially resolved PED data to characterize subtle structural deformations in individual NWs. The effect of using disoriented wire regions to analyze the fine details of the strain field has been shown. The observed crystal attributes was measured with high precision combining real and reciprocal space information. It is important to emphasize that this study involved a rather beam sensitive material (InP) and electron irradiation has been minimized by the fact the PED analysis does not require a carefully oriented sample. We have applied precession diffraction very close to its sensitivity and precision limits and, experimental results were compared with full dynamical diffraction simulations to understand the observed crystal misorientations.
Diffraction patterns were recorded by recording the microscope phosphor viewing screen using an external high speed CCD camera binned to 144 × 144 pixels for twist assessment. Precession diffractions patterns were acquired at 0. 5° precession angle and 50 ms exposure per image with a precession frequency of 100 Hz. The HSR-PED data sets were processed to obtain the orientation mapping by means of the ASTAR pattern matching software and libraries describing at least 150 orientations along each edge of the stereographic triangle were generated from the InP wurtzite crystal structure to achieve the needed angular resolution for crystal rotation measurements. HSR-PED experiments (shown in Fig. 5 ) were preformed to optimize the spatial and angular resolution to determine c-axis misorientation.
HSR-PED The orientation maps were derived using the ASTAR software (template matching) in order to yield the three Euler angles (2, Φ, 1) for each pixel. Subsequently, a rotation matrix was built using the angles that are provided in Bunge notation  [42] ; this step and all further processing discussed below were performed using a homemade software in Python. This was used for two analyses: In contrast to polycrystalline samples, where Euler angles may present discrete jumps at grain boundaries, the twisted NWs must show a smooth axial evolution of the crystal rotation as a function of the axial position (the Eshelby twist). This smoothness has been used to correct eventual occurrence of the "180° ambiguity" that can appear in the pattern recognition methodology  [12]. Due to the high symmetry of the InP wurzite structure (P63mc space group  [20] ), we were forced to correct other possible ambiguities associated with the equivalent positions in the basal plane in order to get consistent crystal rotation evolution along the wire from the ASTAR matching template procedure.
Acknowledgements We thank  Dr Z. Saghi  for taking the ADF-STEM images of the twisted wires. D. U. acknowledges financial support from the  Brazilian Agencies FAPESP  (No. 2014/01045-0 ) and  CNPq  (No. 302767/2012-6 ). A. E. acknowledges funding from the  Royal Society. P. A. M. acknowledges financial support from  European Research Council  through grant  291522-3DIMAGE  and the  EPSRC  grant number  EP/R025517/1. M. A. C. acknowledges financial support from  FAPESP  (Nos. 2013/02300-1  and  2013/10957-0 ) and  CNPq  (No. 479486/ 2012-3 ). L. H. G. T. and P. A. M. acknowledge funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013 ) under Grant Agreement No. 312483  ( ESTEEM2 ).
Photo Album of Food Portions and Foods Evaluated in the Study The photo album of food portions previously developed for the Brazilian population  [15]  was used in the assessment. It has 96 photos, 32 new photos produced in the first half of 2015 in Brazil and 64 existing photos from the International Agency for Research on Cancer/World Health Organization (IARC/WHO) album developed in Europe. The foods included in the album were identified using the food consumption database obtained in the last National Food Consumption Survey (POF 2008/2009)  [23]. First, variability analyses using a stepwise regression were conducted to detect the between-person variation in the intake of foods (in grams) based on selected nutrients (energy, macronutrients, vitamin A, vitamin C, iron, folate, and calcium) by region and sex  [24].
The selection of these foods was based on the following criteria: represent different food groups and formats (e. g. , amorphous, liquid, solid); be frequently consumed by the local population (i. e. , from Paraná), based on data of POF 2008/2009  [23]  and foods with similar formats evaluated only once (e. g. , boiled cassava and cassava fries). To represent the 20 food photo series, three photos of each food were randomly selected for the evaluation, representing the portion sizes small, medium and large and totalizing 60 different photos/portions.
Data Analysis Data were doubly typed into EpiData software version 3. 1  [26] , with the purpose of standardizing and avoiding possible typing errors. These data were later exported to SPSS software version 22. 0  [27] , where statistical analyses were carried out. The data were analyzed as usually presented in previous similar studies as well as proposed by Nelson and Haraldsdóttir  [25] , comparing the results of the chosen photo by the participant and the true portion size. For that, the difference between the true and the estimated size of the portion was estimated (i. e. , bias) in grams and in percentage (% error: (estimated weight-true weight/true weight) × 100). The photo series was numbered from 1 to 6, with intermediate options of 0. 5 points. In this way, the differences between the amounts have been classified as follows: (a) correct choice-if the bias (difference) was 0; (b) adjacent choice was for bias between -1 and +1; (c) and the distal choice for when bias was less than -1 or greater than +1  [28].
CTCF binding and gene reactivation following mitosis and replication Gene transcription is strongly inhibited during replication and mitosis, and therefore, genes need to be reactivated in an appropriate manner following these processes. We explored two available datasets to study a potential link between CTCF binding and gene reactivation after mitosis  (Teves et al. , 2018)  and replication  (Stewart-Morgan et al. , 2019). For mitosis, we focused on chromatin associated RNAs, enriched in nascent pre-mRNA, in mitotic ES cells and upon release in interphase measured by RNA-seq  (Teves et al. , 2018). We used k-means clustering to find 5 groups of genes displaying different reactivation dynamics, measured as the rate of recovery to the level of pre-mRNA detected in bulk asynchronous cells (Figure  7A ).
1% Tween-20) 5% BSA for 1 hr at room temperature and incubated overnight at 4˚C with primary antibodies diluted in PBST 5% BSA (1:5000 anti-H3 Abcam ab1791; 1:500 anti-CTCF Active Motif 61311). Excess antibodies were washed off with PBST (five washes, 5 min each) and incubated for 1 hr at room temperature in secondary antibodies HRPconjugated diluted in PBST 5% BSA (1:10000 anti-Rabbit IgG-HRP Thermo Fisher RB230254). Membranes were washed five times, 10 min each at room temperature and incubated with PIERCE ECL2 Western Blotting Substrate (80196 Thermo Scientific) 5 min in dark. After excess reagent was removed, proteins were visualized using the BIO-RAD Chemidoc MP Imaging System and processed using the Image Lab Software (BIO-RAD).
1% triton-X100, 10% donkey serum. The following antibodies were used: anti-Nanog (1:200; eBioscience 14-5761-80), anti-CTCF (1:300; Active Motif 61311), donkey anti-rat Alexa Fluor 488 (1:300; Invitrogen A21208) and donkey anti-rabbit Alexa Fluor 546 (1:300; Invitrogen A10040). Nuclei were counterstained with Hoechst 33342 (1. 6mM; Sigma Aldrich 14533), and embryos were placed individually in glass bottom microwell dishes (MatTek Corporation P35G-1. 5-20 C) in 1 Â PBS. Fluorescent images were obtained using a confocal laser-scanning microscope (LSM800; Zeiss) with the objective Plan-apochromat 20Â/NA 0. 8, speed 6, pinhole one airy unit, and laser intensities suited for optical section thickness of 2 mm. Images were analyzed and processed using Fiji and Photoshop CS6 softwares. Pictures correspond to a projection of 5 confocal optical slices.
Data and availability Samples are summarized in Supplementary file 1. Briefly for ChIP-seq, we sequenced 3 x Interphase and 3 x Mitosis wild-type CTCF; 2 x Interphase and 2 x Mitosis wild-type SMC1; 2 x Interphase and 2 x Mitosis CTCF in CTCF-aid for -IAA and +IAA (2 hr); 1 x interphase and 1 x mitosis of CTCF in C2C12 and NIH3T3 cells. For MINCE-seq we sequenced 2 x input and IP pairs for pulse and chase samples. Finally, MNase H3 ChIP-seq in CTCF-aid 2 x Interphase and 2 x mitosis for both -IAA and +IAA (2 hr). We previously generated wild-type MNase-seq: 3 x Interphase and 3 x Mitosis; MNase H3 ChIP-seq: 2 x Interphase and 2 x Mitosis; and ATAC-seq: 2 x Interphase and 2 x Mitosis. All previously generated datasets are described  (Festuccia et al. , 2019)  and are available from GEO accession: GSE122589.
CTCF and SMC1 binding in interphase and in mitosis Data Processing Reads were aligned with Bowtie 2  (Langmead and Salzberg, 2012)  to the mm10 genome, with options '-k 10'. Reads were additionally filtered for those with a single discovered alignment (in Bowtie 2 'k' mode this is mapping quality = 255) and an edit distance less than 4. All libraries were constructed with custom unique molecular identifier barcode as described before  (Festuccia et al. , 2019). Therefore, reads aligning with identical position, strand and barcode were treated as duplicates and collapsed.
Peak calling We used a strategy previously described  (Festuccia et al. , 2019) , where peaks were called against relevant inputs/controls for all samples using MACS2  (Feng et al. , 2012)  with 'callpeak -q 0. 2 g mm'. Peaks intersecting with the mm10 blacklist (ENCODE Project Consortium, 2012) were excluded along with those on chrM and chrY. To determine a set of candidate binding regions for each factor we merged all peaks in interphase and mitosis. Peaks were further filtered to have MACS2 FDR < 0. 01 in both replicates in either interphase or mitosis, and a height >0. 8 reads per million (RPM) in at least one sample (a height of ~16 raw reads at our mean mapped read depth of ~20 million reads).
Bookmarking analysis We used a strategy previously described  (Festuccia et al. , 2019) , where we take an approach similar to differential expression analysis in RNA-seq to determine the set of peaks bookmarked CTCF and SMC1. We combine the read counts for input and ChIP samples for a given TF in a single generalized linear model (GLM) aimed at assessing the difference between input and ChIP signal in both interphase and mitosis at each peak. We implement this GLM using DESeq2  (Love et al. , 2014). We expect that nearly all peaks will have significant differences between input and ChIP, and that the majority of peaks will have significantly different occupancy between interphase and mitosis. Therefore, we set the size factors to the total mapped reads of each sample. We encode ChIP versus Input by a factor ChipTF and mitosis samples by a factor ChipM, and then we ran Wald tests on the model ~ChipTF + ChipM.
Therefore, we optimized hyperparameters on the union [-500, -100] U [100, 500] bp. We then use these optimized hyperparameters to predict over the full region. We selected hyperparameters by sparse Gaussian process regression employing a variational approximation to the marginal likelihood (as offered by https://github. com/SheffieldML/GPy) (Titsias, 2009) , with an initial inducing inputs every 10bp. To account for the overdispersed count-data nature of the nucleosome fragment mid-points, we employ a log transform to stabilize variance and then assume a Gaussian likelihood for Gaussian process regression. We transform the total midpoints m i at site i data by: y i ¼ log am i þ b ð Þ, we select a ¼ 10 3 and b ¼ 1. We make predictions and invert the transform, mi ¼ e ŷi À b À Á =a where ŷi is the Gaussian process mean, thus mi reflects the median of the transformed random variable and we plot this median.
Chromatin accessibility in interphase and in mitosis Paired end reads were trimmed by aligning read pairs to discover regions of reverse complementarity surrounded by Nextera sequencing adapters for ATAC-seq. Alignment and trimming was performed with the BioSequences package for Julia 0. 6  (Bezanson et al. , 2017). Reads were aligned to mm10 genome using Bowtie 2  (Langmead and Salzberg, 2012)  with options '-k 10 -I 0 -X 1000 -nodiscordant -no-mixed', and filtered for reads with a single alignment mean edit distance less than four between read pairs. To generate heatmaps, the two end points (cut sites) of fragments in the 0-100 bp range, shifted inward by +/-4 bp as recommended  (Buenrostro et al. , 2013 ) and piled at base pair resolution. Heatmaps are visualized at a resolution of 100 sites per pixel using inferno colormap scaled to 0.
Genomic features All heatmaps are metaplots are centered on CTCF maximal motifs (Supplementary file 2); SMC1 summits (Supplementary file 2); Esrrb motifs (Figure  4A ) for Esrrb bookmarked regions were Esrrb dictates nucleosomal organization as previously defined  (Festuccia et al. , 2019) ; Oct4/Sox2 composite motifs (Figure  4A ) at Oct4 and Sox2 interphase binding regions as defined  (Festuccia et al. , 2019) ; P300 binding regions centered of summits derived from the Encode portal (ENCODE Project Consortium, 2012;  Davis et al. , 2018) , experiment identifier: ENCSR000CCD, file: ENCFF179FJG. We further restricted P300 binding regions by ChromHMM ES enhancers  (Ernst and Kellis, 2012)  (https://github. com/guifengwei/ChromHMM_mESC_mm1) (Pintacuda et al. , 2017). We retain p300 binding regions that intersected as regions annotated as 'Enhancer', 'Strong Enhancer' or 'Weak/Poised Enhancer' and no other ChromHMM categories.
Post-mitotic reactivation -Chromatin associated RNA-seq Chromatin associated RNA-seq reads were taken from GSE109964  (Teves et al. , 2018)  and aligned to an index comprising the mm10 genome and the ERCC spikes, using STAR  (Dobin et al. , 2013)  as part of the RSEM-STAR pipeline  (Li and Dewey, 2011)  with additional options '-seed 1618 -staroutput-genome-bam -calc-pme -calc-ci -estimate-rspd'. To focus on pre-mRNA signal STAR genome bam files were further quantified to count intronic reads. First duplicates were removed using Picard (https://broadinstitute. github. io/picard/) MarkDuplicates with options "TAGGING_PO-LICY=All ASO=coordinate READ_NAME_REGEX=null". Reads arising unambiguously from spliced mRNAs or unspliced pre-mRNAs were counted using VERSE  (Zhu, 2016) , we generated a custom gtf file with additional with the feature field transcript entries marking the extent of each isoform in the Ensembl  (Zerbino et al.
Genes were filtered for those in which all replicates in at least two conditions had a spike-corrected depth normalized transcript count of greater than 10, and whose interphase mean spike corrected RPKM was greater than 2/15. RPKMs were calculated by the normalizing counts by the total number of non-exonic bases per transcript model. This resulted in 13,233 filtered genes. To determine the groups of genes with differing reactivation behavior we used k-means clustering as offered by the Clustering package of Julia  (Bezanson et al. , 2017). We normalized spike corrected mean counts of mitosis, 30 min release and 60 min release to interphase as log 2 fold changes. We clustered with 2 k 20, and compared cluster assignments for k to k þ 1 we found the Rand Index  (Rand, 1971) , defined as the proportion of pairs of genes assigned to the equivalent clusters over the total numbers of pairs exceeded 0.
85 for k ! 5 and we selected k ¼ 5. To determine enrichments of CTCF bookmarked and lost peaks in proximity to these clusters, we calculated Fisher Exact test p-values for the genes of a given cluster within xbp of a CTCF peak to a background of all genes clustered within xbp of a CTCF peak, for x in  [1, 1e+6]  bp. Post-replication reactivation -repli-ATAC-seq repli-ATAC-seq reads were taken from GSE128643  (Stewart-Morgan et al. , 2019). Paired end reads were trimmed by aligning read pairs to discover regions of reverse complementarity surrounded by Nextera sequencing adapters for ATAC-seq. Alignment and trimming was performed with the BioSequences package for Julia 0. 6  (Bezanson et al. , 2017). Reads were aligned to mm10 genome using Bowtie 2  (Langmead and Salzberg, 2012)  with options '-k 10 -I 0 -X 1000 -no-discordant -no-mixed', and filtered for reads with a single alignment mean edit distance less than four between read pairs.
For comparison to MINCEseq we quantified the total number of cutsites from 0 to 100 bp repli-ATAC-seq fragments normalized to total 0-100 bp fragments per library, falling within +/-100 bp of CTCF maximal motif in CTCF peaks, Esrrb maximal motif in Esrrb Peaks, Oct4/Sox2 maximal motif in Oct4/Sox2 peaks and the summit of p300 peaks. To determine groups with differing post-replication reactivation dynamics we took total cutsites of 0-100 bp repli-ATAC-seq fragments, normalized to total 0-100 bp fragments, within [À200, 0] bp of active promoters as determined by chromHMM ES cell data  (Pintacuda et al. , 2017) , as used in Figure  3D. We filtered for promoters with normalized total accessibility over the [À200, 0]bp region of 0. 15 in all samples and >0. 5 in steady state. We employed a strategy identical to our analysis of post-mitotic gene reactivation.
(D) Mean +/-1,2,3 nucleosome positions relative to wildtype interphase, for datasets shown in (C), complete unnormalized datasets shown in Figure 6-figure supplement 1D. Circles denote nucleosome movements < 5 bp; arrow direction, size and color describes movements > 5 bp. (E) Nucleosome signal in NDR ([À40, 40]bp of the motif) at Bookmarked sites in mitosis CTCFaid -/+ IAA, normalized to WT interphase; unnormalized data shown in Figure 6-figure supplement 1C. DOI: https://doi. org/10. 7554/eLife. 47898. 016 The following figure supplement is available for figure 6: Figure supplement 1. Additional information on CTCF binding and nucleosome positioning in CTCF-aid ES cells. DOI: https://doi. org/10. 7554/eLife. 47898. 017.
Acknowledgements The authors acknowledge the  Imagopole France-BioImaging infrastructure , supported by the  French National Research Agency  ( ANR 10-INSB-04-01 , Investments for the Future), for advice and access to the UltraVIEW VOX system. We also acknowledge the  Transcriptome and EpiGenome, BioMics, Center for Innovation and Technological Research of the Institut Pasteur for NGS. We thank  Steven Henikoff  and  Srinivas Ramachandran  for advice on setting up MINCE-seq;  Andrea Voigt  for sharing protocols for EdU incorporation and click reactions;  Shahragim Tajbakhsh  for C2C12 and 3T3 cells;  Marlies Oomen ,  Job Dekker  and  Gerd Blobel  for discussions and critical reading of the manuscript. This work was supported by recurrent funding from the  Institut Pasteur , the  CNRS, and Revive  ( Investissement d'Avenir ;  ANR-10-LABX-73 ).
EPN was supported by  EMBO  ( ALTF523-2013 ), HSFP, and the  Roddenberry Stem Cell Center at Gladstone. NO is supported by  Revive. PN acknowledges financial support from the  Fondation Schlumberger  ( FRM FSER 2017 ), the  Agence Nationale de la Recherche  ( ANR 16 CE12 0004 01  MITMAT), the  Ligue contre le Cancer  ( LNCC EL2018 NAVARRO ) and the  European Research Council  ( ERC-CoG-2017   BIND ).
bp. Database and Author(s) Year Dataset title Dataset URL Identifier Owens N, Navarro 2019 CTCF confers local nucleosome https://www. ncbi. nlm. NCBI Gene P resiliency after DNA replication and nih. gov/geo/query/acc. Expression Omnibus, during mitosis cgi?acc=GSE131356 GSE131356 The following previously published datasets were used: Database and Author(s) Year Dataset title Dataset URL Identifier Teves SS, Tjian R 2018 Role of TBP in reactivation of https://www. ncbi. nlm. NCBI Gene transcription following mitosis nih. gov/geo/query/acc. Expression Omnibus, [RNA-Seq] cgi?acc=GSE109963 GSE109963 Owens N, Navarro 2019 Transcription factor activity and https://www. ncbi. nlm. NCBI Gene P nucleosome organisation in mitosis nih. gov/geo/query/acc. Expression Omnibus, cgi?acc=GSE122589 GSE122589 Stewart-Morgan KR, 2019 Transcription Restart Establishes https://www.
As these were major features and symbols of the GH in ancient Dunhuang, observing the linear traces in VHR satellite RS data required a proper method for identifying them in different environmental settings. In this study, it was crucial to identify the linear traces so that spatial patterns and defense policies employed along the Hexi Corridor could be better understood. Those traces had a mean width of around 10-12 m. This allowed an extraction approach for identifying them from VHR satellite remote sensing imagery to be designed.
Remote Sensing Data A total of 10 GF-1 images (see Table  1 , Figure  6 ), acquired between 30 July and 25 September 2014 were procured for use in this study. The GF-1 satellite RS data used in this study was obtained from the Centre for REsources Satellite Data and Application (CRESDA), China. The GF-1 satellite is fitted with two panchromatic and multispectral (PMS) cameras (Table  2 ) which can provide PAN and MS data with nadir resolutions of 2 m and 8 m, respectively, across an imaging swath of 60 km. It is worth noting that all the GF-1 images used in this study are cloud-free and are imaged at approximately the same local time (10:30 a. m. )  Remote sensing archaeologists interested in detecting, mapping and documenting AOIs are increasingly employing very high-resolution commercial satellite optical imagery.
The SPOT, IKONOS, QuickBird, GeoEye and WorldView data have been widely used in previous studies  [19, 33, 34, 54, 55] , and will continue to play a constructive role in the identification of archaeological features in the future  [29] , lacking applications of the latest GF-1 optical data. Compare with abovementioned commercial satellite data, the unique advantage of GF-1 data is that it is free to access by the registered users. Gaofen-1 Constellation is currently composed of four satellites (Gaofen-1 A, B, C, D), which jointly construct the operational constellation of land resources survey and monitoring. It shortens the global imaging period to 11 days and the revisit period to 1 day. In case of emergency, the target area can be revisited for several hours. Based on an analysis of the GF-1 PAN image, which has a spatial resolution of 2 m, it was easily found that the linear traces between two adjacent beacon towers were almost linear and had clear edges, and that the remnants of the GH on the ground produced obvious archaeological soil marks.
Compare with above-mentioned commercial satellite data, the unique advantage of GF-1 data is that it is free to access by the registered users. Gaofen-1 Constellation is currently composed of four satellites (Gaofen-1 A, B, C, D), which jointly construct the operational constellation of land resources survey and monitoring. It shortens the global imaging period to 11 days and the revisit period to 1 day. In case of emergency, the target area can be revisited for several hours. Based on an analysis of the GF-1 PAN image, which has a spatial resolution of 2 m, it was easily found that the linear traces between two adjacent beacon towers were almost linear and had clear edges, and that the remnants of the GH on the ground produced obvious archaeological soil marks. In view of these common features of linear traces, a method of automatically identifying linear traces of the GH that included image segmentation using the Otsu threshold segmentation algorithm and the extraction of linear traces based on the Hough Transform was developed.