dataesr/scientific-paragraphs-categorization huggingface dataset:
Then we used a graph-based algorithm to find a set of barcodes where there was no pairwise alignment (blastn algorithm, word size ¼ 6) between any two barcodes (Figure  S1B ). Each barcode was a node in the graph. Initially, all nodes are connected in the undirected graph. We performed an all-vs-all alignment of the barcode sequences using the blastn algorithm (word size ¼ 6). We removed the edge between two barcodes (nodes) if the two barcodes were aligned. The remaining edges only connect barcode pairs that have no alignment. Therefore, a complete subgraph (clique) is a set of barcodes in which any two barcodes have no alignment. We used the networkx python package to find the cliques with enough nodes (number of barcodes). We first designed a set of 16-bp barcodes for amplicon-1 (Table  S1 ; Figure  S1 ).
Barcoded long-range PCR to amplify the region flanking exon-1 of the HTT gene For the French HD cohort, the target region was covered by two overlapping amplicons (amplicon-1 and amplicon-2; Figure  1F ). Amplicon-1 mainly covers the upstream region of the CAG repeat region (GRCh38, chr4:3069608-3075517). The amplicon length (without barcode) is 5,910 bp. Amplicon-2 mainly covers the downstream region of the CAG repeat region (GRCh38, chr4:3071119-3079972). The amplicon length (without barcode) is 8,854 bp. For each amplicon, ten different barcodes were added to the 5 0 side of both forward and backward primers. The complete primer sequences are listed in Tables  S1  and S2. The combination of the both forward and backward primers can be used to multiplex 100 samples. The French HD cohort contains 396 HD  Sequencing and data analysis workflow (A) Barcodes were added to both forward and backward PCR primers and the pooled PCR products of multiple samples were sequenced with Oxford nanopore sequencing.
(B) De-multiplexing strategy of NanoBinner. NanoBinner aligns a read to barcode sequences with the 256-bp amplicon sequence immediately next to it. The 256-bp amplicon sequence acts as an anchor so that the matching of barcodes is at the correct position. NanoBinner assigns a read to a barcode if the Phred scale mapping quality score is R30. (C) Joint quantification of CAG and CCG repeat sizes using NanoRepeat. NanoRepeat first estimates the upper and lower bounds of the CAG and CCG repeat sizes separately, and then performs a joint quantification to refine the repeat sizes. In the joint quantification step, NanoRepeat generates a series of template sequences with m CAG repeats and n CCG repeats, where m and n are all integers within the upper and lower bounds determined in the first step.
Demultiplexing of the sequencing data with NanoBinner We developed NanoBinner, a tool for de-multiplexing of barcoded amplicons from long-read sequencing data. Given the moderate error rate in long reads, there might be random matches of barcodes inside the amplicon sequence. To avoid this potential issue, NanoBinner aligns the barcode sequence as well as the 256-bp amplicon sequence next to it. The 256-bp amplicon sequence acts as an anchor so that the matching of barcodes is at the correct position. The alignment is performed using minimap2  27  with the parameter for nanopore reads (-x map-ont). This parameter can be changed if the input is PacBio reads. Minimap2 calculates a mapping quality score for each barcode, which is the Phred scale of the probability that a read is misplaced. NanoBinner assigns a read to a barcode if the Phred scale mapping quality score is R30.
DNA transfection was done by electroporation using an Invitrogen Neon transfection reagent using the electroporation conditions (ND33392: 1,450 V, 20 ms, 2 pulses), following the guidelines provided by the manufacturer. Fibroblasts were selected with puromycin (2 mM) for 24 h and subsequently expanded for genomic DNA extraction. Cells were not authenticated or tested for Mycoplasma by the investigators since they previously passed the quality controls of CHOP Research Vector Core and the Coriell Institute for Medical Research cell repository. None of the cells used in the study were listed in the ICLAC database of commonly misidentified cell lines.
5 mL ddH 2 O. The PCR thermal cycling program was 95 C for 2 min, followed by 25 cycles of 95 C for 15 s, 57 C for 15 s, 72 C for 45 s, and a final extension at 72 C for 5 min. The PCR products were separated by electrophoresis through a 1. 5% agarose gel stained with ethidium bromide. The gel bands were quantified using the Image Lab software (Bio-Rad).
The barcoded amplicons were pooled, size selected, and then sequenced in a MinION flow cell using the Oxford Nanopore GridION sequencer. The sequencing data were de-multiplexed with NanoBinner, which was originally developed for this work. Given the moderately high error rate and the flexibility of alignment for long reads, there may be random barcode matches inside the amplicon sequence. To avoid potential random matching, NanoBinner compares the barcode sequence as well as the 256-bp amplicon sequence immediately next to the barcode. The 256-bp amplicon sequence acts as an anchor such that the matching of barcodes is at the correct position. NanoBinner also uses a new algorithm to assign barcodes, which is described in the material and methods section. After de-multiplexing, CAG repeat sizes are detected by NanoRepeat, a novel repeat detection tool originally developed for this study.
Assessment of computational methods We first tested the accuracy of repeat quantification in whole-genome sequencing data. We used the Oxford Nanopore dataset of the CHM13 genome. 35 The CHM13 cell line has near-complete homozygosity, with only a few exceptions. The telomere-to-telomere consortium has finished the de novo assembly of CHM13 primarily based on PacBio HiFi reads, but supplemented with data from other sequencing platforms. As the CHM13 v. 1. 1 assembly sequence is highly reliable, repeat counts from the assembly sequence can be considered as a truth set for method assessment. We benchmarked NanoRepeat along with two other widely used repeat detection tools, namely RepeatHMM  36  and STRique. 37 Repeat detection was performed on 2,370 short tandem repeat (STR) regions in the CHM13 genome.
SNPs with high allele frequency in HD samples After the reads were phased according to the CAG and CCG repeats, SNP detection was performed for each allele (see material and methods section), generating a list of haplotyped SNPs of 10. 3 kb. The complete list of the SNPs is shown in Tables S4, S13, and S14. A total of 110 SNPs were identified from the two HD cohorts; 56 SNPs are in the upstream HTT gene region; one SNPs is in 5 0 UTR; 7 SNPs are in the exon-1 coding region, and 46 SNPs are in intron-1. Eight SNPs are novel and have not been found in dbSNP, the Genome Aggregation Database (gnomAD), or the 1000 Genomes Project. 38 We validated the eight novel SNPs by examining the alignments using the Integrative Genomics Viewer (IGV  39  ), we acknowledge that such novel SNPs could be still due to errors in longrange PCR and subsequent sequencing.
The IGV screenshots are displayed in Figures  S5-S12. We first analyzed the SNPs identified from the French cohort. Among those identified, 19 are common with allele frequencies (AFs) of R5% in the normal or mHTT chromosomes, or the gnomAD database (non-Finnish European [NFE] population). The positions of the 19 SNPs are shown in Figure  4A. Notably, their AFs in normal and mHTT chromosomes are dramatically different (Figure  4B ; Table  2 ). To verify SNP detection, the AFs of all identified SNPs were compared with their AFs in the gnomAD database. The AFs of SNPs in the normal chromosomes are highly correlated (R 2 ¼ 0. 9725) with their AFs in NFE population in the gnomAD database. However, the correlation between the AFs of SNPs in the mHTT chromosomes and those in the gnomAD database are low (R 2 ¼ 0.
Analysis of haplotypes of the HD samples To better understand the percentage of HD individuals who could benefit from our editing approach, the haplotypes of normal and mHTT chromosomes of the HD individuals as well as those in the 1000 Genomes Project phase 3 dataset (denoted as 1KG dataset hereafter) were analyzed. The 1KG dataset provides phased SNP/indel calls of 2,504 individuals, of whom 404 individuals are from the NFE population. In the following haplotype analysis, we used all SNPs listed in Table  2  except SNP11, as it resides in the CAG-CCG repeat region and was not genotyped in the 1KG dataset. In total, we observed 37 different haplotypes in the French HD cohort (including normal and mHTT chromosomes) and individuals of NFE population in the phase 3 dataset. The SNPs carried by each haplotype are shown in Table  S6.
The frequencies of the top 10 most abundant haplotypes are shown in Figure  6A. Hap1 (no SNP across the region) is the predominant haplotype (82. 58%) in the mHTT chromosomes. It is also the most abundant haplotype in normal chromosomes (frequency ¼ 48. 39%). The frequencies of minor haplotypes (haplotypes 2-37) are dramatically different between normal and  mHTT chromosomes. The frequencies of the minor haplotypes in the normal chromosomes are highly correlated (R 2 ¼ 0. 8036) with those in the 1KG dataset. In contrast, the haplotype frequencies in the mHTT chromosomes have no correlation (R 2 ¼ 0. 0031) with those in the 1KG dataset (Figures  6C  and 6D ). To validate these discoveries, we examined the haplotypes of the CHDI cohort, which contains HD samples from multiple continents and ethnicities.
Searching for potential editing sites for individuals with hap1 in both alleles We tried additional strategies to find potential editing sites for individuals with hap1 in both alleles. First, we called indels and structural variants (SVs) from our sequencing data. However, all common Indels/SVs are located in tandem repeat regions where the guide RNAs are not specific. Next, we analyzed SNPs within a greater range than our earlier 10-kb limit using the 1KG dataset (NFE population). Since the 1KG dataset provides phased SNPs, we can extract the SNPs from chromosome 4 with different haplotypes. We found that, in hap1 chromosomes, there are fewer SNPs in the HTT gene (167 kb) compared with the intergenic region. (Figure  S4 ). In regions upstream of the HTT gene, the nearest high-frequency SNP (AF > 20%) in hap1 chromosomes is 12.
Cumulatively, our data provide a comprehensive analysis of allele-specific target sites for CRISPR-based gene editing, which relies on the ability of an SNP to provide a PAM site for targeted editing of the expanded allele. For effective editing, we focused on genomic regions within 5 kb of exon-1, because previous work showed that the distance between upstream and downstream guides influenced editing efficacy. 20 ong reads have a higher per-base error rate than short reads. However, the sequencing error tends to be random and the consensus sequence of high-coverage long reads can be very accurate. 46 We required at least 503 coverage per allele for analysis, and the vast majority of samples have more than 2003 coverage per allele. In our results, the AFs of the SNPs in normal chromosomes are highly correlated with those in the gnomAD database, indicating that the SNP detection is correct.
The haplotype frequencies in normal chromosomes are also highly correlated with those in the 1KG dataset. In addition, we validated the genotypes of SNP16 using Sanger sequencing and a TaqMan SNP genotyping assay, both of which gener-ated results identical to the ONT long reads. Of note, ONT sequencing produces more Indel errors in homopolymer regions (e. g. , the poly(A) sequence). Therefore, indel detection from ONT reads may be less accurate in homopolymer regions. As our study focuses on SNP detection, our data and are less affected by this limitation. NanoBinner and NanoRepeat are novel computational tools developed for this work. NanoBinner is a demultiplexer for amplicon sequencing data. Existing tools, such as DeepBinner  47  and qcat, only support the barcoding kits provided by ONT.
NanoBinner is a general tool and can work with any user-provided barcodes. NanoRepeat is a tool for repeat detection from amplicon sequencing. It uses alignment-based quantification and can jointly quantify two adjacent tandem repeats, phase the reads, and report haplotypes. We evaluated NanoRepeat on both whole-genome sequencing data and amplicon sequencing data of our HD cohort. In the whole-genome data, NanoRepeat outperformed other repeat quantification tools and reduced the average quantification error by 3. 5-fold. In our amplicon sequencing data of the CHDI cohort, NanoRepeat is highly consistent with PCRbased fragment analysis, which is commonly used in clinical labs for diagnostic purposes. sgRNAs targeting SNPs1-6 were validated previously. 20 n this study, we developed sgRNAs targeting SNP16 and tested it in cell lines.
Figure1. Sequencing and data analysis workflow (A) Barcodes were added to both forward and backward PCR primers and the pooled PCR products of multiple samples were sequenced with Oxford nanopore sequencing. (B) De-multiplexing strategy of NanoBinner. NanoBinner aligns a read to barcode sequences with the 256-bp amplicon sequence immediately next to it. The 256-bp amplicon sequence acts as an anchor so that the matching of barcodes is at the correct position. NanoBinner assigns a read to a barcode if the Phred scale mapping quality score is R30. (C) Joint quantification of CAG and CCG repeat sizes using NanoRepeat. NanoRepeat first estimates the upper and lower bounds of the CAG and CCG repeat sizes separately, and then performs a joint quantification to refine the repeat sizes. In the joint quantification step, NanoRepeat generates a series of template sequences with m CAG repeats and n CCG repeats, where m and n are all integers within the upper and lower bounds determined in the first step.
Acknowledgments We thank the participants and their family members for contributing samples for this research. Participants of the French cohort were recruited at the  PitieSalpetriere Hospital in Paris, France. Samples for the CHDI cohort was generously provided by the participants in the Enroll-HD study and made available by the  CHDI Foundation , a non-profit biomedical research organization exclusively dedicated to collaboratively developing therapeutics for HD. This study is in part supported by the  NIH/NIGMS  grant  GM132713  (to KW. ) and the  CHOP Research Institute.
The greatest amount of seismic compression is expected in the funicular regime due to the presence of both air and water. Also, undrained cyclic simple shear test data for sand specimens initially in the funicular regime of the SWRC are available for calibration and validation of the model  [7, 8]. This constitutive model is intended to be simple and based on an existing constitutive model (UBCSAND) so that properties can be calibrated with non-specialized laboratory tests and to facilitate implementation into software packages used by practitioners in geotechnical earthquake engineering (e. g. , OpenSees, Plaxis, FLAC, etc. ). This paper presents the details of the model formulation including the prediction of key variables. The model was calibrated using experimental shear stress-strain backbone curves from drained cyclic simple shear tests and transient SWRC scanning path measurements from undrained cyclic simple shear tests.
This paper presents SEAHORS, an R shiny application available as an R package, dedicated to the intra-site spatial analysis of piece-plotted archaeological remains. This opensource script generates 2D and 3D scatter and density plots for archaeological objects located with cartesian coordinates. Many different GIS software already exist for this, but they mostly require specific skills and training to be used and are rarely designed for the particular needs of archaeological applications. The goal of SEAHORS is to make the two and three-dimensional intra-site spatial exploration of archaeological data as user-friendly as possible, in order to give the opportunity to researchers not familiar with GIS and R software to utilise such approaches. SEAHORS has an easily accessible interface and can import data from text and Excel files (.
csv and. xls/xlsx respectively) without preformatting. The application includes functions to concatenate columns and to merge databases, for example when spatial data (XYZ coordinates) and analytical data (e. g. taxonomical attribution of faunal remains, typo-technological attributes of artifacts, etc. ) are stored in separate files. SEAHORS can generate five types of plots: 3D, 2D and density plots, as well as 2D plots by slices (i. e. subdivisions according to a third dimension) and 2D plots with a modification of the angle of projection (i. e. to explore spatial organization without the constraints of the field grid orientation). SEAHORS has visualization tools with several sorting and formatting options (color, size, etc. ) applicable to coordinates and all possible analytical variables (i.
2023  for a recent review of such use). Following early calls for the development of spatial analyses of archaeological objects (e. g. Laplace & Méroc 1954), this practice was renewed when computer power became increasingly accessible to archaeologists at the end of the 1980s. Software dedicated to spatial analysis were developed in the next decades: Esri ARC/INFO in 1982  (Morehouse 1985) ; Newplot developed since 1986  (Dibble & McPherron 1988) ; Paleo III (by Pierre-Alain Gillioz, see application in  Chadelle et al. 1996) ; FrAcTool  (Houllier & Arnoux 2001) ; the adaptation of DataDesk to archaeological applications (Lacrampe-Cuyaubère 1997); QGIS in 2002 (QGIS. org 2023); the web application and R package archeoViz  (Plutniak 2023a; Plutniak 2023b ). More recently, web applications dedicated to specific archaeological sites were developed, such as Plotit for Combe-Capelle and others sites (R shiny application; https://oldstoneage.
com); PyCoCu for Combe-Cullier (Python script;  Sécher et al. 2020) , Virtual Poeymaü for the Poeymaü Cave (R shiny application; Plutniak 2021b) and an R shiny application for the La Roche à Pierrot site  (Couillet et al. 2022 ). However, using these software for analyzing spatial distributions of archaeological remains can be challenging due to several factors; such as complex interfaces, difficulties in adapting software to specific needs or databases to the required format, lack of user training, cost, or the absence of a support community. This paper presents SEAHORS (Spatial Exploration of ArcHaeological Objects in R Shiny), a free R Shiny open-source application, that allows easy and quick exploration of the spatial distribution of archaeological objects. SEAHORS is available as an R package on CRAN web server (https://cran.
SEAHORS overview SEAHORS is an R shiny application available as an R package. This application is distributed under the General Public License (GPL) license v3. 0. R is a free and open-source statistical environment, with a large supportive community (R core team 2022). However, using a programming language can be challenging for untrained archaeologists. The Shiny package offers the possibility to build a point-and-click interactive interface, allowing for a more user-friendly experience  (Chang et al. 2022). SEAHORS also relies on the Plotly package  (Sievert et al. 2017) , offering interactive data visualization by displaying information associated with each point directly on the visualized plots. To showcase SEAHORS' main features, spatial data from the Paleolithic site of Cassenade (Dordogne, France;  Discamps et al.
Data import: the Load data panel The Load data panel allows the import of different types of data for spatial exploration. This panel is divided into five subpanels: The first subpanel, Import XYZ data, is the most important and is a mandatory step before spatial exploration. It allows the user to set the four basic data to import, namely the XYZ coordinates and the ID of objects. If the three columns of the XYZ coordinates are named "X", "Y" and "Z", respectively, in the imported data frame, the script will automatically load them. Otherwise the column names can be set up manually. Note that only rows with numeric values for the three X, Y and Z coordinates will be taken into account by SEAHORS. The values of the X, Y and Z coordinates can be inverted (i. e. multiplied by -1) by checking the boxes in front of these data: this is useful, for instance, when Z values were recorded in the field as depth and not altitude.
The box in front of each variable allows the user to select the corresponding column of the imported sheet. The second subpanel, entitled Merge additional data, offers the possibility to import supplementary data from a second dataset (i. e. a second Excel or text file). To do this, determining unique object IDs in the Import XYZ data subpanel is required. These unique IDs are used to merge the XYZ dataset and the supplementary dataset; it must then be present in the two files. After merging, a report displayed in the bottom part of the subpanel describes the result of the merging by listing: the IDs of objects that are not unique 1) in the XYZ dataset, 2) the IDs of objects not unique in the supplementary dataset, 3) the objects present in the XYZ dataset only (i. e. no object ID correspondence in the supplementary dataset), and 4) the objects from the supplementary dataset not merged because of a lack of correspondence in the XYZ dataset.
The third subpanel, Import orthophoto, allows loading an orthophotographic image that can then be used in the 2D plot and Density plot. The orthophotographic image should be obtained from a georeferenced model using the same excavation grid as the XYZ dataset. The orthophotographic image must be in. TIFF and smaller than 150 MB. The fourth subpanel is used to Import refit data. Users can either import a separate file that lists refit data or select the appropriate column from the main dataset (the one already imported using the Import XYZ data subpanel). The refit data should be organized as a spreadsheet listing refitted objects (one row per object) with one column for the object's unique ID and one column for the ID of the refit group to which the object belongs. Groups of refitted objects can link two objects or more.
For instance, if we consider that three objects (unique IDs 1300, 1502 and 1603) refit together, then they belong to one refit group ID, which we name "refit group 1". The refit database should then consist of 2 columns (unique IDs, refit group ID) with three rows with "1300", "1502" and "1603" in the first column, and in the second column "refit_group_1", "refit_group_1", "refit_group_1". The last subpanel (Concatenate two columns) gives the possibility to concatenate two columns. This can be very handy, for example, to merge the square values (e. g. C50) and the object number (e. g. 3) to generate unique object IDs (e. g. C50-3).
The "Quick sidebar" panel The "Quick sidebar" panel allows the user to easily subset the dataset to facilitate visualization, as well as to modify point size, color or shape in the different plots. Modifying the sliders and check-boxes directly modifies the objects that are included in projections. This panel is subdivided into five subpanels easily accessible with buttons (Figure  2 ): The first subpanel is dedicated to subset the dataset shown in the plots. Numeric variables will be displayed with sliders and categorical variables as check-box lists; The second subpanel can be used to adjust the point size according to their XYZ coordinates or to categories (i. e. variable modalities); The third subpanel is used to color the points according to the modalities of a variable; The fourth subpanel is used to change the point shapes (circle, triangle, square, diamond) either for all points or for specific variable modalities; The last one offers some additional display options, such as the possibility to change the size of axis labels and tick marks, the name of axis legends, the space between major tick marks as well as the breaks of minor tick marks.