dataesr/scientific-paragraphs-categorization huggingface dataset:
DNA binding (FRET) FRET experiments were conducted simultaneously with the FP experiments, as the 147 bp DNA used was labelled on the opposite end of the Alexa488 with an Alexa647 fluorophore. Data were collected on a BMG Labtech CLARIOstar by excitation of 488 nm light while recording the emission at 647 nm. Data were analysed in Prism and fit to an [Inhibitor] versus response, four-parameter nonlinear regression curve. Binding at different buffer conditions was determined via FP experiments using custom scripts for the OpenTrons OT-2 liquid handling robot, using 10 nM of 40 bp dsDNA Alexa488-labelled DNA (made by IDT) and screening four pHs (5, 6, 7 and 8) at seven different NaCl (0-250 mM) concentrations.
X-ray diffraction data were collected at the ALS synchrotron (12,397. 9 eV, 225 mm detector, ∆Φ = 0. 25) and indexed in DIALS  57. Molecular replacement was done using Phenix  58  and structures were refined in Coot  59  (as implemented through SBGrid  60  ). The apo Bd0055 structure was used as a search model along with 2 single-stranded DNA (ssDNA) fragments (2 and 3 bp). The data indicated an asymmetric unit containing 2 ss nt + 3 ss nt (that are not paired), bound to one Bd0055 dimer. The high degree of symmetry in the crystal lattice is dictated by the protein, which overrules breaks in symmetry from DNA sequence.
AUC AUC experiments were carried out in a Beckman Coulter Proteomelab XL-A analytical ultracentrifuge using an An50TI rotor. Samples were prepared in 50 mM MES, 10 mM NaCl and 1 mM EDTA pH 6. 0 using 500 nM 147 bp 601-sequence DNA (roughly 0. 6 OD) and spun in a sapphire-windowed cell. Data were collected by monitoring absorbance during 150 scans at 30,000 r. p. m. , 20 °C. Data were processed in UltraScan3, following standard processing protocols (2DSA, GA, GA-MC)  61. Concentrations that crashed out of solution before first scans were acquired were not processed.
Acknowledgements We thank the  LMS Genomics Facility  for RNA sequencing, and  A. Erbse  at  CU Boulder  for help with crystallography. This study was funded by a  Wellcome Trust   Investigator Award in Science  ( 209437/Z/17/Z , P. R. , J. T. , C. L. , R. E. S. ), a  UKRI   MRC core grant  ( MC-A658-5TY40 , T. W. , A. H. , K. M. S. ), and the  Howard Hughes Medical Institute  (K. L. , S. P. L. ).
Data availability All datasets are publicly available. Proteomics data have been deposited to PRIDE (PXD039405) and RNA-seq data to GEO under accession GSE220534. All genomes used were publicly available with no usage restriction (Supplementary Table  4 ). Structure data have been deposited at the Protein Data Bank (PDB 8FVX for apo Bd0055 and 8FW7 for DNA-bound Bd0055). Primers and oligos used in this study are provided in Supplementary Table  3. Raw microscopy images have been deposited in Zenodo (https://zenodo. org/record/8255694). Correspondence and requests for materials can be addressed to K. L. (karolin. luger@ colorado. edu) or T. W. (tobias. warnecke@lms. mrc. ac. uk). Source data are provided with this paper.
Bioinformatics: Read Mapping and Microbial Species Raw reads obtained from Ion Torrent sequencing technology were quality trimmed using AlienTrimmer v2. 0  [27]  with the following parameters: "-k 10 -l 45 -m 5 -p 40 -q 20" and human-related reads were removed using Bowtie2 v2. 5. 1  [28]  using the Human genome reference GRCh38 (accession GCA_000001405. 15, nucleotide identity > 90%). High quality reads were mapped onto the 8. 4 million gene oral catalog (https://entrepot. recherche. data. gouv. fr/dataset. xhtml?persistentId=doi:10. 15454/WQ4UTV (accessed on 2 October 2023))  [29]  using the METEOR software v1 (https://forgemia. inra. fr/metagenopolis/ meteor (accessed on 2 October 2023)), using a two-step procedure as described before  [30]. The resulting gene count table was then downsized to 1 million mapped reads using the R package MetaOMineR v1.
31 (https://github. com/eprifti/momr (accessed on 2 October 2023) or https://forgemia. inra. fr/metagenopolis/momr (accessed on 2 October 2023)) to take into account differences in sequencing depth; this was normalized for gene length and finally transformed into a frequency matrix using the FPKM strategy. The 8. 4 million gene oral catalog was previously organized, using a MSPminer tool, into 853 MSP species  [29, 31, 32] , corresponding to clusters of co-abundant genes used as proxies for microbial species, and containing core and accessory genes. Taxonomical annotation was assigned using the Genome Taxonomy Database  [31]. MSP definition and taxonomy are available from Data INRAe (https: //entrepot. recherche. data. gouv. fr/dataset. xhtml?persistentId=doi:10. 15454/WQ4UTV (accessed on 2 October 2023))  [29].
Biostatistics Statistical analysis was carried out using the R software v3. 6. 0  [32]. Comparisons between two groups were performed using unpaired Wilcoxon ranked tests, and FDR was controlled by p-values correction using the Benjamini-Hochberg (BH) procedure. Results were declared significantly different for a p-value less than 0. 05. Correlations between variables were determined using Spearman's correlations. Alpha and beta diversity indices were determined by using the vegan R package v2. 5. 7  [33]. PCA analysis was performed on a log10 transformed msp count table using FactoMineR v2. 4 and Factoextra v1. 0. 5 packages  [34, 35]. Box plots were computed using the ggpubr R package v0. 2. 1  [36] , heatmaps were created with the ComplexHeatmap v2. 2. 0 and pheatmap package v1. 0. 12  [37, 38] , and metagenomic analyses were performed using the momr R package v1.
Funding: This research was funded, in part, by  Aligning Science Across Parkinson's  (Grant number:  ASAP-000420 ) through the  Michael J. Fox Foundation for Parkinson's Research (MJFF). For the purpose of open access, the author has applied a CC BY 4. 0 public copyright license to all Author Accepted Manuscripts arising from this submission. E. M. is supported by a  Royal Free Charity Fellowship. M. A. provided additional funding from the  MetaGenoPolis  grant  ANR-11-DPBS-0001.
There was no significant difference in demographic data between participants included at M0 and the remaining participants evaluated at M12 (Supplementary Table  3 ). The inclusion criteria for Huntington's disease were (i) confirmed cytosine-adenine-guanine (CAG) expansion (≥ 36 CAG repeats) and (ii) stages 1 or 2 of the disease according to the Unified Huntington's Disease Rating Scale (UHDRS) Total Functional Capacity (TFC) scores (TFC ≥ 7). 25  Matched healthy controls were spouses or partners of Huntington's disease participants, gene-negative siblings or persons unrelated to the Huntington's disease participants with a Mattis Dementia Rating Scale (MDRS) global score ≥ 136. 26 Exclusion criteria for controls included alcohol or substance abuse and neurological co-morbidity. Participants' demographics and pathological disease burden scores (DBS)  27  are reported in Table  1.
IES consists of the mean cognitive RT (in seconds) divided by the test accuracy, a higher IES indicating a lower performance. We also calculated the global cognitive IES (mean of all cognitive IES). Missing data in classical cognitive paper-and-pencil tests were imputed using 10 iterations of the non-parametric random forest imputation algorithm  32  (Supplementary Table  4 ). The comparison of performance on paper-andpencil and SelfCog tests between healthy controls and Huntington's disease participants at M0 was assessed using linear models. The associations between SelfCog and classical scores in Huntington's disease participants at M0 and the reliability of these measures in all participants between M0 and M1 evaluations were calculated using Spearman's correlation coefficients and false discovery rate (FDR)-corrected P-values.
MRI data acquisition, processing and analysis The detailed description of MRI acquisition and processing is provided in Supplementary Appendix 2. Images were analysed using tools from the FMRIB Software Library (FSL). 34 rain MRI scans were obtained for 51 Huntington's disease participants at M0 and for 24 at M12 in France and Germany (Supplementary Tables  5  and 6 ). Structural 3D-T1 data were analysed with the standard (M0) and adapted (for longitudinal analysis) processing stream of FSL-voxel-based morphometry (VBM) (https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/FSLVBM). [34] [35] [36] [37]  Diffusion-weighted imaging data were processed and analysed with a tract-based spatial statistics (TBSS) pipeline at baseline  38  and using an optimized registration approach to register baseline and follow-up fractional anisotropy (FA) images for the longitudinal analysis.
39 The output used in the statistical longitudinal MRI data analysis consisted of the subtraction of the baseline from the follow-up images to obtain a difference image for each subject for grey matter (GM) volumes and white matter (WM) FA. We applied a voxel-wise general linear model (GLM) with non-parametric permutation tests (10 000 for FSL-VBM and 5000 for TBSS). We identified the GM or WM areas contributing to the regression models of the SelfCog scores and of the classical test performance at baseline in Huntington's disease participants. A second regression analysis was performed between the change in SelfCog scores or classical test scores and change in GM volumes or FA in Huntington's disease participants. Family-wise error correction for multiple comparisons was performed, implementing threshold-free cluster enhancement using a significance threshold of P < 0.
The variety of impairments in cognitive functions in Huntington's disease has shown that exhaustive evaluation should take into account at least language, executive functions, memory, visuo-perceptive function like in the CAPIT-HD, the CANTAB or the Huntington's Disease-Cognitive Assessment Battery (HD-CAB). 12 However, none of them was able to provide a unique composite endpoint reflecting the global cognitive capacity. Additionally, neuroscience can distinguish the stages of perception, function processing, decision-making and motor responses to analyse the function of interest in statistical accumulation models. 40 These models are not applicable in the type of neuropsychological assessment we provide here, given the low amount of data by individuals. An alternative consists in neutralizing the steps common to each condition by subtracting the motor condition which also includes a perceptual and decision phase and by analysing the remaining components, namely, language, memory, visuo-spatial processing and executive functions.
Acknowledgements We thank the Repair-HD consortium partners and the Repair-HD site staff who assisted in clinical data collection at the sites. We acknowledge  Enroll-HD  and REGISTRY methods (https://www. enroll-hd. org/); Huntington 's disease participants were recruited from these two studies supported by the  CHDI Foundation Inc. We also acknowledge additional contributors, namely,  Laurie Lemoine ,  Florian Jennequin ,  Alexis Gabadhino ,  Agnès Sliwinski ,  Justine Montillot ,  Nicolas Fraisse ,  Rima Belhamdi ,  Etienne Audureau ,  Rebecca Cousins ,  Laura Mills  and  Cheney Drew , for their help in data collection or data analysis. T. M. organized the study on site, and D. S. the regulatory aspects. M. L. designed the pictures for the SelfCog.
Funding Repair-HD  is funded from the  European Union 's  Seventh Framework Programme  ( FP7 ) for research, technological development and demonstration under grant agreement number  602245  and grants from the  Agence Nationale pour la Recherche  ( ANR-17-EURE-0017   Frontcog ,  ANR-10-IDEX-0001-02 PSL* ) and from  NeurATRIS  ( ANR-11-INBS-0011 ).
For ANDV and SNV infection on the control cell line, U2OS PCDH1-KO cells complemented with WT, n = 12 (SNV) and n = 18 (ANDV) wells of infected cells were examined over four independent experiments. Infectivities were compared by one-way ANOVA with Dunnett's test for multiple comparisons. Source data are provided as a Source Data file. (Fig. 7g, h ). These findings suggest that both monomeric and dimeric forms of PCDH1 provide suitable entry receptors for hantaviruses but also raise the possibility that viral particles may preferentially engage PCDH1 monomers.
03 IUs per cell and allowed infection to proceed for 12-14 h. For all infections, cells were fixed 12-14 h post-infection with 4% formaldehyde (Sigma) and counter-stained with Hoechst nuclear stain (Invitrogen). Number of infected cells (GFP+ cell counts) and viral infectivity (%GFP+ cells) was measured by automated enumeration of eGFP-expressing cells from captured images from multiwell plates, using a CellInsight CX5 automated fluorescence microscope with onboard HCS Studio software (Thermo Fisher, V. 6. 6. 0) or Cytation5 cell imaging multi-mode reader with Agilent Biotek Gen5 Microplate Reader and Imager software (BioTek, V. 3. 2). For sEC1-2 infection-inhibition experiments, pretitrated amounts of rVSV particles (MOI of 0. 1) were incubated with increasing concentrations of WT or mutant sEC1-2 at room temperature (RT) for 1 h prior to the addition to HUVEC monolayers in 96-well plates.
PCDH1 EC1 sequence alignment Alignment of amino acid sequences of the EC1 domain of PCDH1 from six different species was generated by Clustal Omega. The PCDH1 sequences used for the alignment, along with their GenBank accession numbers, were as follows: Mesocricetus auratus (golden or Syrian hamster), XP_021082321. 1; Peromyscus maniculatus bairdii (prairie deer mouse), XP_015863073. 1; Mus musculus (house mouse), NP_001390724. 1; Rattus norvegicus (Norway rat), XP_038953281. 1; Macaca fascicularis (crab-eating macaque), XP_045250283. 1; Homo sapiens (human), NP_002578. 2.
ELISA signal was developed using 1-Step TM Ultra TMB-ELISA substrate solution (Thermo Scientific) and measured at an absorbance at 450 nm on a Perkin Elmer Wallac 1420 Victor2 TM microplate reader or Cytation5 cell imaging multi-mode reader (Agilent BioTek Gen5 Microplate Reader and Imager software, V. 3. 2).
Hierarchical data clustering An in-house algorithm was used to perform hierarchical clustering of two experimental readouts: sEC1-2 competition ELISA (absorbance at 450 nm) and infection-inhibition assay (GFP-positive cells [%]). A sigmoidal function (A) (see below) was fitted to the normalized experimental readouts using a non-linear least square analysis as implemented in the SciPy package  55. Hierarchical clustering and a heatmap comparing each sigmoidal curve to all other sigmoidal curves were built using the clustermap (method = average; metric = Euclidean distance) function as implemented in the seaborn python package (https://seaborn. pydata. org/index. html): y = y min + ðy max À y min Þ=ð1 + 10 log 10ðEC50ÀxÞxHillÞ Þ h i where y corresponds to the experimental readout (ER) (relative ELISA signal or percent infectivity); y min and y max are the minimum and maximum ERs, respectively; EC 50 is the value that gives half-maximum ER (half-y max ); Hill describes the slope of the curve, and x is the amount of soluble protein, log 10 (sEC1-2), at the particular y (ER).
Stable cells expressing PCDH1 variants cDNA constructs encoding full-length human PCDH1 (isoform 1, Genbank accession number NM_002587) or lacking the fourth extracellular cadherin (EC) repeat ΔEC4 (residues 328-446) were synthesized in frame with Myc and Flag epitope tags at the C-terminus (Epoch Biolabs or Twist Bioscience) and cloned into the pBABE-puro retroviral vector  57. Residue numbers are based on the full-length sequence of PCDH1 starting from the signal sequence (i. e. , residue D85 in this study matches PDB 6MGA residue D28, and E137 in this study matches PDB 6MGA residue E80). Mutations for the PCDH1 variants were introduced into the pBABE-puro-PCDH1 plasmid using standard molecular techniques and confirmed by Sanger sequencing. Human U2OS PCDH1-KO cells ectopically expressing the above PCDH1 variants were generated by transduction with pBABE-puro-based retroviral vectors.
PA5-83876, Thermo Fisher) by rocking for 1 h at RT, washing with PBS with 0. 01% Tween 20 (PBST) and incubating with an anti-rabbit IgG Alexa Fluor TM Plus 800 pAb (1:10,000 dilution, catalog No. A32735, Thermo Fisher) for 1 h, rocking at RT. After thoroughly washing with PBST, western blots were imaged on a LI-COR OdysseyⓇ Fc Imager (LI-COR Biosciences, LICOR Image Studio software, V. 1. 0. 19) and the uncropped images are included in the Source Data file.
Sections were then rinsed, and a peroxidase-labeled polymer (secondary) was applied for 40 min. Slides were rinsed and a brown chromogenic substrate 3,3′ Diaminobenzidine (DAB) solution (Dako Agilent Pathology Solutions) was applied for 8 min. After the substratechromogen solution was rinsed off, the slides were counterstained with hematoxylin and rinsed. The sections were dehydrated, cleared with Xyless, and then coverslipped. In situ hybridization (ISH) was performed to detect ANDV RNA using the RNAscope 2. 5 HD RED kit (Advanced Cell Diagnostics) according to the manufacturer's instructions. An ISH probe targeting ANDV S segment (GenBank accession number: NC_003466. 1) was designed and synthesized by Advanced Cell Diagnostics (#900241, Advanced Cell Diagnostics). Tissue sections were deparaffinized with xylene, underwent a series of ethanol washes and peroxidase blocking, and heated in kit-provided, antigen retrieval buffer followed by digestion by kit-provided protease.
Acknowledgements The authors thank  Estefania Valencia ,  Laura Polanco ,  Javier Janer , and  Isabel Gutierrez  for technical support,  Eva Mittler  for valuable input, and the  Einstein Flow Cytometry Core. This work was supported by  NIH  grant  AI132633  (to K. C. , J. M. D. , and Z. W. ). R. K. J. was partly supported by  NIH  grant  P20GM134974.
Data availability The authors declare that the data and data sets supporting the findings of this study are available within the paper, its Supplementary Information files, and in the Figshare repository. The interfacial prediction data set generated in this study has been deposited in the Figshare database under accession code https://doi. org/10. 6084/m9. figshare. 23401916. v1  58  , the PDB with the two modeled EC1 loop conformations has been deposited in the Figshare database under accession code https://doi. org/10. 6084/m9. figshare. 23398358  59. The raw data generated in this study is provided in the Source Data file. Source data are provided with this paper.
Detection of PCDH1 surface expression by flow cytometryHuman U2OS cells expressing variant PCDH1 were seeded in 6-well plates 24 h prior to immunostaining. Cells were chilled on ice for 10 min (min) and blocked with chilled PBS/10% FBS for 30 min at 4 °C. Surface PCDH1 was stained using human anti-EC7 mAb-3677 (5 µg/mL, Donnelly Centre and Department of Molecular Genetics, University of Toronto) followed by anti-human IgG Alexa Fluor TM 488 pAb (1:500 dilution, catalog No. A-11013, Thermo Fisher) for 1 h at 4 °C. After washing, cells were resuspended in PBS/2% FBS, and those intended to be sorted were stained with TO-PRO TM 3 Ready Flow TM Reagent (Invitrogen) to identify and exclude dead cells. Cells were passed through a 0. 41 µm Nylon Net hamster kidney cells stably expressing Cas9. The best candidate sgRNA (sgRNA2: 5′-GACTACGGTTTTCCAGACTGGG-3′) targeted sequences encoding the homologs of human F83 and D85 in the hamster PCDH1 gene (F79 and D81, respectively) (accession number NW_024429184.
With new bed topography, they refined some promising oldest ice sites proposed by Van Liefferinge and Pattyn (2013) using the same model and suggested that especially the region immediately south of DF station is promising for holding old ice. A 1-D ice flow model called IcIES-2 was adapted to DF and the DF-New Dome Fuji (NDF) site transect by  Obase et al. (2023). They examined the influence of ice thickness and GHF on the age of the ice and pointed out that ice thickness is one of the most critical factors for the preservation of old ice. They suggested that analyzable 1. 5 Myr old ice could be expected at DF when the GHF is small enough to keep the basal ice from melting. Tsutaki et al. (2022)  compiled a new ice thickness data set collected by ground-based radar surveys over the last 30 years, which revealed with higher resolution the complex landscapes compared with the previous data sets.
The AWI RES system transmits radar waves with a center frequency of 150 MHz, a band width of 20 MHz and an amplitude of 1. 6 kW. During the survey it effectively operated as a pulse-limited radar with the 600 ns wide pulse. The theoretical vertical resolution in ice for the 600 ns burst is 50 m  (Nixdorf et al. , 1999). In this study we use radar returns of the 600 ns long burst from internal reflection horizons (IRHs), as well as from the ice-bedrock interface. The raw radar data have a mean spacing of 5 m along the flight line (which varies with real speed and direction of aircraft) and are sampled at a time interval of 4 ns  (Karlsson et al. , 2018). Before picking the IRHs and the ice-bed interface, the radar data are resampled to 12 ns and stacked 7-fold, which leads to a mean trace spacing of ∼ 35 m.
Horizon picking and dating In order to provide age markers (i. e. , the age of IRHs) and ice thickness to use as constraints in our 1-D flow model, IRHs are traced in the two-way travel time (TWT) domain. The surface reflection is picked automatically in the program Echos and then subtracted in all traces to shift the first break of the radar data to time zero. The maximum reflection power of IRHs is traced manually in the seismic software package "Section", which allows IRHs to be continuously traced in different radar profiles through intersections between profiles. This ensures that the same isochronous horizons are traced. We trace six (H1, H2, H4-H7) or seven (H1-H7) relatively distinct and continuous IRHs in the radar profiles, since the third IRH, H3, is not clear and continuous enough to be traced in some profiles.
Ice-bed returns were picked by  Karlsson et al. (2018)  through semiautomatic detection routines in MATLAB. These ice thickness data are available on Pangaea  (Eisen et al. , 2020). The ice-bed returns are often diffuse, especially around mountain peaks, which results in disagreements when using different methods to trace ice-bed returns and thus in differences in ice thickness and modeling results. We emphasize that  Karlsson et al. (2018)  picked the first (uppermost) ice-bed return when there were uncertainties, so ice thickness estimates can be considered a minimum ice thickness in some places. TWT is converted to depth assuming a constant radar wave speed of 168. 5 m µs -1 in ice  (Winter et al. , 2017; Lilien et al. , 2021 ) and a 15. 5 m firn correction calculated from the depth-density curve in the B53 ice core.
Our sensitivity study also showed the correspondence between the age of basal ice and ice thickness as being a crucial input in the ice flow model. Accurate ice thickness can improve the reliability of the modeling results. Our radar data were collected with an incoherent burst radar system, which means hyperbolic effects in signals are strong and affect the accuracy of subglacial topography. According to  Tsutaki et al. (2022) , the average difference between ice thickness observed from the JARE radar system (with high-gain and high-directivity antennae) and the AWI RES system is -8 m, and the standard deviation is 108 m. The high standard deviation implies the details of the bed topography observed by two radar systems could be significantly different, which may cause the misalignment in modeling results.