dataesr/scientific-paragraphs-categorization huggingface dataset:
Animal behaviour The rotarod protocol was slightly adapted from  Menalled et al. (2009) : mice were tested over three consecutive days, each daily session included a 5 min training trial at 4 rpm on the rotarod apparatus. One hour later, the animals were tested for three consecutive accelerating trials of 5 min with the rotarod speed linearly increasing from 4 to 45 rpm over 300 s. The latency to fall from the rod was recorded for each trial. Mice remaining on the rod for more than 300 s were removed and their time scored as 300 s. Data from the training trial were not included. For hind limb clasping, mice were tested once a week from 6 to 11 weeks old. They were suspended by the tail for 30 s and the clasping phenotype was graded according to the following scale: level 0, no clasping; level 1, clasping of the forelimbs only or both fore-and hindlimbs once or twice; and level 2, clasping of both foreand hindlimbs more than three times or more than 5 s.
Image acquisition For DARPP32 depletion studies in shCYP46A1 experiments, images of immunostained sections were acquired with LAS V3. 8 (Leica) software, with a bright-field Leica DM 4000B microscope equipped with 40 Â/0. 75 and 100 Â /1. 30 lenses and a Leica DFC500 digital camera. Confocal images were acquired with an Olympus BX 61 microscope equipped with 60 Â/1. 35 lens and Fluoview FV-1000 image acquisition system. The extent of shCYP46A1-induced lesions in the striatum was analysed in six DARPP32-stained sections per animal (320 mm between 40-mm thick sections), selected to obtain a complete rostrocaudal sampling of the striatum and by quantifying the area of the lesion with a semi-automated image-analysis program (ImageJ software, NIH). Sections throughout the entire striatum were analysed.
The area of the striatum showing a loss of DARPP32 staining was measured for each animal with an operator-independent macro. The volume was then estimated using the following formula: volume = d(a1 + a2 + a3 + Á Á Á), where d is the distance between serial sections (320 mm), and a1, a2, a3 etc. are DARPP32-depleted areas for individual serial sections. The average grey value of all pixels measured in the lesioned area was recorded for each depleted area. Results are presented as the calculated value for volume of the DARPP32 depleted region for each animal. For studies in R6/2 mice, the analysis of DARPP32 and EM48 staining, images were acquired from a confocal laser scanning microscope (TCS, SP5, Leica Microsystems). DARPP32-stained neurons were manually traced and MSN area quantified using ImageJ software.
For the counting of aggregates, EM48-stained images were segmented using the same global threshold and quantified using ImageJ software. An x, y pixel size of 150 nm and a z-step of 500 nm were chosen, laser intensity and detector gain were constant for all images. Aggregates were segmented in 3D using the same global threshold. The ImageJ plugin 3D object counter was used to measure volume and mean intensity of each aggregate. For each aggregate, mean intensity is the mean of all voxel intensities in an 8-bit scale.
1H ), therefore reproducing previous observations in the caudate of patients with Huntington's disease (Del Toro et al. , 2010). Comparison of cellular cholesterol localization in the striatum of wild-type and R6/2 mice was assessed by filipin staining, a naturally fluorescent polyene probe that binds to cholesterol but not to esterified sterols. Cholesterol labelling showed a marked staining of plasma membrane in both wild-type and R6/2 striatal cells. Cholesterol accumulation in internal organelles was not observed in R6/2 mice (Supplementary Fig. 1 ). Despite decreased expression of CYP46A1, 24S-OHC was not altered in the striatum of R6/2 mice (Fig. 1H ). Altogether, these data indicate that CYP46A1 levels are reduced in the striatum from Huntington's disease patients, mouse and cellular models.
Acknowledgements We acknowledge  Philippe Mailly  for his contribution to 3D reconstruction of microscope images of AAVrh10-GFP injected mice,  Ariel Di Nardo  for critical reading of the manuscript,  Peter Vanhoutte  for helpful scientific discussions and  Emmanuel Brouillet  for fruitful comments on transcriptomic data from the literature. We thank the vectorology platform (Inserm  U1089 ,  Nantes, France)  for the production of all AAV vectors.
, 2015; Kreibich et al. , 2014). Models based on the shallow-water equations are the most common approach for detailed inundation modelling  (Costabile and Macchione, 2015). They are considered as state-ofthe-art for large-scale real-world applications and have been the focus of much research over the last 2 decades. They also benefit from widely available detailed datasets obtained from remote sensing techniques, such as laser altimetry  (Dottori et al. , 2013). Difficulties remain nonetheless for estimating the roughness parameters which may vary significantly in space, particularly in floodplains. Published by Copernicus Publications on behalf of the European Geosciences Union. Different state-of-the-art hydraulic models exist to compute the inundation characteristics. However, their validation for urban flood configurations remains incomplete as reference data from the field are relatively scarce and difficult to obtain  (Dottori et al.
The present set-up enables the inflow discharge to be controlled independently in each street. We focus on steady flow conditions, which constitute a realistic approximation for long-duration floods. The study provides new insights into the distribution of water depths and the discharge partition in a more general urban setting than considered in previous research. The experimental dataset is available for testing numerical models and we provide here a comparison with the results of one specific model, namely WOLF 2-D, which has been widely used for inundation mapping  (Erpicum et al. , 2010a)  and in flood risk analysis research  (Beckers et al. , 2013; Bruwier et al. , 2015; Detrembleur et al. , 2015; Ernst et al. , 2010). The experimental and numerical models are presented in Sect. 2. The influence of varying the total inflow discharge is analysed in Sect.
Acknowledgements. The Icube Laboratory experimental model was funded by the  Alsacian network of laboratories in Environmental Engineering and Sciences  and the  Fluid Mechanics research team of the Icube laboratory. The authors are grateful to the  Icube Laboratory technicians  ( Martin Fisher ,  Johary Rasamimanana  and  Abdel Azizi ) and the PhD students ( Hakim Ben-Slimane ,  Vivien Schmitt ,  Noëlle Duclos ,  Alain Petit-Jean  and  Sandra Isel ) for their priceless contribution to the building of the experimental set-up. Special thanks are also expressed to  Quentin Araud  who conducted most of the experiments. Part of this research was funded through the  ARC grant for Concerted Research Actions , financed by the  Wallonia-Brussels Federation. The authors gratefully acknowledge  Mathieu Debaucheron  who contributed to some of the numerical computations.
Data Collection The study area was imaged twice on 28 th September 2015 in the interval 12h00-12h54 (CEST) using two coupled cameras mounted on the UAV md4-1000 quadrocopter (table 1):  digital RGB OLYMPUS EP-2 camera  Rikola 2D spectral sensor  (Makelainen, Saari, Hippi, Sarkeala and Soukkamaki, 2013). The Rikola 2D imaging system is a VNIR sensor based on the Piezo-Actuated Fabry-Perot Interferometer (FPI), working in the spectral range 500 nm -900 nm. This allows the user to select the central wavelengths of the bands to be recorded by setting up the appropriate "air gaps" in the interferometer. The CCD/CMOS matrix consists of 1024x1024 detectors. Each sensing element has the size of 5. 5x5. 5 μm. The camera is characterized by FOV=37°, focal length f=9mm and F-number=2. 8. The ADC (analog-to-digital converter) is operating in 12 bit mode.
Data Pre-processing Acquired RGB images underwent orthorectification process with Agisoft Photoscan Professional software. The process consisted of digital aerotriangulation, image matching, 3D cloud point and Digital Surface Model generation and the final ortho-correction. The final RGB orthophotomap had pixel size 5x5 cm and it was considered as a background supporting the geometric processing of acquired hyperspectral data. The exposition time for Rikola camera is usually set between 10 and 25 ms depending on sunlight intensity. In our experiment the exposition was set at 15 ms. Such a value is suitable for taking nonblurred pictures from moving platform but the technology of image formation and recording on the memory card leads to the situation where every band of the given hypercube has a slightly different position and external orientation.
The last step in geometric processing was the adjustment of all spectral orthophotomaps to the common frame by affine transformation based on RGB orthophotomap. Prior to the orthorectification at each spectral band, the radiometric processing needed to be performed. The first step was the radiometric calibration of each hypercube to remove the influence of the black current from measured signals. The second step was the radiometric normalization i. e. comparison of the recorded spectral luminance for each band with the luminance of the white standardized target. In our case the Zenith Lite TM panel 50x50 cm covered by BaSO4based white paint was used. No other atmospheric or radiometric corrections were applied. Some spectral bands from second Rikola dataset taken in halfframe mode were eliminated due to the encountered errors in pictures recording.
2014). Quantification and identification of FAMEs and the stable carbonisotopic composition of the individual PLFAs were determined on a Thermo GC/C-IRMS system composed of a Trace GC Ultra gas chromatograph (Thermo Electron Corp. , Milan, Italy) coupled to a Delta V Advantage isotope ratio mass spectrometer through a GC/C-III interface (Thermo Fisher Corp. , Bremen, Germany). The PLFA δ 13 C data were corrected for the addition of the methyl group by a simple mass balance. The offset between the total biomass and PLFA due to fractionation effects during fatty acid synthesis was corrected  (Boschker, de Brouwer and Cappenberg 1999; Bouillon and Boschker 2006 ) by assuming a depletion in 13 C of -3. 7 ± 2. 1 compared with the bacterial substrate  (Bouillon and Boschker 2006 ) and, therefore, calculated the isotopic composition of the microbial biomass as follows: δ 13 C = δ 13 C fatty acid + 3.
Bacterial biomass and cell counts The bacterial biomass and cells counts were analysed by epifluorescence microscopy  (Buesing 2005 ) on a Carl Zeiss Axio Imager Z1 microscope (1000×). Sampling and analysis were performed at day 0, 29, 31, 74 and 77. Digital images were taken randomly (10 fields using an AxioCam HR3 camera) and processed in Axio-Vision Version 6. 1. 7601 (Carl Zeiss MicroImaging, Germany). Every cell was manually marked, and the software delivered the spatial dimensions for each marked cell. The cell volumes were calculated as rods or spheres according to the closest geometric shape. The biomass was calculated as the cell carbon content from mean biovolumes and abundance using a relationship proposed by  Romanova and Sazhin (2010) : fg C cell -1 = 133. 754V 0. 438 , where V is the cell volume in μm 3.
Functional annotation of highly expressed gene sub-datasets in human and mouse laser microdissected choroidal epithelium cells yielded several significant canonical pathways  [66]. Among those are the "clathrin mediated endocytosis signaling" and the "caveolin mediated endocytosis signaling" pathways. This strong endocytotic activity is believed to pertain to the vigorous metabolic and synthetic functions of the BCSFB. The exact relevance of this vesicular abundance to the transcellular transport of selected endogenous or therapeutic macromolecules between blood and CSF in either direction remains completely unknown, at least for fluid-phase endocytosis as investigated with horseradish peroxidase  [67, 68]. Transcytosis pathways have raised considerable interest in the field of CNS delivery for their potential to deliver large cargoes including biotherapeutic agents, nanobodies, liposomes, nanoparticles, as well as vectorized forms of these nanotechnologies-based systems.
Peptide and antibody libraries need to be screened again to identify ligands for potential basolateral membrane proteins. Databases generated from transcriptomic and proteomic studies on choroid plexus, microdissected choroidal epithelial cells, and CSF exosomes are available and can be thoroughly probed to select receptor candidates highly expressed at the BCSFB. Developmental regulation should be taken into account, as stage-specific properties of this barrier could be exploited to specifically address perinatal and pediatric diseases. Mechanisms supporting transcytosis pathways must be fully dissected in order to understand at the molecular level, the subsequent events that control vesicular trafficking and the respective sorting of the receptor and its cargo within the endosomal network.
Expression quantitative trait locus analysis (eQTL) in breast tissue In order to identify associations between candidate variants and expression levels of genes within the 4q21 region, we analyzed all genotyped and imputed SNPs within a 1Mb region centered around the most significant SNP (rs11099601), in normal and breast cancer tissue. Significant eQTL associations were observed for numerous SNPs in the region in both normal breast and tumors (Figure  5 ). In the breast cancer tissue dataset BC241, the most strongly expression-associated SNP at this locus was our top risk SNP rs11099601, which was associated with expression levels of HELQ, (with P = 8. 28x10 -14 and r 2 = 0. 20, where the r 2 value indicates the percentage of variance in HELQ expression levels explained by rs11099601) (Figure  6A ).
A decrease in HELQ expression levels was observed with increasing copy number of the rs11099601 (C) allele (Figure  6A ). Multiple SNPs within the 1 Mb region were also associated with expression of HELQ, all of which were correlated with rs11099601 (r 2 > 0. 3). No significant eQTLs were observed between rs11099601 and other genes in this region, namely COQ2, HPSE, MRPS18C, FAM175A, or AGPAT9, using data from the BC241 sample set. In the TCGA BC765 breast cancer dataset, HELQ expression levels were not associated with rs11099601 (P = 0. 34 and r 2 = 0. 00099) or with any other SNPs in this region. Weak associations were only observed between rs11099601 and expression levels for MRPS18C (P = 1. 25x10 -4 and r 2 = 0. 02) (Figure  6D ) and FAM175A (P = 3. 83x10 -3 and r 2 = 0. 011) (Figure6F).
Further isoform-specific analysis was performed in the TCGA BC765 breast cancer dataset. In contrast to the expression data generated from the Norwegian sample sets, which were obtained using expression arrays, expression data from the TCGA datasets used in the current study were obtained by RNA-Sequencing, thus allowing further analysis of different gene isoforms. Thus, in the BC765 dataset, these analyses resulted in the identification of significant eQTLs for an isoform of HELQ (uc101ikb) (P = 2. 71x10 -11 and r 2 = 0. 056) (Figure  6B ), corresponding to a long isoform of the gene with one exon lacking. These analyses also further revealed highly significant associations for the MRPS18C isoform uc003hor (P = 1. 94x10 -27 and r 2 = 0. 143) (Figure  6E ). Similar to what is observed in the TCGA BC765 breast cancer dataset, gene-normalized analysis in the TCGA normal breast tissue dataset NB93 did not reveal associations between HELQ expression levels and rs11099601 while isoform-normalized analysis showed associations with HELQ isoform uc101ikb (P = 9.
90x10 - 05 and r 2 = 0. 153) (Figure  6C ). In normal breast tissue from the NBCS (NB116), the strongest eQTLs were observed for HPSE, where rs11099601 was associated with a decrease in HPSE expression levels (P = 4. 57x10 -3 , r 2 = 0. 0645) (Figure  6G ). rs11099601 was not associated with the expression levels of any other genes in this region. Although associations were detected between several genes and our top risk SNP in the different sample sets, a lack of consistency in eQTL associations between the two breast cancer sample sets was observed. It should be noted that expression data were obtained trough different approaches as previously mentioned, i. e expression array (44K Agilent array) for BC241 and RNA-Sequencing for BC765 (Illumina RNAseq). Moreover, there are differences in the overall PAM50 subtype distributions between these two sample sets.
Another study by the same group analyzed the role of DNA repair pathways in upper aero-digestive tract cancers  [56]. This study showed that the polymerase pathway, to which the HELQ gene belongs, is the only pathway significant for all upper aero-digestive tract cancer sites combined and that this association is entirely explained by the association with rs1494961 (P = 2. 65×10 -4 )  [56]. Finally, a recent study reported the mutation screening of HELQ in 185 Finnish breast or ovarian cancer families  [57]. This study did not provide evidence for a role of HELQ in breast cancer susceptibility in the Finnish population, but analyses in other populations and larger datasets are needed to further assess its role in breast cancer predisposition  [57] , especially with regard to the involvement of rare variants.
Indeed, SNPs associated with variation in gene expression have now been mapped for a variety of tissues, highlighting their tissue dependent properties and the need for expression profiling of a diverse panel of cell types. Hence, further functional characterization of the 4q21 locus, and replication in a larger dataset, would be relevant to provide more robust evidence of the involvement of this region in breast cancer susceptibility as well as identify the gene(s) and biological mechanism(s) underlying this susceptibility.
SNP selection SNP selection was performed by first identifying a list of genes of interest, which was determined by the involvement of these genes in cancer related pathways and/or mechanisms. The list of genes was established by researching published results and/or by using available public databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www. genome. jp/ kegg/). Thereafter, DEA SNPs falling within these gene regions were identified using previously reported data on allelic expression cis-associations, derived using: 1) the llumina Human1M-duo BeadChip for lymphoblastoid cell lines from Caucasians (CEU population) (n = 53)  [47] , the Illumina Human 1M Omni-quad for primary skin fibroblasts derived from Caucasian donors (n = 62)  [49, 70] , and the Illumina Infinium II assay with Human 1.
2 M Duo custom BeadChips v1 for human primary monocytes (n = 188)  [71]. Briefly, 1000 Genomes project data was used as a reference set (release 1000G Phase I v3) for the imputation of genotypes from HapMap individuals. Untyped markers were inferred using algorithms implemented in IMPUTE2. The unrelated fibroblast panel consisted of 31 parent-offspring trios, where the genotypes of offspring were used to allow for accurate phasing. Mapping of each allelic expression trait was carried out by first normalizing allelic expression ratios at each SNP using a polynomial method  [72]  and then calculating averaged phased allelic expression scores across annotated transcripts, followed by correlation of these scores to local (transcript +/-500 kb) SNP genotypes in fibroblasts as described earlier  [70].
8 or greater were considered. SNPs were preferentially accepted if they had a design score of 1. 1 (i. e. had previously been genotyped on an Illumina platform). If not, we sought SNPs with r 2 = 1 with the selected SNP, and selected the SNP with the best design score. If no such SNP was available, we selected SNPs with r 2 > 0. 8 with the chosen SNP, and selected the SNP with the best design score. For the COGS project overall, genotyping of 211,155 SNPs in samples was conducted using a custom Illumina Infinium array (iCOGS) in four centers. Genotypes were called using Illumina's proprietary GenCall algorithm. Standard quality control measures were applied across all SNPs and all samples genotyped as part of the COGS project  [35]. After quality control, genotype data were available for 48 155 breast cancer cases and 43 612 controls, and call rates for all SNPs were > 95%.
Statistical analysis Per-allele log-odds ratios (ORs) were estimated using logistic regression, adjusted for principal components and study, as described previously  [35]. P-values were estimated using Wald test. For imputation, genotype data from 48,155 breast cancer cases and 43,612 controls were used to estimate genotypes for other common variants across a 500 kb region on chromosome 4 (chr4: 84,132,763-84,632,763 -NCBI build 37 assembly), with IMPUTE v. 2. 2 and the March 2012 release of the 1,000 Genomes Project as reference panel. In all analyses, only SNPs with imputation information/accuracy r2 > 0. 30 were considered  [40].
Linkage disequilibrium LD values were computed using 118 independent individuals from the CEU population of the 1,000 Genome dataset (v3, release 20110521, downloaded from 1000genomes. ebi. ac. uk on April 2013)  [73]. The relevant subset was extracted from the raw data using VCFtools (v0. 1. 7) [74]  and the paired r 2 statistics were obtained for all target loci using PLINK! (v1. 07)  [75]. The linkage heatmaps and the association plots were produced on the R platform (v3. 0) using the package LDheatmap  [76].
Functional annotation Two publicly available tools, RegulomeDB  [79]  and HaploReg V4  [80] , were also used to evaluate candidate variants. For a full description of the RegulomeDB scoring scheme refer to (http://www. regulomedb. org). Publicly available genomic data was also used to annotate each SNP most strongly associated with breast cancer risk at locus 4q21 (for data sources refer to S6 Table ). The following regulatory features were obtained for breast cell types from ENCODE and NIH Roadmap Epigenomics data through the UCSC Genome Browser: DNase I hypersensitivity sites, Chromatin Hidden Markov Modelling (ChromHMM) states, histone modifications of epigenetic markers more specifically commonly used marks associated with enhancers (H3K4Me1 and H3K27Ac) and promoters (H3K4Me3 and H3K9Ac), and transcription factor ChiP-seq data.
To identify putative target genes, we examined potential functional chromatin interactions between distal and proximal regulatory transcription-factor binding sites and the promoters at the risk loci, using the Chromatin Interaction Analysis by Paired End Tag (ChiA-PET) and Genome conformation capture (Hi-C, 3C and 5C) datasets downloaded from GEO (for data sources refer to S6 Table ). Maps of active mammary super-enhancer regions in HMEC cells were obtained from Hnisz et al. [81]. Predicted enhancer-promoter determined interactions were obtained from the integrated method for predicting enhancer targets (IM-PET) described in He et al. [82]. RNA-Seq data from ENCODE was used to evaluate the expression of exons across the 4q21 locus in HMEC and MCF7 cell lines. For HMEC and MCF7, alignment files from 4 and 19 expression datasets respectively were downloaded from ENCODE using a rest API wrapper (ENCODExplorer R package)  [83]  in the bam format and processed using metagene R packages  [84]  to normalize in Reads per Millions aligned, and to convert in coverages.