dataesr/scientific-paragraphs-categorization huggingface dataset:
The dissociation profile was monitored for 20 min, and then the surface was regenerated with 10 mM glycine HCl at pH 1. 7. To avoid any mass transfer effect, the experiments were performed at a high flow rate of 30 μ l/min using freshly prepared Aβ peptides. Reference subtracted SPR data were evaluated with the Biacore T200 Evaluation Software (version 2. 0) using a 1:1 Langmuir binding model. Cell based neuroprotection assays. Neurones were prepared from the cortex of 18 day old embryonic CD rat brains using a papain digestion kit (Worthington, Biochemical Corporation). Cells were plated at 3 × 10 5 cells/ml in 96 well Cell Coat, Poly-D-Lysine coated plates (Greiner) in Neurobasal media with Pen/Strep, L-glutamine and B27 supplement with antioxidants (Gibco, ThermoFisher). The plates were incubated in a humidified incubator at 37 o C, 5% CO 2 for two days prior to adding the antibodies and amyloid peptides.
The relative Aβ load was evaluated in the frontal cortex using an Olympus BX-51 microscope equipped with an Olympus DP-50 camera and the ImageJ software (NIH, USA). Representative pictures of 100× magnification were systematically captured. Using ImageJ the pictures were binarized to 8-bit black and white pictures and a fixed intensity threshold was applied defining the DAB staining. Measurements were performed for a percentage area covered by DAB staining, as well as for the number of grains per mm 2 and the average size of the grains. Abeta ELISA. Brains were homogenized in 8-fold amount of TBS Lysis Buffer (50 mM Tris, pH 8. 0 supplemented with 1 tablet/10 ml of Complete Protease Inhibitor cocktail (Roche)) and homogenates were spun down for 20 min at 17. 000× g, 4 °C in a stratos Biofuge (Thermo Fisher).
The CMs were loaded with a fluorescent Ca 2 þ sensitive dye, Fura-2, and the ratios of fluorescence intensities (excited at 340 and 380 nm) were recorded using the Ionoptix system (Ionoptix). The electrically induced Ca 2 þ transients ([Ca 2 þ ] i ) were triggered by pulses generated from a field stimulator (20 ms duration, 0. 5 Hz). The Ca 2 þ traces were analysed using IonWizard software to calculate the irregularity of the timing (Beating rate), the percentage of irregular beating areas and the number of irregular waveforms. TALEN construction. TALEN genomic binding sites were designed using the TALEN Hit software (Cellectis Bioresearch). The TALEN genomic binding sites were 17 bp in length and the target sequence between the two binding sites was 17 bp in length (TALEN left: 3 0 -TTTATCAATTTCTGTCT-5 0 ; TALEN right: 3 0 -TGATACAGATCAGCAAG-5 0 ) separated by a 15 bp spacer.
Data analysis was performed using the hPSC Scorecard software (Life Technologies). Pyrosequencing analysis. PCR templates for pyrosequencing were amplified from cDNA using PrimeSTAR GXL DNA Polymerase (Clontech) following the manufacturer's instructions. The pyrosequencing was performed using the Pyro-Mark Q24 (Qiagen) and pyrograms were analysed using the PyroMark Q24 Software 2. 0. SNP karyotyping. Single nucleotide polymorphism (SNP) karyotype analysis was performed on the Illumina's CytoSNP-850K genotyping microarrays, which measure B850,000 SNPs across the genome. All genomic DNA was isolated from iPSC clones according to the manufacturer's protocol (QIAGEN). Input genomic DNA (500 ng) was processed, hybridized to the array and scanned on an Illumina HiScan according to the manufacturer's instructions.
Copy number variations (CNVs) were identified using the cnvPartition Pluginv. 3. 2. 0 in GenomeStudio (Illumina) by assessing both the B-allele frequency and Log R ratios. Single-cell electrophysiology and Ca 2 þ imaging. The patch-clamp and Ca 2 þ imaging assays were performed on single CMs isolated from the differentiated cardiospheres and seeded on matrigel-coated glass coverslips or glass bottom dish. Assays were performed 5-7 days post seeding (30-50 days post differentiation). Patch-clamp studies. The action potentials were recorded by whole-cell patchclamp technique using the EPC10 amplifier and Pulse/PulseFit software (HEKA, Germany) at 37 °C. The patch pipettes were prepared to have a typical resistance of 3-6 MO. The internal pipette solution contained 110 mM potassium aspartate, 20 mM KCl, 10 mM HEPES, 1 mM MgCl 2 , 0.
5 Hz); caffeine-induced Ca 2 þ transients were triggered with a puff of caffeine (10 mM). Fluorescence intensity of CMs in sequences of time-lapse images recorded was converted to numerical data with Image J (NIH, US), followed by mathematical treatment with Origin (OriginLab, Northampton, MA) to obtain the Ca 2 þ transient parameters including the transient amplitude, resting Ca 2 þ , maximum upstroke and decay velocity and decay constant. RNA-seq analysis. Cardiomyocytes derived from PLN-R41del iPSC cells were infected with AAV6-EGFP-miR-PLN or AAV6-EGFP-miR-luc as a negative control at an MOI of 10 4. One week after infection, total RNA was extracted using Zymo columns and 2 mg were used to generate a RNA-seq sequencing library. Poly-A selection and mRNA-SEQ library preparation were performed at the Mount Sinai Genomics Core Facility.
Sequencing (50 bases, paired ends) was performed using a Illumina HiSeq2500. Annotated reads were obtained using STAR and HTSeq and normalized to full library size. Scatter plots of read counts were realized using Graphpad PRISM. For the Venn diagram analysis, 41 human transcripts showing significant homology to the PLN miRNA target sequence (3 0 -GCTA-TAAGAGCCTCAACCATT-5 0 ) were identified using BLASTn, and compared with the 381 transcripts significantly downregulated in cells expressing miR-PLN versus control cells expressing miR-Luc. A surface-proportional Venn diagram was obtained using the BioVenn application (http://www. cmbi. ru. nl/cdd/biovenn/ index. php). RNA-seq data have been deposited in GEO (Gene Expression Omnibus) under accession code GSE65763. Exome sequencing. Genomic DNA samples were assessed for quantity by Qubit fluorometry (Life Technologies, Grand Island, NY) and for quality by the 2100 Bioanalyzer system (Agilent).
Exome sequencing data have been deposited in GEO under accession code GSE65763. Identification of novel indels. Off-target indels were identified by computational approach  35. Paired-end reads were aligned to the human reference genome hg19 using Bowtie-2. 1. 0, followed by manipulation using PicardTools-1. 110 (sorting, duplicate marking, readGroup addition and indexing). The Genome Analysis ToolKit (GATK), version 3. 1. 1, was used for indel identification. Sample processing included local realignment via 'RealignmentTargetCreator' and 'IndelRealigner', base score recalibration via 'BaseRecalibrator', variant calling across the three samples via 'HaplotypeCaller' and variant score recalibration via 'VariantRecalibrator' and 'ApplyRecalibration'. The set of identified indels was filtered in several steps.
To remove indels near low-complexity regions the hg19 RepeatMasker database was downloaded from UCSC Genome Browser website. Every indel that contained a masked base pair within a distance of 10 bp or 433 masked basepairs within a distance of 50 bp was removed. None of the identified indels caused expansions or compression of long (46 bp) homopolymer, so no indels were removed at this step. To generate a list of sample specific indels only indels with differing alternative alleles in both clones (L2GC1 and L2GC2) were kept. In addition, we removed all those indels in which the alternate allele was not covered by at least three reads. To identify indels that lie close to possible off-target TALEN binding sites, we generated a list of sequences by substituting r5 bp of each monomer's on-target binding site and aligned them to the reference genome hg19 using bowtie.
Acknowledgements This work is supported by  NIH   R01 HL117505 ,  HL 119046 , a  NHLBI Program of Excellence in Nanotechnology (PEN)  Award Contract #  HHSN268201000045C , a  P50 HL112324  and a  Transatlantic Fondation Leducq  grant (R. J. H. );  NIH   K99 HL104002  and  AHA 15BGIA22730027  (I. K. );  Research Grant Council-Theme-Based Research Scheme   T13-706/11  (RAL);  R01 HL123968  and  R01 HL126527  (JCW);  NIH HL26057  and the  Hellenic Cardiological Society (E. K).
JLS Method Evaluation by Simulation We conducted extensive simulation analyses to evaluate the performance of the proposed JLS-Fisher and JLS-minP tests for single-variant analysis and compared them with the individual location-only and scale-only tests, as well as the distribution test  16  and the LRT. 17 We also conducted a sensitivity analysis on the various tests, studying the impact of poorly captured genotypes that can be expected from imputed datasets. Simulation details are provided in Appendix A.
31  Given loss of function in CF, SLC9A3 regulators might not be mediating their effects via CFTR; however, it is possible that some of the remaining significant apical membrane components, beyond SLC9A3, utilize the EZR and/or the SLC9A3 regulator scaffold. 32, 33 e demonstrated the potential advantage of using the JLS framework over the conventional location-only or scale-only approach in practice. As proof of principle, we used examples from an association study of a complex trait (glycemic control in type 1 diabetes) and a modifier study of a Mendelian disease (lung function in CF). Both of these datasets provided examples of a genetic effect on a quantitative phenotype that varied based on another variable (i. e. , treatment regimen and age, respectively). Our results showed that if the interacting exposure(s) were not known or available, the JLS method could have improved power over conventional approaches in detecting the association.
tified the SLC9A3 regulatory complex as an important contributor to CF lung disease, which was completely missed in previous analyses of the same data. The JLS association testing method should be considered for future gene-set, pathway, and wholegenome association scans. Further, JLS should be employed to re-examine datasets previously analyzed via the conventional location-only or scale-only testing approaches, for complex traits or complex secondary phenotypes of Mendelian diseases. The method will help researchers pinpoint susceptibility loci for additional analysis toward the understanding of the genetic architecture of a complex trait.
For comparison, empirical type 1 error rates of the individual location-only and scale-only tests are also studied, in addition to the JLS-Fisher and JLS-minP tests, and the LRT of Cao et al. ;  17  the distribution test of Aschard et al. 16  has the correct type 1 error by design. Type 1 error control at the genome-wide level was assessed by phenotype-permutation analysis of the 866,995 T1D GWAS SNPs and 565,884 CF GWAS SNPs. For the sensitivity analysis of genotype imputation uncertainty, simulated true genotypes were converted to probabilistic genotype data using a Dirichlet distribution with scale parameters a for the correct genotype category and (1 À a)/2 for the other two;  39  a was fixed at values of 1, 0. 9, 0. 8, 0. 7, 0. 6, and 0. 5. Based on the simulated posterior probabilities, the most-likely genotype for each subject was the genotype with the highest posterior probability (i.
Figure1. Apical Gene-Set Association Analysis of CF Lung Disease Severity as Measured by SaKnorm in the Canadian Gene Modifier Study Discovery Sample of 1,409 Subjects SaKnorm 24 (sex-, age-, and mortalityadjusted, normalized forced expiratory volume in 1 s; FEV1) was used as the lung function measure. In total, 3,814 GWAS SNPs (MAF > 0. 02) were annotated to within 510 kb of the 155 apical genes obtained from the Gene Ontology database. 5 (A, C, E) Quantile-quantile plots (QQplots) of the SNP-specific association test statistics of the apical SNPs via (A) the proposed joint location-scale test (JLS-Fisher), (C) the regression based location-only test, and (E) Levene's scale-only test. QQ-plots of the observed association statistics are shown in red, and the QQ-plots of the statistics calculated from the 10,000 phenotype-permutated replicates are shown in gray.
Acknowledgments The authors are grateful to the subjects in the  CGS, French CF , and DCCT studies for their participation. The authors also thank  Drs. Hughes Aschard  and  Peter Kraft  for providing their simulation results; the  CF Gene Modifier Consortium  and  Drs. Michael Knowles ,  Garry Cutting , and  Mitchell Drumm  for helpful discussions; and the  Canadian  and  US CF Foundations  for their generous support of the genotyping. This data resource was also supported in part by  Genome Canada , through the  Ontario Genomics Institute  per research agreement  2004-OGI-3-05  (to P. R. D. ), with the  Ontario Research Fund ,  Research Excellence Program. This work was funded by the  Canadian Institutes of Health Research (CIHR ;  201309MOP-310732-G-CEAA-117978  to L. S. and  MOP-258916  to L.
Filters were examined and photographed on a Leica DM4000B microscope. The number of nuclei detected on the filter was determined using the ImageJ software, the Fast Morphology plug-in, and a threshold size of 400 pixels square. When required, cell clusters were resolved manually using the Cell Counter plug-in. The data were normalized to the number of nuclei obtained in the control (non infected, non treated) experiment. Two independent experiments were performed. Statistical analysis was performed using the Mann-Whitney and Kruskal-Wallis tests.
5 min and then to 100% B at 9 min. Isocratic elution continued for 1 min at 100% B, then 100% A was reached at 10. 2 min and maintained to 11 min. The flow rate was 0. 35 ml/min. The autosampler was set at 5°C and the injection volume was 5 μL. Source conditions were as follows: negative ESI mode; source temperature = 325°C, nebulizer gas (nitrogen) flow rate = 10 L/min, sheath gas (nitrogen) flow rate = 12 L/min, sheath gas temperature = 400°C and spray voltage = -3500 V. Data were acquired in MRM mode. For each compound the best conditions of separation and quantification were defined: retention time in minute (RT), specific Q1/Q3 transition (T) fragmentor (F) and collision energy (CE). Peak detection, integration and quantitative analysis were performed using Mass Hunter Quantitative analysis software (Agilent Technologies).
Therefore we elected to use AD169 rather than generate high passage VHL/E for the initial screening in HIPEC, and we used low passage VHL/E for the screening in placental explants. Noteworthy, 13-HODE and 15-HETE were the predominant lipids generated by HIPEC infected by AD169 and by placental explants infected with VHL/E respectively. Also, the results of transwell migration assays, performed with HIPEC infected by VHL/E HCMV, supported those found in wound healing experiments with AD169. Together, these data suggest that AD169 genome alterations do not result in detectable changes respective of HIPEC permissivity and migration abilities, PPARγ activation and 13-HODE and 15-HETE generation. HIPEC are SV-40 immortalized human EVCT  [10]. As such, they probably bear transcriptome or genomic alterations.
We extracted a surfacebrightness image of the observation, subtracting a model for the instrumental background and accounting for variation of the telescope efficiency across the field of view. Figure  1  shows the resulting surface-brightness image in the 0. 5-1. 2 keV band obtained combining the data from the three detectors of the European Photon Imaging Camera (EPIC) on board XMM-Newton. X-ray point sources were masked and the data were adaptively smoothed to highlight the diffuse emission. The high sensitivity achieved during this observation, thanks to a minimal number of solar flares, allowed us to identify several previously unreported features. Near the virial radius of the cluster (∼ 2h  -1  70 Mpc) and beyond, several high significance (> 6σ ) regions of diffuse emission are detected and appear to be connected to the cluster core.
This suggests Shown is the XMM-Newton image of Abell 2744 (same data as Fig. 1 ); also shown are the positions of member galaxies with spectroscopic redshift within ±5, 000 km s -1 of the cluster mean  [18]  (red dots); red curves show galaxy number density contours. that the gas observed in the structures is cooler than that of the central regions. To identify the structures detected in X-rays, we used a collection of published spectroscopic redshifts within the XMM-Newton field of view. Spectroscopic redshifts are available for 1,500 galaxies in the field  [17, 18]. We selected galaxies with velocities falling within ±5, 000 km/s of the cluster mean to capture the cluster and its accretion region in their entirety. In Fig. 2  we show the XMM-Newton brightness image together with the position of selected cluster members and galaxy density contours.
9 ± 0. 1) × 10 13 25. 9 (1. 2 ± 0. 3) × 10  14  3. 3 0. 15 ± 0. 04 z T M gas S/N M tot S/N f gas [10 6 K] [h -1 70 M ] X-ray [h -1 70 M ] lensing E 0. Table  1 : Properties of the filaments discovered in this study. X-ray and lensing properties of the regions defined in Extended Data Figure  2. Note that because of the uncertainty in the geometry of the filaments the provided gas mass, total mass, and gas fraction should be considered as indicative. The masses reported here were obtained by combining strong and weak lensing. A comparison with weak-lensing-only measurements is provided in Extended Data Table  2. NSFC of China under grant 11103011. TE is supported by the Deutsche Forschungsgemeinschaft through the Transregional Collaborative Research Centre TR 33 -" The Dark Universe ". EJ is supported by CNES.
Imaging X-ray analysis Abell 2744 was observed by XMM-Newton in late 2014 for a total observing time of 110 ks (PI: J. -P. K. ; OBSID 074385). At the redshift of A2744 (0. 306), the size of the XMM-Newton field of view corresponds to 8h -1 70 Mpc. We processed the data using the XMM-Newton Scientific Analysis System (XMMSAS) v14. 0. We excluded flaring periods from the event files by creating a light curve for each instrument separately and filtering out the time periods for which the observed count rate exceeded the mean by more than 2σ. The observation was very mildly affected by softproton flares, allowing us to reach a flare-free observing time of 96 ks, 97 ks, and 87 ks for EPIC detectors MOS1, MOS2 and pn, respectively. We extracted raw images in the 0. 5-1. 2 keV band for all three EPIC detectors using the Extended Source Analysis Software (ESAS) package  [29].
This energy band maximizes the source-to-background ratio and avoids the bright Al and Si background emission lines, while maintaining a large effective area since the collecting power of the XMM-Newton telescopes peaks at 1 keV. Exposure maps for each instrument were created, taking into account the variations of the vignetting across the field of view. A model image of the non X-ray background (NXB) was computed using a collection of closed-filter observations, and was adjusted to each individual observation by comparing the count rates in the corner of the field of view. X-ray point sources were detected using the XMMSAS tool ewavelet and masked during the analysis. Additionally, we used the existing Chandra observations of the cluster  [18, 19]  to detect point sources down to fainter X-ray fluxes (∼ 5 × 10 -16 ergs cm -2 s -1 ) and mask the corresponding areas.
Such a flux threshold for point-source removal corresponds to a resolved fraction of 80% of the cosmic X-ray background  [30]  , which is associated with a cosmic variance of about 5%. This ensures that the extended features reported here are indeed caused by diffuse emission. We computed surface-brightness images by subtracting the NXB from the raw images and dividing them by the exposure maps. To maximize the signal-to-noise ratio (SNR), we then combined the surface-brightness images of the three EPIC detectors by weighting each detector by its relative effective area. The resulting image was then adaptively smoothed using the XMMSAS tool asmooth, requiring an SNR of 5 for all features above the local background. The total XMM-Newton/EPIC image of A2744 is shown in Fig. 1. To confirm the presence of the filamentary structures shown in Fig.
Radius [arcmin] Extended Data Figure  2 : Regions used for the analysis of the thermodynamic properties of the filaments. The 0. 5-2 keV surface brightness is colour coded. Spectra were extracted from the regions indicated as E, S, SW, NW1 and NW2 by the white ellipses. The green circles show the regions labelled as Offset1-4 used to estimate the local background components (see Extended Data Table  1 ). The dashed cyan sectors show the regions used to extract the radial profiles along the filaments for Extended Data Figs 1, 3 and 5. The grey ellipses show background/foreground structures masked during the analysis (see text). For comparison, we extracted radial profiles of galaxy density from spectroscopically-confirmed members  [18]  in exactly the same sectors. The resulting profiles are shown in Extended Data Fig.
Analysis of ESO and CFHT optical data We used the colours of galaxies in archival optical imaging of the Abell 2744 field to identify members of the cluster and its associated filaments. We constructed a photometric catalogue from observations obtained in the B,V and R filters using the WFI instrument at the ESO 2. 2-m telescope at La Silla Observatory, combined with i-band data obtained with MegaCam/MegaPrime at the CFHT. For the WFI BVR filters, we were able to use existing co-added images (B: 9,200 s, V: 8,700 s, R: 21,000 s) from a weak lensing followup of clusters detected in the Sunyaev-Zeldovich (SZ) effect. Observations spanning three campaigns between September 2000 and October 2011 were bias-subtracted and flat-fielded using the THELI processing pipeline  [38, 39]. THELI also includes astrometric, relative and absolute photometric calibration.
all filters, the co-added images were post-processed, and saturated stars and otherwise unreliable image areas were masked out  [41]. Source catalogues were distilled from the co-added images using the weak lensing pipeline from ref 22. Because of the different field-of-view of the cameras involved (34 × 34 for WFI versus 60 × 60 for CFHT MegaCam), it proved useful to adopt the following strategy: we measured source photometry in all three WFI passbands in one go, making use of the "double detection" mode in SEXTRACTOR  [42]  , with the deep R-band data as the detection image. In order to obtain consistent magnitudes, photometric quantities were measured after having matched the seeing in the other filters to the poorest seeing among them. A separate detection run was performed for the CFHT i'-data.
Lensing Analysis of HST and CFHT data Lensing Constraints : HST field of view The strong lensing constraints used to model the inner core of Abell 2744 consist of a set of 51 multiply-imaged systems (159 images  [21]  ). The weak lensing catalogue for the HST field of view was built following the methods described in ref 43, and the details of the Abell 2744 weak-lensing catalogue will be given elsewhere (M. J. et al. , manuscript in preparation). Here we give a brief summary of the different steps. The weak lensing analysis is based on shape measurements in the Advanced Camera for Surveys (ACS)/F814W band. Following a method developed for the analysis of data obtained for the COSMOS survey  [43]  , the SEXTRACTOR photometry package  [42]  was used for the detection of the sources. The resulting catalogue was then cleaned by removing spurious sources, duplicate detections, and any sources in the vicinity of stars or saturated pixels.