dataesr/scientific-paragraphs-categorization huggingface dataset:
Area rescaling A determination of whether or not clustering redshifts can constrain the σ( z ) of Euclid tomographic redshift bins to less than 0. 002 (1 + z) requires extrapolation. We estimated the relation between σ( z ) and tracer-target overlap area by measuring z within subregions of the Flagship region (see Fig. 4 ). We fitted the σ( z ) data with power laws and then extrapolated to the total projected overlap. The relations can be seen in Fig. 8  for the shifted-true model, calibrated by method 2 (Sect. 3. 1) clustering redshifts measured on the 'mean Rubin' photometric catalogue. The extrapolation projects the uncertainties to be significantly smaller than the required uncertainties for Euclid. We can be confident of this statement, as even the 402 deg 2 Flagship area yields uncertainties approaching or surpassing the Euclid requirement.
Conclusion We measured clustering redshifts in the Flagship simulation to test their uncertainties in determining the mean redshifts, z , for Euclid tomographic bins. The method uses cross-correlations with mock spectroscopic samples, modelled after BOSS, DESI, and Euclid NISP-S. Clustering redshifts were determined using the YAW software, for transverse pair separations between 100 kpc and 1000 kpc, using simple galaxy bias correction schemes. Simulated photometric samples were constructed using the DNF photometric redshift code (Euclid Collaboration 2021b). The redshift determinations were constructed from two sets of training samples, one that is fully representative in redshift and magnitude and a second that has a completeness drop off in I E magnitudes similar to surveys such as Rubin LSST (denoted with 'Rubin').
Acknowledgements. K. Naidoo  acknowledges support from the  Science and Technology Facilities Council  grant  ST/N50449X  and from the  (Polish) National Science Centre  grant # 2018/31/G/ST9/03388. H. Johnston  acknowledges support from the  Delta ITP  consortium, a program of the  Netherlands Organisation for Scientific Research (NWO)  that is funded by the  Dutch Ministry of Education, Culture and Science (OCW). O. Lahav  acknowledges support from an  STFC  Consolidated Grant  ST/R000476/1. K. Naidoo ,  H. Johnston ,  B. Joachimi , and  O. Lahav  were supported by the  UK Space Agency. H. Hildebrandt is supported by a Heisenberg grant of the  Deutsche Forschungsgemeinschaft  ( Hi 1495/5-1 ) as well as an  ERC Consolidator Grant  (No. 770935 ). J. L. van den Busch acknowledges support from the  European Research Council  under grant numbers  770935.
On the synthetic test dataset, we show that our svFCS diffusion law yields precise estimates for  2D and   , with an accuracy around 12 %. The accuracy of our method on real experimental measurements cannot be assessed directly, but our results confirm the capacity of our 2D/3D diffusion and binding svFCS diffusion law to correctly estimate  2D and   in the case of HIV-1 and HTLV-1 Gag proteins binding to model lipid SLB and to HEK-293T cells. Notably, our estimates confirm previous results obtained in the literature with other methods by different teams. Therefore, our results highlight the benefits of our empirical svFCS diffusion law for the determination of  2D and   in the membrane binding problem with diffusion both at the membrane and in the cytosol.
ACKNOWLEDGMENTS The authors acknowledge the  Imabio CNRS (GdR Imabio) consortium  for their continuous support of the project and for inititally granting EB. AM Ph. D. fellowship is granted by  CNRS Prime 80. EB was then granted by  CNRS. CA is a recipient of  Université Montpellier Ph. D. fellowship. RD Ph. D. fellowship is granted by  Sidaction. The project was initially granted by  ANR Fluobuds  and then by  CNRS. Authors acknowledge  Montpellier RIO Imaging  ( MRI, Biocampus, UAR CNRS ) microscopy facility. Authors acknowledge  C. Eggeling ,  H. de Rocquigny ,  M. May ,  D. Derse  and  N. Jouvenet  for the gift of the different fluorescent lipids, dyes and plasmids.
Synchrotron single-crystal diffraction measurements ( = 0. 69127 A ˚) in the ranges 108-350 K for Rb 2 V 3 O 8 and 88-298 K for K 2 V 3 O 8 with a temperature step of 2 K at ambient pressure were performed on the BM01 station of Swiss-Norwegian Beamlines (SNBL) at the European Synchrotron Radiation Facility (Grenoble, France)  (Dyadkin et al. , 2016). The crystals were mounted on glass pins and placed in a stream of nitrogen from an Oxford Cryostream 700+ instrument. The data (a full rotation of 360 ) were collected using a Pilatus 2M detector. After several test exposures, our data collection strategy was to measure frames with an angular step of 1 and an exposure time was 0. 1 seconds per frame. Such a combination of an angular slicing and exposure time is a compromise that (i) keeps the detector in a linear response range even for strong reflections, (ii) uses rotation speed (10 s À1 ) that provides precise angular positioning, (iii) reduces the number of files to a reasonable and manageable number.
Finer angular slicing is always better, but to stay with the same dose accumulation rate (same exposure per 0. 1 ) we would have to use the 0. 1 /0. 01 s strategy, which is still possible at BM01. Unfortunately, this implies 10 times more data are collected which is not very practical if the goal is to collect many datasets for mapping temperature or time evolution of the crystal structures. A discussion of data collection with pixel photon counting detectors at third-generation synchrotron sources was presented by  Krause et al. (2020). With an angular step of 1 and an exposure time of 0. 1 seconds per frame, one data collection, i. e. one scan, took 36 s. The relatively high starting temperature (350 K) for the measurements on Rb 2 V 3 O 8 was chosen so that several data points could be collected above the postulated phase transition at 270 K  (Withers et al.
Results and discussion A series of measurements on K 2 V 3 O 8 was performed with single scans in the temperature range 88-298 K. Afterwards, the crystal was heated to 100 K and then to 150 K and 20 consecutive scans were collected at both temperatures. Analysis of reciprocal space in all the data shows that first-order satellite reflections can only be observed when the frames from 20 separate scans at 100 K are binned into one single data set (Fig. 2 ).
Temperature dependence of the average structure of K 2 V 3 O 8 Considering solely the main reflections above and below the phase transition at 115 K, there is no indication for a change of space group P4bm of the average structure. Consequently, all the data collected with one scan between 88 K and 298 K were processed in space group P4bm using the cyclic refinement option in Jana2020 (Petr ˇı ´c ˇek et al. , 2014). The starting data set for the cyclic refinement was the one measured at 88 K. It was refined first and, once a satisfactory agreement was achieved, the resulting structural parameters were then automatically passed on to the next higher temperature by the program. The next temperature point was then refined and this was subsequently repeated until the dataset at the highest temperature was reached.
Temperature and radiation dose dependence of the structure of Rb 2 V 3 O 8 A similar approach as the one described above for K 2 V 3 O 8 was followed to determine the temperature-dependent behaviour of Rb 2 V 3 O 8. The data with one scan were recorded on cooling in the temperature range 108-350 K and then refined   in a cyclic way using the program Jana2020 (Petr ˇı ´c ˇek et al. , 2014). Based on the analysis of the reciprocal space at all temperatures, there is no evidence for a change of space group P4bm of the average structure. It is striking that the anomalies in the a and c unit-cell parameters occur in Rb 2 V 3 O 8 at about 180 K (Fig. 3 ). The bulk thermal contraction also changes its slope at this temperature indicating higher thermal contraction below 180 K. The volume contraction above and below 180 K is V(T) = 438.
We finally conclude with a note on synchrotron data collection strategies suitable to measure coexisting strong and weak diffraction features. Bright synchrotron light and modern low-noise pixel area detectors make diffraction experiments very fast, but there is a limitation on the linear detector response of the dynamic range. One strategy is to collect data with different attenuations of the incoming beam and then merge the datasets, taking the strong intensities from the attenuated data and weak intensities from the un-attenuated measurement. Another strategy, the one applied here, is to perform the same data collection many times with an attenuated beam. These data can then be summed partially or totally into a single dataset with improved statistics. Such binning of the data is a technique perfectly suited to the zeroreadout noise pixel area detectors, such as the Pilatus 2M used in our case.
Diffusion Tensor Imaging (DTI) Ex vivo MRI experiments were performed on a 14. 1 T magnet (Bruker) with a homemade saddle coil of 2 cm diameter. A multi-b-value shell protocol was acquired using a spin-echo sequence (FOV = 21 × 16 mm2, matrix size = 128 × 92, 12 slices of 0. 6 mm, 3 averages with TE/ TR = 45/2000 ms). 89 DWI were acquired, 5 b0 images and 84 separated in 3 shells (noncollinear and uniformly distributed in each shell) with number of directions/b-value in s/mm 2 : 28/2000, 28/4000 and 28/5000. Acquired data were fitted using the white matter tract integrity model based on diffusion kurtosis imaging (DKI)  (Jespersen, Olesen, Hansen, & Shemesh, 2018). Cortex (CX), external capsule (EC), and medial cingulate white matter (cWM) were assessed. DTI derived parameters (Axial diffusivity (AD), Radial diffusivity (RD), Mean diffusivity (AD) and Fractional anisotropy (FA)), DKI derived metrics (mean (MK), axial (AK), and radial (RK) kurtosis) as well as WMTI derived metrics axial and radial diffusivity in the extra axonal space (Depar and Deperp, respectively), intraxonal diffusivity (Daxint), intra-axonal volume fraction (fin) and C2 (the square of the cosine of the angle between the fibers considered as the fanning of the fibers) were averaged in the different regions assessed for all animals.
Autoradiograms were analyzed using Fujifilm BAS-1800II phosphorimager using Aida Software V4. 06 (Raytest Isotopenmessgerate GmbH) in presence of homemade 125 I calibration curves. Specific binding ratio (SBR) was calculated as follows: (Average radioactivity in ROI over 12 sections/radioactivity in ROI over 4 slices in the presence of 10 μM of unlabeled radiotracer) -1. For each radiotracer, quantification was performed individually on the ROI.
Western Blot Frozen tissue was homogenized on ice in RIPA lysis buffer containing protease inhibitors (Sigma-Aldrich) and total proteins were quantified using Bradford (Bio-Rad). Samples were electrophoresed through a 4-12 % acrylamide SDS-PAGE by loading equal amounts of proteins (25 μg) on a Bio-Rad Mini-PROTEAN TGX Stain-Free Gel (Bio-Rad). Proteins were transferred to nitrocellulose membranes and detected with either overnight incubation at 4 • C or 1 h at room temperature of primary antibodies (1:1000 in blocking buffer containing 0. 1 % casein) followed by 2 h incubation at room temperature with a fluorescent secondary antibody (1:10,000), conjugated with either DyeLight™-800 or AlexaFluor®-680. Detection was achieved using iBright reader (Thermo Fisher) and densitometric protein band quantification was performed with Image Studio™ Lite software (LI-COR Biosciences).
RT-qPCR Total RNA was extracted using Quiagen Mini RNA kit from 30 mg of tissue. 3 μg RNA were retrotranscribed into cDNA with random hexamers (Promega), dNTP (Promega), RNAsin, DTT and M-MLV reverse transcriptase (Invitrogen). Quantitative PCR was performed using 15 ng of cDNA, 2× SYBR-Green Master Mix (PowerUp®, Invitrogen) on the QuantStudio™ 5 System and elaborated with the Design&Analysis Software 2. 5. 1 (Thermo Fisher). Amplification parameters: 95 • C for 2 min, (95 • C for 15 s, 58 • C for 1 min) X 40 cycles, 58 • C for 5 s, 65 • C for 10 s, 95 • C for 30 s. Negative controls included omission of reverse transcriptase at the cDNA synthesis step and omission of the template at the PCR step. Additional controls performed for each cDNA amplification included assessment of amplification efficiency and detection of possible primer dimerization through analysis of dissociation curves.
The relative molecular weight (M) is calculated by comparison with a standard curve. Profiles were obtained with the software ImageJ. Panel B: on the left, superimposition of the GFC profiles of intracellular HS from control (0 μg/ml A5_3) and cells treated with 20 μg/ml A5_3. Treatment causes a shift towards higher M. On the right, superimposition of the GFC profiles of extracellular HS from control and treated cells. The relative M reported in the main text was calculated by comparison with a standard curve. 75,000 cpm of HS were applied to the column. Fig. 3. Brain metabolism assessed by 1 H-MRS in the cortex and hippocampus of control and MPSIIIA mice. Quantification of metabolites in the cortex of 4-week-old mice (panels A-E: control, n = 12 vs MPSIIIA, n = 20) and of 12-week-old mice (panels F-J: control, n = 12 vs MPSIIIA, n = 11, treated MPSIIIA, n = 10).
Acknowledgements Research was supported by the  Fondation Sanfilippo Suisse. We thank  Dr. Analina Hausin ,  Dr. Mario Lepore  and  Dr. Stefanita Mitrea  from the  CIBM in Lausanne  for their precious help with animals. We thank  Annamaria Naggi  from  Ronzoni Institute  for providing heparin derivatives and  Dr. Edwin Yates  of  Liverpool University  for critical reading. All authors have read the journal's policy on disclosure of potential conflicts of interest and declare no conflict of interest. All authors have read and approved the final version of the manuscript as submitted.
Moreover, CXRD may allow a phasing of the diffraction patterns to obtain an image of the strain field. In our case, we acquired three-dimensional Bragg ptychography data sets but we did not succeed in obtaining any reliable reconstruction. Three reasons can explain the inefficiency of the reconstruction: the contribution of the pedestal mentioned above, the thickness of the pillar, which is large with respect to the longitudinal coherence length, and the initial crystalline quality. Examples with a few dislocations have already been presented, but it is generally accepted that CXRD will apply only for low defect density. We think that it will be difficult to obtain information on individual defects with CXRD on these samples: on virgin crystals, the scattered intensities from the micropillar and from the pedestal overlap; shortly after the yield point, there are too many defects to be interpreted with CXRD.
Funding information This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 701647 , as well as  SNSF  grant No. 200021L_169753 , and from  ANR  grant  Nos. ANR-16-CE93-0006  and  ANR-19-CE08-0007 , ' Investissement d'Avenir'  ( LABEX INTERACTIFS ,  ANR-11-LABX-0017-01 ), and  Nouvelle Aquitaine Region/European Structural and Investment Funds  ( ERDF No. P-2016-BAFE-94/95 ).
(84, 108, 110, 115)  In these patients, to counteract the recurrence of the infection due to the persistence of active colonies especially of S. aureus and E. faecalis the use of a biological substitute such us cryopreserved aortic homograft (CAH) (as in our case) or another full root xenograft is recommended. However, whether the use of CAH in the field of infective endocarditis can improve clinical outcomes remains questionable because of the lack of RCTs (103,108, 114-121) However, the latest report from the Cleveland Clinic's Endocarditis Study Group suggested that allogeneic tissue reduces the risk of infection relapse. (122) The use of a homograft in first-time aortic valve replacement for IE decreased over time (9,4% to 5,6%) and in reoperation (37,5% to 28,5%) in a report from the STS database between 2005-2011.
Log 2 fold-changes were computed using day 3 as the baseline estimate and normalized by subtracting the median log 2 fold-change of all non-targeting hairpins. Replicates were z-mad normalized and collapsed by computing their mean for a final hairpin-level matrix. Gene-level matrices were obtained using DEMETER2 with C911 hairpins provided for adjustment of the seed effect and used for downstream analyses  58. Dependency Map CRISPR/RNAi datasets & dependency analyses Ceres and DEMETER2-processed CRISPR & RNAi datasets from the Dependency Map (v. 18Q4) were accessed on 12/12/2018  59. The CRISPR dataset contained the MM cell lines below: EJM, JJN3, KMS11, KMS20, KMS26, KMS27, LP1, MM1S. The RNAi dataset contained the MM cell lines below: KMS18, SKMM2, MM1S, OPM2, RPMI8226, OPM1, KMS27. Genes with a negative dependency score and whose median score in 1q-amplified MM cell lines was more negative compared to non-amplified controls in the Dependency Map datasets and the in-house genome-wide CRISPR screen, were considered for further filtering.
CCLE gene expression data (v. 18Q2, accessed on 12/12/2018) were used to identify genes whose difference in median expression levels between 1qamplified and non-amplified MM cell lines (n=22 and n=3, respectively) was > 2 or < -2, which correspond to the ~95 th and 5 th quantiles of the distribution, respectively. Differentially essential genes were then filtered for genes with an up-or downregulation in expression & and an upregulation in expression only for those located on chr1q. Genes that hit in at least two datasets (n=1,523) were considered for downstream analyses. Gene set enrichment analysis was performed using hypergeometric tests against the Hallmark pathway collection from MSigDB (https://www. gseamsigdb. org/gsea/msigdb/human/collections. jsp, accessed on 4/21/2023) and p-values were corrected using the Benjamini-Hochberg approach.
Pathways with a q-value < 0. 05 (n=14) were considered enriched in MM cell lines with Amp1q. Connectivity Map-guided drug screen A lineage-agnostic Amp1q gene expression signature was derived by performing differential expression analysis between tumors with and without Amp1q across patients with MM (GSE2658), breast cancer, and lung squamous cell carcinoma (TCGA). Breast cancer and lung squamous cell carcinoma were selected as examples of solid malignancies that can present with Amp1q. Differential expression analysis was performed using t-tests and genes were ranked according to their -log 10 (p-value) multiplied by the sign of their log 2 fold-change. Genes located in chr1q were removed from consideration and gene ranks were averaged across the three cohorts. The top 150 up-and down-regulated genes by average rank were used to query the Connectivity Map (CMAP, clue.
Testing was performed in a 96-well plate-format with 10,000 cells per well and using dose-response matrices in a 10x10 grid manner (0-10uM in two-fold dilutions) and cell viability was measured with CellTiter-Glo (Promega). The dose-response matrices were then used to compute synergy and antagonism scores using Bliss synergy modelling with Combenefit software. Single-cell RNA and B cell receptor (BCR)-sequencing of patient samples with subclonal Amp1q Two patients with Monoclonal Gammopathy of Undetermined Significance (MGUS) and three patients with Smoldering Multiple Myeloma (SMM) who had subclonal Amp1q by Fluorescence In Situ Hybridization (FISH) were selected for this cohort from the PCROWD study (IRB #14-174). All patients provided informed consent. The review boards of all participating centers approved the study in accordance with the Declaration of Helsinki and the International Conference of Harmonization Guideline for Good Clinical Practice.
7 M Tris, 1. 25 M glycine pH 9. 1) for 20 minutes at RT to neutralize the PFA. Next, 10uL of staining cocktail containing 1x Tween20, anti-CytC-Alexa Fluor 488 (#612308, Biolegend) and Hoechst-33342 (#H3570, Invitrogen) were added to each well and incubated overnight prior to reading on BD FACS Fortessa and analyzing using the FlowJo software. Dynamic BH3 profiling was performed after treating cells with the PI3K inhibitor AZD8186 at a final concentration of 5uM.
Systematic characterization of genetic dependencies in Multiple Myeloma with Amp1q We set out to systematically map genetic dependencies associated with Amp1q in MM. Due to the technical challenges associated with generating isogenic cell lines for a chromosomal amplification event and the rarity of MM cell lines without Amp1q, the discovery of genetic dependencies requires the comparison of non-isogenic cell lines with and without Amp1q  25, 28. Such comparisons are inherently noisy due to inter-tumor variability that is unrelated to Amp1q, as well as technical variables associated with the technology used (RNAi vs CRISPR) and the pooled format  67. To control for technical noise and the low number of controls per dataset, we leveraged both RNAi and CRISPR screen data from the Cancer Dependency Map (DepMap) (Amp1q: n=12; Non-Amp1q: n=3) and performed a genome-wide CRISPR screen on a MM cell line with Amp1q and one without, which allowed us to filter for genes that hit in at least two of the three datasets.
To control for noise due to differences unrelated to Amp1q, we filtered for genes whose expression was either up-or down-regulated in MM cell lines with Amp1q, reasoning that Amp1q could result in differential dependencies due to either up-or down-regulation of genes in trans. We defined hits as genes with: (i) a negative median dependency score in MM cell lines with Amp1q and a negative difference compared to the median dependency score in MM cell lines without Amp1q, and (ii) a difference in median expression between MM cell lines with and without Amp1q of > 2 or < -2. Overall, 2,101 genes hit in one dataset, 1,243 genes hit in two datasets, and 280 genes hit in all three datasets, without clear technology-based bias (Figure  1A, B ). Considering genes that hit in at least two datasets, we split each chromosome into 30 equal-sized bins and identified three segments with at least 20 hits and significant positional enrichment (hypergeometric, q < 0.
Prior efforts have focused on genes located on chr1q, such as MCL1, BCL9, CKS1B, ILF2, and PBX1  [46] [47] [48] [49] [50] [51] [52] [53] [54] [55] [56]. Here, we have performed a large-scale systematic characterization of genetic dependencies and therapeutic vulnerabilities associated with Amp1q and identified a broad range of opportunities for targeted therapy. To reduce technical noise and control for irrelevant inter-tumor variability in our dependency analyses, we performed a genome-wide CRISPR screen and a targeted shRNA screen of genes located in 1q21-1q23, and filtered for genes that hit in multiple datasets and were differentially expressed between MM cell lines with and without Amp1q. This approach led to minimal positional bias across the genome, while enriching for hits associated with Amp1q, as demonstrated by the fact that 1q21-1q23 was the region with the highest density of genetic dependencies across the genome in MM cell lines with Amp1q.
F) Heatmap of cytochrome c loss (%) in cell lines with (n=3) and without Amp1q (n=2). G) Barplots of cytochrome c loss (%) in cell lines with (n=3) and without Amp1q (n=2) incubated with 5uM of the MS-1 peptide. Error bars correspond to the standard deviation across three replicates and the p-value was computed with Wilcoxon's rank-sum test. H) Scatterplot of log 2 fold-change in expression between MM cell lines with and without Amp1q (x-axis) and differential dependency (i. e. , the difference between median dependency estimates in MM cell lines with and without Amp1q) in the targeted shRNA screen dataset (y-axis). Genes with a positive foldchange are colored in red, while genes with a negative fold-change are colored in blue. Genes that hit in both the targeted shRNA dataset and the genome-wide screens are marked with yellow stars.