dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Data are presented as mean6SD, unless otherwise stated. We used Prism 5 for unpaired T-test or ANOVA with Bonferroni posttest. Survival curves were analyzed by log rank test. Tumor size differences in PyMT cohorts were analyzed by Mann-Whitney, because the groups were not normally distributed. Two-tailed two-way ANOVA and logistic regression models were used to compare tumor volumes, weights and take between sorted EPCR + and EPCR 2 cells using R version 2. 15. 0 (http://www. Rproject. org/) with p = 0. 05.
EPCR-expression defines a distinct subpopulation in mammary fat pad-enhanced MDA-MB-231 mfp cells Passage through the mammary fat pad can be employed to select more aggressive tumor cell populations for tumor transplantation studies. A mammary fat pad-enhanced derivative of the triple negative breast cancer cell line MDA-MB-231 (MDA-MB-231 mfp) with markedly improved tumor growth properties was previously characterized  [40]  and used to study contributions of TF-FVIIa-PAR2 coagulation protease signaling pathways to tumor progression  [8]. Our more recent data showed that the stem cell receptor EPCR is required for signaling of another TF protease complex, i. e. TF-FVIIa-FXa in which FXa cleaves PAR2 or PAR1  [21, 41]. In order to begin to evaluate potential contributions of EPCR to TF signaling in cancer progression, we determined the co-expression of TF and EPCR by FACS.
The sorts of passage 22 and 23 cells were treated as replicates in the data analysis and the clustering for the mean value for these samples and 4 additional earlier and later sorts is shown. Hierarchical Clustering Subbranches: The dendrogram was cut at a defined height to yield 10 gene clusters. Of these, 6 had transcripts upregulated in EPCR + cells (Branch 1-6) and 4 had transcripts upregulated in EPCR 2 cells (Branch 7-10). Gene counts were: Branch 1, n = 405; Branch 2, n = 198; Branch 3, n = 59; Branch 4, n = 54; Branch 5, n = 624; Branch 6, n = 2; Branch 7, n = 107; Branch 8, n = 844; Branch 9, n = 85; Branch 10, n = 264 (see TableS1for gene lists). The genes for each cluster were used as input into Ingenuity IPA to identify pathway annotations or representation of gene signatures (see TableS2).
RNA preparation and expression analysis The ectopic gene expression in the stable cell lines was induced with doxycycline, and the cells were collected at different time points post-induction. Total RNA was extracted using TRI-REAGENT (Euromedex), and Poly(A)+ RNA was enriched from total RNA using mMACS mRNA Isolation Kit (Miltenyi Biotec). The cDNA obtained by reverse transcription with random primers (dN9) was amplified with specific primers on the CFX96 Touch TM Real-Time PCR Detection System (Bio-Rad) using EvaGreen qPCR Mix Plus (Euromedex). Primer sequences are available in Supplementary Table  S1. All reactions were carried out in triplicate. The relative expression ratio was calculated using the CFX Manager TM Software (Bio-Rad). The tissue qPCR arrays gene expression analysis was performed on human disease tissues (TissueScan TM Cancer Survey Tissue qPCR Panel 96 -I CSRT101, OriGene) and on mouse tissues (TissueScan TM Mouse developmental Tissue qPCR Panel I MDRT101, OriGene).
Of the 118 ENCODE transcription factors  (49)  we selected only the sequence-specific transcription factors and as for ZNF143, we were interested in the regulatory region located between the TSS and the CDS, including the eventual non-coding exons and introns. Using UCSC table browser  (50)  we extracted the ENCODE ChIPseq signal track and overlapped with the transcription factors regulatory regions. For each factor, the cluster score represents the strength of the ChIP-seq signal that is influenced by the affinity of the factor to one or more binding sites present in the cluster. The resulting table is available in supplementary material (Supplementary Table  S2 ). Surprisingly, of the 87 sequence-specific transcription factors, we found that 39 of them have at least one binding event in their own regulatory region (Figure  7A  and B ).
ACKNOWLEDGEMENTS We are grateful to  Dr B. Se´raphin  for the gift of the pBS2415 plasmid,  Dr I. Davidson  for the anti-TBP antibodies and  Dr S. Pffefer  for supplied material. We are thankful to  Evelyne Myslinski  for discussions and critical reading of the manuscript. We also thank  E. Vanlioglu  and  M. Gierens  undergraduate students for involvement in several experiments. We also thank  S. Baudrey  and  A. Schweigert  for valuable technical assistance. We are also grateful to  E. Westhof  for his interest in our study. The overall study was conceived, designed and coordinated by P. C. , with important contributions from R. P. N. R. P. N. performed the experiments. R. P. N. designed the figures. P. C. and R. P. N. wrote the paper.
Acknowledgements We thank  M. Cacciari  for providing the FONLL predictions for the B meson production cross-sections. We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  NSFC (China) ;  CNRS/IN2P3  and  Region Auvergne (France) ;  BMBF ,  DFG ,  HGF  and  MPG (Germany) ;  SFI (Ireland) ;  INFN (Italy) ;  FOM  and  NWO  (The Netherlands); SCSR (Poland);  ANCS/IFA (Romania) ;  MinES, Rosatom ,  RFBR  and  NRC "Kurchatov Institute" (Russia) ;  MinECo, XuntaGal and GENCAT (Spain) ;  SNSF  and  SER (Switzerland) ;  NAS Ukraine (Ukraine) ;  STFC  ( United Kingdom );  NSF (USA).
II. THE MEMPHYS DETECTOR MEMPHYS is a proposed large-scale water-Cherenkov detector with a fiducial mass of the order of half a megaton. The detector could be installed at the Frejus site, near the existing Laboratoire Souterrain de Modane (LSM), in the tunnel connecting France to Italy, located at 130 km from CERN and with a rock overburden of 4800 meters water equivalent. Possible installation at other European sites was studied in the context of the LAGUNA EU-FP7 Design Study  [14]. The original plan  [4]  envisaged three cylindrical detector modules of 65 meters in diameter and 60 meters in height. At the Frejus site, the characteristics of the rock allow for a larger excavation in the vertical direction. With an updated design of 80 m height, for example, the fiducial volume would be increased to 572 kilotons (30% larger).
III. R&D TOWARDS LARGE LIQUID-BASED DETECTORS One important R&D item towards the construction of MEMPHYS and other large liquid-based detectors, such as LENA  [17]  or GLACIER  [18] , is focused on the reduction of the number of electronics channels for power supply and signal readout of the PMTs, which is expected to be one of the major costs of the experiment. The PMm 2 R&D program  [19]  has developed an integrated readout electronics circuit (an ASIC called PARISROC  [20] ) for groups of PMTs (matrix of 4 Â 4). The electronics and acquisition are being fully tested with the MEMPHYNO prototype  [21] , a test bench for light sensor or electronics solution for next generation large size experiments. MEMPHYNO is installed at the APC Laboratory in Paris. It consists of a high-density polyethylene tank of 2 Â 2 Â 2 m 3 , filled with water and placed inside a muon hodoscope.
This is simpler to compute that the likelihood function based on charge distributions, used by SK, relying on detailed comparisons of data and simulation, but proves to be very effective for electron/muon separation. Distributions of this PID quantity for single electron and single muon events are shown in Fig. 11. The distributions obtained when the simulation is run with the SK geometry parameters are also shown, for comparison; the edges of the Cherenkov cone are less sharp than in SK, due to more light scattering with the larger size of the detector, however the separation between the two types of leptons is better in MEMPHYS, mainly due to the larger number of PMTs providing better granularity when reconstructing the pattern of detected light. A cut at a value of 0. 8 on this variable provides an efficiency of 96.
ACKNOWLEDGMENTS We are grateful to  Enrique Fernandez Martinez  for useful discussions and cross-checks and to  Jaime Dawson  for detailed reading of the manuscript. We acknowledge the financial support from the  UnivEarthS Labex program of Sorbonne Paris Cite ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ) and from the  European Community  under the  European Commission   Framework Programme 7 Design Study: EUROnu , Project No. 212372. The EC is not liable for any use that may be made of the information contained above.
(  6 )) is determined by the sign of cos ψ which can be positive or negative during OTHG process. Nevertheless, J 3 is too small compared with K 1 , so K 1 P 1 and K 3 P 3 lead to a fast phase variation. While the OTHG buildup region is restricted to -π < ψ < 0, ψ cannot stay in this favorable region for a long propagating length. Therefore, it is difficult to realize efficient OTHG. To verify our analysis mentioned above, we used the Runge-Kutta method in MATLAB to numerically calculate Eqs. (  2 ) and (3) to investigate the designed fiber with different doping concentrations. The pump and signal powers are set to be 500 W, and 35 W, respectively and initial phase difference ψ(0) = -π/2. For a lossy interaction, the conversion efficiency is given by η = (P 1out -P 1 (0))/P 3 (0), where P 1out is the power of the one-third harmonic at the output.
Without standardized API, each operating system has a specific approach to set and configure flow labels, and it is not easy to write portable software. This is why our work focuses only on the Linux kernel. The Linux Flow Label API is 13 years old, and part of the Kernel since the version 2. 2. 7, released in April 1999. Design decisions of the implementation are explained in  [2] , and there is nothing really new since this time. This document is still the reference documentation for this API.
How to patch applications on Linux Our proposition is simple: each application discriminates flows and sets flow labels for outgoing sockets, and if it is not done, the operating system has to set a flow label for all sockets of an application. The user can deactivate this kernel behavior (for example with the help of an environment variable). Thanks to the GNU linker ld, it is possible to preload some libraries for dynamically linked software (the kind of linkage used by nearly all Linux distributions). We can intercept the call to the connect() function. Here we check if a flow label is already set, in this case we continue with the standard connect() function. Otherwise, we enable the flow label and set its value (derived from the process identifier PID) via the setsockopt() function. Using this approach, we do not have to patch every application to use the spreading.
CPU consumption on the middlebox To evaluate the CPU consumption of our solution, we made a profiling of the Kernel. We used the OProfile software, part of the standard tools of the Linux Kernel package. Our results show that our module needs about 10% of the time needed by the network card driver itself. With a bandwidth of 2. 2Gb/s our module uses less than 2. 5% of the CPU time of the 2. 4Ghz CPU used. Additionnaly, we install our solution in a home network on a standard router (Asus-rt16) and we do not see any CPU consumption overhead 6.
Statistical analyses The statistical parameters for the concentrations (mean, median and standard deviation) were calculated from all the analyzed samples of each matrix and not only from samples for which the residues were detected or quantified. When a compound was not detected (, LOD), the concentration used for statistical analysis was K LOD  [35]. When a compound was not quantified (. LOD and , LOQ), the concentration used was K (LOD + LOQ). For each matrix, statistical analyses were performed only for residues that were detected or quantified at least once. We transformed the data into a present/absent dataset and considered the number of compounds detected and/or quantified in each sample as an explicative variable in the following models. Linear mixed effects models were used to perform a comparison between the number of residues detected or quantified (i) in honey bees (n = 141), honey (n = 141) and pollen (n = 128); (ii) in different landscape structures (rural-grassland, rural-cultivated, urban and island); and (iii) for different sampling periods (AM8, JJ8, JA8, SO8, AM9, JJ9, JA9 and SO9).
These models were the best way to take into account the repeated measurements on each apiary. The linear mixed effect models are the theoretical presentation of ANOVA for repeated measurements. For each of these three models, the assumption of independence and normality of the residues and random effects was checked through diagnostic graphs generated by the parametric estimation theory of mixed effects models (data not shown)  [36]. Then, Tukey post-hoc tests (a specific version designed for mixed effects models) were used to implement multiple comparisons of the means in each model, (i) difference in matrix, (ii) difference in landscape and (iii) difference in sampling periods. The statistical analyses were performed using R software with the ''nlme package'' for the mixed effects and the ''multcomp package'' for the post-hoc tests  [37].
Gain and Loss of dTDP-43 Cause Significantly Overlapping Transcriptome Alterations To identify molecular pathways underlying the specific late pupal neurodegenerative phenotypes, we sequenced transcriptomes of heads with altered dTDP-43 expression at the developmental stage when the CCAP/bursicon neuronal defect was observed (73-90 hr APF; Gene Expression Omnibus [GEO] accession number GSE42844). Pupae overexpressing dTDP-43 were generated by crossing the ubiquitous act5c-GAL4 driver to UAS-dTDP-43-Flag (dTDP-43 GOF ) (Figure  S3A ), resulting in moderately increased dTDP-43 transcript and protein levels (Figures  S3B  and S3C ). To produce pupae with ubiquitously decreased dTDP-43 expression, we used transheterozygous combinations of dTDP-43 D23 and dTDP-43 D142 , two reported mutants  (Feiguin et al.
, 2010; Herrmann et al. , 2012). In the list of upregulated genes we identified 151 out of 6,383 tested PWM motifs with an enrichment score (E-score) above 2. 5 (Table  S6 ). The list contained three highly similar motifs: 5 0 -CAAGGTC-3 0 (ranked first, E-score: 4. 75), 3 0 -GAAGGTCA-5 0 (ranked tenth, E-score: 4. 27), and 5 0 -AAGGTCA-3 0 (ranked 54 th , E-score: 3. 30). The latter two corresponded to the recognition motifs of the nuclear hormone receptors Hr39 and the estrogen-related receptor (ERR) and strongly resemble the archetypical AGGTCA half-site consensus motif for nuclear hormone receptors, including EcR (King-Jones and Thummel, 2005) (Figure  4B ; Table  S6 ). Next, we used i-cisTarget to detect enrichment of experimentally validated in vivo transcription factor binding sites (derived from modENCODE ChiP-seq data) and retrieved five hits of which two corresponded to EcR (Table  S7 ).
Image Quantification To measure EcR cytoplasmic/nuclear ratios in CCAP/bursicon neurons, ImageJ Software was used to measure the fluorescence intensity (mean gray values) of confocal images. Images were made with equal laser intensity unless noted otherwise with a Fluoview FV1000 confocal microscope (Olympus). Ten CCAP/bursicon neurons of different VNCs were analyzed. Mean gray values of nucleus and cytoplasma were measured and the cytoplasmic versus nuclear intensity ratio was calculated. Statistical analysis was done using one-way ANOVA with post hoc Bonferroni's multiple comparison test.
Mapping and Measurement of Gene Expression The Bioscope Whole Transcriptome (WT) Pipeline (Applied Biosystems) was used for read mapping using default parameters. Briefly, color space reads were mapped against the complete Drosophila melanogaster reference genome (dm3, BDGP Release 5). After this initial mapping, this pipeline performs several additional mappings against specific sequence subsets, namely, exon junctions and exon sequences obtained from the FlyBase Release 5. 12 annotations (FlyBase track from the UCSC Genome Browser). Contaminating sequences such as adapters and repeat sequences were filtered out. To count the number of reads per gene, we used the HTSeq package version 0. 5. 3 (function htseq-count) (http://www-huber. embl. de/users/anders/HTSeq/ doc/overview. html), with gene annotation from Drosophila melanogaster FlyBase Release 5.
Differential Gene Expression and Splicing Analysis Differential expression analysis between dTDP-43 LOF (four replicates) and w 1118 (two replicates) and between dTDP-43 GOF (two replicates) and w 1118 was performed using the Bioconductor package DESeq version 1. 2. 1 (R version 2. 12)  (Anders and Huber, 2010). DESeq first normalizes the data to correct for different library sizes and then estimates biological variability across samples and tests for differential expression based on the negative binomial distribution model. Genes are considered as differentially expressed when the Benjamini-Hochberg corrected p value is below 0. 05. Overlap between gene lists was statistically evaluated by calculating the representation factor (RF) and associated probability. The RF is the number of overlapping genes divided by the expected number of overlapping genes drawn from two independent groups.
An RF > 1 indicates more overlap than expected of two independent groups, and an RF < 1 indicates less overlap than expected. Probability was calculated using the exact hypergeometric probability formula. To quantify and determine isoform abundance and isoform differential expression between dTDP-43 LOF and wild-type control and between dTDP-43 GOF and wild-type control, we used Cufflinks and Cuffdiff  (Trapnell et al. , 2012) , with the Drosophila melanogaster reference annotation (D. melanogaster FlyBase r5. 25), hence avoiding novel isoform identification. To identify genes with altered splicing pattern, we made use of the Cuffdiff output file. We selected genes with multiple alternative transcripts having at least one transcript with FC > 2 (FDR < 0. 05) and at least one with FC < À2 (FDR < 0.
qRT-PCR RNA was isolated from 50 heads of w1118, act5c > dTDP-43, dTDP-43 D23/D142 , dTDP-43 D142/Df(2R)106. Heads were collected in 1 ml of TRI reagent (Sigma-Aldrich, St. Louis, MO, USA) and were grounded with a plastic disposable pestle grounded. Total RNA was isolated by using standard procedures. cDNA was generated from 1 mg of RNA of each sample by using an anchored oligo(dT)18 primer and a hexamer primer according to the instructions of the Transcriptor first-strand cDNA synthesis kit (Roche). qRT-PCRs were performed on an ABI7000 instrument with qPCR Mastermix Plus for SYBR Green I (Eurogentec, San Diego, CA, USA) with primers designed by PrimerExpress software (Applied Biosystems) (see the Extended Experimental Procedures). Expression levels of transcripts from the various samples were normalized to the housekeeping genes Rpl32, Rps13, and Gadph.
(legend continued on next page) lysis Buffer from the Magna RIP Kit (Millipore, Billerica, MA, USA). The beads were prepared according to protocol using rabbit dTDP-43 antibody (1:3,000), and normal rabbit IgG was used as a negative control. The pretreated beads and brain extracts were mixed and incubated ON at 4 C. Bound RNA transcripts were extracted according to protocol. First-strand synthesis was carried out according to the instructions of the Transcriptor first-strand cDNA synthesis kit (Roche). qRT-PCRs were performed with gene specific primers (see the Extended Experimental Procedures). In order to calculate the enrichment, the data were normalized to the inputs. The signal was represented by how many more fold increase was measured compared to the control signal. The error bars represent standard deviations on the normalized ratios.
ACKNOWLEDGMENTS L. V. B. and M. G. received PhD fellowships of the  Agency for Innovation by Science and Technology in Flanders (IWT). M. N. S. and G. K. hold a PhD fellowship from the  Foundation for Research Flanders (FWO) in Belgium. B. D. received a postdoctoral fellowship of the  FWO  and received support from the  Foundation for Alzheimer Research (SAO/FRMA) ,  FWO  and the  VIB Technology Watch Team in Belgium , and from  Inserm , the  University of Lille 2 ,  DN2M  ( Demences des maladies Neurologiques et Mentales ) as part of the contract project of the  Etat Region Nord/Pas-de-Calais  (grant  11005007 ) in France. P. C. is supported by  VIB ,  FWO , and the  Interuniversity Attraction Poles program   P6/43  of the  Belgian Science Policy Office (BELSPO). S. A. received support from the  FWO  (grant  G.
Immunofluorescence and Western Blotting Cells were prepared for immunofluorescence either by fixation with paraformaldehyde followed by permeabilization with Triton, or by fixation/permeabilization with methanol at 220uC, and antibody labeling was carried out as previously described  [17]. Images were acquired using a Zeiss Axiophot fluorescence microscope equipped with a CCD camera (Princeton Instruments), and photographs were recorded using IP Laboratories software and then moved into Photoshop. SDS-PAGE and Western blotting were performed as described by Gordon et al. [15]. Primary antibodies used in this study included an in-house affinity-purified rabbit antiserum against clathrin heavy chain  [18] , a commercial goat polyclonal antibody against CALM (Santa Cruz), commercial rabbit polyclonal antibodies against Dab2 (Santa Cruz) and actin (Sigma), and commercial mouse monoclonal antibodies against the HA epitope (Covance), the myc eptiope (Millipore), syntaxin 4, AP-2 a, and AP-2 m2 (all from BD).
No. 6607120) were used as a focusing reference, as described by Kozik et al. [21]. The images were acquired on the Arrayscan platform (Cellomics, ThermoFisher) with a 406 objective lens using the Spot Detector bioapplication. The Cy5 channel was used to determine a focus plane, and the images of Dab2 were acquired with a 23. 5 mm offset. The total intensity per spot was used for analysis; triplicate values for each clone were averaged for each experiment. The data shown are an average of three independent experiments.
The Dlongin VAMP7 showed the expected phenotype: it was somewhat elevated on the plasma membrane when compared with wild-type, and it was strongly affected by the CALM knockdown. The effect of the CALM knockdown on wild-type VAMP7 was surprising, because we had assumed that the longin domain would occlude the binding site on VAMP7 for CALM. The interaction between VAMP7 and CALM was further investigated in vitro using purified proteins. Although we were unable to detect any binding between CALM and VAMP7 by ITC, we could pull down CALM with sufficiently high concentrations of VAMP7 (Figure  3f ), indicating that CALM can interact directly with wild-type VAMP7 in spite of its longin domain. Together, these data show that CALM is capable of trafficking VAMP4, but the dileucine motif is such a strong sorting signal normally that CALM is not required.
Related literature For the pharmaceutical and medicinal activity of Schiff bases, see:  Dao et al. (2000) ;  Sriram et al. (2006) ;  Karthikeyan et al. (2006). For the coordination chemistry of Schiff bases, see:  Ali et al. (2008) ;  Kargar et al. (2009) ;  Yeap et al. (2009). For the crystal structures of related Schiff base compounds, see:  Damous et al. (2011) ;  Fun et al. (2009) ;  Nadeem et al. (2009) ;  Eltayeb et al. (2008). For a description of the Cambridge Structural Database, see:  Allen (2002).
Refinement All non-H atoms were refined with anisotropic atomic displacement parameters. The remaining H atoms were localized on Fourier maps but introduced in calculated positions and treated as riding on their parent atoms (C, O and N) with C-H = 0. 93 Å, N-H = 0. 86 Å and O-H = 0. 82 Å and U iso (H)=1. 2U eq (C or N); U iso (H)=1. 5U eq (O). The crystal used is an inversion twin with refined components 0. 57 and 0. 43. (Farrugia, 2012)  and DIAMOND  (Brandenburg & Berndt, 2001) ; software used to prepare material for publication: WinGX publication routines  (Farrugia, 1012).