dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS. We thank  S. van der Mije  (Naturalis),  F. Boschin  ( University of Siena ),  G. Perry  ( Pennsylvania State University ),  P. Kosintsev  ( Russian Academy of Sciences ),  J. -J Cleyet-Merle  ( Musée National de Préhistoire ), and  A. Lister  ( Natural History Museum ) for providing COL1 reference samples;  A. Reiner ,  L. Klausnitzer ,  S. Steinbrenner ,  L. Westphal ,  B. Höber ,  B. Nickel ,  U. Stenzel , and  A. Weihmann  for technical support; and  S. McPherron ,  M. P. Richards , and our reviewers for their constructive comments, which greatly improved the paper. This research was supported by funding through the  Max-Planck-Gesellschaft ,  European Research Council Advanced Award CodeX, Deutsche Forschungsgemeinschaft  ( SFB1052 , project  A02 ), and  Engineering and Physical Sciences Research Council   NE/G012237/1.
Planck Collaboration: Full Focal Plane Simulations The second release of Planck data (hereafter PR2-2015) is based on five HFI and eight LFI surveys (LFI Survey 9 having been reserved for atypical scans designed to assist in the understanding of systematic effects), and for the first time includes results in polarization as well as intensity. PR2-2015 is accompanied by a suite of papers, of which this is one, together with an online explanatory supplement, an ESA legacy data archive (http://pla. esac. esa. int/pla/) and NASA partial mirror (http://irsa. ipac. caltech. edu/Missions/planck. html), as well as resources to access and manipulate the full simulation suite described here (http://crd. lbl. gov/ cmb-data). Simulations play a number of important roles in the analysis of these Planck data, including: 1.
FFP8 pipelines The two Planck DPCs have distinct internal data formats and access protocols, including elements that are either impossible or impractical to port to supercomputing facilities. Moreover, data access can be a significant computational bottleneck, especially in an HPC environment, so aggressive IO optimization is necessary for the most compute-intensive operations. To address these issues, we use the Planck exchange format 10  (EF), developed for inter-DPC exchanges and public releases, for all time-domain data, adding the necessary interfaces to EF files to the TOAST data abstraction layer. TOAST allows any analysis code to access any supported data set -including Planck -without worrying about the details of the underlying file formats or data distributions on disk. Instead the path to, format of, and metadata about, each data file required in a particular analysis is stored in an XML document called a run configuration file, or "runconfig".
Any request by a TOAST-interfaced analysis code for data is then passed to TOAST to resolve and satisfy, using the information in the runconfig. This data abstraction also enables TOAST to cache heavily re-used data (such as the telescope pointing information) and to generate synthetic data on-the-fly (bypassing the crippling IO costs associated with performing distinct time-domain simulation and analysis phases), and to do both of those things transparently to the calling analysis code. Since the general analysis of large, correlated data sets involves global operations on all of the data, requiring very massive parallelism, TOAST uses a hybrid MPI/OpenMP programming model to enable these operations to be efficiently executed on the architectures of the current largestscale computing resources.
Fiducial realizations The FFP8 fiducial realization is generated in two steps: (1) simulation of the full mission TOD for every detector; and (2) calculation of maps from the various detector subsets, intervals, and data cuts (Fig. 11 ). Simulation of explicit TODs allows us to incorporate each detector's full beam (including its far sidelobes) and unique input sky (including its bandpass). As noted above, the fiducial realization is generated in six separate components -the three CMB components (lensed scalar, tensor, and non-Gaussian complement), two foreground realizations (with and without bandpass mismatch), and noise. The first five of these are simulated as explicit TODs and then mapped, while the noise is generated using the on-the-fly approach described in the noise MC subsection below.
TOD generation for any detector proceeds by first convolving the appropriate sky component with the beam at every point in a uniformly sampled data cube of Euler angle triplets (encoding the pointing and polarization orientation) to produce the "beamskyset", and then generating the time-ordered data by interpolating over the beamskyset data cube to the exact pointing and polarization orientation of each sample. Previous FFP simulations, including FFP6, accompanying the 2013 Planck data release, used the LevelS software package  (Reinecke et al. 2006)  to do this, using the conviqt  (Prezeau & Reinecke 2010)  and multimod tools, respectively. However, this required format conversions for the input pointing data (from exchange to LevelS) and the output time-ordered data (from LevelS to exchange), at significant IO and disk space costs.
For FFP8 we have therefore embedded the critical parts of these routines into a new code, IScalm2TOD, which uses TOAST to interface directly with exchange format data. As an additional benefit, by combining the two steps into a single code the beamskyset never has to be written to disk, further reducing the IO and disk space costs. This approach also allows us trivially to handle the LFI multi-component beams (subsection 3. 1. 3), where each beam is expressed at a different ( max , m max ) resolution. Previously this meant generating distinct TODs and adding them only after they had been written, tripling the IO and disk space requirements, whereas now they are generated separately but combined within IScalm2TOD prior to being written. All of the FFP8 fiducial maps are produced using Madam/TOAST, a TOAST port of the Madam generalized destriping code  (Keihänen et al.
2005 (Keihänen et al. , 2010)) , which allows for destriping with an arbitrary baseline length, with or without a prior on the baseline distribution (or noise filter). Madam is used to produce the official LFI maps, and its destriping parameters can be chosen so that it reproduces the behaviour of Polkapix, the official HFI mapmaking code. Comparison of the official maps and Madam/TOAST maps run using exchange data show that mapmaker differences are negligible compared to small differences in pointing and (for HFI) dipole subtraction that do not impact the simulation. The sky components are mapped from the TODs generated by IScalm2TOD, while the fiducial noise is taken to be realization 10 000 of the noise MC (with realizations 0000-9999 reserved for the noise MC itself). Summarizing the key differences in the map making parameters for each Planck frequency: -30 GHz is destriped with 0.
25 s baselines; 44 and 70 GHz are destriped using 1 s baselines; and 100-857 GHz are destriped using pointing-period baselines (30-75 min). -30-70 GHz are destriped with a 1/ f -shape noise prior, while 100-857 GHz are destriped without a noise prior. -30, 44, and 70 GHz have separate destriping masks, while 100-857 GHz use the same 15 % galaxy+point source mask. -30-70 GHz maps are destriped using baselines derived exclusively from the data going into the particular map, while 100 -857 GHz maps are destriped using baselines derived from the full data set. The baseline (r = f NL = 0) fiducial channel/mission/full maps for temperature and total polarization are shown in Figs. 12 and 13, respectively.
Consequently when Madam requests the noise TOD for a particular interval of a particular realization, the process can skip ahead to the appropriate location in the overall sequence; furthermore the random number generation itself can be threaded, with each thread then skipping ahead to the appropriate place within the subsequence. Madam supports MC runs by enabling the pointing and flag data common to all realizations to be read once, before looping over the realizations that share them. However, to support maximum MC throughput some changes were also made to the standard Madam implementation: once the full data for any time span are loaded, Madam/TOAST can independently destripe and map the full and two half ring data sets; communication at high concurrency is more efficiently supported through use of the advanced MPI collective operation alltoallv; the map is distributed between the MPI processes based on pixels covered by the distributed TOD rather than using a simple round-robin scheme; -HFI full-mission destriping is supported on-the-fly so that once the full mission data have been destriped, all subset maps can be binned as part of the same run; -OpenMP/MPI hybridization allows for runs at higher concurrencies without suffering from MPI communication congestion.
CMB Monte Carlos The FFP8 CMB MCs are generated using the FEBeCoP software package  (Mitra et al. 2011) , which produces beam-convolved maps directly in the pixel domain rather than sample-by-sample,   as is done for the fiducial maps. The goal of this approach is to reduce the computational cost by the ratio of time-samples to map-pixels (i. e. , the number of hits per pixel). As illustrated in Fig. 15 , FEBeCoP proceeds in 3 steps: 1. Given the satellite pointing and flags and the focal plane (accessed through the TOAST interface), for every channel FEBeCoP first re-orders all of the samples in the mission by pixel instead of time, localizing all of the observations of each pixel, and writes the resulting pixel-ordered detector dngles (PODA) to disk. Note that since the PODA also contains the detector, time-stamp, and weight of each obser-vation this is a one-time operation for each frequency, and does not need to be re-run for different time intervals or detector subsets, or for changes in the beam model or its chosen cut-off radius.
Since this missing HFI power is not picked up in the noise estimation, it must be skysynchronous (frequency bins corresponding to sky-synchronous signals being discarded when fitting the noise PSDs due to their contamination by signal residuals). This is now understood to be a systematic effect introduced in the HFI pre-processing pipeline, and we are working both to incorporate it as a systematic component in existing simulations and to ameliorate if for future data releases. Finally, the various analyses of the FFP8 maps in conjuction with the flight data provide powerful incidental validation. To date the only issues observed here are the known mismatch between the FFP8 and PR2-2015 cosmologies, and the missing systematic component in the HFI maps. As noted above, the former is readily addressed by rescaling or using FFP8.
In 2014, Planck was awarded about 75 000 000 NERSC CPU-hours, which were shared by the 170 Planck data analysts who have requested NERSC accounts. For FFP8, the fiducial realization, all HFI noise MCs, the first 1 000 LFI channel noise MCs, and all CMB MCs were performed on NERSC's 133 824-core Cray XC30 Edison. Subsequently, all of the FFP8. 1 CMB MCs were generated there too.
The concurrencies and runtimes for each frequency are listed in Table  6. With five components generated via explicit timelines and one via on-the-fly simulation within the mapmaking, the total cost was approximately 250 000 CPUhours. This same set-up also allowed us to generate each individual foreground component to cross-check the component separation pipelines. Table  6. Computational cost of generating one component of the FFP8 fiducial realization for each frequency. TOD generation includes all of the detectors, and mapmaking all of the maps, at that frequency. Note the much higher cost of LFI mapmaking, due to its shorter baselines and run-baseline destriping. For the noise MCs, we noted that mapmaking runs most efficiently at the lowest concurrency with sufficient memory to hold all of the data at a given frequency.
Uses of FFP8 As well as supporting the validation and verification of all of the Planck analysis codes, FFP8 is widely used in the analysis of Planck data. Fiducial realizations are used to: - Noise MCs are used to: measure the accuracy of the LFI pixel-pixel noise covariance matrix construction (Planck Collaboration VI 2015); and inform low-data selection by measuring the distribution of low-multipole amplitudes due to noise as well as confirming the anomalous nature of Surveys 2 and 4 (Planck Collaboration II 2015); CMB MCs are used to: produce beam transfer functions for each map type listed in Table  1  by averaging over the corresponding MC realizations  (Planck Collaboration IV 2015; Planck Collaboration VII 2015). In addition, the noise and CMB MC maps are combined using the same component separation pipelines as the real and fiducial data.
Community access to FFP8 As was the case with FFP6 for the first Planck data release, FFP8/FFP8. 1 is being made available to the scientific community. A simulation set of this size requires significant computational resources to store and analyse, so once again we are making it available at NERSC, where there is also an allocation of compute cycles available to support its exploitation. Full details on this, including how to obtain a NERSC account, can be found at http://crd. lbl. gov/cmb-data.
Conclusions Simulations play a critical role in the analysis of CMB data sets, most particularly in the validation and verification of analysis pipelines and the debiasing and uncertainty quantification of analysis results. The former requires a simulation that is as true to the mission as possible, while the latter requires enormous Monte Carlo sets of mission realizations. FFP8 provides the 2015 Planck data release with both. The fiducial realization takes the most realistic model of the microwave sky available to us and simulates the full mission observation of that sky for every Planck detector, incorporating our best estimates of that detector's beam, bandpass, and time-varying noise properties. The noise and CMB MC sets include 10 4 realizations of the most important maps (namely those that incorporate all of the data at a given frequency), together with 10 2 -10 4 realizations of all of the other maps that include subsets of the observations and detectors at each frequency.
Acknowledgements. The Planck Collaboration acknowledges the support of:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MINECO , JA, and  RES (Spain) ;  Tekes, AoF , and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ;  ERC  and  PRACE (EU). A description of the Planck Collaboration and a list of its members, indicating which technical or scientific activities they have been involved in, can be found at http://www. cosmos. esa. int/web/planck/planck-collaboration. Particular thanks are due to the extraordinary NERSC staff, who have supported the Planck mission for over a decade and who facilitated the production and distribution of FFP8 in numerous ways.
RT-qPCR RT-qPCR was performed as previously described  [19]. One μg of total RNA was reversed transcribed using SuperScript III Reverse Transcriptase (Invitrogen). Primers 5'-GCTGTGAAGT GGAAGCCCCTACA-3' (forward) and 5'-TGCCACCTGTGAGCTGGAAGTCA-3' (reverse) were used to specifically amplify NRP1-FS in human cells and tissues. Primers 5'-GCTGTAA GTGGAAGCCCCTACA-3' (forward) and 5'-TGGTGCCACCTGTTCCACTGTG-3' (reverse) are specific for human NRP1-Δ7. In mice, 5'-GGCTGTGAAGTGGAAGCACCTAC-3' (forward) and 5'-TGCCTCCTGTGAGCTGGAAGTCA-3' (reverse) amplified NRP1-FS whereas 5'-GGCTGTGAAGTGGAAGCACCTAC-3' (forward) and 5'-TGGTGCCTCCTGTGCCAC TGTG-3' (reverse) amplified NRP1-Δ7. Primers specific for human and mouse GAPDH were described previously  [20]. Real-time qPCR was performed in a final volume of 20 μl containing 2 μl cDNA (corresponding to 10 ng of total RNA), 300 nM of each primer and 10 μl qPCR MasterMix Plus for SYBR green assays (Eurogentec, Belgium) in the StepOne Real-Time PCR system (Applied Biosystems, Belgium).
The polymerase was activated at 95°C for 3 min and the PCR conditions for amplification were 15 sec at 95°C, 20 sec at 66°C and 20 sec at 72°C. The results were analyzed with the StepOne Software. qPCR amplifications were performed in triplicate. The amplification efficiencies of both variants were close to 100% and not significantly different. The relative expression of the two variants was calculated using the formula 2 (-ΔΔCt). The specificity of the amplification products was assessed by gel electrophoresis.
Deep Sequencing MCF7 were isolated and prepared for next-generation sequencing analysis, as described in a previous publication  [20]. The average number of reads approached or exceeded 20 millions. Alignment of transcripts to the genome indicated that 16733 genes were expressed in the sample. Detailed protocol and sequencing data are available in the ArrayExpress database (www. ebi. ac. uk/arrayexpress) under accession number E-MTAB-5104. Generation of PC3 and MDA-MB-231 Cells Expressing NRP1-FS, NRP1-Δ7, FLAG-NRP1-FS and FLAG-NRP1-Δ7 in a Doxycycline-Dependent Way MDA-MB-231 cells were transfected with pcDNA6/TR (Invitrogen) and selected in medium supplemented with blasticidin (4 μg/ml). One clone expressing a high level of tetracycline repressor (MDA-MB-231/TR) was isolated. PC3 cells expressing a high level of tetracycline repressor (PC3/TR) were described previously  [19].
Scratch Assay A suspension of 50 000 PC3 or 35 000 MDA-MB-231, each inducible for NRP1-FS or -Δ7, was seeded into culture-inserts (#80209, Ibidi, Germany) with or without 200 ng/ml doxycycline in the medium. This type of insert allows the creation of a homogeneous 500 μm width cell-free space in the middle of a confluent cell monolayer. Twenty-four hours after seeding, the inserts were removed and pictures by phase-contrast microscopy of the resulting scratches were taken at T0 (2 pictures/insert, 6 inserts/condition and experiment for each tested clone was performed at least twice). PC3 and MDA-MB-231 were cultured for a further 24h and 8h, respectively. Cell migration was evaluated by measuring the cell-free surface at the beginning of the experiment (T0) and the surface covered by the cells at the end of the experiment by using ImageJ Software (National Institute of Health, USA).
Cell Apoptosis PC3/NRP1-FS and PC3/NRP1-Δ7 cells were cultured in absence and presence of doxycycline and apoptosis was evaluated by FACS after annexin V-FITC and propidium iodide staining, as previously described  [19]  using a FACSCanto II double LASER flow cytometer (UV, 488 nm, 633 nm) (BD Biosciences, Belgium). Data were analyzed using FACSDiva Software (BD Biosciences).
The actual existence of this variant of NRP1 was further confirmed by different independent observations. Its presence was first evaluated in various cell lines and tissues of human or mouse origin by regular RT-PCR followed by gel electrophoresis. The relative proportion of NRP1-Δ7 was also determined by RT-qPCR using variant-specific primers. The ratio Δ7/FS varies between 0. 13 and 0. 43 (Fig  2 ). As an additional approach, the existence and presence of NRP1-Δ7 was also evaluated in "Deep Sequencing" data previously obtained using MCF7 cells mRNA  [20]. From such data, the abundance of alternative splicing variants can be calculated by comparing their FPKM (Fragments Per Kilobase Of Exon Per Million Fragments Mapped). Values of 29. 13 and 12. 18 were obtained for NRP1-FS and NRP1-Δ7, which correspond to a ratio of 29%.
Finally, the existence of NRP1-Δ7 is further assessed by its identification by the German Genome Project (clone DKFZp686E15115; http://www. ncbi. nlm. nih. gov/ nuccore/BX648250. 1) by entries in Genbank under the accession numbers NM_001244973 (NRP1 variant 5) and NP_001231902 (NRP1 isoform E). Based on the exon content of the previously described variants of NRP1, NRP1-s12 and NRP1-ΔE16 should be also affected by the 21 bases deletion. It was confirmed for NRP1-s12 (NRP1-s12-Δ7), while it was not further investigated for NRP1-ΔE16 which is barely detectable in our cell models (S1 Fig).
Acknowledgments We gratefully acknowledge the  GIGA-Genotranscriptomics facility  of the  University of Liège. We thank  Betty Nusgens  for providing advice in this work and reviewing the manuscript,  Antoine Heyeres  and  Fabrice Olivier  for their technical support,  Charles Lambert ,  Lauriane Janssen ,  Romain Delcombel ,  Nor Eddine Sounni  and  Carine Munaut  for providing material.
For the three days of the event, we shadowed and videotaped the activities of two teams of participants (comprised of six members each), from the moment the teams were formed to the moment they presented their prototypes to the visitors of the museum. We also collected various documents that were used for organizing the event, including snapshots from Museomix's website and copies of the participants' handbook. Using the qualitative analysis software Transana, we then watched the videos, created a content log for each video file, and transcribed passages where the ideas developed by the participants seemed to undergo significant changes. We used Jefferson's transcript symbols  [56]  for the transcriptions. We then analysed the transcribed passages. Our process of analysis can be described as follows: for each passage, we detected the relations through which an idea materialized itself by acquiring specific properties (starting with that of being an idea).
To help our reader follow our data analysis, we divided the analytical section into two parts. In the first part, we present the requirements that participants were supposed to meet when pitching their ideas. These requirements were communicated to the participants in three main ways: through (a) Museomix's website, (b) the participants' handbook, and (c) instructions given by the organizers over the course of the event. In the second part, we analyse the pitch of the idea using elements from the transcript of the pitch and from the pitch requirements.
populi, a sister species of C. tremula, to identify ABC proteins expressed in the larval midgut. The CpABC12 gene of C. populi had the highest expression and encoded a full transporter of the B subfamily  [15]. We obtained the full-length cDNA sequence of the C. tremula homolog which we named CtABCB1 (GenBank Accession GU462154), which shared more than 90% amino acid identity with CpABC12. The open reading frame (ORF) of CtABCB1 is 3780 bp long (Figure  S1 ) and encodes a protein of 1259 amino acids possessing all general features of full-transporter ABC proteins (Figure  1  and Figure  S2 ), such as two transmembrane domains each composed of six transmembrane helices, and two nucleotide binding folds (NBF1 and 2) each composed of an ATP binding domain (ATP) and a transporter motif (TpM1 and 2).
We then PCR-amplified the ORF of CtABCB1 from larval midgut cDNAs prepared from Cry3Aa-resistant insects. These showed a four-base-pair deletion at position 1561 (GenBank Accession KX686490, Figure  S1 ), introducing a frame shift with a premature stop codon leading to loss of the TpM1 transporter motif as well as the complete second transmembrane domain (Figure  1 ). A homolog of CtABCB1 in the western corn rootworm, Diabrotica virgifera virgifera, was shown to be genetically linked to resistance to the Bt Cry3Bb1 toxin; however, the resistance-conferring mutation was not reported  [16]  (Figure  S4 ). The existence of cross-resistance between Cry3Bb1 and mCry3A (a modified version of Cry3Aa) in the western corn rootworm  [17]  suggested that CtABCB1 could be involved in resistance to Cry3Aa in C.