dataesr/scientific-paragraphs-categorization huggingface dataset:
2004) primers (Cytbf, COIIrev592, 12So, and COII28for). Amplicons were pooled in equimolar amounts for each sample and barcoded. Sequence libraries were prepared using the NexteraXT kit (Illumina) according to the manufacturer's instructions and sequenced using Illumina MiSeq with 150-bp paired-end reads. Reads were mapped to the previously published mitochondrial assemblies of the corresponding species (chimpanzee: NC_001643. 1; bonobo: NC_001644. 1; gorilla: NC_011120. 1; Sumatran orangutan: X97707. 1; Bornean orangutan: NC_001646. 1). Average coverage across samples was high (from 1200× to 1560×; mean 1460×). Data processing and variant calling were done as described above. For filters, see Supplemental Table  S8 , and for mitochondrial sequences, see Supplemental File S1. All confident sites, single-nucleotide variants, and indels were called using the SAMtools  (Li et al.
2009 ) mpileup v1. 1 multisample option with the following general parameters: minimum base quality 20 and minimum mapping quality 30. Raw variants were filtered using VCFtools v0. 1. 12a (Danecek et al. 2011 ) and inhouse Perl scripts (Supplemental File S2). Details and phylogenetic positions of all MSY variants shown in the trees in Figure  3  can be found by consulting Supplemental Figure  S8  and Supplemental Tables  S9-S13. mtDNA sequences from all three data sets were aligned using Clustal Omega  (Goujon et al. 2010; Sievers et al. 2011; McWilliam et al. 2013) , and alignments were manually edited using AliView v1. 17. 1  (Larsson 2014 ). D-loop sequences were excluded from all analyses.
Other data sets Published data for multiple (sub)species  (Prado-Martinez et al. 2013)  were available as BAM files mapped to human genome reference hg18. All the variant calling and filtering steps used were identical to our data (see above). For filtered VCF files containing all confident sites, liftOver from hg18 to hg19 was done using Crossmap v0. 1. 5 software (http://crossmap. sourceforge. net/), followed by merging the overlapping sites with our filtered data set. Data for mountain gorillas  (Xue et al. 2015)  were available as prefiltered VCF files mapped to human genome reference hg19 containing all confident sites. These were additionally filtered to match our data processing with no missing data allowed, followed by merging the overlapping sites from all data sets. mtDNA data were available as FASTA files for both published data sets  (Prado-Martinez et al.
Principal component analysis Principal component analysis (PCA) was performed with the function "prcomp" in R environment version 3. 0. 2 (R Core Team 2014). For autosomal data sites with heterozygous calls in >80% of samples, triallelic sites and missing data were discarded to minimize background noise and uncertainty. Phylogenetic inference PHYLIP v3. 69 was used to create maximum parsimony phylogenetic trees  (Felsenstein 2005)  for both MSY and mtDNA. Three independent trees were constructed with DNAPARS using randomization of input order with different seeds, each 10 times. Output trees of these runs were used to build a consensus tree with the consense program included in the PHYLIP package. Intraspecific MSY trees were rooted using the ancestral sequence generated and described in the Supplemental Text.
Acknowledgments We thank  Javier Prado-Martinez ,  Tomàs Marques-Bonet , and  Yali Xue  for assistance with great ape sequence data extraction;  Maarten Larmuseau  for helpful discussions;  Lorna Gregory  and the  Oxford Genomics Centre for Illumina HiSeq  library preparation, target enrichment, and sequencing; the  NUCLEUS Genomics Facility of the University of Leicester  for access to Illumina MiSeq sequencing; and three anonymous reviewers for helpful comments. This research used the  ALICE High Performance Computing Facility at the University of Leicester. C. B. , P. H. , D. Z. , and M. A. J. were supported by  Wellcome Trust   Senior Fellowship Grant Number   087576 , and P. M. D. by a  College of Medicine, Biological Sciences  and  Psychology studentship  from the  University of Leicester.
Data access Raw sequence data from this study have been submitted to the European Nucleotide Archive (ENA; http://www. ebi. ac. uk/ena/) under accession number PRJEB12247. Mitochondrial DNA sequences from this study have been submitted to Genbank (http://www. ncbi. nlm. nih. gov/genbank/) under accession numbers KU353708-KU353726 and are also in Supplemental File S1; MSY variant sites and genotypes in all samples and species are available in Supplemental Tables  S9-S13. MSY variant sites from chimpanzees based on mapping to the chimpanzee genome reference (panTro4) have been submitted to dbSNP (http://www. ncbi. nlm. nih. gov/SNP/) under ss numbers given in Supplemental Table  S13.
Aerial photographs An aerial photographs survey was launched the day after the flood. DREAL PACA decided to lead it in order to help defining the flood extent and localize the flood major damages and its indirect effects (e. g. landslides, bank erosion …). This aerial survey was carried out by the IGN (French National Geographical Institute), the results expected were: -A fully ortho-rectified photographs in true colours (RGB) and in infra-red (IRD) with 10 cm resolution -A post treated topographic dataset using LIDAR (Light Ranging and Detection) technology with 5m resolution For the coastal streams, on the request of State services, two sectors were covered : •FUVAR survey covering Gapeau, Roubaud, Réal Martin, Maravenne and Batailler •FUARGENS survey covering Argens and its tributaries.
In order to allow discussion and dialogue with the local actors and inhabitants, the time required should not be underestimated. It is worth planning further field visits, focused on specific themes or areas and based on experience and analysis from the first field visits. (  The purpose of collecting High Water Marks is to help mapping flood inundation areas and identifying the flood flows and its frequency. Before the first field visit, Sate services had contacted some local actors in order to ensure that the High Water Mark collection operated by various organisms concerned by the post-event management (i) would not be redundant. Because of the multiplicity of heterogeneous High Water Mark collections, a key issue was to coordinate the collection and gather data. All collecting organisms produced data sheet related to each HWM.
In total, 398 HWM with their data sheet were produced. Among those 398 HWM, 250 were leveled by a surveyor (geo location X,Y and Z of the water levels). Figure  2  shows the set of the 398 HWM collected on the studied area: 168 in the Argens watershed, 230 in the valley of others coastal streams. Cerema compiled all the HWM data in a regional database owned by DREAL PACA. This database will be incorporated into the future national HWM database being set up by the Central Service of Hydrometeorology and support for the flood forecasting (SCHAPI). Thus, a simple and interoperable format (Excel) has been chosen with clearly identified database fields.
Cataclysmic cosmic events can be plausible sources of both gravitational waves (GW) and highenergy neutrinos (HEN), alternative cosmic messengers carrying information from the innermost regions of the astrophysical engines. Possible sources include long and short gamma-ray bursts (GRBs) but also low-luminosity or choked GRBs, with no or low gamma-ray emissions. Combining directional and timing informations on HEN events and GW bursts through GW+HEN coincidences provides a novel way of constraining the processes at play in the sources. It also enables to improve the sensitivity of both channels relying on the independence of backgrounds in each experiment. A first search was performed with concomitant data from 2007, when ANTARES was half its final size. This contribution focuses on the second, optimised search performed with data taken in 2009-2010, during the Virgo VSR2-3 and LIGO L6 science runs (with improved sensitivity) and with ANTARES in its final configuration.
The list of HEN triggers included their arrival time, direction on the sky, and an event-by-event estimation of the angular accuracy, which was used to define the angular search window for the GW search. This list was then processed by the X-pipeline  [8] , an algorithm which performs coherent searches for unmodelled bursts of GWs on the combined data stream coming from all interferometers. The background estimation and the optimization of the selection strategy were performed using time-shifted data from the off-source region in order to avoid contamination by a potential GW signal. Once the search parameters were tuned, the analysis was applied to the on-source dataset, consisting of data recorded within a time window of [-500s, +500s] around the time of each HEN trigger. This time interval was chosen on basis of conservative estimations of the time delay between the HEN and GW signals expected for long GRBs, based on BATSE, Swift and Fermi observations  [9].
No GW candidate was observed in coincidence with the selected HEN events from the 2007 data sample. A binomial test was also performed to look for an accumulation of weak GW signals, with negative results. This allowed to extract GW exclusion distances for typical source scenarios. Converting this null observation into a density of GW+HEN emitters yielded a limit ranging from 10 -2 Mpc -3 yr -1 for short GRB-like signals down to 10 -3 Mpc -3 yr -1 for long GRB-like emissions  [6]. This contribution focuses on the second search that is being finalized with data taken with the full ANTARES detector in 2009-2010, concomitant with the Virgo VSR2/VSR3 and LIGO S6 joint science runs, with upgraded GW detectors. Building on the experience of the pioneering 2007 search, and following the joint 2009 IceCube-LIGO-Virgo analysis  [10]  which introduced a more complete and symmetrical characterisation of the GW and HEN events  [11] , a new strategy has been adopted for the optimisation of the HEN trigger list in order to maximise the number of sources detectable by the search.
A new HEN reconstruction algorithm (AAfit) has been used in In both cases, the neutrino candidates (with their time and directional information) act as external triggers for a given GW analysis pipeline, which searches the combined GW data flow from all active interferometers (ITFs) for a possible concomitant signal. The background estimation and the optimization of the selection strategy are performed using time-shifted data from the off-source region in order to avoid contamination by a potential GW signal. Once the search parameters are tuned, the box is opened and the analysis is applied to the on-source dataset. order to reduce the angular error  [12]. A different GW pipeline, the skymask coherent WaveBurst (s-cWB), has also been developed to allow the analysis with only 2 interferometers taking data, and the realisation of joint simulations -a necessary step to optimise the joint analysis  [13].
Detectors and associated datasets 2. 1 The ANTARES neutrino telescope and associated dataset The ANTARES telescope  [5]  is located at a depth of 2475m in the Mediterranean Sea off the coast of Toulon, at 42 • 48 N, 6 • 10 E. It comprises 885 optical modules consisting in 17" glass spheres, each of them housing one 10" photomultiplier, and installed on 12 vertical strings. The dataset used in this analysis covers the period from July 7th 2009 to October 20th 2010 for a total observation time of 266 days. The sample consists in events originating from muon neutrino charged-current interactions, which produce a muon that leaves a track-like signal in the detector. It is the most suited to this kind of directional searches, as the current reconstruction algorithms for this class of events achieve a sub-degree angular resolution (defined as the median angle between the neutrino and the reconstructed muon).
2. 2 The LIGO and Virgo gravitational wave interferometers and associated data set LIGO  [2] , with two sites in the United States, and Virgo  [3] , with one site in Italy, consist of perpendicular km-size Fabry-Perot cavities forming a Michelson interferometer tuned to the dark fringe. Any gravitational wave passing through the detector would induce a difference of path length in the two arms, thus changing the interference pattern. The direction of an event is reconstructed by time-of-flight techniques which imply the use of at least two detectors. Figure  2  (right) shows the typical sensitivity for the LIGO and Virgo science runs taken in 2009 and 2010, compared to the one achieved in 2007. The GW data used in this search are the S6-VSR2/3 LIGO-Virgo data, which were collected between July 07, 2009 and October 21, 2010 by three detectors: LIGO-Livingston, LIGO-Hanford and Virgo.
PoS(ICRC2015)1112 p GWHEN = ∞ Max(X 2 i ) P bg (max(X 2 ))dX 2 (3. 9) The background probability density function P bg (max(X 2 )) is estimated by a Monte Carlo simulation of 10 4 pseudo-experiments of 773 joint triggers (the remaining of the 1986 neutrinos coincident with data taking periods of GW interferometers) obtained by applying the analysis on time-shifted GW data. It will determine the significance of the loudest event once the box will be opened and the real, non-time-shifted data will be scrutinized. An accumulation of weaker signals can also be looked for, as was performed for the 2007 joint search.
This work describes the overall data flow of the pipeline implemented at the LFI data processing centre (DPC), including scientific telemetry from the instrument, housekeeping data, and frequency maps, as well as the tests applied to validate the data products. Detailed descriptions of critical aspects of the data analysis and products, including improvements in some of the algorithms used in the pipeline, are given in four companion papers. These discuss, respectively: systematic effects and the overall error budget  (Planck Collaboration III 2016) ; the determination of the LFI main beams and window functions from in-flight planet-crossing measurements and optical modelling  (Planck Collaboration IV 2016) ; photometric calibration, including methods adopted and related uncertainties (Planck Collaboration V 2016); and mapmaking, including the process used to obtain the low-resolution maps and their associated full noise covariance matrices  (Planck Collaboration VI 2016).
Operations The data set released together with this paper was acquired from 12 August 2009 to 3 August 2013, roughly four years of observations. The first two years of data (from Survey 1 to Survey 4) were acquired scanning the sky with a phase angle of 340 • , whereas for the last two years (from Survey 5 to Survey 8) the phase angle was shifted to 250 • (see Planck Collaboration I 2 http://pla. esac. esa. int/pla/index. html 2016, for details). This shift has allowed for more thorough investigation of systematic effects, including better characterization of the beam and the related Galactic straylight (see Sect. 7. 4) using null tests based on survey differences. During the last three Jupiter crossing, the scanning strategy was optimized to obtain a better beam determination (see  Sect. 6 ).
ADC non-linearity correction The ADCs convert the analogue detector voltages to numbers, their linearity is as important as that of the receivers and detectors, with any departure appearing as a distortion in the system power response curve. While the algorithm for determining the ADC corrections remains the same as described in Planck Collaboration IV (2014), some changes were made in its implementation and execution. First, the full mission data are now used, so that when detector voltages are revisited there will be an improvement in signal to noise (although in the particular case of radiometer 21M, some of the voltages were too poorly sampled to generate an adequate solution). Second, instead of determining the white noise amplitude via a Fourier transform, we now use the difference between the sum of the variances and twice the covariance of adjacent paired points in the timestream, such that white noise variance σ 2 WN = Var[X o ] + Var[X e ] -2Cov[X o , X e ], where X o and X e are data points with odd and even indices, respectively.
Corrections for electronic spikes Electronic spikes are caused by the interaction between the electronics clock and the scientific data lines. They occur in the data acquisition electronics (DAE) after the detector diodes and before the analogue-to-digital converters (ADC,  Meinhold et al. 2009; Mennella et al. 2010 Mennella et al. , 2011)). The signal is detected in all the LFI radiometers time-domain outputs as a 1 s square wave with a rising edge near 0. 5 s and a falling edge near 0. 75 s, synchronous with the on-board time signal. In the frequency domain it appears as a spike signal at multiples of 1 Hz. The 44 GHz channels are the only one that are significantly affected by this effect. Consequently the spike signal is removed from the data only in this channel. The procedure consists of the subtraction of a fitted square wave template from the time-domain data as described in Planck Collaboration III (2016).
We are evaluating the possibility of further reducing the residual effect of the spikes signal at the map level, as described in Planck Collaboration III (2016), for the next Planck data release by adopting one or more of the following approaches: increasing the resolution of square wave template, at the moment at 80 Hz; using time varying template instead of the fixed one over the whole mission; removing spikes signal from the 30 GHz and 70 GHz channels.
Combining diodes Two detector diodes provide the output for each Planck LFI receiver channel. To minimize the impact of imperfect isolation between the two diodes, we perform a weighted average of the time-ordered data from the two diodes of each receiver just before the differencing. The procedure applied is the same described in Planck Collaboration II (2014) and for the sake of completeness, we report in Table  3  the values of weights used; the receiver channels are indicated either with M (main) or S (side). The weights are kept fixed during the whole mission.
Main beams and the geometrical calibration of the focal plane The in-flight assessment of the LFI main beams relies on the measurements performed during seven Jupiter crossings; the first four transits ("J1" to "J4") occurred in nominal scan mode (spin shift 2 arcmin, 1 deg per day), and the last three scans ("J5" to "J7") in a deeper coverage mode (spin shift 0. 5 arcmin, 15 arcmin per day). The period of time corresponding to each Jupiter observation is reported in Table  4. By stacking data from the seven scans, we measure the main beam profiles down to -25 dB at 30 and 44 GHz, and down to -30 dB at 70 GHz. If we fit the main beam shapes with elliptical Gaussian profiles, the uncertainties of the measured scanning beams can be expressed in terms of statistical errors on these Gaussian parameters.
Effective beams The GRASP combined co-and cross-polar main beams are used to calculate the "effective beams", which take into account the specific scanning strategy and pointing information in order to include any smearing and orientation effects on the beams themselves. We compute the effective beam at each LFI frequency, using the scanning beam and scan history in real space using the FEBeCoP  (Mitra et al. 2011)   7  lists the mean and rms variation across the sky of the main parameters computed with FEBeCoP. Note that the FWHM and ellipticity in Table  7  differ slightly from the values reported in Table  6. This results from the different way in which the Gaussian fit was applied. The scanning beam fit is determined by fitting the profile of Jupiter to timelines and limiting the fit to the data with signal-to-noise ratio greater than 3, while the fit of the effective beam is computed on GRASP maps projected in several positions of the sky (Planck Collaboration IV 2016).
Planck's calibration source has always been the dipole term, D. However, since the previous Planck data release  (Planck Collaboration V 2014)  we have implemented a number of important changes in the pipeline used to calibrate the voltages measured by the LFI radiometers. In this section we provide an overview of the most important result; we refer the reader interested in further details to Planck Collaboration V (2016). We use as a calibration source the signal B * D in Eq. (  1 ), which is induced by the combined motion of the spacecraft and the Solar System with respect to the CMB rest frame. We have characterized the dipole by means of Planck data and have estimated the amplitude to be (3364. 5 ± 2. 0) µK CMB in the direction l = 264. • 00 ± 0. • 03, b = 48. • 24 ± 0. • 20 in Galactic coordinates  (Planck Collaboration I 2016).
(2010)  and http://tskisner. github. io/TOAST/. The TOAST interface was chosen on the basis of added flexibility and speed; see  (Planck Collaboration VI 2016). The outputs of Madam/TOAST software are inverse-noise covariance matrices, specifically one inverse matrix per radiometer for a given time period. Because inverse NCVMs are additive, the individual inverse matrices are merged together to form the actual inverse NCVM. To obtain the noise covariance matrix from its inverse, the matrices are inverted using the eigendecomposition of a matrix. These intermediate-resolution matrices are then downgraded using the same downgrading scheme as applied to the maps. The matrices are regularized by adding the same level of white noise to the diagonal elements of the covariance matrix as to the low-resolution maps.
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/. Finally, we thank  Benjamin Walter  for a careful reading of the manuscript.
Microsatellite analysis Individual genotypes were scored for 11 previously published microsatellite loci: AC1, AC2, AC4, AG1, AG2, AG5, CT2  [26] , A1, B2, B3  [27]  and 145TAAA1  [28]. DNA was amplified in a Veriti 96 well Thermal Cycler (Applied Biosystems) using the GoTaq G2 Flexi DNA Polymerase (Promega) as described in previous studies with slight modifications  [26] [27] [28]. PCR products were analyzed using a Genetic Analyser 3130xl (Applied Biosystems). The results were genotyped with Peak Scanner software (Applied Biosystems) and double-checked (i. e. , read by two independent people). The deviation from Hardy-Weinberg equilibrium for each locus was tested with GenAlex 6. 5  [29]. MicroChecker v2. 2. 3  [30]  was used to calculate the probability of null allele occurrence in each locus within each population.
The number of alleles and the estimated allele richness  [31]  were determined by FSTAT2. 9. 3 software  [32]. The F IS for each population for all loci were calculated using Genetix  [33]  and Arlequin v3. 5. 1. 2 [34]. The Analysis of Molecular Variance (AMOVA) and the F ST were computed using Arlequin v3. 5. 1. 2 software  [34]. The population genetic structure was determined using STRUCTURE software  [35]. The Bayesian approach was chosen to infer the number of genetic clusters (K). We performed twenty independent runs, K from 1 to 15, with a burn-in period of 100,000 iterations and a total of 1,000,000 Markov Chain Monte Carlo iterations. The program Structure Harvester  [36]  was used to determine the most probable number of clusters by calculating the ΔK value  [37]. The web server CLUMPAK was used to summarize and visualize the STRUCTURE results  [38].