dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion We have analyzed data from O1 to look for GWs coincident with GRBs that occurred during this period, using both a modeled search for BNS and NS-BH systems and an unmodeled search for GW transients. No GW was detected in coincidence with a GRB by either search. We set lower limits on the distance for each GRB for various GW emission models. The median of these distance limits is higher than distance limits placed by all previous modeled and unmodeled searches (e. g. ,  Abadie et al. 2012a; Aasi et al. 2014b ). We also combined these lower limits into an exclusion on the GRB redshift distribution. This exclusion is a factor of a few away from the short and long GRB distributions measured by γ-ray satellites. With 2years of observation at design sensitivity, Advanced LIGO will probe the observed redshift distribution.
At that point, either a GW detection in association with a short GRB will take place, or the result will be in tension with the NS binary merger progenitor scenario for short GRBs. For long GRBs, a lack of detection would only constrain the most extreme scenarios of GW emission from a strongly rotating stellar core collapse. We also analyzed data from the LIGO Hanford detector to look for a GW signal associated with GRB 150906B. No evidence was found for a GW signal associated with this GRB. The sensitivity of the modeled search allows us to confidently exclude the hypothesis that an NS binary in NGC3313 was the progenitor of GRB 150906B. If the event indeed occurred in NGC 3313, then it would have had to defy the setup of the modeled search. In this case, and in light of the problematic classification of GRB150906B discussed in Section 2, this GRB may most probably have been due to a stellar core collapse or a giant flare from a soft-gamma repeater.
Gels were destained in a 30% ethanol (v/v) and 5 % acetic acid (v/v) solution until the background was sufficiently cleared. After staining in a Coomassie Blue dye solution, the gels were scanned and the proportions of the different MyHCs bands were quantified by densitometry with ImageQuant Software 5500 (Amersham Biosciences/GE Healthcare) and expressed in percentage. The quantification of the bands revealed no existence of MyHC-IIb isoform in PDO Maine-Anjou breed, thus only MyHC-I, IIa and IIx isoforms are reported. The muscle mean cross sectional fiber area for all the animals was determined on 10-µm thick sections cut perpendicular to the muscle fibers with a cryotome. 14  Between 100 and 200 fibers in each of two different locations in the muscle were used to determine the mean fiber area (in µm²) by computerized image-analysis.
Briefly, the protein extracts (15 µg) of each muscle sample were spotted in quadruplicate on a nitrocellulose membrane with the Minifold I Dot-Blot apparatus from Schleicher & Schuell Biosciences (Germany) in a random order on the 96-spot membrane. In addition, a mixed standard sample (15 µg) was deposited for data normalization. The Dot-Blot membranes were air-dried for 5 min, blocked in 10% PBS milk buffer at 37°C for 20 min, and then incubated to be hybridized with the specific primary antibody of each protein. Subsequently, the membranes were incubated at 37°C for 30 min with the anti-mouse fluorochrome-conjugated LICOR-antibody IRDye 800CW (1 mg/mL). Infrared fluorescence detection was used for quantification of the relative protein abundances. Subsequently, the membranes were scanned using the Odyssey NIR imager (LI-COR Biosciences), with an 800 nm laser, a 169 µm spatial resolution and a fixed gain of 5.
Finally, the Dot-Blot images were quantified with GenePix PRO v6. 0 (Axon). Each dot volume was calculated as the total dot intensity from which the median local background value multiplied by the dot area was subtracted. Because Dot-Blot offers the possibility of replicates, a data-prefiltering approach was implemented to eliminate outlier values mainly due to dust. The exclusion technique of outliers was based on the Medium Absolute Difference (MAD) and applied before repeated values were averaged. Finally, to make the data comparable between assays, the data were normalized using a regression-approach based on the used mix standard specific for each muscle. Thus, the relative protein abundances were based on the normalized volume and were all expressed in arbitrary units (A. U).
The sessions were carried out in a sensory analysis room equipped with individual booths under artificial red light to reduce the influence of the appearance of the samples. At each session, a monadic presentation of 6 samples was done, each sample being selected in random order. Each tasting booth was equipped with computer terminals linked to a fileserver running a sensory software program (Fizz v 2. 20 h, Biosystemes, Couternon, France) that facilitated the direct entry of assessor ratings, which were later formatted in Excel. Within each sensory session, scores were averaged across panelists for each steak, and the means were used in the statistical analyses. For objective tenderness, the Warner-Bratzler shear force (WBSF) was measured according to Lepetit and Culioli,  18  Briefly, from the sample cuts cooked as previously described, two to five 1 x 1 x 4 cm cores per steak sample were removed parallel to the longitudinal orientation of the muscle fiber of LT muscle.
Statistical analysis The collected data concern the animal life and fattening period of 110 PDO Maine-Anjou cows data (p = 16), carcass characteristics (p = 7), muscle properties (p = 27) and ribeye steak (Longissimus thoracis) quality traits (p = 11). Rearing practices classes were created using an innovative statistical approach based on principal component analysis (PCA). We first projected the 16  (k = 3) via the variability explained by PCA allowed the clustering of the animals into distinct clusters. K-means clustering is a widely used classification method that generates a specific number of classes (nonhierarchical). This method assumes a certain number of clusters, k, fixed a priori and produces a separation of the objects into nonoverlapping groups coming from Euclidean distances minimized at each step of an iterative procedure  10.
To determine the significance of differences between the least squares means of the animal life and fattening period, carcass, muscle and meat quality traits between the three rearing practices classes, PROC GLM procedure of SAS was performed. The significant differences were performed using Tukey's test at a significance level of P < 0. 05. For better understandings of the relationships and hence of mechanisms, simple and multiple regression analyses were performed using the PROC REG of SAS to create best regression equations for meat quality traits and carcass characteristics (as dependent variables, x) using the protein muscle characteristics and protein biomarkers (as independent variables, y) per rearing practice class. For that, the option "optimal model" was chosen to produce the model with the highest r 2 value.
Acknowledgments The financial support given to MG from  S3-23000846  project is highly acknowledged. The authors would thank also " Pays de Loire Region" , the  defense Trade Union of PDO Maine-Anjou ,  Adema  and  Elivia  for the use of data. The authors thank the many colleagues involved in this project, namely  Guillain Le Bec  and  Ghislain Aminot  from  SICA Rouge des Prés  and  Didier Micol  from  INRA  for their assistance in data collection, the management and slaughter of animals, muscle sampling, and biochemical measurements. Many thanks to  Nicole Dunoyer  and  Jeremy Huant  for their technical assistance concerning the biomarkers quantifications by Dot-Blot.
DISCUSSION The Denisova 2 specimen adds a deciduous molar to the meager Denisovan fossil record, which so far included one distal manual phalanx (Denisova 3) (2, 3, 16) and two permanent molars (Denisova 4 and 8)  (2, 17). The fact that only 47 Mb of nuclear sequences could be retrieved from ~10 mg of tooth powder removed from Denisova 2 precludes many analyses. For example, subsampling DNA sequences from Denisova 3 shows that the power to detect gene flow from Denisovans into modern humans is limited (section S4, table  S8 , and fig. S9 ). Sequence errors in the low-coverage data also hinder our ability to ask whether the gene flow from an unknown archaic hominin into Denisovans detected in the Denisova 3 genome (7) also affected the ancestors of Denisova 2 (section S4 and fig. S10 ). Denisova 2 was found in layer 22.
Support for each branch is based on 500 bootstrap replications. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. Accession codes for the comparative data and the geographical origins of ancient individuals are presented in table  S5. suggest that Denisova 2 lived at least 100 ka, making it one of the oldest hominin remains discovered in Central Asia to date. Beyond reinforcing the idea that both Neandertals and Denisovans lived in the cave or its vicinity  (7, 17) , our findings indicate that Denisovans were present over an extended period in the Altai region, where the two archaic groups may have met and mixed. The seemingly great difference in age between the Denisovan individuals is congruent with previous indications that Denisovans inhabited the Altai region over tens of thousands of years  (17).
A DNA extraction blank and one library preparation blank were carried along each experiment. Sequencing and processing of sequence data Shotgun sequencing of library A4891 was first performed on 12% of an Illumina HiSeq lane and then sequenced deeper on four HiSeq lanes. Libraries A4944 to A4947 were sequenced on 90% of a MiSeq (Illumina) platform and then sequenced together on four HiSeq lanes. The captured library A4928 was sequenced on 12% of a MiSeq lane. Sequencing was performed using paired-end runs with double-index configuration  (46). Base calling was carried out using freeIbis  (50)  and Bustard (Illumina) for HiSeq and MiSeq runs, respectively. Adapter sequences were removed and overlapping forward and reverse reads were merged using leeHom  (51). Sequences that did not perfectly match the expected barcode combinations were discarded.
For each library, sequences originating from different sequencing runs were combined using SAMtools  (52). Mapping to a reference genome was performed using Burrows-Wheeler Aligner (BWA)  (53) , with parameters "-n 0. 01 -o 2 -l 16500" (3). To recover nuclear DNA fragments, sequences originating from shotgun sequencing were aligned to the human reference genome [UCSC (University of California, Santa Cruz) version hg19]. For mtDNA analyses, sequences from all libraries were aligned to the Denisovan mitochondrial reference sequence [National Center for Biotechnology Information (NCBI) reference NC_013993. 1] (16). Because BWA cannot use circularized references, the mitochondrial reference sequence was altered by copying the first 1000 bases to its end  (39). After mapping, PCR duplicates were removed by collapsing sequences starting and ending at identical coordinates using bam-rmdup (https://bitbucket.
Phylogenetic analysis The Denisova 2 mtDNA was aligned to the mtDNAs of 3 Denisovans (2, 16, 17), 5 present-day humans from a variety of geographical origins (54), 5 ancient modern humans  (4, 6, 55, 56) , 10 Neandertals (7, 34, 57-59), 1 Middle Pleistocene hominin  (39) , and 1 chimpanzee (Pan troglodytes, NC_001643) (60) using MAFFT  (61). NCBI accession codes for the comparative sequence data and the geographical origin of the individuals are presented in table  S5. A maximum likelihood phylogenetic tree with 500 bootstrap replications  (62)  was reconstructed in MEGA6 (63) using the Hasegawa-Kishino-Yano substitution model  (64)  with gamma distribution and allowing for invariable sites (HKY + G + I), as determined by jModelTest2  (65, 66). The numbers of pairwise base differences observed between mtDNAs were determined using MEGA6  (63).
Molecular dating The Denisova 2 mitochondrial genome was aligned to the other three Denisovan mtDNA sequences  (2, 16, 17)  and to the mtDNA of a Middle Pleistocene hominin from Sima de los Huesos (Spain) (39) using MAFFT  (61). The number of substitutions occurring on each branch since the split from the most recent common ancestor of all Denisovan mtDNAs was inferred by maximum parsimony in the R package "phangorn," using an accelerated transformation ("ACCTRAN") algorithm to optimize ambiguous character reconstructions  (67). The difference in the number of mutations inferred to have occurred on each branch was translated to differences in time based on a mitochondrial mutation rate for the modern human lineage of 2. 53 × 10 -8 substitutions per site per year (95% highest posterior density, 1.
(7) ]. We calculated how many of the substitutions inferred to have occurred from the human-chimpanzee ancestral sequences to the highcoverage genomes occurred after the split from the Denisova 2 genome, that is, the fraction of derived sites that Denisova 2 does not share with each high-coverage genome  (1, 7). CIs were calculated by jackknife resampling in 5-Mb windows along the high-coverage genomes. For the high-coverage genomes, the homozygous genotype was selected at homozygous sites, and a random allele was selected at heterozygous sites, whereas for the Denisova 2 data, a random allele was selected in the rare cases when more than one DNA fragment covered a site. For comparative purposes, we repeated this calculation while replacing the Denisova 2 data with low-coverage sequence data from two other Denisovans (Denisova 4 and Denisova 8) (17) and six Neandertals (Feldhofer 1, Mezmaiskaya 1, El Sidron 1253, Vindija 33.
16, Vindija 33. 25, and Vindija 33. 26) (1, 7). We reprocessed these sequences using the same filtering scheme that we applied to the Denisova 2 data. For data generated with the single-stranded DNA library preparation protocol (Denisova 4 and 8), a terminal C-to-T substitution to the reference genome at either end was required. For data generated with a double-stranded DNA library preparation scheme (Feldhofer 1, Mezmaiskaya 1, El Sidron 1253, Vindija 33. 16, Vindija 33. 25, and Vindija 33. 26), putative deamination was identified as a C-to-T substitution at the 5′ end of fragments or a G-to-A substitution at their 3′ end  (32, 36, 48). The base quality of terminal thymines or adenines was reduced to 2, and subsequent processing was limited to bases with a quality higher than 30. To compute estimates of diversity among present-day humans that would be comparable to our estimates for the archaic genomes, we subsampled each of the 12 present-day high-coverage human genomes  (7)  to retain only positions covered by at least 1 of the low-coverage archaic genomes in our data set.
Acknowledgments: We thank  B. Höber ,  B. Nickel , and  A. Weihmann  for help in the laboratory;  U. Stenzel  for raw data processing;  M. Hajdinjak ,  A. Hübner ,  F. Mafessoni ,  M. Petr , and  F. Romagné  for help in analyzing the data;  P. Korlević  for graphics; and  H. Temming  for mCT scans. We are grateful to  A. Andrés ,  M. Dannemann ,  C. de Filippo ,  F. -M. Key ,  M. Mednikova ,  F. Racimo ,  D. Reich ,  M. Slatkin ,  U. Stenzel , and  M. Stiller  for helpful comments. Funding: This work was supported by the  Max Planck Society , the  Max Planck Foundation  (grant  31-12LMP Pääbo  to S. P. ), the  European Research Council  (grant agreement no. 694707  to S. P. ), the  Social Sciences and Humanities Research Council  ( IDG 430-2016-00590  to B. V. ), and the  Russian Science Foundation  (project no.
A temperature adjustment of + 1 • C (respectively -1 • C) was, for example, needed at the MAR boundaries with ERA-20C (20CRv2) reanalysis, given that ERA-20C (20CRv2) is ∼ 1 • C colder (warmer) than ERA-Interim over Greenland during the period 1980-2010. Comparisons with daily PROMICE (Programme for Monitoring of the Greenland Ice Sheet) near-surface observations support these adjustments. Comparisons with SMB measurements, ice cores and satellite-derived melt extent reveal the most accurate forcing datasets for the simulation of the GrIS SMB to be ERA-Interim and NCEP-NCARv1. However, some biases remain in MAR, suggesting that some improvements are still needed in its cloudiness and radiative schemes as well as in the representation of the bare ice albedo. Results from all MAR simulations indicate that (i) the period , commonly chosen as a stable reference period for Greenland SMB and ice dynamics, is actually a period of anomalously positive SMB (∼ +40 Gt yr -1 ) compared to 1900-2010; (ii) SMB has decreased significantly after this reference period due to increasing and unprecedented melt reaching the highest rates in the 120-year common period; (iii) before 1960, both ERA-20C and 20CRv2forced MAR simulations suggest a significant precipitation increase over  1900-1950, but  this increase could be the result of an artefact in the reanalyses that are not well-enough constrained by observations during this period and (iv) since the 1980s, snowfall is quite stable after having reached a maximum in the 1970s.
The recent development of a new reanalysis dataset covering the entire last century and constrained by observations from both Greenland and outside Greenland offers a new opportunity to evaluate SMB over GrIS through the last century  (Hanna et al. , 2011). Regional climate models (RCMs) especially developed for polar regions  (Rae et al. , 2012)  and coupled with complex snow models are powerful tools to physically downscale the 6-hourly reanalyses and estimate SMB. Both the spatial resolution and the snow model used in reanalyses are not yet adequate to directly derive SMB from reanalyses  (Cullather et al. , 2016). Among the "polar" RCMs, there is the MAR (Modèle Atmosphérique Régional) model, fully coupled with a snow energy balance model and developed and extensively evaluated to study the present Greenland climate as well as to perform future projections of GrIS SMB for the last IPCC report  (Fettweis et al.
MAR forcings In  Fettweis et al. (2013b) , summer temperatures at 600 hPa, geopotential height at 500 hPa and wind speed at 500 hPa from the different MAR forcing fields were compared over 1980-1999 to explain the discrepancies between the MAR simulations using different forcings. According to  Fettweis et al. (2013a) , the JJA (June-July-August) mean 700 hPa (T700) or 600 hPa temperatures (T600) in the free atmosphere over Greenland are a good predictor of the melt variability in MAR. Therefore, temperature biases at the MAR boundaries will directly impact the amount of melt simulated by MAR  (Fettweis et al. , 2013b). Since free atmosphere temperatures are not assimilated either in 20CRv2c or in ERA-20C reanalysis, a comparison of this field is presented in Fig. 1  over our reference period  (1980) (1981) (1982) (1983) (1984) (1985) (1986) (1987) (1988) (1989) (1990) (1991) (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999) , which is covered by all datasets used here and during which SMB has been relatively stable.
While SMB has already started to decrease at the end of the 1990s, a comparison over a period longer than 20 years does not change the conclusions of this comparison as justified in  Fettweis et al. (2013b). The mean 1980-1999 free atmosphere JJA temperature from ERA-40 and NCEPv1 compares very well with ERA-Interim over Greenland (see Fig. 1b  and e ). Surprisingly, the second generation of the NCEP reanalysis does not compare as well as with ERA-Interim and NCEPv1 because NCEP2 is too warm in summer, except in the vicinity of Iceland (see Fig. 1f ). As specific humidity (used as forcing at the MAR lateral boundaries) needs to be derived from relative humid- No comparison is shown above 2000 m a. s. l. due to the aim of only showing comparisons in the free atmosphere (700 hPa), and the datasets are shown here by using their native lat-long projection.
Finally, as the warm bias from 20CRv2 is partly corrected in 20CRv2c and is now centred around zero on average with a too-warm atmosphere at the north of Greenland and too cold at the south-west, unmodified 20CRv2c temperatures are then used to force MAR at its lateral boundaries. Since the surface pressure has been assimilated in all reanalyses used here, the general circulation (gauged here by the 500 hPa geopotential height in Fig. 2 ) including the North Atlantic Oscillation (NAO) compares well over the recent decades  (Belleflamme et al. , 2013) , except for 20CRv2(c), which underestimates wind speed at 500 hPa, inducing anticlockwise circulation anomalies over Greenland (see Fig. 2g  and h ). Moreover, as a consequence of the lack of (or less reliable) assimilated data before 1940, the general circulation variability from 20CRv2 and ERA-20C diverges according to  Belleflamme et al.
Surface mass balance As validation of the SMB simulated by MAR over 1958-2010 (the period covered by seven of the datasets here), we use the following. 1. The ice core measurements in the accumulation area from  Bales et al. (2001 Bales et al. ( , 2009) )  and  Ohmura et al. (1999). The MAR accumulations values (here in m. w. e. yr -1 ) for each of the 246 records are averaged over the years listed in the three previous references (the mean from 1958-2010 is used if the period is not given or before 1958) and come from an interpolation of the four nearest inverse-distance-weighted MAR grid cells. 2. The new SMB database (hereafter MACHGUTH16) compiled under the auspice of PROMICE and available through the PROMICE web portal (http://www. When MAR is forced by reanalyses other than ERA-40 and ERA-Interim, we find that (i) MAR NCEPv1 is the most accurate because, over 1958-1978, NCEPv1 is not affected by the humidity bias present in ERA-40 and impacting the MAR precipitation in the non-homogeneous ECMWF time series, (ii) the use of CORR-ERA-20c partially corrects the SMB overestimation (due to the underestimation of melt) obtained when MAR is forced by unadjusted ERA-20C, (iii) MAR 20CRv2 is more accurate than MAR CORR-20CRv2 because the overestimation of melt in MAR 20CRv2 compensates for the SMB overestimation in the ablation area due to albedo overestimation and (iv) the results of MAR NCEPv2 are worse than MAR results using less constrained reanalyses (e.
g. 20CRv2) or first generation reanalysis (e. g. NCEPv1), as a result of the temperature biases in NCEPv2. Moreover, while some of these data were used in the  Box (2013)  reconstruction, the comparison of ice core measurements with BOX13 shows the same agreement with MAR (see Table  3 ). Regarding the comparison with the SMB MACHGUTH16 database, the SMB values from BOX13 were corrected as a function of the elevation difference with the MACHGUTH16 database as done for MAR. However, to match the exact period of the MACHGUTH16 database, we have simply derived daily values from the monthly BOX13 values by dividing them by the number of days in every month. It is clear that this approximation smoothing the melt variability can be problematic when the period of measurements covers only a few weeks in the melt season and very likely explains why   Bales et al.
(2001 Bales et al. ( , 2009) )  and  Ohmura et al. (1999) , and satellite-derived melt extent over 1979-2010. MAR ERA-Interim (MAR ERA-40 ) means that MAR was forced by  ERA-40 over 1958 ERA-40 over -1978 ERA-40 over (1958 ERA-40 over -2000) )  and  ERA-Interim over 1979 -2015 (2001 -2015). Finally, MAR ERA-Interim * means that the extrapolation of  Franco et al. (2012)  was not used to correct the MAR SMB with respect to the elevation differences between MAR and the MACHGUTH16 measurement sites. BOX13 is less correlated with the MACHGUTH16 dataset than MAR. Finally, it is interesting to note that the comparison with MACHGUTH16 and ice core measurements is quite constant over the entire century (see Table  S2 ) and not better in the recent decades than before despite the larger amount of assimilated data.