dataesr/scientific-paragraphs-categorization huggingface dataset:
In most cases here, there were no appreciable differences between the different sets of replicates within a laboratory. One difficulty is developing the database so that the QC charts can be based upon the order the experiments were conducted in (i. e. in a time-ordered format). In practice, it should be the responsibility of the laboratory to make the case for combining the results and doing this in a transparent way so that others can judge whether this is appropriate. One of the complexities of cross laboratory comparisons is the standardization of the endpoints. Different laboratories use different denominators (e. g. 1000, 2000 or variable numbers of cells. ) Results presented in the form of the number of micronucleated cells/cells scored expressed as proportions are also common; sometimes with different numbers of cells per replicate.
Attempts to detect shifts in the mean within this 'normal range' would require large experiments to have sufficient power. Based upon these results, it can be argued that an acceptable level of variability for the background control incidence of micronuclei would be one that fell within the control limits such as the ± 2SD range (i. e. the LWL and UWL) provided that the dataset does not show excessive variability. Obviously the mean level within a specific laboratory affects the size of effect detectable and the design of a study with the power to detect a doubling over the negative control level. In the combined groups there were five cases (A, E, G, K and L) where the calculated LCL was 'below zero'. This means that there is no lower value that would be outside the limits. However, in all these cases the calculated 'negative' LCLs values were just below zero indicating that zero values, while not 'triggering a warning' were close to the LCL.
These incidences appear to be based upon a single replicate negative control in each experiment. Fowler et al. [16]  indicated negative control incidences based upon quadruplicate cultures (in Tables 1-3 of their paper) of 4. 5-9. 5/1000 cells in human TK6 cells in the presence and absence of cytochalasin B. In conclusion, this study has identified the range of results collected by 13 proficient laboratories in conducting the in vitro micronucleus assay using TK6 cells. In addition, it demonstrates, that the variability between laboratories does not appear to be due to a number of differences in how the data were produced. This study supports the need for data to be produced from about 20 experiments to create a negative control database, it also provided evidence to justify the OECD guideline recommendation that at least 2000 cells (from two cultures) per concentration should be scored in order to provide an acceptable range for historical control databases.
Acknowledgements The authors wish to thank the participating laboratories and their staff who provided data:  AstraZeneca, BSRC, Canon Inc, Covance, Janssen A, Janssen B, Litron, Lundbeck, NCTR ,  Novartis ,  Pfizer ,  Sanofi , and  Takeda. The authors also thank the  HESI GTTC  and the Data Interpretation Workgroup for intellectual and financial support. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data The Mean experimental conditions and samples initial dimensions was gathered in Table  1. The Mean measured dimensions of different experiment are reported in Table  2. Tables  3 4 5 summarizes the experimental data of different tests. This data contains valuable information of temperature air and samples thus that loss mass product. The Figs. 1 2 3 shown the instrumentation procedure and the surface evolution of potato samples from different tests. In Fig. 4 , the characteristic drying curves are depicted for different thickness. These data were used to evaluate moisture diffusivity as described in Section 2. 4. Whereas, the drying rate and surface temperature of samples are plotted in Fig. 5. After an initial short warming-up period the drying rate reached a maximum value and then it followed by three falling rate period in all experiments.
Moisture content and drying rate The moisture content for each experiment showing in Fig. 4  was calculated from the dry matter and the weighings reported in Tables 3-5 using Eq. (  1 ) X ¼ m t -m d m d ð1Þ T 4 ¼ Average surface temperature ¼ T2 þ T3 2. The drying rates (DR) of potato samples during the drying experiments showing in Fig. 5  were calculated by using the following equation  [6, 7] : DR ¼ d m t þ dt -m t À Á dt ð2Þ where m t is the dry matter at any time t (kg), m d is the dry matter (kg), m t þ dt is the dry matter at time tþ dt (kg) To obtain the drying rate data, it's necessary to go through a numerical processing on the derivative of the mass in order to overcome the error. At first, the time step is lengthened during the derivation  [8]. After lengthening the time step to 500 s, filtering is performed by a MATLAB program using the function "smooth (y)".
Read pairs with evidence of neither were labelled as wild type. Indel length and type (insertion or deletion) were extracted from the NHEJ read pairs via a custom Perl script. Bisulfite sequencing of unedited samples. Trimmed and de-duplicated read pairs were aligned to the bisulfite conversion indexed mouse genome build GRCm38 using Bismark v0. 16. 3  [49]  with Bowtie v2. 2. 6  [50]. Read pairs that did not align were then separated and each end of the pair aligned as single end reads. The three resulting alignments were merged. Read pairs were extracted by the expected genomic region for each experiment, and assigned to the C57BL/6J or JF1 chromosome based on nucleotide identity at known polymorphic SNPs. The number of methylated CpGs in each read pair was counted using a custom Perl script examining the XM tag for each read in the relevant BAM file.
All sequencing data have been deposited in the Sequence Read Archive under Study Accession SRP126405. post-transfection for experiments using an sgRNA targeting KvDMR (sgKvDMR#1 -Figure  S1A ). Asterisks denote p < 0. 05 for unpaired t-tests on the fold difference in maternal versus paternal allele mutation frequencies between timepoints. D. As above, using an sgRNA targeting the imprinted Inpp5f_v2 promoter (Figure  S3A ). Note that a majority of maternal chromosomes had lost imprinting at this locus in BxJ cells (Figure  S3E ), hence, only JxB data are shown. E. Pie charts show mutation frequencies observed 24 hours post transfection, expressed as a percentage of the mutation frequency in cells collected after 96 hours. Data are derived from the experiment described in Figures  2  and S4 , with mutation frequencies broken down by both parental allele and Cas9 expression level.
Acknowledgements We thank  Feng Zhang 's laboratory for sharing the CRISPR plasmids used in this study through Addgene,  Edinburgh Genomics  for high throughput sequencing, and the  IGMM Flow Cytometry Facility  for FACS. We are also grateful to  Wendy Bickmore  and  Rebecca Holmes  for comments on the manuscript, and to  Nick Gilbert  for useful discussions. AW's laboratory is funded by a  Sir Henry Dale Fellowship  from the  Wellcome Trust and Royal Society  ( 102560/Z/13/Z ). RF Acknowledges grant funding from the  Fondation Recherche Medicale (FRM , grant  DEQ20150331703 ).
Third, birds might exhibit lower predictability because they are less sensitive to climate change than plants. They have broader thermal tolerances  (Araújo et al. , 2013) , and the relevance of dynamic equilibrium responses as a conceptual model to explain birds responses to climate change is controversial. For example, their realized niche may not necessarily match the observed climate  (Beale et al. , 2008). Evidence of breeding bird responses to recent climate change are mixed and appear highly idiosyncratic  (Stephens et al. , 2016; Currie and Venne, 2017). Climatic data are used to extract observed temperature of the site through time (C), and the community-inferred temperature through time. These time series are combined in a time-implicit parametric plot to build the CRD (D). For each time bin, deviation from the 1:1 line value and the number of community-inferred value existing for each observed temperature value are used to compute three statistics (E) summarizing different aspects of predictability.
Community Data Plant assemblage composition data across the Late Quaternary and Holocene (21 Ka-present) were compiled from the Neotoma paleoecology database  (Goring et al. , 2015) , relying on the fossil pollen data sets used in  Maguire et al. (2016). Sites represent highquality assemblages and were primarily located in eastern North America. We used  Blois et al. (2011)  selection of sites and revised chronologies. Pollen assemblages obtained from lake sediments can provide a rough proxy for the composition of communities, despite issues on spatial and taxonomical scale integration, species abundance vs. pollen abundance, and detectability of rare taxa  (Birks and Seppä, 2004). These issues might influence the quantification of disequilibrium state because rarer taxa with lower dispersal abilities might be undetected.
More details on the data processing, spatial and temporal distributions of site are provided in Figure  S1. The overall process yielded a presence/absence dataset comprising 425 sites, 103 plant taxa, and 45 time bins (500 yr each) spanning 21 Ka-present. Bird assemblage composition across the last 50 years were compiled from the North American Breeding Bird Survey (BBS,  Sauer et al. , 2013 , data and protocol at http://www. pwrc. usgs. gov/ bbs/). We used data processed from  Barnagaud et al. (2017) : firstyear observer effects were removed by excluding the first survey performed by a given observer on a given route. The dataset was restricted to 807 routes monitored at least 8 years and once every 5 years during the 1970-2011 period. Coastal, pelagic and species which accounted together for <1% of the records were not taken into account (87 species).
Temperature time series in North-America Paleoclimate time-series data were obtained from SynTraCE-21, a set of transient simulations runs using the CCSM3 model  (Liu et al. , 2009) , with decadal averages stored from 22 Ka to the present. These simulations are reasonably congruent with sitebased climate reconstruction  (Harrison et al. , 2014). Simulations were statistically downscaled to a 0. 5 • by 0. 5 • grid cell following the CRU TS 3. 20 dataset, and then for every 500 years from 21 to 0 Ka, average maximum temperature was calculated based on a 200 year window centered on the 500 year time step  (Lorenz et al. , 2016). Contemporary temperature time-series data were extracted from the Parameter Regression of Independent Slope Model (PRISM) (Oregon Climate Service, Corvallis, OR, USA) for the continental United States  (Daly et al.
Climate Niches We inferred realized plant climate niches for mean maximum temperature for each taxon using independent contemporary occurrence data for each species or genus in the paleocommunity dataset from the BIEN3 database. We choose maximum temperature as a unique climatic niche axis because it is a strong predictor of the species' responses to temperature increase  (Jiguet et al. , 2007; Lorenz et al. , 2016; Maguire et al. , 2016). Note that maximum temperature was strongly correlated with mean temperature and minimum temperature in both paleo and contemporary temperature data (correlation coefficients ranging from 0. 89 to 0. 98). BIEN3 contains more than 30,000,000 geo-referenced vascular plant observations  (Enquist et al. , 2009)  from a much broader geographic scope than represented by the pollen dataset.
Contemporary occurrence data were filtered to include only New World records that did not come from cultivated areas. Genus-level distributions were pooled for taxa with multiple names (e. g. , "Ostrya/Carpinus"). For each taxa, we estimated the niche maximum temperature value as the mean of the average monthly maximum temperature values ( • C, extracted from the WorldClim raster) over all sites where the taxa was detected. Such realized-niche estimates can be affected by sampling bias, but have the benefit of being estimable from broad-scale and commonly available presence-only data. To ensure that sampling bias does not affect our estimates, we tested the correlation between species' thermal niches estimated from raw data bounded to the 95% confidence interval (i. e. , incorporating the density of sampling) with species' thermal niches sampled from a uniform distribution bounded to the 95% confidence interval of real distribution (i.
We used an independent dataset combining global extent-of-occurrence maps for 9,886 bird species (Bird Life International Handbook of the Birds of the World, 2017). BirdLife's species range maps are produced to provide robust, reliable geographic extents of a species range. They are used to present bird distributions on the BirdLife Datazone, the IUCN Red List website and for assessment of individual species Red List status (Bird Life International Handbook of the Birds of the World, 2017). These data have been compiled from multiple sources, including specimens, distribution atlases, survey reports, published literature, and expert opinion, and currently represent the most comprehensive assessment of global bird species occurrences. BirdLife International and HBW endeavor to maintain clean, accurate, and up-to-date data at all times.
Community Response Diagrams We used community-inferred temperatures and observed temperatures to build CRDs for the plant and bird datasets, as shown in Figure  1. First, we computed community-inferred temperature values using the climate niches previously inferred via the species distribution data. For each site monitored at a given time, the community-inferred temperature corresponds to the average climate niche value of all species present in a community. community-inferred temperature was calculated as the average maximum temperature niche value of all species present in a community. We also computed the standard deviation associated with each community-inferred community value. For birds, we additionally calculated community-inferred temperature based on the abundance-weighted mean of specie's maximum temperature values.
This method takes into account the relative abundance of each species in the community and could provide a more sensitive estimation on community-inferred temperature change. Abundance weighted community-inferred temperatures were strongly correlated to estimates based on occurrence only (see Figure  S3 , Pearson's correlation coefficient = 0. 95, t = 540. 94, df = 32264, P < 0. 0001). We therefore used occurrence-based communityinferred temperature in order to increase the comparability of results between plants and birds. We then extracted observed maximum temperature for each site and each time bin from paleoclimate and modern time-series data (see Data subsection). In order to reduce the inter-annual variability and temporal stochasticity that might undermine the identification of community and climate dynamics, we smoothed community-inferred and observed temperature times series by using Local Polynomial Regression Fitting (LOESS).
Topography To estimate topography, we extracted digital elevation data (DEMs) from the NASA Shuttle Radar Topographic Mission (SRTM). The NASA SRTM provides elevation as 3 arc second (∼90 m) resolution. For each site, we calculated a topography index as the difference between highest and lowest elevation within a 50 km buffer around the site. We tested the influence of the buffer size on our estimate by computing topography on 100 and 150 km buffer and comparing these values to the 50 km buffer.
Human Imprint on Ecosystems To estimate the human imprint on ecosystems over the Holocene period, we obtained population density data over the Holocene for the period ranging from 21 to 0 Ka from the HYDE 3. 2 database  (Klein Goldewijk et al. , 2011). These data are chosen to provide an approximate proxy for the upper bound of human impacts on communities across the last 21 Ka. We extracted population density value for each site from Neotoma dataset. To estimate the contemporary human imprint on ecosystems, we used the Global Human Influence Index (HII) from the NASA Socioeconomic Data and Applications Center. This index estimates the direct human footprint on ecosystems  (Sanderson et al. , 2002)  as a proxy of recent human pressure on biodiversity. HII incorporates nine global data layers corresponding to population density, human land use, infrastructure, and human access.
Species Characteristics For plants, we computed the community mean seed mass and plant height  (Honnay et al. , 2002; Matlack, 2005; Normand et al. , 2011)  using traits values compiled from the TRY database  (Kattge et al. , 2011). For birds, we used mean body mass and the percent of migrants of each community  (Jiguet et al. , 2007; Angert et al. , 2011)  using traits from the Encyclopedia Of Life (http://www. eol. org), the Animal Diversity Web (http://www. animaldiversity. org) and the field guide to North American birds  (Sibley, 2014)  previously compiled in  Barnagaud et al. (2017).
Because state number (n) values take positive integer values, we used Poisson GAMs to analyze these data. Strong outliers corresponding to site with very low number of taxa, or strong variations of number of taxa through time were deleted for the analysis (between one and five points depending on models). P-values reported for parametric and smoothed model terms were based on Wald tests. All analyses were performed using the mgcv library in R statistical software (R Development Core Team, 2013). The code written for data analysis can be accessed at https://github. com/pgauzere/ Predictability_CRD.
Our work suggests that because many community dynamics are diverging from equilibrium, the space-for-time substitution approach should be used with caution to infer future community state. Temporal dynamics might provide fundamentally different insights than spatial patterns  (Bonthoux et al. , 2013; Bjorkman et al. , 2018). While ecology is undergoing a major transformation to leverage and synthesize more spatial datasets  (Hampton et al. , 2013) , timeseries data and analysis are more than ever needed to reach a better understanding and predictability of non-equilibrium biodiversity responses to climate change. For plants, the most common type of dynamics reported were constant-relationship scenarios. Such dynamics were impaired with strong lagged responses. The current distribution of North American plants are heavily affected by climatic conditions at the Last Glacial Maximum  (Ordonez, 2013) , and plants are known to show dispersal lags in post-glaciated areas  (Normand et al.
ACKNOWLEDGMENTS We sincerely and warmly thank the  NEOTOMA database group  and the thousands of dedicated volunteers who took part in the  North American Breeding Bird Survey (BBS)  to collect the valuable data used in our analysis. We also thanks  Jessica Blois  for providing the paleo datasets,  Carolyn Flower  for kindly editing the English, all the  Macrosystems Ecology Lab  (http://benjaminblonder. org/research/) for their insights on the project, and reviewers and editor  Giovanni Rapacciuolo  for their valuable comments. J-CS considers this work a contribution to his VILLUM Investigator project  Biodiversity Dynamics in a Changing World  funded by  VILLUM FONDEN  (grant  16549 ). LI was supported by the  Carlsberg Foundation  (grant  CF17-0155 ).
The parton shower was performed with P 6. 428  [44]  using the Perugia 2012 set of tuned parameters  [45]. The production of top-quark pairs in association with a vector boson was simulated using M G 5_aMC@NLO 2. 2. 3 and the NNPDF3. 0NLO PDF set, whereas P 8. 186 was used for the parton shower with the A14 tune. Production of WW, Z Z and W Z pairs was simulated using S 2. 2. 1 and the NNPDF3. 0NNLO PDF set. Finally, SM Higgs boson production in association with a Z boson was generated with P -B v2 and the NNPDF3. 0NLO PDF set, whereas the parton shower was performed with P 8. 186 using the AZNLO tune  [46]. The modelling of bottom-and charm-hadron decays was performed with the EvtGen v1. 2. 0 package  [47]  for all samples apart from those simulated with S. The simulated events were overlaid with inelastic proton-proton collisions to account for the effect of multiple interactions occurring in the same and neighbouring bunch crossings ('pile-up').
Conclusion Data recorded by the ATLAS experiment at the LHC, corresponding to an integrated luminosity of 36. 1 fb -1 from proton-proton collisions at a centre-of-mass energy 13 TeV, are used to search for a heavy Higgs boson, A, decaying into Z H, where H denotes a heavy Higgs boson with mass m H > 125 GeV. The A boson is assumed to be produced via either gluon-gluon fusion or b-associated production. No significant deviation from the SM background predictions are observed in the Z H → bb final state that is considered in this search. Considering each production process separately, upper limits are set at the 95% confidence level for σ × B(A → Z H) × B(H → bb) of 14-830 fb for gluon-gluon fusion and 26-570 fb for b-associated production of a narrow A boson for the mass ranges 130-700 GeV of the H boson and 230-800 GeV of the A boson.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC, China ;  COLCIENCIAS, Colombia; MSMT CR, MPO CR  and  VSC CR, Czech Republic ;  DNRF  and  DNSRC, Denmark ;  IN2P3-CNRS ,  CEA-DRF/IRFU , France; SRNSFG, Georgia; BMBF,  HGF , and  MPG, Germany ;  GSRT, Greece; RGC, Hong Kong SAR, China ;  ISF, I-CORE  and  Benoziyo Center , Israel; INFN, Italy;  MEXT  and  JSPS, Japan ; CNRST, Morocco; NWO, Netherlands; RCN, Norway;  MNiSW  and  NCN , Poland; FCT, Portugal; MNE/IFA, Romania;  MES of Russia  and  NRC KI , Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SERI ,  SNSF  and  Cantons of Bern and Geneva, Switzerland; MOST, Taiwan ;  TAEK, Turkey; STFC, United Kingdom ;  DOE  and  NSF ,  United States of America.
Phosphoproteomics Proteins from the spinal ligament cells of wild-type and CXCL7-null mice were separated using two-dimensional gel electrophoresis and stained using the Pro-Q Diamond phosphoprotein gel stain (Thermo Fisher Scientific) followed by SYPRO Ruby protein gel stain (Thermo Fisher Scientific). The gel was visualized using a Molecular Imager FX Pro Plus multi imager system (Bio-Rad, Hercules, CA, USA), and the images were acquired using PDQuest software, version 8. 0 (Bio-Rad). The composite images were digitally pseudo-colored and overlaid. The gels were treated with Pro-Q Diamond, and the phosphorylation spots were verified using LC-MS/MS after in-gel digestion using trypsin and peptide extraction  [20] , both of which were performed according to a previously published protocol  [21].
The levels of CaMK II were examined because bone morphometry measurements suggested that osteoclast activity may be affected and CamK II is known to be involved in osteoclast regulation  [43]. RNA and microRNA were obtained from blood obtained from OPLL patients. The data from the microRNA array analysis showed that miR-340 is associated with the CXCL7 deficiency found between OPLL patients and healthy subjects (S3H Fig and  S2  Dataset. Supplementary Dataset GSE57592. pdf). miR-340 did not induce the degradation of CXCL7 in primary cells derived from the spine ligament of healthy mice (S3K Fig) , suggesting that this mechanism is not responsible for the decreased levels of CXCL7 seen in OPLL. We also performed a kidney tissue RNA array analysis to determine the molecular mechanisms underlying CXCL7 deficiencyinduced OPLL.