dataesr/scientific-paragraphs-categorization huggingface dataset:
The pattern of negative balances continued into 2015/16 in Arctic Canada and is captured in the time series       (Derksen et al. 2016)  with negative anomalies (i. e. , below-normal snow depths) across the sub-Arctic surrounded by mainly positive anomalies over the high latitude regions of Siberia and North America. By May, the North America snow depth anomalies changed to strongly negative (average anomaly of -10. 7%) consistent with the record low SCE values reported above. May snow depth anomalies over Eurasia were near normal (-0. 6%) but plummeted to -29. 2% in June. The link between temperature and snow cover extent is straightforward: there is a strong association between trends in surface temperature and snow cover extent in both observational datasets (R 2 = 0. 64 in  Mudryk et al.
The data here are from the Global Inventory Modeling and Mapping Studies (GIMMS) version 3g dataset based largely on the AVHRR sensor onboard NOAA satel-lites  (Pinzon and Tucker 2014). The GIMMS product is a biweekly, maximum-value composited dataset of the normalized difference vegetation index (NDVI); NDVI is highly correlated with aboveground vegetation (e. g. ,  Raynolds et al. 2012). Two metrics based on the NDVI are used: MaxNDVI (peak NDVI for the yearly growing season, related to yearly maximum aboveground vegetation biomass) and time-integrated NDVI (TI-NDVI; sum of the biweekly NDVI values for the growing season, related to the total aboveground vegetation productivity). This section reports only through the end of the 2015 growing season (May-September), as a complete 2016 dataset was not available at the time of writing.
Examining the overall trend in tundra greenness for the 34-year record, both MaxNDVI and TI-NDVI are found to have increased on the North Slope of Alaska, in the southern Canadian tundra, and in much of the central and eastern Siberian tundra, whereas tundra greenness has decreased (i. e. , "browning") in western Alaska (Yukon-Kuskokwim Delta), the higher Arctic Canadian Archipelago, and western Siberian tundra (Fig. 5. 20). Using the same NDVI dataset (albeit with a different vegetation map and a slightly shorter period of 1982-2012),  Loranty et al. (2016)  found that a much greater fraction of tundra areas overlying continuous permafrost exhibited long-term greening (42%) compared to browning (5%); in tundra areas overlying discontinuous permafrost the areal difference was not as great (27% greening and 10% browning).
UVI values <5 indicate low to moderate risk of erythema  (WHO 2002). Maps shown in Figs. 5. 28c,d quantify differences of monthly average noontime UVIs from historical (2005-15) averages and are based on OMI measurements. The OMI UV algorithm uses a surface albedo climatology  (Tanskanen et al. 2003 ) that does not change from year to year. At places where the actual surface albedo deviates greatly from the OMI albedo climatology (e. g. , when snow melt occurred earlier than usual), OMI UVI data may be biased by more than 50%  (Bernhard et al. 2015). Figures 5. 28c,d therefore also compare UVI anomalies measured by OMI and ground-based instruments deployed at 11 stations throughout the Arctic and Scandinavia. Anomalies derived from the two datasets agree to within ±15% at all locations, with the exception of Barrow for April.
2010)  to provide a comprehensive assessment of the contemporaneous carbon cycle. The SOSE output will then be used to improve projections of future carbon cycle using a coupled Earth system model. These improvements will be taken up by the climate modeling centers in both countries, with output made available to the Southern Ocean Model Intercomparison Project and IPCC. This combination of programs will leave a lasting legacy that will guide the future of research on this globally important topic. The datasets gathered will constitute benchmarks for the Southern Ocean's role in capturing anthropogenic carbon and heat from the atmosphere and will have value in perpetuity. The improved mechanistic understanding of key processes will be incorporated into the coupled models used for climate predictions, upon which advice to policy makers is ultimately based.
Note that the ERA-Interim values are biased low with respect to the MERRA-2 data for the period up to 1998, but the two datasets are in excellent agreement from 1999 to the present. The figure shows that the 2016 average temperature was a couple of degrees higher than the average over 1991-2006, the peak ozone hole period. The 2016 stratospheric dynamical conditions were slightly more active than the 1980-2015 average. The 100-hPa eddy heat flux is a metric of both wave propagation into the stratosphere and the strength of the downward motion over Antarctica. In 2016 the magnitude of the 100-hPa eddy heat flux was above average for the August-September period (not shown). Consequently, the Antarctic stratospheric vortex was warmer than average (Fig. 6. 11c) and its jet flow was slightly weaker than average (not shown).
7. 43c). In autumn, negative anomalies of 500-hPa geopotential height and of 850-hPa temperature were observed over the midlatitudes from central Asia to the Kamchatka Peninsula (Fig. 7. 44d), associated with a strongly negative NAO/AO period. 2) rUSSia-O. N. Bulygina, N. N. Korshunova, and N. M. Arzhanova Observational information was obtained from the Roshydromet Observation Network. Official datasets are available from the RIHMI-WDC site: http:// meteo. ru/english/climate/cl_data. php. The mean annual Russia-averaged air temperature for 2016 was 0. 83°С above the 1981-2010 normal (Fig. 7. 45; see also  Fig. 7. 40) , the fifth highest such value in the period 1936-2016. Winter was abnormally warm, especially in western and central regions (Fig. 7. 42a), with the seasonal average 2. 1°С above normal, the second warmest since 1936.
Global (or representative) average time series for essential climate variables through 2016. Anomalies are shown relative to the base period in parentheses although base periods used in other sections of the report may differ. The numbers in the square brackets that follow in this caption indicate how many reanalysis (blue), satellite (red), and in situ (black) datasets are used to create each time series in that order. (a) burning [0,3,0]; (ad) Soil moisture [0,1,0]; (ae) Terrestrial groundwater storage [0,1,0]; (af) Fraction of absorbed photosynthetically active radiation (FAPAR) [0,1,0]; (ag) Land surface albedo -visible (solid) and infrared (dashed) [0,1,0]. ESSENTIAL CLIMATE VARIABLES-J. BLUNDEN, D. S. ARNDT, AND R. J. H. DUNN Time series of major climate indicators are again presented in this introductory chapter.
8% before 2000. Currently it is not clear how much of this change is attributable to the influx of records taken from more modern satellites versus actual variability in cloudiness in the 1980s and 1990s. Strong El Niño events and the eruptions of El Chichón (1982) and Mount Pinatubo (1991) could explain positive cloudiness anomalies during this early period (a shift to more negative anomalies post-2000 can be seen in Figure 2. 22), although it does not explain greater variability among the records. Of the records that extend back before 2000, three: PATMOS-x/AVHRR, CLARA-A2 (cloud, albedo and radiation dataset; Karlsson et al. 2017), and SatCORPS (satellite cloud and radiative property retrieval system; Minnis et al. 2016) are derived from AVHRR instruments flown on NOAA POES and more recently the EUMETSAT MetOP series.
All datasets are averaged to monthly 2° × 2° grids for comparison purposes. Annually averaged SST anomaly (SSTA; relative to 1981-2010 climatology) in 2016 exceeded +0. 5°C over much of the tropical oceans and exceeded +1°C near the Gulf of Alaska and the Gulf Stream (Fig. 3. 1a). Higher SSTAs were partly associated with the 2015/16 El Niño event (Xue et al. 2016; L'Heureux et al. 2017), which peaked in November 2015, weakened, and became neutral after June 2016. In contrast, SSTA was slightly lower than average in the Southern Ocean south of 40°S between 120°E and 0°, the northern North Atlantic south of Greenland near 50°N, and the central North Pacific near 40°N. Globally averaged SSTA in 2016 was 0. 378 (±0. 055) °C, marginally higher (0. 013°C) than the record of 0. 365 (±0. 055) °C set in 2015.
ACKNOWLEDGMENTS The report's editors wish to thank the AMS Journals editorial staff, in particular Melissa Fernau, for facilitating the document, and to the NCEI graphics team for laying the document out and executing the countless number of technical edits needed. We also wish to express our sincere and deep gratitude to Dr. Rick Rosen, who served as the AMS special editor for this report. Dr. Rosen's handling of the reviews was at the same time rigorous and responsive, and greatly improved the document. RNF 16-17-00102, 13-05-41509 RGO, 13-05-00811, 13-08-91001, 14-05-00956, 14-17-00037, and 15-55-71004)  and by the government of the Russian Federation. • The chapter editors thank the authors for their contributions and the reviewers for their thoughtful and constructive comments.
Study Population Clinical outcomes were assessed in 1295 recipients of transplants from HLA-identical sibling donors in Spanish centers between 2002 and 2014 and whose clinical data were reported to the Spanish Hematopoietic Transplant Group. DNA samples from recipients and donors were obtained from peripheral blood, prospectively collected, and stored at -80°C until use. Most recipients were prepared for transplantation with a myeloablative regimen (83. 2%). All patients received a non-T cell-depleted graft, and the GVHD prophylaxis was based on cyclosporine A combinations (83. 2% receiving cyclosporine A and methotrexate). Acute GVHD was diagnosed and graded according to the consensus criteria  [21]. Table  1  shows the clinical characteristics of this cohort. All patients and donors provided written informed consent.
All these features are explicitly taken into account in the COMPHEP Monte Carlo (MC) generator  [27]. In this paper, we present a search by the CMS experiment at the CERN LHC for anomalous Wtb couplings and FCNC interactions of the top quark through the u or c quarks and a gluon (tug or tcg vertices), by selecting muons arising from W boson decay (including through a τ lepton) from the top quarks in muon+jets events. Separation of signal and background is achieved through a Bayesian neural network (BNN) technique  [28, 29] , performed using the Flexible Bayesian modelling package  [30]. Limits on Wtb and top quark FCNC anomalous couplings are obtained from the distribution in the BNN discriminants.
The high-level trigger processor farm further decreases the event rate from around 100 kHz to less than 1 kHz, before data storage. A more detailed description of the CMS detector, together with a definition of the coordinate system used and the relevant kinematic variables, can be found in Ref. [31]. The particle-flow event algorithm  [32, 33]  reconstructs and identifies each individual particle with an optimized combination of information from the various elements of the CMS detector. The energy of photons is directly obtained from the ECAL measurement, corrected for zero-suppression effects. The energy of electrons is determined from a combination of the electron momentum at the primary interaction vertex as determined by the tracker, the energy of the corresponding ECAL cluster, and the energy sum of all bremsstrahlung photons spatially compatible with originating from the electron track.
Data and simulated events The analysis is performed using proton-proton collisions recorded with the CMS detector in 2011 and 2012 at centre-of-mass energies of 7 and 8 TeV, respectively, and corresponding to integrated luminosities of 5. 0 and 19. 7 fb -1. The t-channel production of a single top quark is modelled using the COMPHEP 4. 5 package  [27] , supplemented by an additional matching method used to simulate an effective next-to-leading-order (NLO) approach  [36]. The NLO cross sections used for t-channel single top production are σ(7 TeV) = 64. 6 +2. 6  -1. 9 pb  [37]  and σ(8 TeV) = 84. 7 +3. 8 -3. 2 pb  [38, 39]. The POWHEG 1. 0 NLO MC generator  [40]  provides an alternative model to estimate the sensitivity of the analysis to the modelling of the signal. Contributions from anomalous operators are added to the COMPHEP simulation for both the pro-duction and decay of top quarks.
1 ± 1. 7 pb and σ(8 TeV) = 73. 8 ± 1. 9 pb  [45]  are modelled using LO PYTHIA 6. 426  [46]. The contribution from multijet events, with one of the jets misidentified as a lepton, is estimated using a mutually exclusive data sample. The details are given in the next section. Single top quark production in the s channel with σ(7 TeV) = 4. 6 +0. 2 -0. 2 pb, σ(8 TeV) = 5. 5 ± 0. 2 pb, and in the tW channel with σ(7 TeV) = 15. 7 ± 1. 2 pb, σ(8 TeV) = 22. 2 ± 1. 5 pb  [47]  are modelled using the POWHEG generator. The PYTHIA 6. 4 program is also used to simulate parton showers for the hard processes calculated in the COMPHEP, MAD-GRAPH, and POWHEG generators. The PDF4LHC recipe  [48]  is used to reweight all simulated events to the central value of CT10 PDF  [49]. The Z2Star  [50, 51]  set of parameters is used to simulate the underlying-events.
Because of the importance of the W+jets background and the significant difference in the kinematic distributions, the following contributions are considered separately in the analysis: W boson produced together with a pair of b or c quarks (W+QQ); W boson produced in association with a c quark (W+c); W boson events that do not contain heavy quarks (W+light); and events associated with underlying events (UE) that contain heavy quarks originating from the initial parton interaction (W+QX). Different nuisance parameters for the normalization scale factors are used for these components of the complete W+jets MADGRAPH simulation. Simulated events are reweighted to reproduce the observed particle multiplicity from pileup. Small differences between the data and simulation in trigger efficiency  [52, 53] , lepton identification and isolation  [52, 53] , and b tagging  [54]  are corrected via scale factors, which are generally close to unity.
Event selection The following signature is used to identify t-channel single top quark production candidates: exactly one isolated muon  [52] , one light-flavour jet in the forward region (defined below); one b-tagged jet  [54]  from the b quark originating from the decay of the top quark, and an associated "soft" b jet. The "soft" b jet is likely to fail either the p T or η threshold (given below). The presence of a neutrino in the decay of the W boson leads to a significant amount of E miss T , which is used to enhance the signal. The analysis is performed using data collected with a trigger requiring at least one muon in each event. To accommodate the increasing instantaneous luminosity delivered by the LHC in 2011, different triggers were used for various data-taking periods, with the muon p T threshold ranging from 20 to 27 GeV.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing 9 Summary  2014/13/B/ST2/02543  and  2014/15/B/ST2/03998 , Sonata-bis  2012/07/E/ST2/01406 ; the Thalis and Aristeia programmes cofinanced by  EU-ESF  and the  Greek NSRF ; the  National Priorities Research Program  by  Qatar National Research Fund ; the  Programa Clarín-COFUND del Principado de Asturias ; the  Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University  and the  Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand) ; and the  Welch Foundation , contract  C-1845.
The distribution of fitness effects (DFE) encompasses the fraction of deleterious, neutral, and beneficial mutations. It conditions the evolutionary trajectory of populations, as well as the rate of adaptive molecular evolution (a). Inferring DFE and a from patterns of polymorphism, as given through the site frequency spectrum (SFS) and divergence data, has been a longstanding goal of evolutionary genetics. A widespread assumption shared by previous inference methods is that beneficial mutations only contribute negligibly to the polymorphism data. Hence, a DFE comprising only deleterious mutations tends to be estimated from SFS data, and a is then predicted by contrasting the SFS with divergence data from an outgroup. We develop a hierarchical probabilistic framework that extends previous methods to infer DFE and a from polymorphism data alone.
Patterns of nucleotide variation within the ingroup can be summarized via the polymorphism counts occurring in each frequency class and form the site frequency spectrum (SFS), while variation between the ingroup and Figure  1  Schematic of data and the hierarchical model assumed in the inference method. Throughout the figure, gray and blue fill indicates sites that are assumed to be evolving neutrally or potentially under selection, respectively; while red and blue outlines indicates polymorphism and divergence data (expectations), respectively. (A) The history and coalescent tree of two populations: the ingroup (on the left side), for which polymorphism data are collected, and the outgroup (on the right side), for which divergence counts are obtained. A total of n sequences are sampled from the ingroup (marked in red), with the MRCA marked with a red circle.
The proportion of adaptive substitutions, a, is typically obtained as a ratio between an estimate of the number of adaptive substitutions and the observed selected divergence counts  (Loewe et al. 2006; Eyre-Walker and Keightley 2009; Keightley and Eyre-Walker 2012; Galtier 2016). The number of adaptive substitutions is calculated by subtracting the expected counts accrued by fixation of deleterious and neutral mutations from the observed divergence counts at selected sites. These expected counts are calculated from an inferred DFE of deleterious mutations (henceforth denoted deleterious DFE). The deleterious DFE is most often inferred from the SFS data under the assumption that all mutations at selected sites are deleterious. This approach for estimating a heavily relies on the assumption that the ingroup and outgroup species share the same DFE, or more accurately, the same distribution of scaled selection coefficients S ¼ 4N e s: Unfortunately, this assumption of invariance might not often be met in practice, because the DFE might change, or simply because it is unlikely that both the ingroup and outgroup lineages evolved with the same population size.
We show that the assumption that beneficial mutations make negligible contribution to SFS data are unfounded and that a full DFE can also be inferred from polymorphism data alone. Using the estimated full DFE, we show how a can be inferred without relying on divergence data. Performing inference on polymorphism data alone is desirable when assumptions regarding outgroup evolution (for example, invariance of the DFE) are unlikely to be met. We also demonstrate that when the contribution of beneficial mutations to SFS data is ignored, both the inferred deleterious DFE and a can be heavily biased. This point was also made by  Messer and Petrov (2013)  in the context of studies relying on McDonald-Kreitman tests. We compare our method and illustrate the resulting bias using the most widely used inference method, dfe-alpha  (Keightley and Eyre-Walker 2007, 2012; Eyre-Walker and Keightley 2009; Schneider et al.
Inferring a using divergence or polymorphism data alone Once the DFE is estimated, a can be calculated from the observed divergence counts as follows (Eyre-Walker and Keightley 2009): a d sel 2 l d sel l d neut d neut Z 0 2N S 1 2 e 2S fðSÞdS d sel ; (8) where the numerator represents the estimated number of adaptive substitutions, obtained by subtracting the expected deleterious and neutral substitutions from the total observed divergence counts at selected sites. Keightley and Eyre-Walker (2012)  extended Equation  8 to account for misattributed polymorphism. We correct for it by removing the expected number of mutations that are in fact polymorphic from d sel and d neut : These expectations can be readily obtained from Equation 5 by setting l ¼ 0: The new estimate of a is obtained as in Equation  8 , where d sel and d neut are replaced with the readjusted divergence counts d * sel and d * neut given by d * neut ¼ d neut 2 l d neut E½D neut jl ¼ 0; u; r n ; d * sel ¼ d sel 2 l d sel E½D sel jl ¼ 0; u; r n ; f: (9) This approach to calculate a relies heavily on the assumption that the ingroup and outgroup share the same scaled DFE.
Results and Discussion To investigate the statistical performance of our method to infer the DFE, a, and test hypothesis regarding the contribution of beneficial mutations to patterns of polymorphism, we performed extensive simulations using SFS_CODE  (Hernandez 2008). We generated exome-like data, where a 10. 8-Mb genetic segment was simulated per data set, containing multiple independent fragments (typically of 216 sites) for which recombination took place. We sampled 20 sequences (10 diploid individuals) from the ingroup, and one sequence from the outgroup. We explored a wide range of simulated DFEs (12 full and 5 deleterious, Table  1 ), chosen such that the simulated a had one of four possible values (0; 20; 50, and 80%; Figure  3A ). Most simulations were performed using a constant population size and without error in the identification of the ancestral state.
We would expect that support for the presence of beneficial mutations in the SFS data would be stronger if the number of sequenced individuals would have been larger. Our findings of more evidence for positive selection on the X chromosome than on the autosomes is also in line with the original study  (Hvilsom et al. 2012; Bataillon et al. 2015). While for the autosomes, the type of DFE (full or deleterious only) assumed and the type of data used did not affect drastically the inferred discretized DFE (Figure  8A ), the same cannot be said for the X chromosome; further indicating that care needs to be taken when making assumptions of no presence of beneficial mutations in the polymorphism data, or about the invariance of the DFE in the ingroup and outgroup species used. We also observed the same trend in the inferred discretized DFE for the chimpanzee subspecies previously noted for the simulated data: the estimated proportion for S 2 ð21; 0Þ is considerably larger when only a deleterious DFE is estimated (Figure  8A ).
The use of an LRT comparing a model featuring a full DFE and a deleterious DFE would accurately select the reduced model and allow precise inference of the deleterious DFE. This is an important result, as it suggests that using a full DFE for inference from SFS data does not come with a cost when no beneficial mutations contributed to the SFS counts, and that the method does not spuriously infer presence of beneficial mutations. To correct for demography and other forces that can distort the SFS data, such as linkage, we used the nuisance parameters r i : These parameters have the potential of accounting for more complex scenarios without directly modeling the underlying changes in population size and, potentially, other events such as migration and admixture. This correction could prove more robust than just allowing for a few population size changes, as dfe-alpha assumes.
Acknowledgments We thank  Nicolas Galtier ,  Matthew Hartfield , and  Peter Keightley  for useful early discussions and comments on the manuscript. We thank the four reviewers for their constructive suggestions and comments that helped improve the manuscript. This work has been supported by the  European Research Council  under the European Union's  Seventh Framework Program  ( FP7/ 20072013 ,  European Research Council  grant  311341 ). Literature Cited.