dataesr/scientific-paragraphs-categorization huggingface dataset:
The spread of     2007 ) have been added to the ocean model estimates as required (described in text). Uncertainty is the median absolute deviation for the atmospheric inversions and ocean models, 50 % of the mean for the pCO 2 climatology and SOCAT MPR, the mean annual uncertainty for the pCO 2 database trend, and the published uncertainty for the ocean inversion  (Gruber et al. , 2009). Table  4. 1990 to 2009 mean flux in each region and whole Arctic/Atlantic for Tier 1 methodologies (climatology, ocean inversion, atmospheric inversion, and ocean biogeochemical models), and observations (SOCAT MPR and pCO 2 database). Pixel areas for each region are given in the last column of Table  3 ). Atmospheric inversions begin in 1995. The best estimate for each region except the Arctic is the mean of the pCO 2 climatology and ocean inversion, with uncertainty from standard error propagation.
, 2009) , SOCAT MPR and pCO 2 database estimates are derived. climatology (light blue), atmospheric inversions (black), and ocean biogeochemical models (magenta), SOCAT MPR (green), pCO 2 database (cyan), and at the BATS and ESTOC sites in the North Subtropics (dark blue and red, respectively). Shading indicates the spread between maximum and minimum from the atmospheric inversions and ocean biogeochemical models, 50 % of the mean for the pCO 2 climatology and SOCAT, and the uncertainty of the harmonic fit for the pCO 2 database IAV. Means are calculated both spatially and using all years available for each methodology. The seasonal cycles of the South Subtropics (Fig. 5e ) agree well across methodologies, with correlations all being statistically significant. We note the similar patterns of the mean seasonal cycles shown by the atmospheric inversions and the pCO 2 climatology, which should be largely due to the atmospheric inversions using these same climatological CO 2 fluxes as priors and there being very limited atmospheric pCO 2 data in the temperate Southern Hemisphere to move results away from the priors.
Interannual variability (IAV) Figure  6  shows the low-frequency IAV and trends for Tier 1 methodologies for the atmospheric inversions, ocean biogeochemical models, and the pCO 2 database. High-frequency IAV can be found in the Supplement. Note that fluxes at BATS and ESTOC in [Pg C yr -1 ] assume the same flux intensities of each site at each grid point in the whole RECCAP subtropical region. In Table  6 , the amplitude of the interannual variability (IAV) is presented for each approach, calculated as the temporal standard deviation. SOCAT MPR IAV is compared here even though it is not shown in Fig. 6. Variability is smallest in the Arctic (0. 003 to 0. 005 Pg C yr -1 ) and largest in the North and South Subtropics (up to 0. 026 Pg C yr -1 ). The atmospheric inversions suggest the largest variability, and the SOCAT MPR the smallest.
The integrated Arctic/Atlantic regional sink varies by ±0. 055 Pg C yr -1 for the atmospheric inversions, ±0. 029 Pg C yr -1 in the ocean biogeochemical models, ±0. 015 Pg C yr -1 in the SOCAT MPR, and ±0. 046 Pg C yr -1 in the pCO 2 database. Correlation of the low-frequency interannual variability is presented in Table  7  by region. On the whole, correlations are low. In some regions and between some methods, there are significant and positive correlations, but there is not a consistent pattern of strong positive correlations between methods across all regions. For the whole Atlantic and Arctic, the highest positive correlation (0. 87) is between the SOCAT MPR and the pCO 2 database, and this is due to high positive correlations in the North Subpolar and South Subtropics regions. As for the seasonal cycle, this is encouraging because though these estimates are derived from very similar datasets of in situ pCO 2 , the methodologies used to interpolate through space and time are quite different.
Linear trends of the sea-air CO 2 flux Table  8  presents the linear trends of the sea-air CO 2 flux for 1995-2009 in each region from the atmospheric inversions, ocean biogeochemical models, and observation-based trends from the pCO 2 database. The SOCAT MPR is not included as its time-period is shorter than this. For 1995 to 2009, linear trends are generally negative, indicating an increasing sink, or indistinguishable from zero. The exception is the Equatorial Atlantic and South Subtropics where positive trends for the atmospheric inversion indicate increasing outgassing, while for the same regions the ocean biogeochemical model trends are neutral and the pCO 2 database trends are negative. For the whole Atlantic and Arctic, the atmospheric inversions suggest a steady sink while the ocean models and pCO 2 database suggest an increasing sink, with the basin scale difference driven by the Equatorial region.
The strong increasing trend for the pCO 2 database is dominated by the trend in the South Subtropics where data are extremely limited. Even in other regions, the pCO 2 database method suggests the largest trends, and these should be considered upper-bound estimates for the trends because this approach estimates the fluxes from a repeating harmonic seasonal cycle and a steadily changing linear trend in pCO 2 over the full time period, i. e. interannual variability in pCO 2 is suppressed (Sect. 2. 2).
, 2008; Takahashi et al. , 2009; Watson et al. , 2009). Estimates of the seasonal cycle of the CO 2 flux in the North Subpolar region agree between the pCO 2 database and the ocean biogeochemical models, but include a significant CO 2 efflux in the summer that is a subtropical, temperature-driven signal. The RECCAP North Subpolar region, derived from the TRANSCOM project, includes a significant portion of the subtropical gyre, and thus these estimates are affected by the imposed regional boundaries. The atmospheric inversions have a very broad set of estimates for the seasonal cycle, leading to a median with maximum drawdown in September, which is long after the subpolar spring bloom. The two methods based on in situ pCO 2 data have maximum CO 2 uptake at the time of the bloom, and then a relatively flat cycle through the rest of the year.
We recommend that future assessments use regional boundaries that are defined by biogeochemical provinces  (McKinley et al. , 2011; Sarmiento et al. , 2004)  as opposed to lines of latitude. Interannual variability in the North Subpolar region is small, ranging from 0. 004 to 0. 016 Pg C yr -1 (Table  6 ). There is limited correlation between the methodologies (Table 7), which we partially attribute to regional boundaries being sub-optimal. Maximum correlations come between the pCO 2 database and the other methodologies, which is due largely to the fact that wind speed variability is the only source of interannual variability in the pCO 2 database approach. There are no strong correlations of the variability with the NAO. Trends in CO 2 uptake for 1995-2009 are neutral or negative, indicating a steady or increasing sink.
, 2002; Santana-Casiano et al. , 2007). Statistically significant negative linear trends are found for the sea-air CO 2 flux from 1995 to 2009 (Table  8 ) as determined by the atmospheric inversions, ocean biogeochemical models, and the pCO 2 database, indicating a longterm increase in the CO 2 sink. This is in contrast to results from BATS/Station S, where the long-term mean fluxes have remained constant over the last 3 decades  (Bates et al. , 2012). At ESTOC, the rate of increase of surface pCO 2 was higher between  1995 and 2009 (González-Dávila and Santana Casiano, 2012 ) than between 1995 and 2004 (Santana-Casiano et al. , 2007) , potentially indicating a decrease in the sink. The time period over which a trend is determined is crucial when comparing different estimates  (McKinley et al.
Conclusions We have summarized and compared sea-air CO 2 fluxes estimated for the Atlantic and Arctic across many realizations of the dominant methodologies presently in use to quantify regional and global carbon budgets: pCO 2 climatology, ocean inversion, atmospheric inversions and ocean biogeochemical models. Original results based on newly released large databases of in situ pCO 2 observations are also compared. Across the approaches, mean fluxes within the four Atlantic subregions generally agree within uncertainties, but seasonal cycles agree only in the subtropical regions of both hemispheres. We find little detailed agreement with respect to interannual variability or trends. Our estimate for the 1990-2009 long-term net sea-air CO 2 flux in the Atlantic between 40 • S and 79 • N is -0.
Linear trends of the spatially integrated sea-air CO 2 flux for each region and whole Arctic + Atlantic, 1995 to 2009 [Tg C yr -1 decade -1 ], for the atmospheric inversions, the ocean biogeochemical models and the pCO 2 database. Trends are a linear fit to the low-frequency median flux IAV (i. e Fig. 6), with 2σ confidence. Trends distinguishable from zero are indicated in bold.
Acknowledgements. U. Schuster  and  G. A. McKinley  contributed equally to this manuscript and should be considered as joint first authors. We thank  P. Canadell ,  P. Ciais ,  C. Le Quéré , and  C. Sabine  for coordinating the global effort of RECCAP. We thank captains, officers, and crew of all ships on which measurements have been made that contribute to this study. U. Schuster  has been supported by  EU  grants  IP 511176-2  (CARBOOCEAN), 212196 (COCOS), and 264879 (CARBOCHANGE), and  UK   NERC  grant  NE/H017046/1  (UKOARP). G. A. McKinley  and  A. Fay  thank  NASA  for support ( NNX08AR68G ,  NNX11AF53G ). P. Landschützer  has been supported by  EU  grant  238366  (GREENCYCLESII). N. Metzl acknowledges the French national funding program  LEFE/INSU. Support for  N. Gruber  has been provided by  EU  grants  264879  (CARBOCHANGE) and 283080 (GEO-CARBON) S.
14 0. 30 -0. 06 SOCAT MPR 1 0. 08 0. 02 0. 35 0. 20 pCO 2 database 1 0. 20 0. 49 0. 11 NAO 1 0. 17 -0. 35 BATS 1 0. 26 (d) Equatorial Atm. inversions 1 0. 40 0. 39 -0. 39 -0. 51 Ocean models 1 0. 72 0. 13 -0. 43 SOCAT MPR 1 0. 06 0. 11 pCO 2 database 1 0. 35 (e) South Subtropics Atm. inversions 1 0. 56 -0. 50 -0. 31 0. 02 Ocean models 1 0. 15 0. 55 0. 40 SOCAT MPR 1 0. 97 0. 30 pCO 2 database 1 0. 36 (f) Arctic + Atlantic Atm. inversions 1 0. 32 -0. 07 -0. 15 -0. 25 Ocean models 1 0. 55 0. 54 -0. 05 SOCAT MPR 1 0. 87 0. 28 pCO 2 database 1 0. 29.
Bioassays Guidelines for test procedures and interpretation of results are available from the WHOPES 3 (see http://www. who. int/whopes/resistance/en/). It is important that the mosquitoes used for the bioassays are standardized for age, sex and physiological status as all of these can affect the outcome of the tests. Typically either adults raised from isofemale lines or F1 progeny from field collected blood fed females are used. The limitations and advantages of these two alternatives have recently been discussed  [116]. These diagnostic dose assays are simple to perform and provide standardized data sets that, assuming the guidelines are followed, can be readily compared to identify temporal and/or geographical variations in the resistant status of malaria vector populations. However, it is important to recognize some of the limitations of these susceptibility tests.
The WHO/AFRO African Network for Vector Resistance was established in 2000 and amongst its objectives was the important goal of improving the dissemination of resistance data. Accordingly a database was established to store the results of resistance monitoring activities by the African Network for Vector Resistance (ANVR) members but until recently, this database was not readily accessible by outside users. The recent establishment of new data base (see section 6), as an online centralized resource for collating data on insecticide resistance in disease vectors and the integration of this with the ANVR database, will hopefully ensure that both published and unpublished data on resistance in malaria vectors are more readily available to all interested parties.
0 LLINs was initiated in one of the most affected island provinces, Karuzi  [225]. Initially, one round per year of pyrethroid-IRS was carried out in all human dwellings and cattle sheds before the seasonal increase in transmission. LLIN distribution preceded the first IRS round in the same year. The S-form of An. gambiae was the predominant vector species in Karuzi District and showed resistance to pyrethroids due to the kdr mutation. The entomological data showed that the intervention, overall, effectively reduced Anopheles density by 82% and malaria transmission was decreased by 90% despite high frequencies of the L1014S allele in the local An. gambiae population  [173]. In a more recent observational study conducted in Malawi, the impact of pyrethroid resistance on operational malaria control has been assessed with more controversial evidence of resistance impacting pyrethroid-based vector control  [161].
Considered time step Figure  1 : Scheme explaining the nature of the series we study and the procedure used to obtain them. For each time window, we observe the last trade (trades are dashed vertical lines, randomly spaced) and consider the bid and ask prices immediately before the transaction (blue and red large points). The blue line is the bid time series and the red one is the ask time series. The considered midprice is simply the average of the considered bid and ask prices. estimation of the Hurst exponent is recognized as a good indicator, but this is misleading. Strictly speaking, the Hurst exponent is just a measure of the scaling properties of a time series and, alone, does not give any information on the possible persistent behaviour when the analyzed data are not stationary, as is typically the case in the financial world.
The paper is organized as follow. In section 2 we present the dataset we use for the study, together with the sampling rules. In section 3 the main statistical objects (autocorrelation and Hurst exponent) are presented and discussed. Section 4 is devoted to the explanation of the tools and procedures we follow looking for the desired non-efficient basket. Finally, Sections 5 and 6 are dedicated respectively to the results analysis and the conclusions.
Data set The original data set consists of all trades registered in the primary markets of the analyzed stocks. The data are stored in the Thomson Reuters TRTH data base made available to the Chair of Quantitative Finance by Thomson Reuters and BNP Paribas. For the purpose of our study, we extract from the TRTH database records consisting in the time of a transaction, the best bid and best ask prices prior to each transaction, and the traded price. These data, appropriately filtered in order to remove misprints in prices and times of execution, correspond to the trades registered on the NYSE or NASDAQ during 2007, for the 30 shares of the Dow Jones Industrial Average Index, namely, at that time: AA. 28 of those companies were traded primarily on the NYSE. IBM. O and MSFT. O are the only two primarily traded on the NASDAQ.
Quantitative PCR. For detection of viral RNA, we used the one-step real-time RT-PCR upE assay  (30) ; for the detection of mRNA, we used an ORF1-and ORF5-specific SYBR green assay. See SI Materials and Methods for detailed information. Microarray Data and Functional Analysis. RNA extracted from lung tissue samples and PBMC was run on Agilent Rhesus macaque 4 × 44 K microarrays (Agilent Technologies) using the manufacturer's one-color analysis protocol. Raw array data were uploaded to Genedata Analyst 7. 6 (Genedata) and further analyzed. Raw microarray data have been deposited in National Center for Biotechnology Information's Gene Expression Omnibus (GEO) database under GEO accession no. GSE44542, and are also available to the public at http://viromics. washington. edu. For detailed information, see SI Materials and Methods.
ACKNOWLEDGMENTS. We thank  Drs. Bart Haagmans  and  Ron Fouchier  at the  Erasmus Medical Center  for providing Middle East respiratory coronavirus isolate HCoV-EMC/2012;  Ralph Baric  at the  University of North Carolina at Chapel Hill  for subgenomic RNA primer sequences;  Laura Baseler ,  Tina Thomas ,  Dan Long , and  Rebecca Rosenke  for histopathology; the staff of the  Rocky Mountain Veterinary Branch  for assistance with the animal experiments; and  Anita Mora  for figure preparation. This research was supported by the  Intramural Research Program  of the  National Institute of Allergy and Infectious Diseases, National Institutes of Health ; A. L. R. , A. G. B. , A. O. , and M. G. K. are funded through Grant  U54 AI081680 ,  National Institute of Allergy and Infectious Diseases Regional Centers of Excellence, National Institutes of Health/National Institute of Allergy and Infectious Diseases  Contract  HHSN272200800060C , and  National Institutes of Health  Grant  P51OD010425  ( Washington National Primate Center ).
Motivation: Cancer cells are often characterized by epigenetic changes, which include aberrant histone modifications. In particular, local or regional epigenetic silencing is a common mechanism in cancer for silencing expression of tumor suppressor genes. Though several tools have been created to enable detection of histone marks in ChIP-seq data from normal samples, it is unclear whether these tools can be efficiently applied to ChIP-seq data generated from cancer samples. Indeed, cancer genomes are often characterized by frequent copy number alterations: gains and losses of large regions of chromosomal material. Copy number alterations may create a substantial statistical bias in the evaluation of histone mark signal enrichment and result in underdetection of the signal in the regions of loss and overdetection of the signal in the regions of gain.
Results: We present HMCan (Histone modifications in cancer), a tool specially designed to analyze histone modification ChIP-seq data produced from cancer genomes. HMCan corrects for the GC-content and copy number bias and then applies Hidden Markov Models to detect the signal from the corrected data. On simulated data, HMCan outperformed several commonly used tools developed to analyze histone modification data produced from genomes without copy number alterations. HMCan also showed superior results on a ChIP-seq dataset generated for the repressive histone mark H3K27me3 in a bladder cancer cell line. HMCan predictions matched well with experimental data (qPCR validated regions) and included, for example, the previously detected H3K27me3 mark in the promoter of the DLEC1 gene, missed by other tools we tested.
, 2010). Many tools have been developed to facilitate the analysis of histone modification data obtained with the ChIP-Seq technique. Some tools are designed to detect narrow peaks of type of H3K4me3  (Kharchenko et al. , 2008; Rozowsky et al. , 2009; Zhang et al. , 2008). Other methods are able to identify epigenetic marks covering large genomic regions; this is mostly done through clustering  (Zang et al. , 2009) , gene-by-gene quantification  (Hebenstreit et al. , 2011) , Hidden Markov Models (HMMs)  (Qin et al. , 2010; Xu et al. , 2008)  and linear signal-noise models  (Xu et al. , 2010). However, there is no tool specifically developed to detect histone modifications in cancer genomes that takes into account copy number alterations. As we show later, most of the tools tend to detect more signals in the regions of gain and less signal in the regions of loss.
GC-content is known to influence read depth in both Illuminaand SOLiD-generated datasets  (Boeva et al. , 2011; Dohm et al. , 2008). A possible difference in GC-content dependencies between ChIP and control datasets can result in false predictions of enrichment in histone modification marks  (Chen et al. , 2012). Here we present a tool designed to identify histone modifications in genomes with large copy number alterations. HMCan (Histone Modifications in Cancer) corrects for copy number bias and for GC-content bias. It then uses HMMs to detect regions rich in histone modifications. We chose to compare HMCan with three tools commonly used to detect histone modifications with CHIP-seq data: CCAT  (Xu et al. , 2010) , MACS  (Zhang et al. , 2008)  and SICER  (Zang et al. , 2009). We show that HMCan is able to detect signal enrichment in simulated cancer genomes better than these three tools.
Data profile construction Reads of ChIP and control datasets are transformed into density profiles. To construct the profiles, the reads are extended from read starts to the length of DNA fragments. Similarly to FindPeaks, we use the triangular distribution for read extension  (Fejes et al. , 2008). This method allows the user to set minimum median and maximum fragment length used in the original ChIP-seq experiment. After read extension, we keep one density value for each 50 nucleotides (this value can be changed by the user).
Initial peak calling To calculate the correct GC-content profile on the ChIP data and correctly estimate the initial parameters of HMM, preliminary peak (enrichment signal) calling should be applied to serve as a guide for both operations. A one-sided exact Poisson test is used to label whether a bin belongs to a peak or not. As a post-processing step, singleton bins labeled as peaks are removed. Then, the bins labeled as peaks within 1 Kb are merged into a single peak region.
We denote the expected density for each gc value as gc , defined as: gc ¼ D gc =N gc : ð1Þ We will denote the average expected density along the genome by , defined as: ¼ P gc D gc P gc N gc : ð2Þ Fig. 1. HMCan framework. HMCan initially estimates copy numbers from control data and it builds density profiles for both libraries. Then, HMCan performs a set of normalization steps including normalization by copy number, GC-content and background noise. Finally, HMCan detects histone modification regions with HMMs after estimating initial parameters from the normalized profile Then, each density value D can be corrected as follows: D corrected ¼ D Á gc : ð3Þ The correction process is applied to both ChIP and control data independently. This leads to a more accurate correction compared with calculating GC-content bias ( and gc ) for the control data only and then correcting the ChIP and control densities based on the same values.
Applying the described method to the control data is straightforward, as the control data are not supposed to contain any signal. In the case of ChIP data, the process is trickier because the signal contained in the ChIP data may interfere with the GC bias, e. g. some histone modifications can occur more frequently in GC-rich regions. To overcome this issue, we first apply the module of initial peak calling to identify regions that most probably belong to the signal ('peaks'). Then, we apply the described method for CG-bias evaluation to the regions labeled as 'not peaks'. We denote the expected density in the control data as control and in the ChIP data as ChIP. To get the noise values in the ChIP and control data on the same scale, we multiply the values of density in the control by the noise ratio noise , where: noise ¼ ChIP = control : ð4Þ To calculate the final density profile for the ChIP sample, we apply the following normalization: D final ¼ D ChIP corrected À D control corrected Á noise : ð5Þ 2.
Evaluation on simulated data To investigate the performance of HMCan on cancer samples, we constructed a simulated ChIP-seq dataset for a fictional histone mark. The signal covered multiple regions across chromosome 1 (human genome, hg19), with each region being of length from 1 to 20 Kb. These regions comprised 5% of chromosome 1. We simulated histone marks covering different numbers of alleles in the regions of normal copy number, gain (of copy number) and loss (of copy number) (Supplementary Table  S1 ). In our simulations, we set read length ¼ 76 bp, fragment length ¼ 150 bp, $20% of the reads came from the signal regions and $80% of the reads came from the non-signal regions. We assumed a constant length of DNA around one nucleosome to be equal to 185 bp. We simulated more errors at the end of reads using the standard Illumina error distribution.