dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analyses Data are presented as means + standard error (SE). An independent Student's t-test was performed to compare the baseline levels of biochemicals between the two groups of CFS patients and control subjects. For temporally repeated data during rest, exercise, and recovery periods, the changes over time were determined using ANOVA when the variables were normally distributed or a Friedman's test for repeated measures when they were not. When data were normally distributed, a two-way analysis of variance for repeated measures (RM-ANOVA 2) (i. e. groups and time) was used to determine the significance of changes throughout the cycling trial and the post-exercise recovery period. Post-hoc independent ttests with Bonferroni corrections were used to compare time-point specific differences between groups.
This approach avoids uncertainties due to wave height or wind direction because laser altimetry measures vertical height with high precision over dry land. Finally complementary analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) surfaces from 2000 to 2012 combined with bathymetry gives water levels and storage evolution every 8-day. The article is organized as follows: Section 2 describes the data Section 3 describes the methodology for estimating contour elevations. Section 4 presents the results. Conclusions are drawn in Section 5.
Data Through the USGS GLOVIS images archive  [21]  18 cloud free scenes from Landsat 7 ETM+ and Landsat 5 TM were selected between 1999 and 2008 (Section 3. 2). All selected Landsat scenes have Standard Terrain Correction (Level 1T). Image projection is UTM zone 19N, with 30 × 30 m of ground resolution. Since the first Landsat mission in 1972, there are over 150 suitable scenes now available at no cost through the Landsat archives. Terra Moderate Resolution Imaging Spectroradiometer (MODIS) was launched in 1999 on the Terra satellite. Between 2000 and 2012, 560 MODIS scenes were downloaded  [22]. Data from the first laser-ranging instrument GLAS on board the ICESat 2003-2009 mission is used  [23]. With a 91-day orbit, the ICESat GLA06 v33  [17]  global elevation product is used, prior to the Correction to ICESat Data Product Surface Elevations announced in January 2013  [24].
Manual adjustment of the threshold could achieve a more accurate result in the water delineation  [27, 33, 34]. Co-registered water masks were sorted by decreasing surface order and then superposed. Any incoherencies between consecutive masks, e. g. , when a lower surface contour exceeded a greater surface contour, were binary eroded to leave at least one pixel of spacing between masks. Contours from different images were converted into two datasets: one representing the main water body contours, the other representing major interior details such as islands or sandbars. Moreover, the MODIS instrument provides new remote sensing opportunities for global daily monitoring with improved spatial resolution for land observation at 250 and 500 m and improved spectral band placement  [35]. The MODIS products include seven bands in the visible to middle infrared.
Bathymetry The bathymetry reconstruction was performed with Matlab functions: TriScatteredInterp function  [47] , allowing interpolation on a regular grid from irregularly scattered points, and MeshGrid function for grid meshing  [48]. A 5 × 5 pixel median filter was applied to smooth irregularities and remove artifacts. The bathymetry keeps the same pixel size as input Landsat images. For the purposes of comparison, Lake Poopó hypsometry is estimated directly from bathymetry using Heron's formula  [8]. The methodology presented above allowed us to calculate the Lake Poopo bathymetry and water storage variations as shown in the following section.
Acknowledgements F. B and F. P. thank  M. Bernard  and  S. Majrab  for technical support and  B. Jusserand  for fruitful discussion. F. P. acknowledges funding from C' NANO IDF 2009 (SPIN-WAVEDYN ) and ANR 2007 (GOSPININFO). F. B. is supported by a  Fondation CFM-JP Aguilar grant. I. D'A. acknowledges support from  EPSRC  Grant No. EP/F016719/1  and I. D'A. and F. P. acknowledge support from  Royal Society  Grant No. IJP 2008/R1 JP0870232. C. A. U. is supported by  DOE  Grant No. DE-FG02-05ER46213.
Under this definition, trigger efficiencies can be determined from data using the procedure described in Ref. [11]. The ratio of reconstruction and selection efficiencies between K 0 S → µ + µ -and K 0 S → π + π -decays is evaluated in bins of p T and rapidity of the K 0 S meson using simulated events reweighted in order to reproduce the K 0 S p T and rapidity spectra measured in data  [22]. The reconstruction and selection efficiency for K 0 S → π + π -decays is between 60% and 85% (depending on which point in the phase space a given event is from) of that of the K 0 S → µ + µ -decays due to difference in the material interactions of the pions compared to muons. The factor PID is evaluated in bins of the BDT (both for the TOS and TIS categories) by measuring the muon identification efficiency as a function of p and p T using calibration muons.
• The branching fraction of the normalisation channel B(K 0 S → π + π -) = (69. 20 ± 0. 05)%  [4] ; its uncertainty affects coherently the signal expectations of the forty bins of the analysis. • The absolute TOS efficiency, for which the systematic uncertainty is obtained from the comparison to simulation (around 15%, depending on the BDT bin); no correlation is considered among the different bins. • The effective prescale factor of the MB sample, s MB = (2. 70 ± 0. 76) × 10 -6. The uncertainty is evaluated from the difference between the prescale factor as measured in data and the value of the prescale as set in the trigger system. This systematic uncertainty affects coherently the signal expectations of the twenty bins of the TOS analysis. The leading systematic uncertainties are those coming from the absolute TOS efficiency and s MB factor for the TOS analysis and from the ratio of reconstruction and selection efficiencies for the TIS analysis.
Acknowledgements We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at  CERN  and at the LHCb institutes, and acknowledge support from the  National Agencies: CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  CERN ;  NSFC (China) ;  CNRS/IN2P3  (France);  BMBF ,  DFG ,  HGF  and  MPG (Germany) ;  SFI (Ireland) ;  INFN (Italy) ;  FOM  and  NWO  (The Netherlands); SCSR (Poland);  ANCS (Romania) ;  MinES of Russia and Rosatom (Russia) ;  MICINN, XuntaGal and GENCAT (Spain) ;  SNSF  and  SER (Switzerland) ;  NAS Ukraine (Ukraine) ;  STFC  ( United Kingdom );  NSF (USA). We also acknowledge the support received from the  ERC  under  FP7  and the  Region Auvergne.
Since we are lacking ice thickness information, the investigation is limited to the determination of area fluxes derived from state-of-the-art satellite ice motion and ice concentration information. Below we examine how the ice export in the Laptev Sea changed throughout the past two decades and identify the governing mechanisms controlling ice exchange with the surrounding seas. Furthermore, we investigate the role of atmospheric forcing for observed trends in ice transport. The structure of the paper is as follows. A description of the satellite data used to derive ice fluxes along the Laptev Sea boundaries is given in Sect. 2. In addition, the accuracy of the dataset is determined through a comparison with high resolution satellite data and mooring observations. In Sect. 3, we analyze seasonal and interannual flux rates across boundaries and identify the origin of sea ice leaving the Laptev Sea in late winter.
Dataset description and validation The ice area flux out of the Laptev Sea into the Transpolar Drift and East Siberian Sea is calculated based on ice drift velocities and ice concentration at the northern boundary (NB) Taymyr polynya and the North-Eastern Taymyr (NET) polynya. Color coding corresponds to the sea ice concentration as obtained from Advanced Microwave Scanning Radiometer (AMSR-E) on  May 7, 2008 (source: IUP Bremen, Spreen et al. (2008a) ). The position of the moorings used for satellite ice motion data validation are indicated by black stars. and eastern boundary (EB) of the study region (Fig. 1 ). The NB spans a length of 700 km and is positioned at 81 • N, between Cape Arkticheskiy and 140 • E. The EB, with a length of 460 km, connects the eastern end of the NB with Kotelnyy (76.
Sea ice drift dataset Sea ice drift can be estimated using various techniques between time lag satellite images (e. g. Emery et al. , 1991; Kwok and Rothrock. , 1999; Girard-Ardhuin and Ezraty, 2012). Ice drift information obtained from satellites has been applied in various studies to examine Arctic-wide spatial trends in sea ice drift velocity and area flux (e. g. Kwok, 2009; Spreen et al. , 2011). In particular during winter months, when the atmospheric moisture content is low and surface melt processes are absent, the quality of the drift estimates is high. Restrictions may arise from the spatial resolution of the sensors in near-shore regions characterized by a complex coastline, extensive fast ice areas, and polynyas. The accuracy of ice drift data in the Laptev Sea was investigated by  Rozman et al.
(2011)  through a comparison with in situ measurements. The best performance was found in the ice drift product provided by the European Space Agency (ESA) via the Center for Satellite Exploitation and Research (CERSAT) at the Institut francais de recherche pour l'exploitation de la mer (Ifremer), France. Hence, in the following the Ifremer data set is used to calculate winter fluxes at the NB and EB. The motion fields are available on an operational level from 1 September until the end of May, cov-ering the period from 1992 to present. They are based on a combination of drift vectors estimated from scatterometer data (the National Aeronautics and Space Administration SeaWinds/QuikSCAT for 1992-2009 period, and the European Advanced Scatterometer (ASCAT)/MetOp for 2009 to present) and radiometer data (the 85 GHz channel data of Special Sensor Microwave Imager SSM/I on-board the Defense Meteorological Satellite Program, available since 1992).
, 2011b,a). Overall 12 monthly ice drift estimates are available. The comparison with the Ifremer dataset was done by interpolating Ifremer estimates to the locations of SAR ice drift retrieval. The www. the-cryosphere. net/7/1/2013/ The Cryosphere, 7, 1-15, 2013 agreement between Ifremer data and manually extracted Envisat ice drift information is high for both the zonal (U ) and meridional (V ) ice drift components (correlation coefficient R = 0. 87 and 0. 95, Fig. 2 ). The estimated standard deviation (SE) for the Ifremer ice drift velocity is 0. 56 cm s -1 for V and 0. 6 cm s -1 for the U drift components. For comparison, the uncertainty in ice drift velocity reported by  Rozman et al. (2011)  is around 1 cm s -1 for the Laptev Sea. As stated earlier, the accuracy of passive microwave drift products may be reduced in near-shore areas.
However, in Sect. 3 we identify the origin of sea ice leaving the Laptev Sea in late winter by means of a backtracking approach. Therefore the quality of the Ifremer data in shore areas is checked through a comparison with ice drift estimates taken from long-term moorings equipped with Acoustic Doppler Current Profilers (ADCP). The moorings were deployed between 2007 and 2009 near the fast ice edge in the southeastern Laptev Sea (for exact position see Fig. 1 ). The sampling frequency of the device is 30 minutes with a beam width (footprint) of 5 m. An in-depth description of the mooring design and ADCP processing is given in  Hoelemann et al. (2011)  and  Janout et al. (2012). In prior comparisons, the monthly mean ice drift was calculated from the ADCP data, and Ifremer drift data was interpolated to the mooring position.
Results We present ice flux estimates at the northern and eastern Laptev Sea boundaries computed from Ifremer ice drift information. In order to assess the relative consistency and to quantify the importance of Laptev Sea ice export for the total Arctic sea ice budget, the results are compared with flux estimates from NSIDC drift data  (Fowler, 2003)  and export rates through Fram Strait  (Smedsrud et al. , 2011). In the following, we analyze the interannual and seasonal variability of meridional and zonal area fluxes through NB and EB, and relate the observed fluctuations to changes in sea level pressure (SLP) gradients across the boundaries. Finally, the origin of the sea ice contributing to the annual export is examined through a backtracking approach of sea ice leaving the Laptev Sea in early spring.
The correlation coefficient between summer ice coverage and winter ice area flux (February to May) is R = -0. 65. The figure indicates that years of above-average ice export are generally characterized by below-average summer ice extent and vice versa  (e. g. 2004 and 2005). Including ice advection that took place before February results in a weaker negative correlation coefficient. Taking into account early summer ice export rates (e. g. June and July) is not possible, owing to the limited temporal coverage of the Ifremer ice drift dataset. Synoptic scale processes such as cyclones occurring during summer are believed to further accelerate ice retreat and melting (see discussion above). For example,  Maslanik et al. (1996)  report that an increase in cyclonicity in northern Siberia since 1989 coincides with negative sea ice anomalies in the Laptev Sea during the early 1990s.
Acknowledgements. This work was carried out as part of the  Russian-German cooperation "System Laptev Sea ", funded by the  BMBF  under grant  03G0639A  and the  Alfred Wegener Institute. We thank the  European Commission  as part of the project  ArcRisk "Arctic Health Risks: impacts on health in the Arctic and Europe owing to climate-induced changes in contaminant cycling " ( FP7 GA226534 ). Envisat satellite data were obtained through  ESA  Project  EO-9429  " The use of Envisat ASAR imagery for generation and validation of Arctic and Antarctic landfast sea ice masks ". AMSR-E and SSM/I brightness temperatures were provided by the  NSIDC (Boulder, USA). Ice drift information was obtained from  CERSAT/Ifremer (Plouzane, France)  and  NSIDC , respectively. Furthermore, we thank  Charles Fowler  from the  University of Colorado Boulder  for providing an updated version of existing NSIDC drift fields and  Lars Smedsrud  ( University of Bergen ) for Fram Strait outflow estimates.
The upper unit is disturbed and has anomalous organic matter content, grain size and mineralogy. It is interpreted as an earthquake-induced sedimentary event. The 2. 5-m-long AT2007LG core comprises four sequences, and four sedimentary events. Radiogenic 210 Pb and 137 Cs data obtained previously imply that the shallowest event 1 was triggered by the 1939 M = 7. 9 Erzincan earthquake. Radiocarbon dating and correlation to a reference varved record suggest that events 2 and 4 were initiated by the 1668 and 1254 historical earthquakes. Event 3 does not correspond to a large historical earthquake on the North Anatolian Fault.
Our goal was to assess the usefulness of such shallow structural lakes for paleoseismological studies. In the present study, the morphology of Aşağıtepecik Lake and the fault scarps related to the 1939 earthquake rupture are documented through combined sonar data and seismic data. The sedimentary infill of Aşağıtepecik Lake was sampled with sedimentary cores. Gradual and sudden textural and compositional changes in the retrieved sediments were studied with the combination of X-ray imagery, magnetic susceptibility, mineralogy, loss-on-ignition (LOI) analysis, X-ray-fluorescence (XRF) geochemistry, and carbon-nitrogen isotope analysis. The age of the sedimentary sequence was constrained by combining radiocarbon dating, previously published 210 Pb-137 C data  [Boes et al. 2009 ], and correlation with a reference paleoclimatic lacustrine record in central Turkey  [Jones et al.
Sedimentological changes recorded in Aşağıtepecik Lake were further probed using magnetic susceptibility data, which was acquired on all of the cores at 5-mm resolution, using the Geotek multi-sensor core logger of Rhodes Island University (USA). The magnetic susceptibility data and the macroscopic descriptions were used to correlate the different cores (Figure  5 ). To constrain compositional changes, we performed bulk mineralogy analyses using X-ray diffraction (XRD) on 80 samples of the AT2007LG core. To investigate water level changes in relation to regional climate changes, we investigated the organic matter changes related to Phragmites growing along the shoreline, and to Charophyte algae. With the AT2007LG core, we combined LOI data (4 h at 550 ˚C; 135 measurements) with measurements of total organic carbon (TOC), carbon/ nitrogen (C/N) ratios, carbon and nitrogen stable isotopes (60 samples treated with HCl to removed all mineral carbonates before the analysis).
Acknowledgements. This work was carried out in the framework of the  Marie Curie Excellence Grant  Project  Understanding the irregularity of seismic cycles: a case study in Turkey  ( MEXT-CT-2005-025617 :  Seismic Cycles ), centered at the  Seismology Section of the Royal Observatory of Belgium , whom we would like to thank for their support. We are grateful to  Prof. Erhan Altunel  and  Sevgi Altinok  for the assistance in logistics and field work. The seismic lines and the AT2007 cores were retrieved through a fruitful collaboration with EMCOL ( Eastern Mediterranean Centre for Oceanography and Limnology ) and its Director,  Namık Çağatay. We would like to thank  Emre Damci , who helped with the seismic acquisition and coring. We also acknowledge the work of  Xavier Boes , who conducted the analysis of the AT2006 core published in Boes et al.
Database searches and alignments Sequences of Arabidopsis thaliana AP2/ERF genes were retrieved from the TAIR website and the amino acid sequence of the members described by  Nakano et al. (2006)  were used as baits to search the apple genome at the Genome Database for Rosaceae (GDR) using the Batch BLAST search  (Altschul et al. , 1997). In order to retrieve all AP2-containing sequences, the consensus amino acid domain sequence was also employed to search the NCBI database for Malus. The recovered sequences were checked by reverse BLAST analyses against the Arabidopsis genome and sequences failing to retrieve the original bait were eliminated. The presence and the significance of AP2 domains in the recovered apple sequences were determined by SMART analyses  (Letunic et al. , 2008).
Phylogenetic analysis The functionality of Malus genes in comparison to their Arabidopsis counterparts was assessed by genetic distance and phylogenetic studies. Amino acid sequence alignments were performed using ClustalX  (Thompson et al. , 1997). When necessary, alignments were manually adjusted using Lasergene MegAlign (DNASTAR, Madison, WI, USA). Phylogenetic analyses were performed using distance and parsimony methods in the software PAUP* 4. 0b10 (http://paup. csit. fsu. edu/), using the software default parameters. Resampling bootstrap trees containing 1000 random samples were constructed using PSIGNFIT software (http://www. bootstrap-software. org/). Modular functional domains were employed for genetic distance studies for genes previously characterized as having divergent regions and conserved blocks.
Motif analysis and in silico characterization Conserved motifs were further investigated by multiple alignment analyses using ClustalX and the MEME version 4. 7 suite  (Bailey and Elkan, 1994). The presence and sequence conservation of recognizable functional domains was studied employing protein analysis and gene function tools from databases (European Bioinformatics Institute-European Molecular Biology Laboratory -EMBL-EBI; Expert Protein Analysis System -ExPaSy from Swiss Institute of Bioinformatics -SIB; Gene Ontology database -GO; Protein Families database -Pfam).
Prediction of cellular and molecular parameters The physical characteristics; isoelectric point (IP) and predicted molecular weight, of apple ERF sequences were calculated from the deduced amino acid using default parameter of the Lasergene MegAlign software (DNASTAR, Madison, WI, USA). The proteins folding state was predicted by the FoldIndex program (http://bioportal. weizmann. ac. il/fldbin/findex). The prediction of the sub-cellular compartment location of the apple ERF-like proteins was performed using the plant algorithm of the software WoLF PSORT (http://wolfpsort. org/) (Horton et al. , 2007).
Gene expression analysis Qualitative gene expression profiling was performed by in silico analyses of the Malus EST databases using virtual northern blot analyses. The gene of interest was used in queries against reference sequence databases, generating an alignment of the input gene to its orthologs. The resulting alignment was used to find sequences in the entire mRNA input that are specific to the gene (probe). The resulting alignments were collectively used to query the EST database again using BLAST. This heuristic was critical to avoid false-positives, or ESTs from a paralog of the input gene rather than the gene itself. The identity numbers of the ESTs matching the probes were recovered and the databases were used to find the names of the libraries from which those ESTs were derived.
Accuracy of protein AP2/ERF predictions Genome databases employ ab initio protein prediction methods, which are prone to errors. In order to confirm the existence of the identified AP2/ERF proteins, we have checked intron junctions in comparison to the homologs in Arabidopsis and poplar. Subsequently, the predicted mRNAs of all identified proteins were used to query apple EST databases. We found no unrealistic introns and EST sequences matching (e-value lower than 1•e -25 ) all predicted mRNA were found in the databases, except for gene model MDP0000876858.
doi. org/10. 1016/j. scienta. 2012. 12. 017. The folding states of ERF family proteins in apple were predicted by FoldIndex program  (Prilusky et al. , 2005). The majority of the identified apple sequences sharing sequence similarity to ERFs were predicted to be unfolded (59. 1%, 39) under physiological conditions (Table  SIII ). The predicted sub-cellular localization of the ERF proteins identified in Malus was investigated using the software WoLF pSORT  (Horton et al. , 2007). Most of the ERF-like sequences from apple (83. 3%, 55) was predicted to be exclusively nuclear or with ambiguous sub-cellular location for the nucleus and organelle or cytoplasm (Table  SIII ) (Fig. 3 ).
, 2003; Panne et al. , 2004). Thus, the predicted chemical nature of the proteins identified in apple is compatible with a functional role in transcriptional control. The predicted sub-cellular localization of the ERF proteins identified in Malus was investigated using the software WoLF pSORT  (Horton et al. , 2007). The sub-cellular location is an important clue for the biological function of identified sequences, especially for those hypothesized to be involved in gene expression regulation. The majority of the ERF-like sequences from apple were predicted to be exclusively nuclear or exhibiting ambiguous sub-cellular location for the nucleus and organelle or cytoplasm. At this point, the incomplete nature of some of the sequences and the limitations of bioinformatic analyses prevent us from establishing how accurate the predictions are in vivo.
Acknowledgments The authors would like to acknowledge the excellent technical assistance of  Daniela Dal Bosco  and  Iraci Sinski  in plant propagation and acclimation. We also thank  Renata Gava  for fungus cultivation and plant inoculation. This research is funded by an  Embrapa  grant number  02. 07. 05. 001. 00Macroprograma 2 -Agrofuturo  to C. L. G. VQ is recipient of a productivity research grant from  CNPq  ( 307031/2010-1 ).