dataesr/scientific-paragraphs-categorization huggingface dataset:
5% trifluoroacetic acid (v/v) (Aldrich, Dorset, UK) and HPLC-grade water as previously described  [8]. Protein mass profiles were obtained using a Microflex LT MALDI-TOF (Bruker Daltonics, Germany) mass spectrometer, with detection in positive ion linear mode at a laser frequency of 50 Hz in a mass range 2-20 kDa. The acceleration voltage was set to 20 kV, and the time of extraction delay was 200 ns. Each spectrum corresponds to the ions obtained from 240 laser shots fired in six regions of the same deposit on the ground plate and acquired automatically using the function of the Flex Control AutoXecute V. 2. 4 software (Bruker Daltonics). The spectra are displayed with Flex analysis v. 3. 3 software and exported to ClinProTools2. 2 and MALDI Biotyper v. 3. 0 (Bruker Daltonics, Germany) for data processing.
The reproducibility of the spectra of each flea group was evaluated using the ClinProTools 2. 2 software. Reference spectra (MSP, Main Spectrum Profile) were generated by the automated function of the MAL-DI-Biotyper software v3. 0 (Bruker Daltonics, Germany) by combining the results of the spectra of at least three specimens per condition in order to create a database. MSP were produced based on an unbiased algorithm based on the peak position, intensity and frequency. Flea spectra were selected based on their reproducibility and intensity for all MS analyses, including database creation. The database was created by selecting the spectra of at least 3 to 14 specimens from infected, exposed and control groups for both trials.
Blind tests The ability of MALDI-TOF MS to distinguish infected, exposed but uninfected, and control fleas was evaluated by blind test analysis. After sorting the spectra, high quality and reproducible MALDI-TOF MS spectra from the half body of 8 control fleas from the first trial (B. quintana) and 2 from the second trial (B. henselae), 8 fleas infected with B. quintana and 7 fleas infected with B. henselae, and 14 fleas that fed on infected blood with B. quintana and 6 fleas that fed on infected blood with Bartonella henselae but tested negative in qPCR, were introduced into our in-house arthropod database (Table  1 ). This database already contains spectra obtained from protein extracts of the cephalothorax and legs of specimens of 4 species of fleas, including Ctenocephalides felis felis, Archaeopsylla erinacei, Xenopsylla cheopis and Ctenocephalides canis, a total of 6 tick species (Am.
pictipes, R. robustus, Panstrongylus geniculatus, Eratyrus mucronatus) and bedbugs (Cimex lectularius)  [8, 10, 12, [19] [20] [21] [22]. Then, after the elimination of poor quality spectra, 33 spectra of infected fleas with Bartonella quintana, 30 spectra of infected fleas with Bartonella henselae, 44 spectra of fleas that fed on infected blood but that tested negative to Bartonella quintana in qPCR, 22 spectra of fleas that fed on infected blood but that tested negative to Bartonella henselae in qPCR, and a total of 34 spectra of control fleas from the first and second trial were queried against the upgraded database (Table  1 ). The results of the database queries are presented as Log Score Values (LSVs) for each spectrum, corresponding to a matched degree of signal intensities of mass spectra of the query and the reference spectra.
MALDI-TOF MS biomarker mass set To appraise reproducibility of the MS profiles, spectra from the three categories (infected, exposed but PCR-negative, and control) were imported in the ClinProTools 2. 2 software for both pathogens (B. quintana, B. henselae). For the determination of discriminating peak masses associated with infection status, we compared the average spectrum of the three categories of fleas. The software was used to generate a peak list for each group in the 2 to 20 kDa mass range and to identify discriminating peaks. Regarding the discrimination between control profiles and B. quintana-infected flea derived profiles, the parameter settings in ClinProTools 2. 2 software for spectra preparation were as follows: a resolution of 800; a noise threshold of 2. 50; a maximal peak shift of 800 ppm and a match to calibrant peaks of 20%.
MALDI-TOF MS detection of Bartonella spp. in fleas All collected fleas were analyzed by MALDI-TOF MS. Spectra analysis with Flex analysis v. 3. 3 and ClinProTools 2. 2 software revealed reproducibility of the profiles within the same category (Figs  1  and 2 ). All profiles were compared using the ClinProTools 2. 2 software to appraise global reproducibility and create an average profile. Visual inspection revealed that some peaks were present in fleas infected with Bartonella quintana and Bartonella henselae profiles, but absent in control and exposed fleas (Figs  3  and 4 ). The Genetic Algorithm tool of ClinProTools software was used to better identify the discriminating peaks between infected and control fleas. Spectra from 33 Ctenocephalides felis specimens infected with Bartonella quintana were compared to 22 control Ctenocephalides felis, same for the second pathogen; spectra from 24 Ctenocephalides felis specimens infected with Bartonella henselae were compared to 12 control Ctenocephalides felis.
The genetic algorithm model displayed 13 peak masses that discriminated control and infected fleas with Bartonella quintana (Table  2 ), and 20 peak masses that discriminated control and infected fleas with Bartonella henselae (Table  3 ), with recognition capability (RC) and cross validation (CV) values of 100% for both comparisons. Bartonella quintana qPCR positive fleas (n = 33), Bartonella henselae qPCR positive fleas (n = 30), fleas exposed to blood infected with B. quintana but PCR negative (n = 44), fleas exposed to blood infected with B. henselae but PCR negative (n = 22) and control fleas (n = 34) were queried blindly against the MALDI-TOF arthropod database upgraded with reference spectra from these 4 category. All fleas from the artificial infection with Bartonella quintana were correctly identified to the species level and MALDI-TOF MS differentiated correctly the three categories of fleas (Table  1 ).
Discussion Recently, MALDI-TOF MS technology has been successfully used for the identification of arthropods such as fleas  [10] , mosquitoes  [20, 23] , Culicoides  [21] , ticks  [19, 24] , sand flies  [25]  tsetse flies  [26]  and triatomines  [22]. The success of this method requires the standardization of sample preparation protocols to allow sharing and comparison of MS reference spectra and results between research laboratories. As is true for each innovative method, application of this tool is best with some limitations, such as the cost of the device and the comprehensiveness of the databases  [23]. Furthermore, some parameters may play a role in the efficiency of MALDI-TOF MS identification, such as the conservation of arthropods after collection  (18). Several recent studies have shown the efficacy of MALDI-TOF in identifying arthropods conserved in alcohol.
Ticks collected in the field in Ethiopia, which were preserved in 70% ethanol for about two years, were correctly identified by MALDI-TOF. More recently, different species of ticks collected on mammals in Mali, also preserved in alcohol, were successfully identified as well by MALDI TOF, after the development of a de-alcoholization protocol providing even better results for MS identification for samples preserved in alcohol  [27]. Arthropod body part selection for MALDI-TOF arthropod identification is based on the comparison of the spectra quality from different parts of the body. Flea reference spectra in our database were obtained from cephalothorax and leg protein extracts  [10]. In this study, half of the body was chosen for MALDI-TOF MS and the other half for molecular biology, based on previous data indicating that the Bartonella species is localized in the infected flea gut tract  [4].
Intraspecies reproducibility and interspecies specificity are then evaluated and the most reproducible spectra are selected to upgrade the database  [8]. After the emergence of this method to identify arthropods, our research has been oriented toward the detection of associated pathogens. Preliminary encouraging work has shown that MALDI-TOF can differentiate Rhipicephalus sanguineus ticks infected or not by Rickettsia conorii, the agent of Mediterranean spotted fever  [12]. It can detect Plasmodium parasites in anopheles  [23]  and Borrelia spp. in Ornithodoros sonrai ticks  [11]  based on spectra obtained from the cephalothorax or legs, respectively. Here, we obtained reproducible and intense spectra from control fleas, and also from fleas infected with B. quintana and B. henselae whose infection status was previously confirmed by quantitative PCR.
5 The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2018-133 Manuscript under review for journal The Cryosphere Discussion started: 27 July 2018 c Author(s) 2018. CC BY 4. 0 License. Grid point comparisons of SIT anomalies among all reanalyses are quantified by RMSEs calculated over an overlapping period of 15 years, from Jan/1993 to Dec/2007. This time span corresponds to the period during which data is available from all reanalyses. Furthermore, as adopted by  Blanchard-Wrigglesworth and Bitz (2014) , only grid points wherein the mean ice thickness at the time of summer minimum is greater than 0. 1 m are taken into account. This condition is valid for all reanalysisbased results, unless otherwise stated. The time scale (or persistence) is derived from individual time series by calculating the lagged autocorrelation stepped forward by one measurement, equivalent to one month.
Second, it is estimated for the long-term (GECCO2 and MOVE-CORE) pan-Arctic ice volume anomalies. For the latter case, we evaluate how stable the e-folding time scale is over time by applying a moving (stepped by 1 month) and length-variable window (from 5 to 59 years). Here, we also investigate whether the moving time scale is marked by significant band(s) of variability. To do so, we applied wavelet analysis as proposed by  Torrence and Compo (1998). The length scales of the SIT anomalies are estimated for the reanalysis datasets. The first step is to determine one-point correlation maps. In other words, we calculate the cross-correlation between the SIT anomaly from each grid point with the anomaly from all other points. Subsequently, we make use of the e-folding reference and, for every map, we select all grid cells with correlation coefficient higher than 1/e.
Comparison of reanalyses with observations The scatter plots shown in Fig. 4  combine SIT from each reanalysis and the observational datasets from all sources. The latter are separated into two parameters: draft (black dots) and SIT (green dots). The comparisons indicate that all reanalyses are significantly correlated to the observations, whether these are draft or SIT. By comparing SIT and draft, four reanalyses have    42 m 2 )  and GECCO2  (1. 27 m 2 ) provide the lowest agreement. When comparing SIT from both datasets, the reanalyses with higher correlation coefficient are PIOMAS (R = 0. 66), GECCO2 (0. 64) and TP4 (0. 61), while ECDA (0. 43),  and MOVE-G2 (0. 30) are the reanalyses with poorest correlation. In terms of linear fit, PIOMAS (MRSS = 0. 41 m 2 ), TP4 (0. 41 m 2 ), GECCO2 (0.
42 m 2 ), ORAP5 (0. 46 m 2 ) and C-CGLORS05 (0. 49 m 2 ) are the best performing predictors (MRSS < 0. 5 m 2 ), while MOVE-CORE (0. 71 m 2 ) and ECCO-v4 (0. 7 m 2 ) provide the lowest prediction capability. In addition, a direct comparison by means of RMSEs indicates which reanalyses are closer to the ensemble of observations, as follows: PIOMAS (RMSE = 0. 7 m), C-GLORS05 (0. 8 m), GloSea5-GO5 (0. 8 m), ORAP5 (0. 8 m), GECCO2 (0. 9 m), GloSea5 (0. 9 m), MOVE-CORE (0. 9 m), TP4 (0. 9 m), m), ECDA (1. 0 m), MERRA-Ocean (1. 0 m), G2V3 (1. 1 m), MOVE-G2 (1. 1 m) and UR025-4 (1. 1 m). For a detailed overview on how each reanalysis is linked to each observational dataset, in terms of RMSE, MRSS and R, the reader is referred to the tables presented in Appendix B.
Comparison of reanalyses to each other As a first assessment of how well the reanalyses compare to each other, we estimate the RMSE and R between time series of SIT anomaly, at every grid point and between all pairs of products. The results are organized as a square matrix in Fig. 5 , where the number on the top of each panel represents the respective global value estimated by considering the data from all grid points. The lower triangular part of the matrix reveals that the smallest RMSE is found for the pair ECDA-UR025-4 (RMSE = 0. 21 m). Only four other pairs present RMSE ≤ 0. 25, they are the match between the two GloSea5 products (0. 23 m), and the combination of UR025-4 against C-GLORS05 and ECCO-v4 (0. 25 m). The largest error takes place when comparing GECCO2-MOVE-G2 (0. 61 m). At the regional scale, most of the pairs of reanalyses have larger differences off the northern Greenland coast and to the north of the Canadian Archipelago, which are more pronounced in the MERRA-Ocean product.
Time scales An important property inherent to time series in general concerns their time scale and/or persistence, as defined in Sec. 2. 3. In other words, we aim to infer for how long the SIT anomaly maintains a good correlation with future measurements at the same grid cell. Persistence can also be perceived as part of a self-prediction scheme where past data is used to predict future values. In addition, it is a relevant variable to be taken into account when designing the sampling frequency of observational programmes, specially if these programmes target the understanding of the SIT variability. Fig. 6  displays the e-folding time scales for the SIT anomaly at every grid point, and for all reanalyses. The Area Weighted Mean (AWM) time scales (in months) sorted in ascending order are: 2.
6  shows that GloSea5 and GloSea5-G05 are the only reanalyses in which the SIT anomaly persistence is remarkably short all over the Arctic, presenting e-folding time scales higher than 4 months only in a few, not 11 The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2018-133 Manuscript under review for journal The Cryosphere Discussion started: 27 July 2018 c Author(s) 2018. CC BY 4. 0 License. evenly distributed, grid points. By contrast, the SIT from GECCO2 has a marked longer persistence (>15 months) extending from the region off the northen coast of Greenland to the north of the Canadian Archipelago and mid Arctic Ocean. The ECDA product presents a relatively similar pattern of time scale over the region mentioned above, but persisting for a shorter period (∼8 months). SIT anomalies from MOVE-G2 also indicate long persistence off the northern Greenland coast, extending to the central Arctic and East Siberian Sea.
Discussion and conclusions The first aim of this study was to evaluate how the SIT from the reanalyses compares against observational datasets, either draft or SIT. We have used three different metrics to perform this comparison: the correlation coefficient (R), as a measure of the linear correlation between datasets; the Mean Residual Sum of Squares (MRSS), as an indicator of whether reanalysis values are good predictors for the observations; and the Root Mean Square Error (RMSE), which directly compares how the SIT from the reanalyses approaches the SIT from observations. The results show that some of the reanalyses have a relatively good correspondence either comparing SIT and draft or SIT from both sources of data. This is the case, for instance, for the TP4 product. A direct comparison between SIT from both sources indicates RMSEs ranging from 0.
7 to 1. 1 m. PIOMAS has the best agreement with observations. An interesting case is GECCO2, which presents a relatively small RMSE, as well as good correlation and linear relationship to the SIT observational datasets. However, this same product is weakly correlated and has poor predictive skill to the draft observational datasets. One of our main goals in performing such a comparison was to identify whether or not systems built with assimilation of sea ice concentration data are closer to observations, compared to the products built with no sea ice data assimilation. The results are not conclusive and reanalyses with sea ice data assimilation do not necessarily perform better. One could speculate that some reanalyses do not take the best advantage of the covariances between sea ice concentration and SIT.
Another main goal of this work was to characterize the time and length scales of the sea ice thickness, as well as to report whether these parameters are influenced by the fact that a respective reanalysis assimilates or not sea ice concentration data. In this case, sea ice data assimilation plays a clear role in the referred scales: systems with sea ice data assimilation are 5 characterized by shorter time and length scales compared to the systems which do not assimilate sea ice data. Likely, the main reason why this happen is linked to the fact that when a reanalysis assimilates sea ice information, the system is forced towards the assimilated conditions, differently from what occurs with free-running models. Eventually, data assimilation introduces SIT increments that are not necessarily physical, and so contributes to an attenuation in the correlation of this variable at a certain grid cell both in time, with their future estimations, and in space, with the neighboring grid points.
Fig. 11  shows an interesting 10 example where the time scales are estimated for the same system, but built with (G2V3) and without (G2V1) assimilation of sea ice concentration. Again, the results show relatively longer time scales for the system with no data assimilation. The time scales have a marked anti-correlation with the sea ice velocities as shown in Fig. 12 : reanalyses with faster sea ice present a short time scale and the other way around. The same is observed for the length scales, though not so marked as 18 The Cryosphere Discuss. for time scales (not shown). Different specifications from the reanalyses influence the sea ice velocity. High air-ice and low ocean-ice drag coefficients contribute to faster ice velocities, respectively  (Tandon et al. , 2018). For instance, ECCO-v4 has the second highest air-ice (smaller only compared to MOVE-CORE) and the smallest ocean-ice drag coefficients (see Table  1  from  Chevallier et al.
Acknowledgements. The work presented in this paper has received funding from the  European Union 's  Horizon 2020 Research and Innovation programme  under grant agreement No. 727862 :  APPLICATE  project (Advanced prediction in Polar regions and beyond). The python wavelet software is provided by  Evgeniya Predybaylo  based on Torrence and Compo (1998) and is available at URL: http://atoc. colorado. edu/research/wavelets/.
Seven draft datasets were sampled by means ; Rothrock and Wensnahan (2007)), " Beaufort Gyre Exploration Project" (BGEPScience (IOS) -Eastern Beaufort Sea" (IOS-EBS) and "-Chuck Sea (IOS-CHKSea " (AWI-GS; Harms et al. (2001)), " Bedford Institute of Oceanography Lancaster Sound " ((2008)) from  National Aeronautics and Space Administration (NASA)  and the radar altimeter-derived " CryoSat satellite data " (CryoSat-AWI; Ricker et al. (2014)) from the  European Space Agency (ESA). 2.
The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2018-133 Manuscript under review for journal The Cryosphere Discussion started: 27 July 2018 c Author(s) 2018. CC BY 4. 0 License. RMSE Root Mean Squared Error SIT Sea Ice Thickness GMAO TP4 Global Modeling and Assimilation Office TOPAZ4 GS UK-Subs-AN UK Navy Submarines -Analog Greenland Sea GSFC UK-Subs-DG UK Navy Submarines -Digital Goddard Space Flight Center G2V3 GLORYS 2 -version 3 ICESat Ice, Cloud,and land Elevation Satellite IOS Institute of Ocean Science JMA Japan Meteorological Agency JPL Jet Propulsion Laboratory MERRA Modern Era Retrospective-Analysis for Research and Applications ARC MFC Arctic Marine Forecasting Center MIT Massachusetts Institute of Technology MOVE-CORE Multivariate Ocean Variational Estimation -Coordinated Ocean-ice Reference Experiment MOVE-G2 Multivariate Ocean Variational Estimation -Global version 2 MRI Meteorological Research Institute MRSS Mean Residual Sum of Squares NA v No data Assimilation NASA National Aeronautics and Space Administration NOAA National Oceanic and Atmospheric Administration NPEO North Pole Environmental Observatory ORA-IP Ocean Reanalysis Intercomparison project PIOMAS Pan-Arctic Ice-Ocean Modeling and Assimilation System R Correlation Coefficient 23 TableB2.
Radial velocities (RVs) have been measured for each individual spectrum from the position of several metallic lines using the code DAOSPEC  (Stetson & Pancino 2008). After the correction for the corresponding heliocentric velocity, the spectra of each star have been coadded together and used for the chemical analysis. Typical SNR per pixel are 70-100 for HR12, 120-150 for HR13 and 180-230 for HR15N. We excluded from the following chemical analysis 6 stars with RV not compatible with that of Omega Centauri and 4 stars with RV dispersion larger than 1. 7 km/s (while all the other targets have RV dispersions significantly smaller than 1 km/s) and considered as candidate binary stars. Finally, we derived chemical abundances for a total of 201 member stars of Omega Centauri. All the main information (coordinates, magnitudes, RVs) of the member stars are listed in Table  1.
Chemical analysis Effective temperatures (T eff ) and surface gravities (log g) have been derived from the photometric information. T eff have been obtained by averaging T eff derived from (B -V) 0 , (V -I) 0 and (V -K s ) 0 from the Alonso, Arribas & Martinez-Roger (1999) transformations. B, V and I magnitudes are from  Bellini et al. (2009) , K s magnitude from 2MASS database  (Skrutskie et al. 2006). Because the Alonso, Arribas & Martinez-Roger (1999) calibration adopted the Johnson I-filter, the Cousin I-band magnitudes by  Bellini et al. (2009)  have been transformed to the Johnson photometric system adopting the transformation provided by  Bessell (1979). The K s 2MASS magnitudes have been transformed to the Telescopio Carlos Sanchez photometric system (used by Alonso, Arribas & Martinez-Roger 1999) by means of the relations by  Carpenter (2001)  and  Alonso, Arribas & Martinez-Roger (1998).
Gravities have been derived through the Stefan-Boltzmann relation, adopting the photometric T eff described above, a true distance modulus of (m -M) 0 =13. 70 mag  (Bellazzini et al. 2004 ), the bolometric corrections calculated according to  Alonso, Arribas & Martinez-Roger (1999) , while the evolutive mass have been estimated according to a grid of theoretical 2 http://www. eso. org/sci/software/pipelines/ isochrones of different metallicities from the BaSTI database  (Pietrinferni et al. 2006). Microturbulent velocities (v t ) have been derived spectroscopically by erasing any trend between the line strength and the iron abundance using ∼35-40 Fe I lines. Chemical abundances for Fe and Al have been derived with the code GALA  (Mucciarelli et al. 2013)   3  by comparing the theoretical and measured equivalent widths (EWs) of unblended metallic lines.
The adopted model atmospheres have been calculated with the last version of the code ATLAS9 4  (see  Sbordone et al. 2004; Kurucz 2005). EWs have been measured with the code DAOSPEC  (Stetson & Pancino 2008 ) managed through the wrapper 4DAO (Mucciarelli 2013) 5. For some stars, Al lines are too weak to be measured and we computed upper limits adopting as EW 3 times the uncertainty calculated according to the  Cayrel (1988)  formula. Abundances for Li and Na have been derived through a χ 2minimization, performed with our own code SALVADOR, between the observed and synthetic spectra. The synthetic spectra have been computed with the code SYNTHE  (Sbordone et al. 2004; Kurucz 2005) , including all the atomic and molecular transitions from the Kurucz/Castelli linelist. The Li abundances have been derived from the Li resonance doublet at ∼6708 Å , including the corrections for non-local thermodynamical equilibrium from  Lind et al.