dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The HR-MAS MRS experiments were performed at the  MR Core Facility ,  Norwegian University of Science and Technology (NTNU). The authors would like to thank  Institute Curie  for providing xenograft tissue samples and  Dr. Kristin Andersen  for her input in discussion of the gene expression data. This study was supported by the  Liaison Committee between the Central Norway Regional Health Authority (RHA)  and the  Norwegian University of Science and Technology (NTNU) , (grant  46021700 ),  South-Eastern Norway Regional Health Authority  ( 2011042 , and  2011097 ), the  Norwegian Cancer Society  ( 2209251 ,  PR-2006-0272 , and  138296 -PR-2008-0108 ), and the  national Functional Genomics Program (FUGE) of the Research Council of Norway  ( 183621/S10 ).
This proved that the breaking mechanisms applied to the various pieces of the HLS Lagrangian are consistent and well accepted by ≈ 40÷ 50 different data sets covering seven physics spectra up to ≃ 1. 05 GeV -and some more vector or pseudoscalar meson decay properties (see  [23]  and previous references herein). As complementary topics, the HLS model has provided  [21, 24]  valuable predictions for the dipion spectra in the η/η ′ → π + π -γ decays, while mixing fit results from the pion form factor and information from the Wess-Zumino-Witten Lagrangians. On the other hand, several results derived using the HLS model  [19, 25, 26]  are found in good agreement with the corresponding pieces of information derived by ChPT or Extended ChPT (EChPT)  [27, 28]  from quite different input data. Stated otherwise, the broken HLS model (BHLS) has successfully passed a large number of tests and has always been found in good correspondence with independent expectations.
The Reference Set Of Data Samples ({R}) In order to work, the BHLS model needs to have fixed (explicit) breaking parameters and coefficients of several subtraction polynomials of pseudoscalar loops, presently a total 6 of 24 quantities. As there is generally no deep theoretical motivation to fix them precisely, almost all parameter values should be 100 % data driven 7. This poses the problem of having a reliable reference set of data samples ({R}) which allows to derive credible parameter values. Because of the intricacy pattern illustrated above, the only way is to perform a global fit of the corresponding set of data samples, fully using the whole information provided with each data sample (in particular, its full error covariance matrix). In this case, as one can minimize a global χ 2 function reflecting at best the whole experimental knowledge, the probability returned by the minimization procedure is already a first (legitimate) tag.
PoS(Photon 2013)048 HLS Based g -2 Evaluations auxiliary tag specific of A. We have chosen χ 2 A /N A , the average χ 2 at minimum for the data subset A; this is requested not to depart "too much" from 1. Table  3  in  [23]  proves that all existing data 8  covering the 5 annihilation channels e + e -→ π 0 /γ, e + e -→ ηγ, e + e -→ π + π -π 0 , e + e -→ K + K -and e + e -→ K 0 K 0 together with all existing scan data for e + e -→ π + π -(referred to as NSK in the following) provide a quite satisfactory "Reference Set of Data Samples" {R} for BHLS. Indeed, submitting {R} to the global fit procedure constructed from BHLS, all parameters of the model yield satisfactory values and the parameter error covariance matrix allows a motivated estimate of the uncertainties of derived quantities. As for the role of the available τ spectra collected 8 by ALEPH, CLEO and Belle, it deserves to remind that they have not mandatorily to be included in {R} in order for HLS to provide fair evaluations of physics parameters like g -2.
Of course, because of the intricacy phenomenon, this input influences in turn the γ -V transitions amplitudes and thus the description of all data contained in {R ′ } or {R ′ } τ. Using, mostly 11 the accepted values extracted from the Review of Particle Properties (RPP)  [43] , BHLS is able to provide an overall satisfactory prediction for the pion form factor F e π (s) as illustrated by Figure  1. As all data from {R ′ } τ submitted to the BHLS fit only cover the energy region from threshold to the φ mass, the leftmost panel in Figure  1  clearly proves that the so-called 9 The data samples formerly collected with the CMD and OLYA detectors at Novosibirsk and reported in  [33]  are also considered; they influence the fits only marginally. 10 As a whole, the CMD-2, SND, CMD and OLYA are referred to as NSK.
The total LO-HVP is derived by adding the contribution of the region [1. 05, ∞] which amounts to 112. 96 ± 4. 13  [23]. All other contributions to g -2 are listed in Table  7  from  [26]. As the BHLS global fit already considers the full experimental error covariance matrices when dealing with data, the uncertainty it provides merges already all reported experimental statistical and systematic errors; there is no way to split up the two pieces. Figure  3 : Values and significance for ∆a µ = a exp µa th µ derived using as π + π -data the samples those listed in the first column; NSK actually contains also the former data samples from CMD and OLYA. On top, the value for our τ+PDG estimate based on the data from ALEPH (A), CLEO (C) and Belle (B). Evaluations derived by other groups are also shown (see  [26]  for references).
Conclusions The most important conclusion which can be drawn from our work is that Effective Lagrangians allow for significant improvements in physics studies, especially for important quantities like g -2. Even if derived within the BHLS framework, some properties are certainly valid within other global frameworks. For instance, because Effective Lagrangians imply physics correlations among the various cross sections, all spectra contribute to the determination of each of the model parameters. Compared to traditional methods which ignore such correlations, this approach is equivalent to having a much larger statistics; in the case of BHLS, this can be estimated to a 3 ÷ 4 times increase, without any increase of systematics, always a delicate matter. Concerning the results more specifically related with the Broken HLS (BHLS) Model, several conclusions have been reached which can be summarized as follows : • The annihilations channels entering its realm, namely e + e -→ π + π -, e + e -→ π + π -π 0 , e + e -→ π 0 γ, e + e -→ ηγ, e + e -→ K 0 K 0 and e + e -→ K + K -have been successfully and simultaneously accounted for -based on ≈ 50 independent data samples -and with quite good fit probabilities.
Fluorescence and confocal microscopy Both fluorescence and confocal microscopes were used to visualize extracted and internalized M. leprae by all amoebae spp. studied. Fluorescence microscopy was performed with an Olympus IX71 microscope (Center Valley, PA) using Retiga 2000R (Qimaging, Surrey, BC, Canada) and Qcolor3 (Olympus) cameras. Qimaging and Slidebook software (Intelligent Imaging Innovations, Inc. , Denver, CO) were used for image acquisition and analysis on a Macintosh G5 dual processor computer (Apple Computer, Cupertino, CA). Confocal microscopy was performed on a Zeiss LSM 510 confocal microscope. To determine the spatial occupancy of M. leprae within amoebae, serial optical sections were imaged of infected amoebae and were taken at 0. 2 nm intervals using a 514 nm excitation laser and 560620 nm emission filters.
The specific sequences of the primers and probe have been described elsewhere  [34]. All reagents used in the TaqMan assay were recommended by the manufacturer (PE Applied Biosystems), including AmpErase UNG enzyme and AmpliTaq Gold DNA polymerase. PCR cycling conditions were 40 cycles with 60uC annealing/extension temperature for 60 seconds and 95uC denaturing temperature for 15 seconds. PCR and data analyses were performed on a 7300 RealTime PCR System (Applied Biosystems, Foster City, CA).
Data analysis Data routinely entered into the National Diabetes Registry were imported into EpiData version 6 (EpiData Association, Odense, Denmark) for analysis. Variables included age, sex, family history, type of DM, blood pressure, body mass index (BMI) and measures of DM control. To analyse changes in selected variables, we constructed a study group of patients who had been in care for at least 2 years and who had visited the clinic between 1 June 2011 and 31 May 2012, the last full year for which data were available. We compared data from their most recent visit with data recorded at the time of their first visit. For DM control, we compared the first and most recent glycosylated haemoglobin (HbA1c) readings in 625 patients for whom at least two readings, taken at least 6 months apart, were available.
The demographic profile and risk factors in people newly diagnosed with DM are included in Table  2. Data on lifestyle factors such as physical activity, smoking, alcohol consumption and diet, were poorly recorded. More than 50% of patients diagnosed with type 2 DM were obese (BMI  30 kg/m 2 ), but a substantial subgroup (n = 575, 12. 8%) had low to normal BMI values (25 kg/m 2 ). Complications associated with DM were common. A relatively high percentage (272/4480, 6. 1%) of type 2 DM patients required limb amputations during the study period, mostly of the lower leg, equating to 30 DM-associated amputations per year. Cataracts were recorded in 3. 3% (149/4480) of patients and retinal abnormalities (combined non-proliferative and proliferative DM retinopathy and maculopathy) were present in 4.
Public Health Action Spectrum of DM in Tonga S47 least 6 months apart. In 625 patients with two HbA1c readings more than 6 months apart, the mean HbA1c value at the most recent reading (10. 7%) was significantly higher than the first reading (10. 1%; P  0. 001). Although more patients were on a combination of oral hypoglycemics and insulin treatment at the time of their most recent visit, its impact on glucose control could not be established. The use of ACE inhibitors and lipid lowering drugs were, however, well documented; 50. 9% were on ACE inhibitors and 1. 1% on lipid lowering drugs at their most recent visit. Data from the National Deaths Registry were reviewed to supplement the limited outcome data contained in the National Diabetes Registry. These indicated that type 2 DM was a common contributing factor (listed as condition 2 or 3) in patients who died due to ischaemic heart disease (33.
Carioca drifters Between 2003 and 2009, 9 autonomous lagrangian CARbon Interface OCean Atmosphere (Carioca) drifters were deployed in the Southern Ocean, acquiring an extensive dataset of 80 537 individual observations (Fig. 1a ). Carioca drifters measure hourly, at a depth of 2 m, CO 2 fugacity (f CO 2 ), sea surface temperature (SST), sea surface salinity (SSS) and fluorescence  (Copin-Mont égut et al. , 2004; Boutin et al. , 2008). Extensive validation of Carioca measurements in the Southern Ocean indicate an absolute (relative) precision close to 3 (1) microatm (see Appendix of  Boutin et al. , 2008). Alkalinity is derived from SSS and SST using  Lee et al. (2006)  formulation; DIC is derived from f CO 2 , alkalinity, SST and SSS using CO 2 dissociation constants and solubility as described in  Boutin et al.
Assessing small-scale variability: SV100km diagnostic We quantify the variability of pCO 2 and DIC at small-scales using the 9 drifters extensive dataset. Carioca drifters sample with a horizontal resolution of 1 to 3 km (drifting speed of ∼ 0. 3-0. 9 m s -1 and hourly sampling), hence distinctly capturing the variability at the mesoscale (∼ 100 km). The contribution of mesoscale was disentangled from the large-scale and seasonal signal also captured by the drifters using a fast Fourrier transform 20-day high-pass filter. This allows to filter out variations with time scales larger than 20 days (seasonal signal) and with spatial scales larger than ∼ 500-1000 km (distance covered with a drifting speed of ∼ 0. 3-0. 9 m s -1 in 20 days). In the following, X denotes the anomaly of the field X associated with small scales obtained with the high-pass filter.
The spatial variability of pCO 2 and DIC at small spatial scale is then evaluated using the diagnostic SV100km that quantifies the spatial variability on scales of 100 km (see Sect. where X (t, 100 km) is the time-series of the high-pass filtered field X included in a box of 100 km × 100 km centered on t. SV100km was also evaluated using a second method combining drifters measurements and satellite observations (see  Sect. 3. 3). This method relies on the frequent linear correlation found locally between DIC and SST on small spatial scales. The existence of "local" relationship between a carbon variable (namely pCO 2 ) and SST was identified by  Chen et al. (2007)  across one eddy in the tropical Pacific. Here, we derive such "local" linear correlations between DIC and SST using 20-day sliding windows covering the complete dataset (Eq.
, 2006; Lovenduski et al. , 2008; Metzl, 2009; Brix et al. , 2013; S éf érian et al. , 2013). The objective here is not to directly explain variations of DIC by variations in SST and fluorescence (Fluo) but to relate the small-scale spatial variability in DIC to either dynamical or biological factors and identify where and when those variations are dominant. Indeed, surface DIC concentrations generally vary in association with physical and/or biological factors, such as vertical and lateral dynamical transport or biological uptake by photosynthesis. The factors driving the variability of DIC at small-scales are likely to be different depending on the region and time of year. We therefore chose not to compute one PCA with the complete Carioca data set. Instead, we compute "local PCAs" over segments of the drifters trajectory to identify the factors driving the variability at small spatial scales.
This approach differs from those of  Dandonneau (1995) ;  Murata (2006)  and  Lohrenz et al. (2009)  that computed one PCA with their complete data set on relatively smaller regions. Similarly to the method used to assess small-scale variability, PCAs are performed on Fluo, SST and DIC (i. e. filtered from variations with scales > 20 days and > 500-1000 km, see Sect. ∼ 80 000 PCAs gives 3 "local principal components" (noted PC1, PC2 and PC3) and the variance explained by each of the PCs. For example, at 11. 3 • W/-45. 3 • N the PCA gives the following result: PC1 = 0. 63083 • SST n + 0. 46049 • Fluo n -0. 62450 • DIC n , PC2 = 0. 30271 • SST n -0. 88713 • Fluo n -0. 34838 • DIC n , PC3 = 0. 460492 • SST n -0. 887132 • Fluo n + 0. 030727 • DIC n , with PC1, PC2 and PC3 respectively explains 73 %, 22 % and 5 % of the variance.
2j ). To strengthen the results obtained with the SV100km derived from time-series, this second estimate is computed using all SST satellite images co-located with drifters trajectories available when the correlation coefficient r 2 exceeds 0. 7 (Fig. 3c ). This estimate confirms the order of magnitude of DIC variability at small spatial scales obtained from the time-series (Fig. 3b ), with typical gradients of 2-10 µmol kg This variability of DIC identified at small spatial scales can be compared to the variability at larger spatial and temporal scales. Large-scale regional and latitudinal contrasts highlighted by the Glodap database primarily arise from the thermal effect on CO 2 solubility, the Revelle factor and the large scale circulation (Fig. 1e ,  Williams and Follows, 2011). DIC temporal variability is dominated by the seasonality arising from the deepening of the mixed layer (ML) in winter and the biological uptake of DIC in summer.
We expect hypotheses 1 and 3 to be relatively valid in the Southern Ocean because of the predominant effect of winter mixing at high latitudes and because most of the profiles used to derive Glodap were sampled between October and April in this region (see Appendix Fig. A4 ). In contrast, hypothesis 2 may introduce lags in DIC seasonality. The reconstructed seasonal DIC interpolated along the drifters trajectories captures most of the low frequency signal observed in-situ by the drifters (dark grey lines vs. colored lines on Fig. 8 ), which suggests that Glodap spatial variability and seasonal variability reconstructed from the ML climatology give a useful estimate of the regional patterns and seasonality of the DIC in the Southern Ocean. Note that part of the signal captured by the drifters however remains unexplained, most probably because of the interannual and small-scale variability, the assumptions made to compute the seasonal cycle (see 3 hypotheses above) and the biases introduced by undersampling in the ML climatology and the Glodap database.
Discussion and conclusions In this study, the pCO 2 and DIC variability at small spatial scale in the Southern Ocean is estimated using an extensive dataset of surface drifters measurements. Typical gradients on spatial scales of 100 km range from 5 to 50 µatm for pCO 2 and 2 to 30 µmol kg -1 for DIC. The result of DIC variability is supported by a second estimate based on SST satellite images and DIC/SST relationships at spatial scales of ∼ 100 km derived from drifters observations. Regions with highest variability of the CO 2 system mostly correspond to regions of high eddy kinetic energy or frontal zones. These estimates of the CO 2 system variability at small-scale are comparable to estimates obtained in the tropical Pacific (up to 30 µatm on scales of 50 km,  Chen et al. , 2007)  northeast Atlantic (up to 25 µatm on scales of 25 km,  Resplandy et al.
The impact of episodic vertical mixing events, such as eddies or storms, on surface pCO 2 has been estimated in  Mahadevan et al. (2011)  using global monthly climatological datasets. They found that vertical mixing events could induce changes in DIC through the vertical input of DIC-rich subsurface waters and the enhancement of biological uptake. In agreement with previous studies based on observations  (Chen et al. , 2007)  and models  (Mahadevan et al. , 2004; Resplandy et al. , 2009)  atively low impact on pCO 2 (< 5 %) due to compensations between biological uptake, DIC input and thermal effects. This study confirms that seasonal variations are the dominant mode of variability of the CO 2 system in the Southern Ocean  (Lenton et al. , 2006). However, we find that small spatial scales structures are a non negligible source of variability for DIC, with amplitudes of about a third of the variations associated with the seasonality (2-30 µmol kg -1 vs.
, 75 000 surface DIC estimates could be computed from the 4. 4 million individual measurements of the global database of Takahashi et al. (2009) after removing non collocated measurements of pCO 2 , SST and SSS and coastal data. Among these data, only few are located in the Southern Ocean, thus limiting the assessment of the seasonal cycle and small-scale variability of DIC. In this work, the seasonal cycle of surface DIC is reconstructed from the annual Glodap data set and a mixed layer climatology. This methodology has been previously used in Mahadevan et al. (2011) at global scale but was not validated. Here, we provide a validation of this method in the Southern Ocean using the months-to year-long high frequency time-series of the nine drifters deployed in the area. The fair agreement Introduction Discussion Paper | Discussion Paper | Discussion Paper |.
BGD Acknowledgements. Support was provided by the  European Integrated  project  CAR-BOOCEAN  and  EU   FP7  project  CARBOCHANGE "Changes in carbon uptake and emissions by oceans in a changing climate ", which received funding from the  European Commission â Seventh Framework Programme  under grant agreement no. 264879. We thank  L. Beaumont  and  T. Danguy  from technical division of  Institut National des Sciences de l'Univers  for supervising CARIOCA assembly and performing thorough CARIOCA calibrations. We.
At least two independent experiments were performed for each set of reaction conditions. Blank experiments with the oxidant using the same experimental conditions except catalyst were performed. Alkene conversion in absence of the complex was 05%. Crystal Data Collection and Refinement. Intensity data of 1 were collected at 293 K using Mo Kα radiation ( = 0. 71069 ¡) with an Oxford Diffraction Gemini diffractometer, while the data collection of 2 was carried out on a rotating anode diffractometer equipped with Kappa-CCD plate and Cu Kα radiation ( = 1. 54178 ¡). CrysAlis RED 41 and Denzo/ Scalepack 42 programs were used for data reduction of 1 and 2, respectively. The structures were solved by direct methods using the program SIR97  43  and SHELXS-97  44  and refined by full-matrix least-squares methods with programs CRYSTALS  45  and SHELXL-97  44  for 1 and 2, respectively.
Analysis and acquisition were performed using Living Image 3. 1 software (Xenogen Corp. ). Images were acquired using 5 min of integration time with a binning of 8 and with the emission filter in the ''open'' mode. All other parameters were held constant. Quantification was performed using a region of interest defined manually (whole mouse or kidneys) and the results were expressed as photons (P) per second (s) per cm 2 per steradian (SR). Non-infected control mice and infected mice were separately imaged in order to prevent hypothetical contamination. At different time points after infection, 50 ml of blood was collected at the retro-mandibular sinus, added to 20 ml of 100 mM EDTA and imaged immediately after addition of 10 ml of 3 mg/ml Dluciferin in PBS, to detect viable bacteria, or 10 ml of a solution of 3 mg/ml D-luciferin in PBS complemented with 1 mM ATP, to detect both live and dying bacteria.
These data suggest that the biological cost of luciferase cassette expression has only minor effects on the virulence of the L. interrogans MFlum1 transformant. To ascertain whether L. interrogans were present in the blood circulation, a 50 ml drop of blood was collected every day post infection, and imaged ex vivo. Data showed that bioluminescent bacteria were indeed present in the blood (Fig. 1D ). The light emitted was proportional to that measured in the whole mouse, showing exponentially growing L. interrogans in the blood between 1 and 3 dpi, which was confirmed by q-PCR data (Fig. 1D ). Interestingly, although the bacteria used for the inoculation glowed equally well with or without ATP (Fig. 1D , lower panel), confirming that they were all alive at the time of inoculation, more light was emitted in the blood in the presence of ATP at 1 and 2 dpi, suggesting the presence of dying bacteria in the blood.
Kidneys are the reservoir of leptospires at the chronic phase Because of the lower sensitivity of live imaging compared to q-PCR data in the kidneys, we wondered if, at the chronic phase, L. interrogans were restricted to kidneys as suggested by the light images of whole mice (Fig. 3B ). MFlum1 infected mice were sacrificed 2 months post infection and their organs, blood and urine collected to analyze ex vivo the presence of leptospires. Ex vivo imaging of brain, lungs, liver, spleen and blood did not reveal any bioluminescence (Fig. 3F ), suggesting a low amount or absence of leptospires in these organs. The lack of bacteria in these organs and blood was further confirmed by q-PCR data (Fig. 3F ). By opposition, L. interrogans were detected in infected kidneys and urine, both by light imaging and q-PCR, as expected (Fig.
Each cross represents an individual mouse. (E) Ex vivo imaging in presence or absence of ATP of half-kidneys from 10 7 MFlum1 infected mice one month post infection. Data are expressed as the mean 6 SEM of average radiance of light measured in photons/ second/cm 2 for 6 half-kidneys for each group. Below are shown corresponding images of the tracking of half-kidney after sacrifice and addition of Dluciferin. On the left is shown the schematic representation of an infected kidney in longitudinal cross section. (F) Ex vivo live imaging (Left Y axis) and the corresponding number of leptospires measured by q-PCR (Right Y axis) of different organs from 10 7 MFlum1 infected mice, one month post infection. Data are expressed as the mean 6 SEM of average radiance of light measured in photons/second/cm 2.