dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, the scans were batch analyzed using Medimaps TBS clinical Data Analyzer 2. 2. 1. Results: We found that three subjects had to be excluded because of an anatomic anomaly. The TBS precision errors (%CV) for GE and Hologic systems were similar but significantly different: 1. 74% vs. 1. 97%, p<0. 05. The Spearman Rank correction between GE and Hologic TBS scores was rZ0. 73 but with significant slopes and intercepts in the Deming regression relationship (TBSHologic Z 0. 86*TBSGE+0. 20). Using the same scans and analysis, the BMD precision was 1% and the intermachine BMD agreement was rZ0. 99.
For reference we compared this to scan-rescan precision. Methods: To simulate the scan localization process, we recreated the graphical user interface (GUI) of the HR-pQCT acquisition software (XtremeCT, Scanco Medical AG), specifically the scout view visualization and reference line positioning components. Eight operators, from eight centers performed the landmarking procedure in a set of 56 scout images of the radius and tibia. The scouts corresponded to in vivo exams with double-stack (220 slice) acquisitions. Based on the position of each operator's reference line, standard-size acquisition subvolumes (110 slices) were extracted for analysis to determine the impact on bone parameters. Specifically, we evaluated: 1) Intra-operator short-term precision for 8 operators on a set of 15 scout images, repeated thrice; 2) Intra-operator long-term precision (6-24 months) for 2 operators; and 3) Inter-operator variability for 8 operators on 56 scout images.
Apoptosis induction was also investigated via annexin V staining. Figure  8  and Tables  1  and 2  shows same results displayed in Figure  7. The percentage of annexin V-positive cells significantly increased in treated cells with BIBR 1532 without glucose compared to the control cells. These data suggest that the growth inhibitory effect of BIBR 1532 on MDA-MB 231 cells is partly due to its effect on apoptosis induction under restricted glucose conditions. In the MCF-7 cell line, high concentrations of BIBR 1532 did not elicit apoptosis as observed in MDA-MB 231, unless cells incubated in high glucose concentration (4. 5 g/L) are treated with 50 μM BIBR 1532, as shown in Figure  8b  and Tables  1  and 2.
The choice of the optimal cut-off level and number of cluster is not straightforward here, because the distance matrix (OD) is not computed with a distance metric applied to a given dataset. Thus, usual criteria based on dataset variance within clusters cannot be used (e. g. [30] ) because there is no dataset. Instead we took a simple approach comparing results from Ward and flexible linkage. For each partition into n clusters, we compute the proportion of cells classified in the same cluster with Ward and flexible (see Appendix S2). This proportion increases from 82%, to 88% from for n = 2 to n = 6 clusters, then drops to values ,70% for n. 6. Therefore we consider that the optimal cluster number is 6 as it gives more information while keeping consistent results among the two linkages. However, as no absolute criterion is available, we show the maximum number of clusters that we can interpret, which is 22 clusters.
Perspectives The regionalization proposed here will eventually be compared to an ongoing biogeochemistry-based regionalization  [60] , and to zooplankton species distribution as available in database COPE-PODS  [61]. Concerning the methods, several points can be made. With a similar approach but shorter simulations, we can explore the seasonal variability of clusters boundaries that may be significant  [62]. Here we used hierarchical clustering to extract clusters from the oceanographic distance, but clusters could also be computed with other methods such as graph theory that uses the asymmetry of the connectivity matrix (e. g. [49, 63] ). Finally, this method was applied to the Mediterranean Sea but it can be applied anywhere, at any spatial scales as long as accurate and long term model velocity outputs are available.
The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under Grant Agreement No. 287844  for the project '' Towards COast  to  COast NETworks  of marine protected areas (from the shore to the high and deep sea), coupled with sea-based wind energy potential'' (COCONET). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
5 mg/kg every 15 min), resulting in a cumulative dose of 10 mg/kg. The MPG and BPG sequences were acquired before USPIO injection and 5 min after the 4th injection. The diffusion signals from each animal were measured individually in ROIs (Fig. 1b ) located in the substantia nigra (SN), globus pallidus (GP), thalamus (Th) and cortex (Cortex) drawn using MRIcro software  (Rorden and Brett, 2000) , avoiding cerebrospinal fluid filled spaces (visible on the b = 0 diffusion images and the anatomical T1-weighted images).
In order to overcome those pitfalls we have introduced a new general framework for analysis of non-Gaussian diffusion and IVIM (intravoxel incoherent motion) MRI. Instead of fitting the signal data with the diffusion model of Eq. (3) using the standard iterative (fitting) search approach, we directly derive the parameters by comparing the raw signal data acquired at all b values with those of a database of simulated signals built once-for-all for the complete study using an exhaustive set of parameter combinations. A distance is calculated between the measured signal attenuation profile, S(b) and each simulated signal, S db(i) (b) of the database (raw signals were normalized to S(b = 0) = 1000 to have a single database for the whole study). The parameter combination, P i , giving the shortest distance, d i , is deemed to represent the searched parameter estimates.
This approach not only alleviates the issues of local minima and sensitivity to initial values of the iterative approach, but is also much more efficient (hence, faster) in terms of computing requirements, as only a simple distance needs to be calculated while the iterative method requires many complex calculus elements (such as those present in Eq. (  3 )) to be performed for each iteration. The distance was defined as the mean square error  (Park et al. , 2003) : d i ¼ 1=n ð Þ: X b S b ð Þ-S db i ð Þ b ð Þ h i 2 ð5Þ where n is the number of b values. The smallest d i value, d min , was kept as an index of the matching goodness between the best simulated signals and the measured signals. An adaptive database was built with the following parameter ranges for the monkey experiments: 980 ≦ S 0 ≦ 1020 (step 10, signal was normalized to 1000), 0.
0 ≦ K ≦ 0. 40 (step 0. 05), 0. 42 ≦ K ≦ 1. 8 (step 0. 02) and 0. 1 × 10 -4 ≦ D (D′) ≦ 25 ×10 -4 (step 0. 1 × 10 -4 ). The database, thus, consisted in 98,750 simulated signals taking into account all parameter combinations and b values. For the phantom experiment, we used a more accurate database for D (step 0. 05 × 10 -4 ) with identical ranges and steps in the others, resulting in 197,105 signals. Parameters were estimated not only in the selected ROIs, leading to ξ [Fe] according to Eq. (  4 ), but also on a voxel-by-voxel basis to get parametric maps of ξ  [Fe]. Finally, maps of R2 changes, ΔR2, following the USPIO injection were also calculated from the changes of S 0 estimated from the BPG images: ΔR2 ¼ ln S 0post =S 0pre h i =TE ð6Þ where S 0 pre,/post are the S 0 values before and after USPIO injection.
Those maps were obtained only to allow a qualitative comparison with Δξ [Fe] maps, as TE was not optimized for detection of R2 effects. All codes for the analysis were implemented on MATLAB (The MathWorks, Natick, MA, USA). After checking the equivalence of the MPG and BPG sequences to evaluate diffusion parameters (Supplemental Fig. S1 ) and to further increase robustness, especially at voxel level, parameters D and K were first estimated using BPG data, then D′ was estimated using MPG data, fixing K to the value obtained with the BPG data.
(  5 ). The estimated parameters in the selected ROIs were examined using non-parametric statistical tests (Kruskal-Wallis one-way ANOVA, Mann-Whitney U test and Wilcoxon signed-rank test). The relationship between ξ [Fe] and the estimated iron concentration was tested using a linear regression. All statistical analysis was performed using MedCalc (MedCalc Software, Ostend, Belgium) with alpha level = 0. 05.
In vivo experiments Plots of ln(S) versus b (Fig. 2b ) show that the signal remained above noise level in all regions (average SNR at b = 3000 s/mm 2 was 20 for both BPG and MPG signals with a minimum value of 14 after USPIO injection). Typical maps from the BPG sequence (Monkey B) are shown in Fig. 3  revealing good contrast between anatomical structures. Asymmetry for D and K in white matter between left and right hemispheres reflects diffusion anisotropy. Estimated diffusion parameters (BPG sequence) for each animal are shown in Table  1. Mean values were D = 6. 7 ± 0. 88, 7. 3 ± 1. 1, 7. 3 ± 0. 40, and 8. 8 ± 0. 24 × 10 -4 mm 2 /s, and K = 1. 16 ± 0. 17, 1. 26 ± 0. 17, 0. 88 ± 0. 09, and 0. 82 ± 0. 02 in SN, GP, Th and Cortex, respectively. The matching goodness between the raw signals and the database signals was excellent (average minimum mean square error (Eq.
, 2005). Indeed, effects of USPIOs are much larger than those of endogenous deoxyhemoglobin. Iron contrast agent injection is becoming a standard over BOLD for fMRI experiments performed in animals, especially non-human primates  (Leite et al. , 2002; Zhao et al. , 2009; Autio et al. , 2011; Mandeville, 2012)  The present approach has the potential to detect and quantify variations in blood volume in the cortex upon brain activation status, through variations in ξ  [Fe]  reflecting variations in USPIO concentration. An important methodological point in our study is that we have switched for the standard fitting approach, which analyzes MRI and especially diffusion MRI data, to an exhaustive search approach using a unique database. The standard, iterative approach, indeed suffers from some pitfalls, notably its instability in the estimated parameters, especially when the number of parameters to estimate increases and the data become noisy.
Improvements have been suggested  (Hahn et al. , 2013; Yuan and Zhang, 2013) , but results may depend on the presence of local minima and the initial values set for each parameter to estimate. With the new approach there is no requirement for initial values and minima between raw and simulated signals are search over the whole ranges of parameter values. Although, it was not the aim of this study to evaluate the gain in calculation speed over the iterative approach, the exhaustive search approach has also a clear advantage in calculus efficiency (and analysis time), because only simple quadratic distances have to be calculated. The complex simulated signals are determined in advance, once-for-all for the whole study, even if the database is large. In the future, the approach may benefit from some tuning in the definition (and the resulting size) of the database (ranges and steps for the values for each parameters, which can be adaptive, with finer steps near the range centers), to get the best compromise between accuracy and computing speed.
Conclusion Diffusion MRI is sensitive to the presence of iron deposit in tissues and we have shown that it can be used to quantify iron and get maps of iron content in the macaque brain with good accuracy. The method will benefit clinical investigations on the effect of iron overload in some brain structures which have been suspected to induce neurodegenerative disorders, such as PD. Furthermore, diffusion MRI can also be used in the context of fMRI to quantify USPIOs when administered as blood pool contrast agents to estimate variations in the local blood volume induced by brain activation. The method will probably benefit from ultra-high field MRI systems  (Tani et al. , 2011). Finally, a new procedure was introduced to estimate diffusion parameters from a database, a significant shift from the usual data fitting approach.
Acknowledgments We would like to thank  Dr Hauke Kolster  for providing the customized 4-ch coil dedicated to macaque MRI, and  Morgan Dupont  and  Wilfried Pianezzola  for their help with the experiments. This study was supported in part by grant from  The Uehara Memorial Foundation (Tokyo, Japan ) (201140299) for a research fellow (S. F. ). And we acknowledge the support of  Dr. Kuniaki Ogasawara ,  Dr. Akira Ogawa  and the colleagues of  Department of Neurosurgery in Iwate Medical University.
using the SPAdes software  (12, 13). Contigs obtained were combined by using SSPACE  (14)  and Opera software version 1. 2  (15) , helped by GapFiller software version 1. 10 (  16 ). Some manual refinements using CLC Genomics version 6 software (CLC bio, Aarhus, Denmark) and homemade tools improved the genome. It was found that the M. cosmeticum draft genome consists of five contigs without gaps, containing 6,462,090 bp and a GϩC content of 68. 24%. Noncoding genes and miscellaneous features were predicted using RNAmmer  (17) , Aragorn  (18) , Rfam  (19) , and Pfam  (20). Open reading frames were predicted using Prodigal  (21) , and functional annotation was achieved using BLASTp against the GenBank database (  22 ) and the Clusters of Orthologous Groups (COG) database  (23, 24). Using these tools, it was found that the M.
cosmeticum genome contains Ն75 predicted RNAs, including six rRNAs, 53 tRNAs, one transfer-messenger RNA, and 15 miscellaneous RNAs. A total of 6,281 genes were also identified, representing a coding capacity of 5,995,551 bp (coding percentage, 92. 7%). Among these genes, 926 (14. 74%) were found to encode putative proteins and 1,033 (16. 44%) were assigned as genes for hypothetical proteins. Moreover, 6,211 genes matched at least one sequence in the COG database using BLASTp default parameters. Nucleotide sequence accession numbers. The M. cosmeticum strain DSM 44829 genome sequence has been deposited at DDBJ/ EMBL/GenBank under accession no. CCBB010000001 to CCBB010000005.
Statistical Analysis Data analysis involved use of Systat 9 software (SPSS Inc. , Chicago, IL, USA). Quantitative variables were described with median [inter-quartile range (IQR)] and qualitative variables with counts and percentages. To identify parameters associated with clinically significant symptoms of depression and anxiety, demographic, clinical and functional characteristics were compared between patients with or without clinically significant symptoms of depression and between patients with or without clinically significant symptoms of anxiety, respectively. For bivariable analysis, comparisons involved Pearson Chi-Square test for qualitative variables and Wilcoxon test for quantitative data. Bonferroni adjustment was used for multiple comparisons  (40 comparisons) ; therefore a p value less than 0.
Other clinical diseaserelated variables related to pain (myalgia and arthralgia) or that yielded a p value less than 0. 15 on bivariable analysis were also included. On step 3, we added SF-36 physical component score and function-related variables that yielded a p value less than 0. 15 on bivariable analysis. SF-36 mental component scores were not included in the model because of obvious strong association with both clinically significant symptoms of depression and anxiety that could mask other associations. MHISS and CHFS were not included because of missing data for more than half of patients. Entered variables were selected using backward stepwise regression with values of 0. 15 to enter in the model and of 0. 15 to stay. A p value less than 0. 05 was considered significant in the final multivariable model.
Among all clinical parameters evaluated, only general health status assessed by KPS score was found significantly decreased in patients with clinically significant symptoms of depression. Originally developed for cancer patients, because it strongly predicts cancer outcome  [25, 56] , the KPS score is also used in assessing acute or chronic conditions  [57, 58]. As for cancer, our data support the use of the KPS score to predict outcome in SSc, because in addition to providing clinical estimates of patient's physical state, performance, and prognosis, it is also associated with clinically significant symptoms of depression. Our work has limitations including its cross-sectional design and the use of a self-report questionnaire for clinical features. Furthermore, our study did not assess association with social or economic status and was not designed to explore the cause for the observed differences, since the concurrent assessment of both outcome and associated variables did not allow for the evaluation of pathways of influence.
This analysis showed no significant effect of seawater pCO 2 on any of these parameters and pCO 2 was not correlated to the composite axes 1 and 2. Most of the variance in the dataset was explained by the depth at which parameters were measured in the sediment with significant decreases of N tot as well as of Chl a, b and c with depth, because of active diagenesis. Results from the flux measurements during the 24 h incubations are shown in Fig. 5. At all pCO 2 levels and for all incubations, the sediment was a source of A T , C T and NO 2 2 and a sink of DO for the overlying water. The sediment was also a source of NH 4 + and NO 3 2 for the overlying water, except for one incubation at 1120 matm and at 3000 matm for NH 4 + and NO 3 2 respectively. The sediment was most of the time a source of Si except at the start of the experiment at 540 and 760 matm.
Acknowledgments We thank  H. Findlay  and  H. Wood  for setting up the mesocosm system,  M. -D. Pizay  and  J. -P. Gattuso  for providing carbonate data,  M. Schwanitz ,  S. Treydte  and  J. Mo  ¨nnich for collecting the sediment cores as well as three reviewers for their constructive comments.
This research has received support from the  Netherlands Organization of Scientific Research , the  Darwin Centre for Biogeosciences  and is a contribution to the '' European Project on Ocean Acidification '' (EPOCA) which received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement nu  211384. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
An in-house LabView software (version 8. 6 or higher) controlled the presentation of odors, pictures and circles and recorded the participants' responses and breathing throughout the experiment. The participants were requested to breathe normally and avoid sniffing behaviors (Figure  2 ). To interact with the software, the participants used a trackball (Kensington, Redwood Shores, CA, USA). When the participants clicked on a circle, the odor stimulus was delivered at the beginning of the subsequent expiration, enabling the odor to be perceived at the beginning of the next inspiration (on average 2 s later). The volume, amplitude and duration of each inspiratory cycle were recorded, and the respiratory frequency was calculated.
Encoding For each participant, the number of clicks was computed per odor. For each odor, the time periods between two consecutive clicks (delay) were measured, and the mean delay was then determined. The time window between the two clicks served as the time frame for the analyses of breathing parameters (e. g. , the volume, amplitude and duration of the inspiratory cycles and the respiratory frequency). The influence of the odor characteristics (intensity, pleasantness and familiarity) on the behavioral and physiological (breathing) data was tested. The relationship between the encoding and the retrieval was investigated by analyzing the behavioral and physiological data during the encoding as a function of the subsequent memory performances.
STATISTICAL ANALYSIS Behavioral and physiological data were z-scored [(x -μ)/σ] at the individual level to remove bias based on inter-individual differences. The number of each response given during the odor recognition and episodic retrieval tasks was further normalized by the number of trials after removal of one odor a posteriori from the data ("Odor intensity, pleasantness and familiarity"). The statistic main effects of the factors and interactions were determined using repeated measurements ANOVAs followed by posthoc bilateral Student t-tests when main effects and/or interactions were significant. The effects were considered significant at p < 0. 05. The relation between perceptual ratings of odors (intensity, pleasantness, familiarity) or memory performances with behavioral measures (number of clicks, delay between clicks) or breathing parameters was tested using Pearson tests.
RECOGNITION AND EPISODIC MEMORY PROCESSES The behavioral data revealed a high ability to recognize odors previously encountered in laboratory settings. The unfamiliar odors freely encoded during episode discovery were proficiently recognized among the new odors encountered afterwards, as indicated by a very high memory score. The good memory recognition performances were supported by the behavioral measures. The participants answered more rapidly when they successfully recognized the target odors than for all the other responses. Moreover, the duration of the participants' breath was shorter and their respiratory frequency was higher when they accurately recognized the odors than when they rejected them. These response times and breathing observations are consistent with previous reports  (Jehl et al.
ACKNOWLEDGMENTS This work was supported by the  Centre National de la Recherche Scientifique (CNRS) , the LABEX Cortex (NR- 11-LABX-0042 ) of  Université de Lyon  within the program " Investissements D'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR)  and research grants from the  Région Rhône-Alpes  ( CIBLE 10 015 772 01 ). Anne-Lise Saive was funded by the  Roudnitska Foundation.
Materials and Methods We used 2008 BTV-1 clinical case records from the French Official Veterinary Services to assess the velocity of BTV-1 spread during the 2008 BTV-1 epizootic in France. A case was defined as a bovine herd or an ovine or goat flock in which BT was clinically suspected and BTV-1 infection later confirmed by serological or virological analyses. Our analysis was performed on a municipality basis (the smallest administrative unit in France). Overall 4, 195  BTV-1 clinical cases were reported in 1,649 municipalities. Due to clinical cases with missing date of report we discarded 54 municipalities leaving 1,595 municipalities belonging to 16 departments. Data on French BT cases are available on request to the French Official Veterinary Services (Direction generale de l'alimentation, bureau de la santeanimale, email: bsa.
Effect of vaccination on velocity of BTV-1 spread Quantifying the effects of vaccination on the velocity of BTV-1 spread required the most accurate estimates of velocity. We assumed that the estimated velocity was close to the true velocity if the date of the first case predicted by the TSA-SAR err model was close to the observed one. Consequently, following what had been done in the BTV-8 study  [23] , we restricted the dataset to the municipalities for which the absolute difference between the observed and TSA-SAR err predicted date of the first clinical case was less than 16 days. We used this threshold as a trade-off between discarding strong outliers whilst keeping most of the variability present in the dataset. We checked that the range and characteristics of the environmental factors (minimum, maximum, 1 st and 3 rd quartiles, median and mean) in the restricted dataset remained close to those of the full dataset (Material S2).