dataesr/scientific-paragraphs-categorization huggingface dataset:
A similar situation was also observed in the recently measured 136 Xe+ 198 Pt  [8]  and 136 Xe+ 238 U  [24]  systems, making use of the most neutron-rich stable 136 Xe isotope, and the 40 Ar+ 208 Pb reaction  [25]. To learn more about the character of the final populated channels we have to compare them with theory. The on-going analysis will allow to extract the experimental yields and compare them with the theoretical predictions obtained with the GRAZING code  [26, 27]. The model takes into account on the same footing surface degrees of freedom and particle transfer and the exchange of many nucleons proceeds via a multi-step mechanism of single nucleons (both, protons and neutrons, via stripping, and pick-up processes). The model has been so far successfully applied in the description of multinucleon transfer reactions  [28] [29] [30]  as well as of fusion reactions and barrier distributions  [31].
Acknowledgements Grateful thanks are due to the staff of the Tandem/PIAVE-ALPI accelerator complex and to M. Loriggiola for preparing 130 Te strip targets of excellent quality. Funds for performing the research leading to the present results were received from the European Union Seventh Framework Programme FP7/2007-2013 under Grant Agreement No. 262010 -ENSAR. This work was also partly supported by the Croatian Science Foundation under the project 7194.
M, SuperSonic Imagine; Aix- en-Provence, France). Programming of custom transmit/receive ultrasound sequences was done in Matlab (MathWorks; Natick, Massachusetts, USA), using software-based architecture of the scanner. Ultrasound sequences. The concept of ultrafast Doppler relies on compounded plane-wave transmissions  52. The mouse brain was insonified with a succession of ultrasound plane waves and the backscattered echoes were recorded and beamformed to produce an echographic image for each transmission. Although the frame rate of ultrafast ultrasound can reach more than 10 kHz, a 500 Hz frame rate was used as it allows correct sampling of the ultrasound signals backscattered by the red blood cells without aliasing in the mouse brain  53. To increase the SNR of each echographic image taken at 500 Hz, the echographic images were compounded by transmitting several tilted plane waves and added their backscattered echoes.
Customized Labview software was used to control imaging paramaters. A mechanical shutter was placed directly before the 580 nm dichroic mirror to shield the PMTs during the photostimulation period. The laser light was blocked for a few additional milliseconds before and after the photostimulation period. Texas Red dextran (70 kDa, Molecular Probes) was administered intravenously by retro-orbital or tail vein injection. Analysis of vessel dilations represents maximum absolute value compared with baseline. Smooth muscle cell calcium decreases were averaged over 1 s surrounding the maximum absolute value compared with baseline. For mean values in pericyte and astrocyte experiments, Ca 2 þ measurements were averaged during 2 s after light delivery, relative to the 2 s time period before light.
6. Matlab scripts were downloaded from: http://www. sciencedirect. com/science/article/pii/S2211124715006488. We first modelled the light distribution in space with the Monte Carlo algorithm (MonteCarloLight and LightHeatPlotter Matlab scripts), using Johansson's model (default). Calculations were made based on the features of the optic fibre used for all our experiments (GIF625; ThorLabs, Germany); a fibre radius of 31 mm and NA ¼ 0. 275, and stimulation protocols (40 pulses of 20 ms at 20 Hz, laser power of 5 or 1 mW at the end of the fibre). Temperature changes were modelled for all wavelengths used (473, 488, 561, 594 and 638 nm) without changing any of the default values. To obtain temperature increases caused in the brain tissue by our stimulation pulses we used the HeatDiffusionLight script and plotted the time evolution of the temperature (time versus depth plot).
Acknowledgements We thank  E. Chaigneau ,  Y. Goulam-Houssen  and  M. Ducros  for technical support, along with  Etienne Audinat ,  Philippe Ascher  and  David Attwell  for their critical comments. We thank  Frank Kirchoff  and  Frank W Pfrieger  for  NG2-CreERT2  and  Cx30-CreERT2  mice respectively. R. L. Rungta  has a  Postdoctoral Fellowship award  from  EMBO  ( ALTF 383-2015 ). Financial support was provided by the  Institut National de la Santeet de la Recherche Medicale (INSERM) , the  European Research Council  ( ERC-2013-AD6; 339513 ) and the  Agence Nationale de la Recherche  ( ANR/NSF 15-NEUC-0003-02 ).
Subjects Data from healthy non-smoking subjects were gathered by the current authors from their earlier publications in which healthy individuals were included as control subjects  [10, 14, 18, 19, [22] [23] [24] [25] [26] [27] [28] [29] [30]. In the majority of these studies measurements of lung function and symptom questionnaires verified the health status. Gender, age and height were noted. The exhaled flow together with corresponding exhaled NO levels were collected. NO analysis F E NO 50 and F E NO values from exhalation with flows of 5-500 ml s -1 for the extended NO analysis were gathered. All data were recalculated either with the linear model (Tsoukias & George, TG) [9] using three flow rates of at least 100 ml s -1 or with the non-linear model (Högman-Meriläinen Algorithm, HMA)  [10, 22]  using a low flow rate of 5, 10 or 20, a median rate of 100 and a high flow rate of 300, 400 or 500 ml s -1.
The bootstrap procedure produces optimism-corrected estimates of R 2 , with a correction factor based on the average difference, in over 5000 bootstrap samples, between the R 2 of the model fitto the bootstrap data and the R 2 of the bootstrap model applied to the original data. Model assumptions of normality and homoscedasticity of residuals were assessed from graphs. A p-value <0. 05 was considered statistically significant. Excel (Microsoft R Office 2011) was used for calculations of the NO parameters. Statistical analyses were performed using SPSS, v. 22 (SPSS Inc. , Chicago, MI, USA), and R  [33]  using the rms package  [34].
Follow-up An experienced pathologist performed histological evaluation of the fTI treated surgically. In case of histologically proven malignant pathology, pTNM staging according to WHO classification was used  (11). Prevalence of fTI in the whole population was calculated. The rate of cancer was assessed on the population showing fTI on FDG PET-CT and was defined as the number of cancer confirmed histologically or cytologically (Class VI) relative to the total number of fTI benefiting from an US + FNAB. To define the characteristics of malignant fTI and compare them with the benign fTI, we classified each fTI according to cytological and histological data as 'benign' or 'malignant'. In the absence of histological data, classification was done according to cytological findings, with Bethesda classification, as benign for thyroid nodules ranged 2 and malignant for thyroid nodules ranged 5 or 6.
Primers of the ubiquitary ribosomic protein P0 (forward 5'-CCT CGT GGA AAG TGA CAT CGT -3', reverse 5'-ATC TGC TTG GAG CCC ACA TT-3'), Runx2 (forward 5'-TCA CCT TGA CCA TAA CCG TCT-3', reverse 5'-CGG GAC ACC TAC TCT CAT ACT-3'), osteopontin (OPN) (forward 5'-GCC GAG GTG ATA GTG TGG TT-3', reverse 5'-AAC GGG GAT GGC CTT GTA TG-3') were used at a final concentration of 200 nM. Data were analyzed using iCycler IQ software and compared by the ΔΔCT method. Q-PCR was performed in duplicate for PCR validation. Data were normalized to P0 expression for each condition. Runx2 and OPN gene expression are expressed in relative expression compared to day 1 for each condition and also compare to Matrix-HA for each time of culture.
Micro-computed tomography (Micro-CT) Samples harvested after 2 and 4 weeks, were fixed in 4% (w/v) paraformaldehyde, and were treated by micro-CT before histological analysis. The X-ray microtomographic devices used in this study is a Quantum FX Caliper (Life Sciences, Perkin Elmer, Waltham, MA). The Xray source was set at 90 V and 160 μA. Sample up to a Field of View of 10 mm diameter and were imaged with a 3D isotropic voxel size of 20 μm. Full 3D high-resolution raw data are obtained by rotating both the X-ray source and the flat panel detector 360˚around the sample, with a rotation step of 0. 1˚(scanning time: 3 min). The corresponding 3,600 image projections were then automatically reconstructed (RigakuSW software, Caliper) into a Dicom stack of 512 files using standard back-projection techniques.
After two washes with PBS 0. 1M pH7. 4, the slides were incubated with according to the manufacturer's instruction (Anti-rabbit ABC kit; Vector Labs, Burlingame, CA, USA), and then revealed using Impact DAB solution (Vector Labs, Burlingame, CA, USA). Staining was stopped in distilled water, samples were then counterstained in Mayer's hematoxylin and washed in running tap water for 10 min. Finally, samples were dehydrated and mounted using Pertex. The number of vessels within the tissue was quantified using NDP view software. The whole surface and the number of vessels were quantified in mm 2. Stained slides from two samples per condition were processed for immunostaining analysis, and three sections were fully imaged and analysed per sample and per condition. Results are shown as average with standard deviation per condition, with time of implantation (weeks 2 and 4).
Cytotoxicity evaluation of the Matrix-HA supplemented with strontium After 3 and 7 days of culture, LIVE/DEAD 1 assay (Fig  4A ) revealed that all matrices supplemented with HA particles doped or not with strontium had no obvious cytotoxicity to MSCs Table  4. Ca and Sr contents for the HA samples measured by ICP-OES. Powders were preliminary dissolved in 0. 5 mL HNO3 and 0. 5 mL HCl for 2-3 days before ICP analysis. The resulting solutions were analysed with respect to Ca and Sr concentration with ICP-OES analysis instruments (ICP-OES 720-ES, Varian) calibrated with standard solutions of known concentrations of the elements. Based on the measured concentrations, the concentration of each individual element per suspension was calculated (n = 3). Data are expressed in μg of element / mg of powder (average ± SD).
MSCs differentiation on the Matrix-HA supplemented with strontium The expression levels of osteogenesis related genes including Runx2 as early gene (Fig 5A and     5. mRNA expression of Runx2 and osteopontin (OPN) gene in MSCs cultured on the three different matrices. Runx 2 and OPN expression were quantified by qPCR after 3 and 7 days of MSCs cultured on Matrix-HA, Matrix-8Sr-HA and Matrix-50Sr-HA. Three separate experiments were performed and each assay was done in duplicate. Data were expressed in relative expression normalized to P0 expression, compared to Matrix-HA for each time. Gene expression were set as "1. 0" for each day (Day 3 and Day 7) for Matrix-HA. Results were expressed as average ± SD. In Table  5 , the relative expression of OPN was normalized for each time point (Day 3 and Day 7) to Matrix-HA deprived of strontium.
Acknowledgments In vivo imaging was performed at  Life Imaging Facility of Paris Descartes University  (Platefor-meImagerie du Vivant-PIV) on micro-CT Platform site ( EA2496 ,  Montrouge, France ) with the technical support of  L. Slimani  and  J. Sadoine. Thanks to the Laboratory Animal Care Facility at the University of Bordeaux  for housing and care of the mice. The help of Laetitia Medan, and their co-workers is highly acknowledged. Thanks to  Alain Garcia  and  Alexandre Fargues  from the  ICMCB (Bordeaux) , for their help on FTIR assays.
Computational details The geometry of cis and trans isomers of investigated molecules was performed using the Gaussian09 software  [22]. The B3LYP functional with 6-311(d,p) basis set was used for the gas phase calculations. The calculations in DCM (dichloromethane) solution were done using the PCM model (Polarizable Continuum Model)  [23]  as implemented in the Gaussian09 package  [24]. The nature of the optimized geometries at the B3LYP level was checked by vibrational frequency calculations. The vibrational frequencies were calculated for all minima on PES (Potencial Energy Surface) which were confirmed to have zero imaginary frequency. Obtained relative energies include zero point vibrational energy (ZPVE) corrections. The abundances of the found conformers at the room temperature were calculated according to the Boltzmann distribution.
Acknowledgements Laboratory (LIA,  NAPOLI)  is acknowledged for a partial financing support. A. S. would like to thank for financially support for dissertation preparation under ETIUDA II program which is financed by  Polish National Science Centre  (doctoral scholarship no. Dec-2014/12/T/ST4/00233 ). B. K. acknowledges the  Pays de la Loire region  for the financial support of research work in the framework of the  "Molecular Systems for Nonlinear Optical Application" (MOSNOA) LUMOMAT project. A grant of computer time from the  Wroclaw Center for Networking and Supercomputing (WCSS)  is gratefully acknowledged.
Numerical Simulation The entire OFI system and the channel are modeled by the ray tracing software ZEMAX. The illumination profile is depicted in Figure  2a. Then a cuboid mesh (14 µm × 14 µm × 100 µm) is built in the channel for 3D mapping, the three dimensions grid intervals are set as: dx = dy = 1 µm, dz = 0. 5 µm. For each coordinates, a 4. 89 µm PS micro sphere is placed at the node position and the ray tracing is performed to calculate scattered light power ratio over the initial output power =. To investigate the position dependence of particle scattering properties, the same simulations are repeated at all particle coordinates in the mesh. The resulting 2D profile in the X-Z' plane is plotted in Figure  2b  while the illumination for each mesh coordinate is depicted in Figure  2a. As depicted in  [2] , the enhancement close to the back wall is due to the reflection on the wall of the forward scattered light.
Data Collection. Data were collected on a sheet before the mammogram exam as part of the routine inquiry. The sheet included information on the purpose of the exam (screening or clinical abnormality such as pain, palpable lump, nipple discharge or retraction, and skin change), patient age, age at menarche, postmenopausal or premenopausal status, hormonal replacement therapy (HRT) or contraceptive uptake, parity, personal or familial history of breast or ovarian cancer, age of onset of personal or familial BC, and gynecological personal history. After the mammogram was done, breast density was assessed visually on the two standard views (craniocaudal and mediolateral oblique) for each side by one reader and noted on the inquiry sheet. Breast density was evaluated on hard copies and computer screen.
Finally, a multivariate analysis was performed to test the association between breast cancer (outcome) and breast density (independent variable), having age, menopause, HRT, parity, breastfeeding, and history of breast cancer as covariates. Statistical significance was two-sided and set at the 5%. The statistical analysis was performed using IBM SPSS version 21 for windows release (IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21. 0. Armonk, NY: IBM Corp. ).
g. , number and differentiation status of the lobular structures  [27] ), histology (e. g. , amount of collagen  [28] ), and biochemistry (e. g. , gene expression patterns  [29, 30] ). Moreover, dense tissue has generally been associated with younger age and premenopausal status, with the assumption that breast density gradually decreases after menopause. These data were confirmed in our study with a  < 0. 001 (ANOVA test) (Table 2(b)). Contrary to the database of 35,019 postmenopausal women enrolled in a population-based mammographyscreening program in the USA  [31] , our analysis revealed that having a family or personal history of BC in Lebanon did not influence mammographic breast density. Other studies have also clearly shown mammographic density as a heritable risk factor  [32, 33]. In particular, twin studies have shown that percent mammographic density, at a given age, is highly heritable, with 60% of the variation in breast density being explained by genetic factors  [15, 34].
However, there is still little consensus between the different HC subfield protocols as shown in  (Yushkevich et al. , 2015a)  where 21 delineation protocols were compared. Some of these protocols have been used to create anatomically labeled MRI datasets which are a fundamental resource to develop new segmentation methods. For example, 9. 4 T ultra-high resolution ex-vivo images were used to create an anatomical atlas  (Yushkevich et al. , 2009)  including the CA1, CA2-3, the DG and the vestigial hippocampal sulcus obtained by manual delineation. In 2013, Winterburn presented a new in-vivo high resolution atlas  (Winterburn et al. , 2013)  to divide the hippocampus in five different subregions: CA1, CA2-3, CA4/DG, Stratum and Subiculum (jointly with 5 manually segmented examples, we call this the Winterburn dataset).
Later in 2015, Kulaga-Yoskovitz developed another segmentation protocol  (Kulaga-Yoskovitz et al. , 2015)  consisting of three structures: CA1-3, CA4/DG and Subiculum (jointly with 25 manually segmented examples, we call this the Kulaga-Yoskovitz dataset). To conduct volumetric studies and apply these delineation protocols, automatic segmentation tools are necessary. It is well known that manual delineation of a new case represents an issue in terms of reproducibility. It is also extremely time consuming as well as it has a high economic cost (it can take from 10 to 20 hours of an expert rater time per subject to manually segment the hippocampus subfields  (Iglesias et al. , 2015) ). Since manual segmentation is not an affordable option, several automatic methods have been developed in the last years.
One of the first HC subfield segmentation methods was proposed by  Van Leemput (Van Leemput et al. , 2009 ) using a generative model of the hippocampus region. This model is produced using a mesh-based probabilistic atlas containing information about where the anatomical labels are most likely to occur. The probabilistic atlas is learned from a set of ultra high resolution training images. Recently,  Iglesias (Inglesias et al. , 2015)  continued this work and improved the model by using a more accurate atlas generated from ultra-high resolution ex-vivo MR images and also using multicontrast data. Pipitone proposed a multi-atlas-based method  (Pipitone et al. , 2014)  using T2w images intended to segment a considerable large dataset (targets) using a few manually labeled cases (atlases). However, this method, named  MAGeT (Chakravrty et al.
, 2013)  has a high temporal cost. In 2015, Yushkevich proposed another method  (Yushkevich et al. , 2015b)  using T2w images where a multi-atlas approach is combined with a similarity-weighted voting and a boosting based error correction. Unfortunately, this method takes hours to produce a segmentation due to the exhaustive use of non-linear registrations as in the case of MAGeT. Recently, in 2016, Caldairou presented a new hybrid method  (Caldairou et al. , 2016)  where a set of training subjects are non-linearly registered to the test case. Then, using patch-correspondences, a surface mesh is generated from the manual labels. These patch correspondences are re-computed for each mesh vertex minimizing the error to adjust a deformable model to the case to be segmented. In this paper, we propose a new patch-based segmentation method which has been validated using two hippocampus subfield segmentation protocols with publically available datasets.
Kulaga-Yoskovitz dataset This dataset includes 25 subjects from a public repository (http://www. nitrc. org/projects/mni-hisub25) (31 ± 7 yrs, 12 males, 13 females) with manually-drawn labels dividing the HC in three parts (CA1-3, DG-CA4 and Subiculum). MR data from each subject consist of an isotropic 3D-MPRAGE T1-weighted (0. 6 mm 3 ) and anisotropic 2D T2-weighted TSE images (0. 4×0. 4×2 mm 3 ). Images underwent automated correction for intensity non-uniformity, intensity standardization and were linearly registered to the MNI152 space. T1w and T2w images were resampled to a resolution of 0. 4 mm 3. To reduce interpolation artifacts, the T2w data was upsampled using a nonlocal superresolution method  (Manjón et al. , 2010a). For more details about the labeling protocol see the original paper  (Kulaga-Yoskovitz et al.
Winterburn dataset This dataset contains 5 subjects with 0. 3x0. 3x0. 3 mm 3 high resolution T1-weighted and T2weighted images obtained by 2x interpolation of 0. 6x0. 6x0. 6 mm 3 acquisitions. and their corresponding manual segmentations. The HR images are publicly available at the CoBrALab website (http://cobralab. ca/atlases). These MR images were taken from 5 healthy volunteers (2 males, 3 females, aged 29-57). High-resolution T1-weighted images were acquired using the 3D inversion-prepared fast spoiled gradient-recalled echo acquisition (TE/TR=4. 3 ms/9. 2 ms, TI=650 ms, α=8°, 2-NEX and isotropic resolution of 0. 6 mm 3 ). High-resolution T2-weighted images were acquired using the 3D fast spin echo acquisition, FSE-CUBE (TE/TR=95. 3 ms/2500 ms, ETL=100 ms, 2NEX, and isotropic resolution of 0.
If the resolution of a new case to be segmented is lower than 0. 5x0. 5x0. 5 mm 3 , the cropped data must be first upsampled to produce HR 0. 5x0. 5x0. 5 mm 3 resolution data. This is performed using a patch-based super-resolution technique called LASR  (Coupé et al. , 2013). If HR data is used as input this step is skipped. To achieve a better match between the different subjects anatomy, a non-linear deformation was estimated between the cropped regions of every subject and the HC cropped MNI125 template. For this, we used a multi-contrast registration framework using T1w and T2w images having both equal weights. The non-linear deformation is estimated using the Advanced Normalization Tools (ANTs)  (Avants et al. , 2009)  using cross correlation metric and pyramidal framework at 8x 4x 2x 1x scales and 200, 200, 200 and 0 iterations at each scale.
Experiments and results In this section, the parameters of the proposed method and its results are presented. The method parameters has been adjusted independently to work with the two different segmentation protocols/datasets and it has been compared to other related state-of-the-art methods. To evaluate the segmentation accuracy, we have used the DICE coefficient  (Zijdenbos et al. , 1994)  measured in the linear MNI space. In order to evaluate the significance of the results we applied a Kruskal-Wallis test to find out if any of the configurations present significant differences. Finally a pair-wise Wilcoxon test was applied to find the specific differences.
MOPAL parameters In all the experiments, we used patch sizes of 3x3x3 and 7x7x7 voxels, for each scale respectively. The search volume was set to 7x7x7 voxels. We used 64 independent Patch Matches with 4 iterations each. All these parameters were optimized for both datasets in as similar manner as done in  (Romero et al. , 2017).
Winterburn dataset In all the experiments using this dataset we used the following multi-scale mixing coefficients (5 structures + background) being α=[0. 4711, 0. 3443, 0. 3826, 0. 3900, 0. 8439, 0. 7715]. These coefficients have been optimized doing a Leave-Two-Out Cross-Validation consisting of 5 rounds of optimization leaving the case under study and its flipped version out (5 rounds of 8 subjects for optimizations and 1 subject for validation). The result were 5 sets of coefficients that we used in the following experiments. For the sake of simplicity, the a values provided correspond to the mean of the 5 optimization rounds as done in  (Romero et al. , 2017). In table 1, it can be seen the results (measured through a LOOCV)comparing both versions of the mono-contrast method (T1w and T2w) and the multi-contrast version based on SSD and MSN.