dataesr/scientific-paragraphs-categorization huggingface dataset:
We used the templatematching algorithm described by  Jonker (2002)  to calculate the skeleton. Dendritic end-points were established by searching for the distant-most compartment with respect to the cell body position. The resulting skeleton was converted and saved as a Neuron hoc-file  (Hines and Carnevale, 2001). Ion channel signal quantification. Quantitation of dendritic ion channel signal was performed using the custom-written program DendriteAnalyser. DendriteAnalyser was started from the command line with the hoc geometry file and the native (or deconvolved) multi-layer tif-file corresponding to the ion channel signal as input. Since the original datasets corresponding to both morphological-and ion channel signal were generated during the same imaging session, the 3D coordinates derived above match the same topographical location in the ion channel image file.
As described above, the geometry of the neuron is represented in a graphical structure in which the edges represent the linear dendritic structures and the nodes represent the bifurcation between the dendrites or cell body. Each dendritic segment is represented as a list of compartments with each compartment containing a vector associated with the original xyz position in the imaging stack. DendriteAnalyser then scans over the graphical structure, selecting one dendrite at a time. Next, the program selects one compartment after the other from the list and registers the corresponding signal in the native ion channel file at each given vector position. As a result of this analysis, a number of datasets are calculated, namely: (1) A single value at the position of the compartment (point value).
(2) The mean value; the mean is calculated by a spherical volumetric window moving along the skeleton, and the sphere (diameter of 6 μm) is scaled by the actual voxel dimensions. (3) The maximum value in the compartment around the sphere. (4) The radius of the dendrite derived from the morphological channel is reported in a separate data set as described above. The calculated values for each compartment (voxel position) of a dendrite are stored and written to disk as a txt file. In addition, a file combining the three-dimensional cell geometry and the measured values was created. The geometry was visualized using the software Amira (VSG, France). Image presentation. Native (unprocessed) and processed (filtered, segmented) images were imported into ImageJ and a z-projection prepared from maximum signal intensity derived from the relevant focal planes.
Images were acquired with settings affording the best approximation of Nyquist-Shannon sampling rates. These settings were defined by the numerical aperture of the objective and by an idealized average light wavelength of 532 nm. Image stacks were acquired using 2048 × 2048 format, an optical zoom of 1. 5 and z-step of 0. 08 μm resulting in a voxel size of 80. 13 × 80. 13 × 83. 92 nm. The isotropic dimensions in x, y, and z were important for the subsequent analysis of fluorescent intensity data obtained from DendriteAnalyser. It is important to note that this resulted in oversampling in the z-dimension, however we were able to avoid problems associated with photobleaching by using a hybrid PMT for the ion channel signal and by keeping the corresponding laser power to a very low level. One shortcoming of these acquisition parameters was the long time required to acquire one image stack (i.
e. , ∼30 min for the acquisition of 90 z-stacks) even when employing bi-directional scanning. Images were typically acquired with line-scan speeds of 400 Hz to allow high quality image acquisition. Higher scanning speeds were tested, including the use of a resonance scanner (scanning at 8000 Hz) to enable more rapid acquisition of tiled images, but this resulted in loss of image quality (data not shown). To reduce the effects of oversampling in the z-dimension and to diminish the long acquisition time, one can reduce image size to 1024 × 1024 format and increase the z-stack size to 0. 17 μm. This has the effect of increasing the voxel size in all three dimensions. The choice of the appropriate format has to be empirically tested in each case.
This algorithm requires the user to provide image size and cell body coordinates, which provide a seed-point for dendrite skeletonization. Where no cell body is present, a proxy location along the dendritic arbor must be given to provide an equivalent seedpoint. Image size was obtained from the descriptive text file extracted from the original Leica lif-file. Cell body coordinates were derived by inspecting the filtered, segmented data in ImageJ. The output from this initial processing was a Neuron hoc-file. This file was inspected in Neuron-selecting "distributed mechanisms" and then "shape names" to display the skeletonized structure. Clicking on a selected segment of a dendrite revealed the name associated with this segment and its length in microns. This data was retained for subsequent analysis using DendriteAnalysis and Igor.
The DendriteAnalyser output results in several datasets (as described in Materials and Methods). Here, we have chosen to present the point fluorescence values plotted in 3D space along the entire dendritic skeleton (Figures  4C, D ). In the first case (Figure  4C ) the skeleton is presented as a realistic structure integrating information from the dataset pertaining to dendrite radius. In the second case (Figure  4D ), the skeleton is represented as a filament, aiding the visualization of color map data. Note that fluorescent intensity values corresponding to the first 5-7 μm of the "dendritic" segment actually correspond to the soma because the center point of the cell body is used as a seedpoint for reconstruction. This data should be excluded from any subsequent analysis. The data from two selected dendrites are presented in Figures  4E-G.
The only decision on the part of the operator concerns the choice of the cell body center-point. Thus, the method is largely without risk of operator bias. Lastly, in certain cases (one example of which is presented in Figure  6 ) it may sometimes be advantageous to normalize the ion channel signal to gain a better idea of its overall distribution along a dendrite. This method has the advantage that the normalizing signal is an intrinsic part of the general methodological approach. To extract fluorescence intensity data associated with the morphological label, it suffices to repeat the DendriteAnalyser procedure for the image stack corresponding to eGFP or mCherry signal (or equivalent marker) and to incorporate the resulting data into the downstream analysis in Igor Pro. The ability to normalize the fluorescence intensity data related to ion channel expression permits the comparison of different physiological states.
Its disadvantage is that the skeleton is presented as a simple wire-frame, rather than retaining real dendritic dimensions. However, this structural information is retained in the dataset related to the dendrite diameter and can be visualized in Amira (see Figure  4C  and also User Manual). Altogether, our method encompasses a rapid, semi-automated method that provides sufficient morphological data to enable the characterization of the cell type under consideration. Variations in dendritic ion channel distribution may also involve their preferential localization to sub-dendritic compartments such as spine heads/necks in addition to the main dendritic shaft. Our approach will not permit this type of analysis because the segmentation step excludes compartments that are smaller than 5 μm in length and thus spines are not reconstructed with the main dendritic branch.
(D) Two-dimensional snapshot taken from Amira viewer window (same dataset as in C) showing reconstructed skeleton (represented as a filament). (E) Magnification of a section of the reconstructed skeleton, showing 3D ion channel profile associated with two individual dendrites (arbitrarily named dendrite 1 and dendrite 2). Here, point fluorescence's values are presented by means of the color map function. The color map has been adjusted to best represent, on a graphical level, the predominant ion channel clusters. (F,G) Fluorescence intensity plotted as a function of dendritic length for the same dendrites. Data is presented as the point fluorescent value as well as mean fluorescent value calculated at each point. Arrows in (E) and (G) indicate a putative ion channel cluster, clearly visible in both representations of the data.
ACKNOWLEDGMENTS We are grateful to  Drs. S. Pouvreau  ( IINS, Bordeaux, France ) and  B. Rózsa  ( Institute of Experimental Medicine of the Hungarian Academy of Sciences, Budapest, Hungary ) for their helpful comments regarding the manuscript. We a grateful to  Prof. K. -K. Conzelmann  ( Max-von-Pettenkofer Institute, Munich, Germany ) for the gift of the SAD G viral stocks and materials for their production. We would also like to thank  Dr. G. Giese  ( MPI for Medical Research, Heidelberg, Germany ) for support in optimizing the imaging parameters. Imaging data was acquired using equipment of the  Bordeaux Imaging Center (BIC) , and we would like to thank  P. Legros  ( BIC ) for advice with image acquisition and  S. Marais  for the introduction to the Autoquant deconvolution software. The following funding sources are acknowledged:  INSERM, Max-Planck-Society, Region of Aquitaine ,  Jérôme Lejeune.
Study design and treatment COU-AA-301 was a phase III, multinational, randomized, double-blind placebo-controlled study comparing AA plus prednisone versus prednisone in men with mCRPC who had progressed post-docetaxel (Clinical Trials. gov identifier NCT00638690). 16, 18 Patients were enrolled from May 2008 through July 2009 at 147 sites in 13 countries, and randomly assigned in a 2:1 ratio to treatment with AA 1000 mg, taken once daily, plus prednisone or prednisone plus placebo. Patients in both groups received prednisone 5 mg twice daily. The randomization was stratified by ECOG performance status (0-1 versus 2), pain (present versus absent), number of prior chemotherapy regimens (one versus two) and type of progression (PSA only versus radiographic/PSA). The primary end point was OS, and prespecified secondary end points included radiographic progression-free survival (rPFS), PSA response rate, objective response rate (ORR) and time to PSA progression.
To evaluate the specific impact of the site of disease, additional analysis was performed for the liver metastases and lung metastases groups. Patients with visceral disease, particularly those with liver metastases, had more clinically significant pain and higher baseline PSA, lactate dehydrogenase and ECOG performance status 2 compared with patients without visceral disease (Table  1 ). The median duration of treatment with AA plus prednisone was 5. 5 months (range: 0. 2-24. 1) compared with 3. 1 months (range: 0. 4-23. 9) for prednisone in the visceral disease subset. The corresponding median durations of treatment in the subset without visceral disease were 8. 2 months (range: 0. 2-25. 6) and 3. 7 months (range: 0. 1-24. 9), respectively (data not shown).
97, Wiener Neustadt) software. Specifically, picture processing involved coloring of PI pictures, creation of corresponding DIC/PI overlays (used to determine inviable cells to exclude from quantification) with Metamorph and cropping of representative areas with IrfanView. C. elegans strains and growth conditions. Experiments were carried out using the Caenorhabditis elegans N2 wild-type strain. The following mutant strain was used: RB2481 spds-1(ok3421) II (referred to in the text as SPDS-1). We followed standard procedures for C. elegans strain maintenance. 49 Nematode rearing temperature was kept at 20°C. C. elegans fertility determination. Preparation of Escherichia coli (OP50) plates for worm feeding was performed as previously described. 49 Briefly, bacteria on seeded 7 ml NGM plates were killed by exposure to UV irradiation for 10 min (0.
Acknowledgments We thank  Ulrike Potocnik  and  Angela Pasparaki  for assistance. We are grateful to the  Austrian Science Fund FWF (Austria)  for grants  P23490-B12  and  P24381-B20. N. T. acknowledges support by grants from the  European Research Council (ERC)  and the  European Commission 7th Framework Programme. This work is supported by grants to G. K. from the  Ligue Nationale contre le Cancer (Equipe labellisée) ,  Agence Nationale pour la Recherche (ANR) ,  AXA Chair for Longevity Research, European Commission (ArtForce, ChemoRes) ,  Fondation pour.
This suggests that the effect of the specific meteorological conditions on the snow chemistry in different parts of the continent is very important for the reconstruction of atmospheric circulation from ice core records. In this paper, we present new snow chemistry data from the Princess Elizabeth Land sector, East Antarctica, obtained during the 53rd Russian Antarctic Expedition (RAE) in 2008 along the first tractor traverse from the Progress station to the Vostok station, one of few traverses extending for more than 1000 km from the coast further inland of Antarctica.
Biogenic compounds in the Antarctic snow such as methanesulfonic acid (MSA) were not analysed in this study. Inter-laboratory comparison of the same samples was performed to estimate the accuracy of analytical data on the chemical composition of the Antarctic snow. This analysis was carried out by two laboratories: the Laboratory of Glaciology and Environmental Geophysics (LGGE, Grenoble, France) and the Laboratory of Hydrochemistry and Atmospheric Chemistry (LIN SB RAS, Irkutsk, Russia). In total, in both laboratories 94 equal snow samples have been analysed for major ions using ion chromatography. Analyses performed at two laboratories showed identical trends in concentration changes from sample to sample and similarity in their values. The mean discrepancy between the samples analysed in the two laboratories was 13 % and 24 % for sulphate and sodium ions, respectively.
Snow accumulation and dating of snow pit sections Snow accumulation was calculated (Table  1 ) at each profile site based on: (a) 2-to 4-year  (2008-2010 and 2006-2010)  snow-stake measurements; (b) density measurements from surface to 20 cm depth; (c) density depth measurements at some sites in January 2008; and (d) scarce data of snow layer stratigraphy. The average thickness of the annual snow layers varies from 5. 3 to 32. 4 cm (maximal value at 337 km), mean accumulation from 2. 0 to 15. 2 g cm -2 yr -1. The errors in determination of the accumulation rate can reach 50 % due to a lack of reliable stratigraphic and snow-stake data. Using snow accumulation and density data (see above), the ages of sampled snow horizons were calculated from the snow surface (January 2008). In accordance with these data, sub-surface snow/firn layers were dated from 2.
Spatial distribution of chemical components in snow Measured concentrations of sulphate ion and elements in snow layers along the traverse are shown in Fig. 3. To investigate spatial differences in the chemical composition of snow, we compared the averaged concentrations in the sub-surface 20-80 cm snow layers spanning approximately 2006-2008. This time interval was present at all sites and was chosen to make the data more homogeneous for further comparison. Using snow accumulation data (see above) and measured ion concentrations, we calculated ion fluxes for this period. Spatial trends of concentrations of sulphate ion and elements and their fluxes along the traverse are shown in Fig. 4  and correlation coefficients between different species in Table  2. To evaluate the relative contribution of each factor (distance inland, elevation, accumulation rate, and slope) to variation in concentration we used linear regression analysis (Table  3 ).
The sulphur concentration was calculated from SO 2- 4 content. EF values closest to unity were revealed at 618 km for K (EF = 1. 0), Mg (EF = 2. 6) and Ca (EF = 6. 1), suggesting their terrestrial origin. EFs for other sites differ by 2-3 orders of magnitude, suggesting more complicated element sources. Prevalence of marine and continental sources of the species is defined by differences in atmospheric circulation. To comprehend the circulation patterns over the studied area, we analysed the 5-day back trajectories of air masses coming to 5000 m over two sites of the traverse (618 km and 1276 km) and to 2000 m over the 55 km site using the HYS-PLIT model  (Rolph, 2013; Draxler and Rolph, 2013). We used the NCEP/NCAR reanalysis data set as input for the trajectory model for the period of 2006-2008.
Acknowledgements. We would like to thank two anonymous reviewers for providing useful comments and suggestions for the initial version of the manuscript. The authors gratefully acknowledge the  NOAA Air Resources Laboratory (ARL)  for the provision of the HYSPLIT transport and dispersion model and/or READY website (http://www. ready. noaa. gov)used in this publication. This work was supported by  Program of RAS  No. 11. 9  and  RFBR  Project No. 10-05-93109. Edited by:  M. Albert.
MATERIALS AND METHODS Data collection involved four centres during the period 2009-2012. Three hundred and thirty male partners of subfertile couples, presenting for semen analysis, were included, after providing written and informed consent. Height and weight were recorded on the day of semen collection and the BMI was calculated (kg m  22  ). Patients provided information on their tobacco consumption. Patients who smoked more than two cigarettes per day were designated as smokers. Semen samples were collected by masturbation into a sterile plastic cup in the laboratory. Samples were allowed to liquefy at room temperature for 30 min and conventional semen quality (semen volume, sperm concentration and mobility) was evaluated according to WHO guidelines. 10 Sperm morphology was assessed by using David's criteria.
Statistical analyses Data are summarized as the median, the first quartile and third quartile for continuous data. Categorical variables are presented as absolute and relative frequencies. Patients were assigned to different groups according to their BMI: normal weight (BMI: 18. 5-24. 9 kg m 22 ), overweight (25. 0-29. 9 kg m 22 ) or obese (o30. 0 kg m 22 ). 12 ssociation between sperm parameter values and BMI was analysed by using linear regression. Secondly, data were categorized according to the DNA fragmentation rate as normal (,14%), borderline (14%-29%) and pathological (o30%). Ordinal logistic regression was performed to assess the impact of BMI across DNA fragmentation categories. Since age and tobacco can affect sperm DNA integrity, 1,13 unadjusted and adjusted analyses for age and smoking status were performed.
9 (0. 2, 7. 6)), but not in overweight men (mean differences (95% CI): 1. 1 (21. 4, 3. 6)). Comparable results were obtained after adjustment for age and tobacco use (obese vs. normal: mean differences (95% CI): 4. 2 (0. 4, 7. 9)); overweight vs. normal: mean differences (95% CI): 1. 2 (21. 4, 3. 8)) (Table  1 ). In a second analysis, data were stratified according to the DNA fragmentation rate as normal (,14. 0%), borderline (14. 0%-29. 9%) and pathological (o30. 0%). Compared with men with normal BMI, OR (95% CI) for moving from one DNA fragmentation category to the next was 1. 3 (0. 8, 2. 1) for overweight and 2. 4 (1. 2, 4. 7) for obese men. After adjustment for age and smoking status, OR (95% CI) for moving from one DNA fragmentation category to the next was 1. 4 (0. 8, 2. 4) for overweight and 2.
3 mM of each forward and reverse primer. For miRNA quantification, total RNA was transcribed with a TaqMan Reverse transcription kit (Applied Biosystems), and then amplified using TaqMan Universal PCR Master Mix II. The primers for eEF1A2 qPCR were described earlier  (Bosutti et al, 2007). Primers for beta-actin were: forward primer 5 0 -GCGGGAAATCGTGCGTGACATT-3 0 , reverse primer 5 0 -GATGGAGTTGAAGGTAGTTTCGTG-3 0. MicroRNA primers were purchased from Applied Biosystems. Quantitative PCR was quantified with an ABI PRISM 7500 real-time PCR system (Applied Biosystems). Data were analysed using qPCR Miner 4. 0 software  (Zhao and Fernald, 2005). Proliferation assay. On the day before the experiment, 3000 MCF7 cells per well were seeded into 96-well plates. The next day, cells were transfected with 15 nM miRNA precursors or siRNA against eEF1A2 using RNAiMAX (Invitrogen) by standard protocol according to manual.
Eukaryotic translation elongation factor 1A2 possesses antiapoptotic properties, possibly due to the direct interaction with Prdx1  (Chang and Wang, 2007). In addition, the ectopic expression of eEF1A2 altered the three-dimensional morphogenesis of MCF10A cells by influencing the phosphatidylinositol localisation  (Pinke and Lee, 2011). Importantly, the correlation between the low level of eEF1A2 (data not shown) and high level of mir-744 expression  (Landgraf et al, 2007)  was observed in MCF10 breast, A549 lung and Jurkat leukaemia cells. On the opposite, in the heart tissue where the expression of eEF1A2 is quite high  (Lee et al, 1992) , the expression of miR-744 was absent  (Landgraf et al, 2007). Unfortunately, it is not possible to track the same correlation for miR-663 as no data about its expression are available in MirZ database  (Hausser et al, 2009).
ACKNOWLEDGEMENTS We are grateful to  Linda Pritchard  for critical reading of the manuscript. AV is thankful to FEBS and French Embassy in the Ukraine for short visiting fellowships. Travel support for BN was provided by the  PICS  and GDRI programs. This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution-NonCommercial-Share Alike 3. 0 Unported License.
Transmission loss evolution with cladding ring number Figure  1 (a) shows computed confinement loss (CL) spectra of Kagome cladding-lattice HC-PCF with different cladding ring-numbers. The fibers are taken to have a hypocycloid-shape core with a constant b curvature parameter of 0. 3 (see  [10, 11]  for the definition of b), and a constant inner diameter of 32. 5 µm. The thickness of the silica web forming the cladding is set to 450 nm and the pitch equal to 20 µm. The computation was carried out using a commercial software (COMSOL Multiphysics) based program on the finite-element-method (FEM) with anisotropic perfectly matched layer  [20] , and which was successfully applied to the analysis of loss and dispersion properties of several IC HC-PCFs  [21]. The spectral range of the computed CL was limited to the first transmission band (i.
ACKNOWLEDGMENTS This work was supported by grants from the  Human Frontier Science Program Organization (Career Development Award) ,  French Research Agency (ANR, Programme Jeunes chercheurs and Programme Blanc ),  FRC  ( Fédération pour la Recherche sur le Cerveau), City of Paris (Programme Emergence ),  La Pitié Salpêtrière  hospital (Translational research contract) and  INSERM  to  Nathalie Rouach ,  Labex Memolife  to  Glenn Dallérac  and  FRM (Fondation pour la Recherche Médicale)  to  Oana Chever.
Statistical Procedures Data were expressed as means ± SEM. Nonparametric unpaired comparisons were performed using the Mann-Whitney U test. For multiple comparisons of HBsAg concentrations before and after immunization, the Friedman test followed by Dunn's post test was used. The significance level α was set 5%. Statistical analysis was carried out using Graphpad Prism 5 software. Values of p < 0. 05 were considered significant.
Acknowledgments This work has been carried out in the framework of the  French-Korean International Laboratory  " CNRS-EWHA Research Center for Ultrafast Optics and Nanoelectronics of Functional Nanostructures ". The authors would like to acknowledge funding from " Campus France" (STAR program ). JCR also acknowledges the support by the  Basic Science Researcher Program  and the  Quantum Metamaterials Research Center (QMMRC)  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Education, Science and Technology  (grants  2011-0008650 ,  2012-0000543 ).