dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene movements were recorded at fast or slow acquisition rates, resulting in different SNR, hence in different tracking error SNR. Starting from raw data, we compensated for the difference in SNR by adding an error to the slow acquisition MSD dataset, which is determined by the difference in tracking precision. Datasets are then fused and filtered using a Gaussian filter (order 11; see Fig. S2 ). The resulting dataset is adjusted with an anomalous diffusion model. MSD curves were ultimately extracted and analyzed using Igor Pro (WaveMetrics). Online supplemental material Fig. S1  depicts the nuclear morphology of the 31 analyzed populations. Fig. S2  shows methods used for tracking genes in living cells. Fig. S3  shows MSD responses for loci 136 kb, 80 kb, CEN, 90 kb, and 170 kb along chromosome XII.
In brief, for IEL analysis, yeast cells were spheroplasted and subjected to partial digestion with micrococcal nuclease. DNA was extracted, digested with HindIII, resolved on 1. 5% agarose gel, and Southern transferred. The probe for hybridization was amplified using oligonucleotides described in Table  S2  and -[ 32 P]ATP in a PCR reaction. A 1-kb radiolabeled DNA ladder was run on the same gel as the size marker. Primer extension on RNA extracted from FROS-tagged and untagged strains to get cDNA preparation was done by using radio-labeled primers. Products were resolved on 8 M urea-10% polyacrylamide gel, bands were visualized by phosphorimaging, and SNR6 transcript was quantified by using Image gauge (Fuji) software as described previously  (Shivaswamy et al. , 2004; Arimbasseri and Bhargava, 2008).
We are grateful to  Mathieu Stouf ,  Héléne Badouin ,  Juliane Klehr , and  Céline Jeziorski  for strain construction and initial characterization of loci positions. This work also benefited from the assistance of the imaging platform from  Toulouse TRI. This work was supported by an  ATIP-plus grant  from  Centre National de la Recherche Scientifique , by the  Agence Nationale de la Recherche (Nucleopol, Ribeuc , and program JCJC), Jeune équipe from  FRM , and  University Paul Sabatier  (a fellowship for J. Mathon). This work is a French-Indian collaborative effort funded by  Indo-French Centre for the Promotion of Advanced Research (IFCPAR)  Project  4103  between the laboratories of  Dr. Bhargava  and  Dr. Gadal. Submitted: 31 August 2012 Accepted: 14 June 2013.
Flow Cytometry Analysis FCM samples were analyzed using a Guava EasyCyte Plus SSC4C flow cytometer (Guava Technologies, Hayward). This instrument is equipped with a 488-nm, 25-mW laser line, forward scatter (FSC, for cell size) and side scatter (SSC, for granularity) detectors; green fluorescence was collected on the FL 1 channel using a 525-nm (630 nm) band-pass filter red fluorescence was collected on the FL 3 channel using a 680-nm (630 nm) bandpass filter. This instrument allows determining accurate cell numbers and population percentages, without the need for reference beads, as described by the manufacturer using only the Guava Cytosoft data acquisition and analysis software. For all analyses, a minimum of 5,000 events was acquired, and all samples were collected as logarithmic signal.
The study was carried out in a modified synthetic wine medium containing 4. 5 mg/L of molecular SO 2. Total and viable cell counts determined by flow cytometry and CFU counts (on SC agar) were compared in order to monitor the difference in the VBNC profile of the three strains. In the absence of SO 2 , more than 95% of total cells remain viable and cultivable during the first three days (data not shown). Entry into the VBNC state was induced by the addition of SO 2 (4. 5 mg/L molecular SO 2 ) 3 days after synthetic wine inoculation (Time 0). The total population remained stable over time for all strains and culturability decreases quickly 30 h after SO 2 addition to undetectable levels only for BYD4742 and BYD4742Dssu1 (Fig. 8 A  and B ). However, for BYD4742Dssu1 pCEL13-SSU1, some cells were still found culturable (5.
Statistical analysis The total population with assessment at term or near term was split into two groups: a training group and a validation group. The items significantly associated with suboptimal neuromotor status in the training group were selected, and sensitivity, specificity, positive and negative likelihood ratios were calculated for these items and for their combination. Next, the number and frequency of children with suboptimal neuromotor status in case of no abnormal item, one or two abnormal items and three or more abnormal items were calculated. To validate our encoding strategy (ie, when an item was not recorded as abnormal, it was considered normal), we compared our results obtained for the association between neurological status at term and outcome at 2 years, with the results obtained with 50 datasets where non-recorded items were imputed using a multiple imputation module included in the SPSS Statistics software.
Finally, two predictive risk stratification trees were built using the training dataset. The purpose was to establish a diagnostic decision tree capable of distinguishing between children with optimal or suboptimal neuromotor status at 2 years of corrected age. In the first tree, gestational age, severe cranial lesions detected by neuroimaging and abnormal neurological status at term, defined as presenting at least one abnormal item, were included in the model and prioritised by χ 2 automatic interaction detector analysis. In the second tree, only birth weight and abnormal neurological status were included. Then, the validation dataset was used to validate the items' prioritisation and the two diagnostic trees. All p values resulted from two-sided tests. The analyses were performed with SPSS V.
65 (0. 62 to 0. 67) and 0. 55 (0. 48 to 0. 61), respectively, for predicting cerebral palsy. The predictive capacity for suboptimal neuromotor status at 2 years increased with the number of items included in the neurological assessment at term (figure  3 ). Also, the predictive capacity for suboptimal outcome increased when calculated with a multiple imputation method in 50 datasets. In addition, we validated the two predictive risk stratification trees built using the training group. The first tree included neurological assessment, gestational age and severe cranial lesions, and identified a subgroup of 526 (32%) preterm infants exhibiting a relatively low rate of suboptimal outcome (3%). The second tree only included neurological assessment and birth weight, and identified a subgroup of 696 (42%) preterm infants exhibiting a relatively low rate of suboptimal outcome (5%) (figures 4 and 5).
Acknowledgements We gratefully acknowledge the contributions  Professor Julie Gosselin  and  Dr Claudine Amiel-Tison , who taught and trained the paediatricians of our regional follow-up network for the assessment of neurodevelopment. Contributors BG drafted the initial manuscript and approved the final manuscript as submitted. SN'G The Tich conceptualised and designed the study, carried out the initial analyses, drafted the initial manuscript and approved the final manuscript as submitted. BB coordinated and supervised data collection in all sites, carried out the initial analyses, drafted the initial manuscript and approved the final manuscript as submitted. GG coordinated and supervised data collection in one site, reviewed and revised the manuscript and approved the final manuscript as submitted.
47 to 0. 62) 0. 65 (0. 62 to 0. 67) 1. 55 (1. 33 to 1. 81) Three items or more 108 0. 19 (0. 14 to 0. 25) 0. 95 (0. 94 to 0. 96) 3. 70 (2. 53 to 5. 40) Figure 3 Rates of suboptimal neuromotor developmental status at 2 years of corrected age according to the number of abnormal items observed during neurological assessment at term. Circles indicate rates observed in the validation group. Intervals indicate the range of virtual rates calculated in 50 datasets by imputation in case of missing items.
Acknowledgements We thank the  MINECO  ( ENE2012-30929 ) and the  EU  ( ROBANODE ,  FP7-FCH-JU-2008-1-CP ) for financial assistance. D. Pérez-Coll  and  A Thursfield  also acknowledge the financial support of a " Ramón y Cajal"  contract ( MINECO, CSIC)  and of the  EPSRC  ( EP/G012865/1 ), respectively.
The impact of the former is estimated by removing from the measurement all tracks found within the same event with similar momentum vectors. It is observed that the charged energy flow drops by less than 1% for all η bins and event classes in case of both data and simulation. For the final results, a global 1% systematic uncertainty for multiply reconstructed tracks is conservatively assigned. The effect of spurious tracks is estimated in simulation by determining the energy flow carried by reconstructed tracks which cannot be associated with particles at generator level and accounting for the difference between the rate of spurious tracks in data and simulation. The corresponding systematic uncertainty is found to vary between 0. 2% and 2%. It has been checked that reversing the LHCb magnet polarity has only an influence on the measurements of the charged energy flow for the diffractive enriched event class, which mainly consists of low-multiplicity events.
Acknowledgements We are thankful to  Colin Baus  and  Ralf Ulrich  from the  Karlsruhe Institute of Technology  for providing the predictions of the cosmic-ray Monte Carlo generators. We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  NSFC (China) ;  CNRS/IN2P3  and  Region Auvergne (France) ;  BMBF ,  DFG ,  HGF  and  MPG (Germany) ;  SFI (Ireland) ;  INFN (Italy) ;  FOM  and  NWO  (The Netherlands); SCSR (Poland);  ANCS/IFA (Romania) ;  MinES, Rosatom ,  RFBR  and  NRC "Kurchatov Institute" (Russia) ;  MinECo, XuntaGal and GENCAT (Spain) ;  SNSF  and  SER (Switzerland) ;  NAS Ukraine (Ukraine) ;  STFC  ( United Kingdom );  NSF (USA).
Hence, this goat farm model does not represent the diversity of practices in field data, but instead represents a technical level that could be achieved by most of the Cabricoop farms. Performance recording has not yet been implemented by farmers. Therefore, the herd performances and production parameters were derived from the 30-year-old database of the Creole goat experimental herd at INRA-Gardel  (Alexandre et al. , 1997; Mahieu et al. , 2008). The prices used in this study were the market prices of 2010. Only variable costs (which depend on the level of animal performance) and fixed cost per animal (which does not vary with the performance level) were considered in the cost function. The fixed costs per herd were not considered because they do not depend on the level of animal performance or the number of animals.
(1998). Standardised economic values were calculated by multiplying the economic values (expressed in euro per physical unit of the traits) by the genetic standard deviations of the traits. The units of the traits used in this study are presented in Table  2. These genetic standard deviations were 1. 89 for BW11, 12. 25 for FER, 1. 60 for PCV and 0. 38 FEC in Creole goats  (Gunia et al. , 2011). For DP, no data were available for Creole goats or other breeds of goat in the literature; hence, the genetic standard deviation (1. 22) given by  Greeff et al. (2008)  in sheep was used. Derivation of the economic value for DP implies only the income equation for meat (3) because DP was not linked to any cost. Derivation of the economic value for BW was based on the income equations (  3 ) and (  4 ), the feed cost equations (equations not detailed here) and the anthelmintic cost equations (  5 ) and (  6 ).
This weighting is a maximum value and does not reflect the economic value at the present time. Nevertheless, it provides a good insight into the longterm benefits of parasite resistance when anthelmintics will no longer be effective. Selection on reduced FEC will become increasingly more important as the drenching frequency reduces when shifting from a preventive to a curative approach. Analysis of the database on production traits and FEC for Creole goats during normal years (when parasites were controlled by anthelmintics) did not show any clear relationship between FEC and production traits. The effects of change in pasture contamination have not yet been evaluated for Creole goats, but it seems that an increase of FEC of one unit during the normal period does not lead to any change in productivity.
Acknowledgements We acknowledge ' la Region Guadeloupe'  and the  European Community (FEOGA)  for the financial support provided,  Donald White  ( ABIES doctoral school ) for the English proofreading, as well as  Regis Alexandre (Chambre d'Agriculture) ,  Rangit Manicom  (Iguavie),  Marylene Madassamy  (Cabricoop),  Stephan Asselin de Beauville (Iguavie)  and  Jerome Fleury (INRA)  for their information and advice about field data,  Harry Archimede and Daniel Sauvant (INRA)  for the help on feeding equations,  Maurice Mahieu  ( INRA ) and  Jean Marie - Felix Cherdieu d'Alexis (Veterinarian)  for information about anthelmintics and integrated methods for parasite control,  Fabien Stark  ( EPLEFPA de la Guadeloupe ) and all  INRA-URZ  colleagues for the relevant advice and ideas given.
Nanoscale Computerized Tomography (nCT) X-ray microscopy allows for high resolution imaging of X-ray transmission, with near 10 nm spatial resolution achievable today  (Rehbein et al. 2009). A comprehensive review on X-ray microscopy can be found in the literature  (Attwood 2000; Howells et al. 1985; Schneider et al. 2012). When nanometer scale X-ray microscopy is combined with a rotational stage, computerized tomography (CT) datasets can be collected. CT, invented by Godfrey Hounsfield in 1972, collects a series of X-ray transmission images of the same sample at numerous rotational angles, and mathematically computes the volumetric locations of the features present in the transmission images to build a 3-dimensional voxel representation of the sample that includes its surface and internal features.
Therefore, an X-ray optical system with FOV of 15 lm and pixel size of 10 nm will limit the ideal sample size to about 10 lm in width in order for the entire sample to fit within the FOV. In addition, an ideal dataset contains transmission images of partially absorbed X-ray images with sufficient contrast between morphological features. Neither extremely high Xray absorbance nor extremely high X-ray transmission is desirable, as the former is usually coupled with insufficient data collection, and the latter is usually coupled with insufficient contrast between sample features and background. X-ray absorption through matter is expressed by the Beer's law: I x ð Þ ¼ I 0 e Àlx ð1Þ where I(x) is the final intensity of the beam after the beam passes through sample thickness x, I 0 is the initial intensity of the beam, and l is the material's linear attenuation coefficient, which can be expressed as: l ¼ X f i l i q ð2Þ where i represents each atomic element, f i is the atomic weight fraction of the element in the material, l i is the mass attenuation coefficient at the incident beam energy, and q is the density of the material.
Soft X-Ray nCT Considering the discussed potential for limitations, an example of soft X-ray TXM and nCT is presented in Fig. 1. The sample is a pure aluminum substituted 11 A ˚tobermorite phase extracted from an ancient sea water harbor Roman concrete that was cast circa 1st century CE  (Jackson et al. 2012). Data was collected from HZB-U41/1-TXM beamline at BESSY. Some limitations in the experimental procedures were imposed. First, the sample was scanned in vacuum, as the absorption of X-rays by the ambient air would be significant considering a 510 eV incident X-ray beam. Second, the sample, in powder form, was dispersed on a custom made copper grid covered with a polyimide membrane *10 nm thick. The effective thickness of the film when it was oriented close to parallel to the direction of the beam increased sufficiently to significantly absorb the beam, and therefore higher angle rotation data had to be discarded from reconstruction.
Essentially, data collection was limited to -75 to ?75°rotation, collected at 1°increments and reconstruction algorithm was computed as a partial angle tomography. Avizo software was used to create volumetric reconstruction, which is seen in Fig. 1c. The reconstruction, with 15. 5 nm voxel length, revealed limited details in the internal structures of the particle which is shown in Fig. 1b. The loss of finer nanostructure detail is evident when compared to a typical TXM image as shown in Fig. 1a. This information loss can be contributed to three major factors. First is the incomplete set of projections. With 30°of rotation information missing from the projections, some sample information is lost. The computation challenges associated with partial angle tomography is discussed elsewhere  (Schneider et al.
The computed attenuation length of the sample for a 10 keV incident beam exceeds 100 lm. While X-ray absorption is no longer an issue for hard X-ray illuminated TXM set up, the opposite problem of high radio transparency becomes a source of concern. Often, data interpretation relies heavily on monitoring fringe effects and texture variations that exist in between phases, rather than observing pure absorption contrast. An example of hard X-ray nCT is shown in Fig. 2. Using the same type of sample from Fig. 1 , a tomography dataset was collected from another 11 A ˚Al-tobermorite particle extracted from ancient Roman sea water harbor concrete, *9 lm thick. Data was collected at the hard X-ray nanoprobe beamline (26-ID-C) operated by the Center for Nanoscale Materials and the APS at Argonne National Laboratory.
The sample was mounted on a 10 lm thick Kapton tip, and was illuminated with a 10 keV incident beam, with magnification at the detector set at 15. 77 nm pixel size. A total of 1801 images were collected at 0. 1°r otation increment. Spatial resolution was *30 nm. The DOF at this setting was *19 lm. Preliminary background normalization, image alignment, and reconstruction were performed using the XMreconstructor software package from Xradia. A sample transmission image and subsequent reconstruction images are shown in Fig. 2. Compared to the results from soft X-ray nCT, the tomogram in Fig. 2b  reveals greater details in inner particle features, although at a significantly reduced contrast relative to the background. The resulting 3-D volume reveals surface topology that is not as vivid as soft X-ray nCT, although it exposes greater details in the internal nanostructure network.
Furthermore, the arrays of diffraction patterns collected by a MAR133 X-ray detector are analyzed to derive the distribution maps of phases. Stress, strain and plastic deformation within the sample can also be mapped  (Rehbein et al. 2009). Further details of the instrument are explained by  Tamura et al. (2009). Data are analyzed with X-ray microdiffraction analysis software (XMAS), which is used to extract quantitative information from polychromatic and monochromatic beam patterns. With XMAS, thousands of patterns can be indexed in one session, and these patterns are converted into 2-D element mapping by individual diffraction patterns  (Tamura et al. 2003). X-ray microdiffraction is an effective tool with applicable methods to study a wide range of scientific fields. We have  applied lXRD technique to study the hydration products of cementitious material based systems and corrosion of reinforcing steel bars in concrete.
Monochromatic 25 keV hard X-ray was selected as the incident beam. Finally, pressure dependent behavior of the sample was measured from structural refinements at different pressures. As shown in Fig. 11 , the diffraction peaks sometimes become diffuse with increasing DAC pressures. Most crystal structures in hydrated cements have interlayer spaces which may contain water and/or various guest molecules. Infiltration of pressure transmitting solution into these interlayer regions, and vibration of atoms under pressure are usually responsible for the diffuse X-ray patterns. Therefore, selecting the appropriate pressure transmitting solution and incident X-ray energy value are crucial for obtaining high-quality data for cementitious materials.
Only a few amorphous materials have been analyzed using this method  (Shen et al. 2002; Xiao et al. 2010; Eggert et al. 2002). Finally, the compressibility of crystals can be theoretically calculated from density function calculations or molecular dynamic simulations  (Moon et al. 2012). Experimentally obtained bulk modulus of cementitious materials will be used to validate the performance of different force-field potentials in molecular dynamic simulations. Precisely designed empirical potentials are important elements to accurately simulate static and time-dependant behavior of materials. HPXRD is one of a few existing experimental techniques to measure the nano-mechanical properties   directly and to provide detailed atomistic information of materials under various conditions. HPXRD produces valuable data which helps us to understand the behavior of cementitious materials at nano-scale and to support the various atomic level simulations.
Acknowledgments This publication is based on studies supported in part by Award No. KUS-11-004021 , made by  King Abdullah University of Science and Technology (KAUST)  and by  National Institute of Standards and Technology (NIST)  Grant  60NANB10D014. We thank  Helmholtz-Zentrum Berlin (HZB)  for the allocation of beamtime at the soft X-ray microscope at Berliner Elektronenspeicherring-Gesellschaft fu ¨r Synchrotronstrahlung (BESSY); and to  Peter Guttmann  and  Katja Henzler  for their scientific support at the HZB-U41/1-TXM beamline. Use of the hard X-ray nanotomography beamline at the  Center for Nanoscale Materials  was supported by the  U. S. Department of Energy, Office of Science, Office of Basic Energy Sciences , under Contract No. DE-AC02-06CH11357. The data for STXM (beamlines 5.
This includes the motion of complex species spanning multiple voxels and compartments. Using these extensions, models can be constructed, which capture the importance of location and movement of species. To support diffusion and transport, SK adds 'next-subvolume' diffusion  (Elf et al. , 2003)  to the Kappa framework. This positions SK among other meso-scale modelling techniques, e. g. MesoRD  (Fange et al. , 2010)  and SmartCell  (Ander et al. , 2004). Several existing approaches handle spatially constrained models  (Burrage et al. , 2011) , but none builds on a rule-based approach, with the notable exception of cBNG which extends the BNG rule-based language  (Harris et al. , 2009). However, unlike cBNG, SK does not assume that the contents of an individual compartment are well stirred; agents and complexes can be distributed heterogeneously.
ACKNOWLEDGMENTS We acknowledge the valuable contribution of  Demon Soft Ltd (Edinburgh)  for their help with programming and drafting the user manual. Funding: SynthSys (a  Centre for Integrative Systems Biology  funded by the  UK Biotechnology and Biological Sciences Research Council  and the  UK Engineering and Physical Sciences Research Council  (reference  BB/D019621/1 );  EU   FP7  grants  HEALTH-F2-2009-241498  (EUROSPIN) and  HEALTH-F4-2010-242167  (SynSys).