dataesr/scientific-paragraphs-categorization huggingface dataset:
G R AV I T Y M O N I T O R I N G Absolute gravity measurements were done on a concrete pillar of 1 m 3 , uncoupled with the shelter basement. The apparatus settings, measurements protocol and applied corrections are the same as presented by  Pfeffer et al. (2011). Several series of data sets, each one consisting of 100 drops of the corner cube every 10 s, produce raw gravity values that are corrected for temporal effects, and averaged. Corrections include solid earth tides (tidal parameters from ET-GTAB software; Wenzel 1996), ocean loading  (Schwiderski 1980) , air pressure effects (using barometric in situ measurements, and a regression coefficient of -0. 3 µGal hPa -1 ) and polar motion contribution from pole positions given by the international earth rotation service, IERS (http://www.
This is because the gravimeter is located on top of a hill, and more masses than in the plane geometry are located 'underneath' the gravimeter, thus increasing the vertical component affecting the gravity measure. For instance,  Creutzfeldt et al. (2008)  found a higher value of 52 µGal for SG Wettzell observatory in Germany. The gravity effect of a 1-m thick layer of water at 1, 3 and 6 m deep with respect to the topography has been calculated with different grid sizes (5, 10, 20, 30 and 40 m) from the same DGPS data set. Discrepancies in gravity never exceed 2 per cent between all different grid sizes (largest discrepancies being between the 5 m and the 40 m). This very high accuracy obtained even with coarse grids is due to the very flat terrain, with almost no high frequency variations.
Error assessment In the following, the relevance of the fit between observed ( g FG5 ) and modelled ( g m ) gravity variation is assessed using the RMSD equation RMSD = n i=1 ( g FG5,i -g m,i ) 2 n , ( 6 ) where n is the number of available time steps. Variances on observed absolute gravity values are obtained from the distribution of hourly set values for each experiment. Variances on gravity variations are obtained from the summation of respective variances for consecutive data points.
Comparison of NP and FG5 data Because NP measurements are made in a single borehole, a 1-D geometry is applied in the first step of this modelling approach. Water content measurements by NP in the 0-7. 5 m layer are uniformly applied for each grid cell of the model, accounting for topography. Let G[WSC(t)] be the transformation of the storage variations through time to gravity variations. Hence, g m ( t) = G WSC NP(0-7. 5m) (t) , ( 7 ) where WSC NP(0-7. 5m) (t) are the storage variations measured by NP through the whole vertical profile where storage variations actually occur, and g m is their modelled gravity effect. The latter is compared to FG5-derived gravity variations, and relevance of the fit is evaluated with the RMSD (eq. 6). The instrument is protected by a 12 m 2 shelter. This modifies the local infiltration in the vicinity of the instrument ('mask effect'), and consequently the lack of close infiltration has been evaluated.
It is then possible to optimize the α parameter (eq. 9) with respect to FG5 data, minimizing the RMSD (eq. 6). It is also possible to derive a 1-D equivalent Sy parameter, by adjusting a single value of Sy over the whole area without taking into account the spatial variability. To derive a 1-D equivalent Sy value is highly interesting for hydrological models, or for any further attempt to use gravity data as a proxy for water storage monitoring.
1-D model: cross-validation of NP and FG5 data The first result of this study arises from the comparison of gravity data with direct water content measurements by NP in the 0-7. 5 m layer (the whole profile where WSC occur), from which the gravity effect is calculated using the aforementioned model (Fig. 10 ). In this figure, as we are dealing with storage variations, each modelled time-series is vertically shifted with an offset that minimizes the distances to FG5 data points. When considering the mask effect of the shelter (i. e. no WSC underneath the shelter), the  calculation produces a time-series that remains most of the time within the error bars of the former (blue curve in Fig. 10 ). The comparison shows a fair agreement when considering respective error bars (RMSD = 1. 61 µGal). Discrepancies between gravity measurements and the water storage model made out of point measurements can be related to the non-representativeness of the NP data (point measurements in a specific borehole) with respect to the larger zone 'seen' by the gravimeter.
Discrepancies between modelled and observed gravity variations (RMSD = 1. 61µGal) may arise from the strong heterogeneity of the investigated medium. The modelled shelter effect (no WSC underneath the shelter) provides a time-series that is very close to the unmasked one. Furthermore, because we focus on the seasonal scale (3 months sampling rate for the FG5 data), it is very likely that redistribution of water underneath the shelter occurs, minimizing this mask effect. However, both time-series fit within their own error bars, providing us with a new validation of gravity data potential for hydrological studies. This validation of WSC quantification using absolute gravity data allows some comparisons with previous studies. For instance, daily gravimetric decrease rates during the dry season (deduced from gravimetric measurement in September and January) are of about 0.
Perspectives Scarce absolute gravity data have proven to provide reliable estimations on both water storage variations and specific yield estimates when used jointly with complementary hydrological data. A SG is available on site since the summer of 2010 and provides us with a high precision (≈0. 1 µGal) continuous time-series of gravity variations. Because of a strong initial drift in this relative gravimeter, data were not available for the timespan of this study. However, they will be used to achieve a precise monitoring of water storage variations, both at seasonal scale and short timescale (rainfall event). Also, microgravity relative measurements are currently undertaken to extend this data intercomparison to the small catchment surrounding the FG5 site. They will be used to infer spatiotemporal variability of recharge processes, and hopefully validating repeated microgravity measurements for hydrological processes studies at the catchment scale.
C O N C L U S I O N In this study, absolute gravity data from FG5 monitoring have been compared to modelled gravity variations derived from WSC measurements by NP and water-table level. Gravity data have been corrected for solid earth tides, ocean loading, air pressure effects, polar motion contribution and non-local hydrology and residuals are associated to local WSC and show seasonal variations of up to 11 µGal. This is somewhat higher than gravity variations deduced from NP only and distributed according to the topography, using a 0. 1-m accuracy DEM. NP data have the advantage to investigate the whole profile where WSC occur in this weathered hard-rock basement context. Drawbacks of comparing NP data to gravity data are the local character of the former, with respect to the integrated nature of the second.
Conclusions The differential cross-section for B + meson production has been studied with 2. 4 fb -1 of pp collision data at √ s = 7 TeV, recorded in 2011 with the ATLAS detector at the LHC. The cross-section was measured as a function of transverse momentum and rapidity in the range 9 GeV < p T < 120 GeV and |y| < 2. 25, and quoted with a total uncertainty of 7%-30% with the main source of uncertainty being systematic. The next-to-leadingorder QCD calculation is compatible with the measured differential cross-section. The predictions are obtained within the Powheg and MC@NLO frameworks and are quoted with an uncertainty from renormalisation and factorisation scales and b-quark mass of the order of 20%-40%. Within these uncertainties, Powheg+Pythia is in agreement with the measured integrated cross-sections and with the dependence on p T and y.
Acknowledgments We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia; ARC, Australia; BMWF and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC , China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF,  DNSRC  and  Lundbeck Foundation , Denmark; EPLANET,  ERC  and  NSRF ,  European Union ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  DFG ,  HGF ,  MPG  and  AvH Foundation, Germany ;  GSRT  and  NSRF, Greece ;  ISF, MINERVA, GIF, DIP and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  BRF  and  RCN, Norway ;  MNiSW , Poland; GRICES and FCT, Portugal; MERYS (MECTS), Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MICINN, Spain;  SRC and Wallenberg Foundation, Sweden ;  SER ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ;  NSC, Taiwan ;  TAEK, Turkey ;  STFC , the  Royal Society and Leverhulme Trust ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
Assessments were based on stationary tests where uphole and downhole data were collected while the tool string was held at a predetermined depth, and during normal logging operations when the tool strings were raised or lowered at conventional logging speeds of a few hundreds of meters per hour. Based on these data, we find that the new AHC system reduces 65%-80% of downhole tool displacement under stationary conditions and 50%-60% during normal logging operations. These results indicate that the new system's compensation efficiency is as good as or better than that of previous systems, with additional advantages that include upgradable compensation control software and the capability for continued assessment under varying logging conditions.
A B C move a piston and sheave that paid out or retrieved logging cable according to acceleration-derived heave. The piston's single sheave limited its stroke to 3 m and its ultimate heave compensation to ~6 m. In 2005, a replacement prototype was tested on the JOIDES Resolution-the rotary Smart Wireline Heave Compensator (SWHC). Previous assessments of wireline heave compensation systems compared the performance of the LWHC and SWHC  (Sarker et al. , 2006)  and reported that the LWHC performed consistently at a high level with a variety of logging tool strings. Results indicated that the LWHC reduced downhole tool displacement by more than 50% of surface heave  (Sarker et al. , 2006). Initial results showed that the SWHC reduced downhole tool motion even further  (Sarker et al. , 2006). However, after analyzing additional data from IODP Phase I operations, it was determined that the SWHC was unable to compensate sufficiently to acquire high-resolution Formation MicroScanner (FMS) images  (Liu et al.
Data Acquisition and Compensation Efficiency (CE) Evaluation Measurements of downhole tool string displacement, uphole (surface) heave of the ship, and ideally a means for real-time comparison of motion dynamics are all required to properly assess the performance of the AHC. Downhole acceleration and borehole inclination data are typically acquired during logging using Schlumberger's General Purpose Inclinometry Tool (GPIT). Modifications to the Schlumberger acquisition software allow for real-time output of these data at a sampling rate of 15 Hz, which is suitable for heave compensation assessment. Surface (ship) acceleration and heave are measured by the Motion Reference Unit (MRU) and recorded using LabView utilities in the Downhole Measurements Laboratory (Fig. 3 ). Lastly, the USIO-LDEO developed the MATLAB-based Wireline Heave Compensation Evaluation System (WHCES), which assesses the performance of the AHC system in real time.
Results The performance assessment of the AHC consisted of four types of tests. • Static CE evaluation with the tool string in a stationary position at different depths in the drill pipe or in open hole. During such tests, the logging tool string is positioned at a pre-determined depth, and uphole and downhole acceleration data are collected with the AHC turned on and off. • Dynamic CE evaluation in open hole when logging up or down with the AHC for the entire logging operation and continuously collecting both uphole and downhole acceleration data • Evaluation of factors that may affect the CE performance while logging up or down at different speeds and using different tool strings • Qualitative analysis of AHC performance by evaluation of field logging data Overall, the performance evaluation was based on logging data obtained aboard the JOIDES Resolution during IODP Expeditions 320T through 340.
Qualitative Analysis Using Logging Data Ultimately, heave compensation efficiency contributes to the quality of logging data recorded, and, therefore, log data quality can provide a qualitative measure of the AHC performance. Under given borehole conditions, the quality of FMS images, which are recorded at a 2. 5-mm sampling interval, is largely controlled by variations in tool speed that must be cor-rected during post-logging processing. Such depth corrections are calculated using acceleration data provided by the GPIT and can also be used as a representative measure to evaluate the effectiveness of heave compensation during logging operations. FMS image data acquired while using the AHC (Fig. 6 ) show excellent resolution between successive passes in Hole U1330A. Distinct features over the lower section of the hole (Passes 1 and 2) show resistive horizontal layers (bright) in thin beds (Fig.
Conclusion Based on the test results obtained from this study, the new AHC system is capable of reducing 65%-80% of downhole tool displacement under stationary conditions and 50%-60% during normal logging operations, a result that is better than that of previous wireline compensation systems used on board the JOIDES Resolution. The highest CE in downhole tool motion reduction achieved so far is 80%. The new AHC system is also more versatile and upgradable in design, and it facilitates real-time assessment of compensation efficiency. Optimal AHC performance reduces downhole tool motion to less than ±0. 5 m, independent of water depth and sea state; this can be effectively corrected with post-logging data processing. The repeatability of high-resolution FMS images during the tests attests to high quality log data acquisition.
Acknowledgments This research project was supported by the  U. S. National Science Foundation  through contract #  OCE-0352500 , IODP scientific ocean drilling vessel (SODV) subcontract  JSC 5-03  and  IODP subcontract JSC 4-03. LDEO subcontracted  Schlumberger Limited  and  Deep Down, Inc. to build the new heave compensation system. We thank the  USIO-LDEO Management , Engineering and  Technical Groups  ( T. Hussein ,  W. Masterson ,  E. Meissner ,  M. Reagan , and  C. Brenner ),  Deep Down ,  Inc. , and  Schlumberger Limited  for their contributions and help with this project. We are also grateful to the scientific parties of IODP Expeditions 320T through 340 for their cooperation during routine heave and CE data acquisition operations.
In Vivo Study Two anatomical models of normal human nasal cavities were used: a plastinated head model (hereafter called NC1 for nasal cast 1), previously described and characterized by Durand et al. (18) , and a 3D geometric replica of the plastinated head model (hereafter called NC2 for nasal cast 2). The NC2 was designed from computed tomography images (CT scan) of the NC1 (Fig. 1 ) and made with Acrylonitrile Butadiene Styrene (ABS) using 3D imaging analysis and 3D printing technique (rapid prototyping by Fused Deposition Modelling (FDM))  (23). Solidworks 2012 software (Dassault Systems, Velizy-Villacoublay, France) was used for 3D reconstruction and Dimension 768 for printing (Dimension, Eden Prairie, USA; layer thickness: 0. 245 mm (0. 010 in. )). As in the in vivo study, three scintigraphic studies were performed with each in vitro model: 81m Kr gas was administered to determine ROIs corresponding to the anatomical volumes, and two aerosol deposition scintigraphies, one with the jet nebulizer and one with the mesh nebulizer.
Image Processing The ROIs were defined from the images obtained from 81m Kr gas administration using Siemens software for the nose, and ImageJ software (ImageJ 1. 43U, National Institutes of Health, USA) for the upper nasal cavity and maxillary sinuses. The upper nasal cavity region was defined as the upper half of the nasal region (lateral image) (Fig. 3 ). It contains the ethmoidal space, upper turbinates, the olfactory region and sphenoid regions. The maxillary sinuses were defined as the regions seen from the coronal view (Fig. 4 ). These ROIs were then applied to aerosol images to determine the radioactivity deposited in each region (nasal, upper nasal cavity and maxillary sinuses). For both in vivo and in vitro studies, the activity thus measured in the three ROIs was expressed in terms of the activity loaded into the nebulizer.
All calculations took into account the background radiation and physical decay of radioactivity. The gamma camera images were analyzed using ImageJ software. The distribution of the aerosol deposited in the nasal cavities was analyzed along three axes: the x-axis from the nostrils to the cavum (Fig. 3 ), the z-axis from the floor to the upper nasal cavities (Fig. 3 ), and the y-axis from the septum to the extremity of the maxillary sinuses (Fig. 4 ). In vitro and in vivo  distributions were normalized taking into account the pixel size and counted radioactivity.
Statistical Analyses Statistical analyses were performed using StatXact® software (StatXact-3 3. 0. 2, Cytel Software Corporation, France). Nonparametric stratified Normal Score tests were used to compare the in vitro and the in vivo aerosol deposition data for the three anatomical regions (nasal, upper nasal cavity and maxillary sinuses). Standard Deviations (SD) of aerosol distribution along the three axes were calculated for the seven volunteers in the in vivo study, and the mean distribution was calculated for the six trials with the models in the in vitro study.
However, the origin of fecal proteolytic activity, host or microbial, was not determined in these studies. In addition, to date there are limited data regarding which specific intestinal bacterial groups are associated with enteric protease activity. Our study investigates the hypothesis that intestinal protease activity in humans correlates with specific enteric bacterial taxa. Thus, we carried out high throughput sequencing of the 16S rRNA gene to characterize the microbiota in fecal samples with a range of protease activity. Given the reported increase in protease activity in irritable bowel syndrome (IBS) patients (refs), we performed our analysis on fecal samples from healthy individuals and patients with various severities of IBS symptoms to enable the capture of a wide range of fecal protease activity.
Analysis of 16S rRNA sequences using the QIIME pipeline 16S rRNA sequence data generated by the 454 GS FLX Titanium sequencer was processed by the quantitative insights into microbial ecology (QIIME) pipeline  [19]. Briefly, sequences that were less than 200 bp or greater than 1,000 bp in length, contained incorrect primer sequences, or contained more than 1 ambiguous base were discarded. Operational taxonomic units (OTUs) were picked using BLAST and the greengenes reference database at a level of 97% similarity  [20]. Principal coordinates were generated using un-weighted and weighted UniFrac distances for samples with high and low FP activity (n=26)  [21, 22]. PCoA plots were used to visualize the similarities or dissimilarities between high and low FP activity groups that best represent the pair-wise distances between sample groups.
Human fecal samples exhibit a wide range of FP activity The source of the fecal samples analyzed in this study included 30 subjects with IBS (23 = diarrhea-predominant, 7 = mixed bowel habit-predominant) and 24 HC, 79% females with a mean age of 35 years. These samples demonstrated a wide range of FP activity (Figure  1 ). FP activity was ranked from low to high (0-450 U Trypsin/mg protein) and divided into quartiles. The top and bottom quartiles were used to separate and further investigate samples with high (n = 13) and low (n = 13) FP activity (Figure  1 ). The FP activity data displayed a strong degree of non-normality. Standard transformations such as log and square root did not remove the non-normality. Accordingly, we applied a normal quantile transformation to FP measurements to normalize these data.
Faecalibacterium prausnitzii is the only microbial species within this genus. Thus, we determined the abundance of this bacterial species in 53 out of 54 stool samples (one sample repeatedly failed to detect to amplify F. prausnitzii sequences) using qPCR. We found a significant (p=0. 01) negative correlation of F. prausnitzii with FP activity (Figure  6 ). Association of FP activity with baseline demographic data (age, race, BMI, and IBS status) revealed no confounding individual predictors (p=0. 67).
Mouse lines Characterization and genotyping of the Fat1 LacZ allele. Fat-LacZ mice, previously generated using the genetrap ES line KST249 (see detailed characterization below), were obtained from Marc Tessier-Lavigne. Initial characterization of the transgene insertion site on Fat1 transcript was performed by 59RACE PCR (as documented on the international genetrap consortium databases (http://www. genetrap. org/cgi-bin/ annotation. py?cellline = KST249) indicated insertion downstream of the first exon. Genome walking experiments (LAM-PCR, GATC Biotech; plasmid rescue, restriction analysis by Southern blot) consistently indicated that multiple copies of the transgene were inserted in tandem, thus preventing so far identification of genomic sequences flanking the transgene. Genotyping was performed on genomic DNA using the following PCR to detect the transgene (primer sets OF47: 59 GGA ACT TCT CAG ATC TGC GGG CTGC 39; and OF48: 59-TCT CAT CTT GGG TGA GGT GGG TCCC-39; or OF49: 59-GGA ACT TCT GGA TCT GCG ATC TGCG-39 and OF57: 59 CCC CAA ACA CTG CCA ACT ATG-39).
Samples were cryoprotected in 25% sucrose (in PBS), embedded in a mix with 7. 5% gelatine and 15% sucrose in PBS, and frozen for cryostat sections. Immunofluorescence was performed using primary antibodies to neurofilament (NF-M, Ab1789, Chemicon), tau (AbCAM), laminin (Sigma), alpha-actinin (Clone EA-53, Sigma), Ryanodine Receptor RyR1 (MA3-925, Thermo scientific), Dyhydropyridine Receptor alpha 1S (MA3-920, Thermo scientific), rabbit anti-GFP (invitrogen). Antibodies against FAT1 were the following: two rabbit polyclonal antibodies raised against human FAT1, HPA001869, HPA023882 from SIGMA (epitopes described in the human protein Atlas (http://www. proteinatlas. org) recognised two regions of the extracellular domain of FAT1 indicated FAT1-1869 and FAT1-23882, respectively, in Figure  2A, B.
For each gene examined, primers were chosen at the junction between two exons, to distinguish by their size the RT-PCR products from the genomic DNA PCR products. For Fat1 primer sets, sizes expected from genomic PCR amplicons, in case of genomic DNA contamination, have been indicated on Figures  S4  and Figures S5 ). Expression of the human FAT1 gene was monitored by a real time quantitative RT-PCR method using TaqMan gene expression assay reference number Hs00170627_m1 targeting the 59 part of the FAT1 sequence (Applied biosystem), or using real-time sybrgreen PCR assay (Roche) (see primers below). The ubiquitous betaglucuronidase (GUS), was used as endogenous reference gene to normalize the data across all samples. FAT1 primers were chosen at the exon2-3 junction: forward primer: 59-CAT TAG AGA TGG CTC TGG CG-39; reverse primer: 59-ATG GGA GGT CGA TTC ACG-39).