dataesr/scientific-paragraphs-categorization huggingface dataset:
Examination of the results show, as expected, that when the pseudo-dataset are fit using the correct template, τ fit is consistent with τ Hc,true sim. Conversely, when the same pseudo-dataset is fit with an alternate template that is produced with a significantly different input value of τ Hc sim , τ fit deviates from τ Hc,true sim. Thus the criterion for choosing the optimal template is to select that in which τ fit is closest to τ Hc sim. The chosen template is also found to have the lowest fit χ 2 , which provides additional support for the method of determining the optimal template. Applying this same criterion to the data, the optimal values of τ Hc sim are found to be τ The results of the fits to the Λ + c , Ξ + c and Ξ 0 c decay-time distributions using the best-fit templates are shown in Fig.
Acknowledgements 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) ;  MOST  and  NSFC (China) ;  CNRS/IN2P3  (France);  BMBF ,  DFG  and  MPG (Germany) ;  INFN (Italy) ;  NWO (Netherlands) ;  MNiSW  and  NCN (Poland) ;  MEN/IFA (Romania) ;  MSHE (Russia) ;  MinECo (Spain) ;  SNSF  and  SER (Switzerland) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE NP  and  NSF (USA). We acknowledge the computing resources that are provided by  CERN ,  IN2P3 (France) ,  KIT  and  DESY (Germany) ,  INFN (Italy) ,  SURF (Netherlands) ,  PIC (Spain) ,  GridPP  ( United Kingdom ),  RRCKI and Yandex LLC (Russia) ,  CSCS (Switzerland) ,  IFIN-HH (Romania) ,  CBPF (Brazil) ,  PL-GRID (Poland)  and  OSC (USA).
Materials and methods In this section, the procedure used to determine the effects of joint simplification in musculoskeletal dynamic simulation is described. This procedure was divided in three steps as shown in Fig 1. The first one was focused on the measurement of kinematics during a flexion-extension gesture on cadaveric specimens (sheep elbow). The measurement was done using a digital image correlation (DIC) system; bones were also digitized using a 3D optical scanner. The second step consisted on data post-processing to develop a realistic kinematic model of the elbow. The third step introduced the developed kinematic model in a musculoskeletal model of the whole sheep forelimb for performing inverse dynamic simulations of a gait trial. These three steps are detailed in the rest of this section.
Data acquisition of elbow flexion motion This subsection describes the experimental protocol adapted in this study (in vitro specimen, experimental devices, experimental procedure). Five fresh cadaveric elbow specimens of healthy female Arles Merino sheep (average weight 30 kg and 1 year of age) were used for this study. No ethical approval was required because the study made use of abattoir specimens (Slaughterhouse: SA d'Exploitation de l'Abattoir Municipal de Sisteron; Location: Abattoir Municipal, 8 All des Romarins 04200 Sisteron, France). The specimens were composed of humerus, ulna and radius as well as synovial capsule and all the ligaments that ensure the cohesion of the joint. The joints were warmed to room temperature (20˚C) for 30 minutes before carrying out the experiments. For initial position definition and motion capturing purposes, each specimen was equipped with two tracking clusters, which were screwed on the humerus and on the radius-ulna (see Fig  2A ).
002 mm). The motion was captured during 40 seconds from a measurement window of 200x200x50 mm 3 with an acquisition frequency of 100 Hz. The measurement was repeated five times for each specimen. In order to associate the acquired motion data with the articular geometry, bones were digitized separately (after the experiments) using a 3D optical scanner (Gom ATOS III, Braunschweig, Germany, resolution of 0. 02 mm). The humerus and the radius-ulna were scanned after cutting the synovial tissue but keeping the cartilage. To express both geometric and kinematic data in the same reference frame, the tracking clusters were provided with two different patterns: a stochastic speckle and GOM stickers (see Fig  2C ). For the tracking system Q400 Dantec, two perpendicular edges of the two stochastic speckles were used to create two datums: Ω h for the humerus and Ω r,i for the radius-ulna.
Musculoskeletal inverse dynamic simulation In order to study the impact of anatomical joint simplification on the prediction of reaction forces during gait simulations, a musculoskeletal model of the right thoracic limb of a sheep was used (Fig  6 ). The model was composed by 4 segments and actuated by 16 Hill-type muscle-tendon units. Each extensor and flexor group was treated as a whole unit as no DoFs at the digital joints were considered. Sheep gait data was adopted from  [37]. For the adopted gait trail, muscle forces were calculated using the static optimization algorithm implemented in OpenSim  [38]. This algorithm resolves the net joint moments into individual muscle forces while minimizing squared muscle activations as objective function  [39]. With the same software tool, joint reaction analyses were performed to estimate the loads acting at the elbow joint.
Fluorescence polarisation microscopy Fluorescence polarisation analyses were performed on a SP8 (Leica) inverted confocal microscope with a 63X oil immersion objective (NA 1. 4), and using the Leica Acquisition Suite (LAS)-AF software. As the excitation light coming out of the laser is naturally polarized, an analyser integrated in the Leica emission filter wheel was placed in the emission pathway before the pinhole. The analyser rotates from a parallel to a perpendicular orientation to perform a sequential acquisition of the two corresponding polarised images. Images of GFP polarisation were acquired using a 488 nm argon laser and emission was selected between 500 and 550 nm. Image analysis was performed using the Fiji (ImageJ) software and fluorescence anisotropy was calculated as in  17  , using the following equation: r = (I -I )/( I + 2I ), where r is the fluorescence anisotropy, I and I are the parallel and the perpendicular polarisations and I + 2I is the total polarisation-independent emission.
FLIM microscopy FLIM analyses were performed in the time domain with a time-gated custom-built setup as described in  16  and driven by the Inscoper hardware (Inscoper). Briefly, cells were excited at 440+/-10 nm and at 480+/-10 nm using a white light laser and emission was selected using a band pass filter of 483/35 nm and of 525/50 nm for mTurquoise2 and GFP, respectively. Fluorescence lifetime was calculated with five sequential temporal gates of 2. 2 nsec each. Mean pixel-by-pixel lifetime was calculated using the following equation: τ =ΣΔti Ii/ΣIi where Δti is the delay time of the ith image acquired following a laser pulse, and I is the pixel-by-pixel fluorescence intensity in each image. Lifetime measurements and online calculations were performed with the Inscoper software (Inscoper).
2c-FCCS measurements were carried out with the Symphotime software (PicoQuant), integratd into the LAS-AF software (Leica). For each cell, the fluctuation of the fluorescence intensity for both donor-acceptor pairs (green/red or cyan/yellow) in the confocal volume was analysed by monitoring three independent points in the cytosol. These points were analysed sequentially and in non-photobleaching conditions for 30 sec each. Autocorrelation and cross-correlation curves were reconstructed from the fluorescence decay with the Symphotime software and the lifetime integration as previously reported  21, 22  , with the Symphotime software. Data were fit using the 3D diffusion model, with one diffusing species  21, 22. The ratio between cross-correlation and green (for GFP/mCherry) or cyan (for mTurquoise2/superYFP) auto-correlation was used to estimate FRET efficiency.
For each cell analysed, we measured three randomly-chosen points in the cytosol to estimate FRET efficiency in this compartment. We did not detect any significant difference between active and kinase-dead AURKA when we compared the ratio of the amplitudes of the red or green auto-correlation curves with the amplitude of the cross-correlation curve (Fig. 3 ). This was in apparent contradiction with previous data acquired in cells where the cytosol was strong enough to perform FRET/FLIM analyses  16  , and where we detected FRET with the GFP-AURKA-mCherry biosensor. Therefore, we concluded that the ratio between the cross-correlation and the green auto-correlation is not sufficiently robust to detect changes due to FRET in the original AURKA biosensor. As FRET cannot be estimated by 2c-FCCS using the original AURKA biosensor, it is possible that mCherry represents a limiting factor for the measurement of FRET via 2c-FCCS, due to its longer dark state compared to the one of EGFP-like proteins  32  , and its lower brightness (15.
ACKNOWLEDGEMENTS We thank all the members of the  Microscopy-Rennes Imaging Center (Biologie, Santé ,  Innovation Technologique, BIOSIT, Rennes, France ) for assistance and in particular  X. Pinson  for providing an ImageJ macro for the analysis of fluorescence anisotropy. We also thank  Laurent Deleurme  from the Flow cytometry and Cell Sorting platform ( BIOSIT, Rennes, France ) for assistance, together with  A. Webb  and  S. Ku  for technical help. This work was supported by the  Comité Nationale de la Recherche Scientifique , by the  Ligue Contre le Cancer Comité d'Ille et Vilaine et Comité des Côtes d'Armor  to G. B. and by the  Comité d'Ille et Vilaine, Comité du Maine et Loire et Comité de la Sarthe  to MT, and by the  Infrastructures en Biologie Santé et Agronomie (IBiSA) , région Bretagne and  Rennes Métropole  for the development of the technology for rapid FLIM measurements.
Time responses were recorded using six PCB Piezoelectric accelerometers, equally distributed over the full length of the beam, as shown in Figure  8. LMS TEST. Lab software was used to acquire the responses from the accelerometers. Modal parameters were identified using the PolyMAX modal identification method implemented in the LMS TEST. Lab software. From the stabilization diagram shown in Figure  9 , modal parameters of the studied cantilever beam were identified. The method based on transmissibility measurements is then applied to the measured data response. The results obtained by the two different methods are shown in Table  4. Results show a good correlation when comparing the values of the natural frequencies and the damping ratios. In order to compare the mode shapes identified by the two different methods, the modal assurance criterion is calculated.
Operational modal analysis In addition to the random impact hammer shocks, the beam is now excited by harmonic excitations provided by a motor turning at 851 rpm. The motor is fixed to the second point of the beam, as can be seen in Figure  11. Experimental tests were carried out under random impact hammer shocks in the presence of harmonic excitation. Data responses were measured using six accelerometers, equally distributed over the full length of the beam, as described in the EMA section. Operational modal analysis was performed   The stabilization diagram is obtained using the PolyMAX method implemented in the LMS TEST. Lab software. Here we find that the distinction between harmonics and structural poles is difficult. It is therefore not possible to decide if a pole is real or spurious.
But even simpler, buffy coats from routine whole peripheral blood donations (PB) are available daily in many blood banks in Europe and may be source material for stem cell isolation  [18]  and in vitro platelet production on a research scale  [19]. In this study, the efficiency and quality of in vitro platelet production in a microfluidic flow chamber chip coated with von Willebrand factor (VWF)  [20]  was evaluated for cells derived from UCB compared to PB. The platelets were studied by differential interference contrast (DIC) and fluorescent microscopy and flow cytometry. Data were compared to standard blood bank platelets (bPLT).
tailed t-test (with Welch's correction for unequal variances), by one-way ANOVA for comparison of mean number of receptors per PLP (Tukey's correction for multiple comparison) or with two-way ANOVA (Sidak's correction for multiple comparison) for comparison of both HSC sources at different time points of culture. Correlation was by Spearman's algorithm. Statistical analyses was performed with Prism version 7. 04 (GraphPad Software Inc. , La Jolla, CA).
Acknowledgements This research was supported by the  Foundation for Scientific Research of the Belgian Red Cross-Flanders , the  Special Research Fund of Ghent University  ( BOF 30290744 ) and the  ME TO YOU foundation, Tielt, Belgium. This work was partially supported by  LFB-Biotechnologies  through a service agreement with PlatOD. Special thanks to all voluntary donors of the  Belgian Red Cross-Flanders  for their invaluable contribution.
The minimum chlorophyll a fluorescence in the light was computed by the Imaging Win V2. 41a software (Heinz Walz GmbH, Effeltrich, Germany) as F o ' = F o /(F v /F m + F o /F m '). By using Win software, we calculated the allocation of absorbed light energy at PSII, that is the effective quantum yield of photochemistry (Φ PSII ), the quantum yield of regulated non-photochemical energy loss (Φ NPQ ), and the quantum yield of non-regulated energy loss (Φ NO ). The relative PSII electron transport rate (ETR), the fraction of open PSII reaction centers, the so-called photochemical quenching (q p ) and the non-photochemical quenching that reflects heat dissipation of excitation energy (NPQ) were also calculated.
5% and 238 U + / 232 Th + intensity ratio was less than 1. 2. For leaf sample analysis optimization of the parameters, such as energy of laser beam, spot size, shot frequency and scanning speed, was performed. The laser ablation conditions were chosen so that the ablation of the sample was completed. In the experiment, the volume of standard ablation chamber was reduced to ~10 mL, which shortened the washout time of the aerosol and improved the LA images. The instrumental and analytical conditions of LA-ICP-MS are summarized in Table  1. For bioimage generation, LA-iMageS software was used [54].
The probe position was placed along the muscle fiber orientation. A semi-automatic method was developed to quantify the ZM muscle elasticity, and the repeatability was analyzed at one-week intervals. The mean elasticity for the two probes was about 15 kPa. The SLH20-6 probe yielded a higher mean elasticity (approximately 6 kPa) and less homogeneous echogenicity than the SL10-2 probe. Two distinct groups of texture profiles as a function of the transducer were obtained. This study will provide some guidance for clinical practices and will allow the construction of a reference database that could be used to evaluate treatments and develop numerical models of facial expression.
Data Processing The Aixplorer Multiwave TM System allows for the automatic quantification of elasticity by placement of a rectangular box. DICOM (Digital Imaging and Communications in Medicine) data were exported and analyzed using custom MATLAB scripts (The MathWorks Inc. , Natick, MA, USA). To quantify the SWE elasticity of the ZM, a semi-automatic method was developed using ImageJ 1. 46/Java 8 software (National Institute of Health, Bethesda, MD, United States)  [23]. A mask was manually traced in the belly region of the ZM from the B-mode image to delineate the region of interest (ROI) along the ZM, which was then superimposed onto B-mode and elasticity images (Figure  1 ). From this ROI, the mean E values were calculated. In addition to the mean elasticity parameters, the To compare the images produced by the two transducers, histograms in the ROIs were further analyzed.
SWE Morphological, Textural and Elastic Properties Figure  2  and Table  1  summarize the ZM muscle thickness (MT), echo intensity (EI) and Young's modulus (E) results. The MT and EI did not significantly differ between the two probes. However, the mean elasticity achieved with the SLH20-6 probe was significantly higher (18. 3 ± 3. 7 kPa) than that achieved with the SL10-2 probe (12 ± 4. 3 kPa). The range (from 4. 6 kPa to 20. 5 kPa) of the elastic data obtained with the SL10-2 probe was larger than that obtained with the SLH20-6 probe (from 13. 3 kPa to 27. 6 kPa). The coefficient of variation in the B-mode images obtained with the SLH20-6 probe showed a larger range of data than that obtained with the SL10-2 probe, revealing less homogeneous echogenicity Figure  2. Zygomaticus muscle thickness (MT), echo intensity (EI) and Young's modulus (E) data for 15 healthy volunteers obtained using two linear transducers, SL10-2 and SLH20-6 (**p < 0.
001). (Figure  3 (a)). Conversely, the coefficient of variation in elasticity obtained with the SLH20-6 probe showed a smaller range of data than that obtained with the SL10-2 probe, demonstrating a more stable result (Figure  3(b) ). Analysis of the textures of the B-mode images provided the following three discriminant texture parameters as determined by Fisher's coefficient (Figure  4 ): parameters of the autoregressive model (sigma and theta1) and the co-occurrence value of correlation S (1, 0). Figure  4  shows the results of comparing the texture profiles of the SL10-2 probe (labeled #1) and the SLH20-6 probe (labeled #2). Two distinct groups of texture profiles as a function of the transducer were obtained. These results demonstrate that the probes were able to differentiate the ZM texture profiles.
CONCLUSION The present study is the first to demonstrate the utility of US elastography in the regular care of facial palsy patients and paves the way for other applications. Development of an US protocol will allow the construction of a reference database composed of the morphological and elastic properties of mimic muscles. These facial quantitative data will enable the adaptation and evaluation of treatments. Future research could be conducted on ZM with different morphologies (bifid, fibrous, etc. ), thicknesses and in different state (stretch and contracted). In addition to clinical applications, these values could be used for the development of numerical facial expression models  [28, 33]  using the true in vivo characteristics of the zygomaticus muscle.
X It is important to note that the variable A is not an arbitrarily created free parameter, but has physical meaning within our experimental design. Each experimental dataset reported in the present study features a unique surface area, meaning that the same mass of newly formed mineral is distributed over distinct total solid phase areas and thus the depth of this newest layer differs. The fluid-interaction depth then dictates the amplitude of kinetic fractionation and the apparent rate of isotopic re-equilibration observed between the fluid and bulk solid (discussed further in Section 4. 4). By adjusting A between experiments, we are thus able to impose unique time intervals to average over, which then provide a proxy for the fluidmineral interaction depth over a known surface area. This correlation between the amount of solid phase precipitated on a given surface area over a given time interval and the depth into the surface that the fluid can isotopically exchange with is a key feature of the modeling framework.
Given the success with which Rayleigh approaches have been used in prior Si studies, we begin our analysis of the present amorphous silica precipitation experiments using this framework. However, it is immediately apparent that a Rayleigh model using a fixed fractionation factor of 0. 9967  (Roerdink et al. , 2015)  fails to capture the variability in both measured fluid δ 30 Si and δ 30 Si of the cumulative precipitated amorphous silica calculated according to closed system, mass balance (Section 3. 3, eq. 6) as a function of reaction progress (Fig. 3 ). In all three batch datasets, the Rayleigh model reasonably reproduces the trend in fluid isotope enrichment early in the experiment (when precipitation rates are high), but systematically over-estimates the evolution of fluid δ 30 Si as precipitation rates slow down in the approach to metastable equilibrium.
A transient model for mixed kinetic and equilibrium behavior To explore the effects of surface area and solid to fluid ratios in a fully transient framework, we developed a numerical model (described in Section 2. 3) using CrunchTope, an isotope-enabled, multi-component reactive transport software  (Steefel et al. , 2015). The input parameters used for our numerical simulations corresponded to the experimental conditions for each of the three datasets with distinct surface area values (Table  3 ). Across all simulations we used one common set of reaction rate constants, initial aqueous silica concentrations, and kinetic and equilibrium fractionation factors for all three experiments. The only parameters that vary between the three cases are the surface area and the solid to fluid ratios. Before applying our transient model to the δ 30 Si batch data, we first verified that the simulation reproduced our experimental SiO2(aq) concentrations and found good agreement (Fig.
As a final check we attempted to fit our present dataset using the Rb = Rp 1/2 functional form proposed for calcite (DePaolo, 2011) , but failed to reproduce observed α variability with this approach. Thus, further work is necessary before we can defensibly suggest any variability in Rb for amorphous silica precipitation and associated implications for observed fractionation factors. The initial observed fractionation for the high SA experiment (9obs = -3. 73‰) is smaller than those observed at the start of the medium (9obs = -4. 12‰) and low (9obs = -4. 42‰) SA experiments. The corresponding α kin (0. 9967) value which reasonably captures both the Roerdink et al. (2015)  dataset and our high SA experiment (Fig. 4, solid black line) similarly fails to capture the evolution of the initially high α obs values for the low (0.
ACKNOWLEDGMENTS This material is based upon work supported by the  National Science Foundation Graduate Research Fellowship Program  under Grant No. DGE -1144245  and by the  Chateaubriand Fellowship of the Office for Science & Technology of the Embassy of France in the United States. Special acknowledgment is given to  Jérôme Gaillardet ,  Julien Bouchez , and the  G2E (Géochimie des enveloppes externs) group  for their assistance and allowing us access to the facilities at the  Ínstitut de Physique du Globe de Paris (IPGP) in Paris, France  to perform measurements necessary for this study. We are thankful to  Jessica Dallas  and  Pascale Louvat  for their support with silicon isotopic analyses and  Pierre Burckel  for silica concentration analyses.
Data collection Data were obtained from medical records and interviews with patients, relatives and/or general practitioners. Patients received a written explanation of the study protocol. The study protocol was approved by the Committee for the Protection of Persons (DC 2016/55), the French National Agency Regulating Data Protection (n° 1935307 v 0) and the Ethics Committee of Bordeaux University Hospital (n°CE-GP-2018/16). All clinical signs were noted at the time of blood collection. The presence of geriatric syndromes (delirium, falling, acute urine retention or incontinence and decreased general condition/functional decline) was also recorded at that time. Data on patients' demographic characteristics and reasons for admission to hospital were collected. Comorbidities were assessed using the Charlson Comorbidity Index (CCI)  (22)  and the number of medications prescribed.