dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistics We retrospectively extracted data from the electronic medical records system. The primary outcome variable was the total opioid consumption in the PACU. Patients requiring an IV morphine titration in the PACU were regrouped into a subgroup called M+, while those who did not receive morphine were assigned into the M-subgroup. The secondary outcomes were the total opioid consumption during the first and second postoperative days (D1 and D2) and the worst pain score that was recorded at any time during the PACU stay on D1 and on D2. As this was a study with a rather pragmatic nature, we did not predetermine a patient population size. Based on a preliminary evaluation of 125 patients  [22] , an analysis of every 50 new patients did reveal any variations in the consumption of postoperative morphine in the PACU [expressed as the mean ± the standard deviation (SD)].
Results We retrospectively evaluated the anesthesia files of 362 consecutive patients who underwent the OFA protocol between June 2015 and May 2019. Forty-eight patients were excluded because they had received an epidural (OFA-epidural). 314 remaining patients received a total IV-OFA protocol (11 in the last 6 months of 2015, 41 in 2016, 69 in 2017, 139 in 2018, and 54 in the first 5 months of 2019). However, three patients were excluded due to missing PACU data (see Fig. 1 , flow chart). Irrespective of the surgery, no patient scheduled to undergo the OFA protocol received intraoperative opioids (i. e. , 0% OFA protocol failure).
The muon spectrometer (MS) surrounds the calorimeters and is based on three large air-core toroidal superconducting magnets with eight coils each. The muon spectrometer includes a system of precision tracking chambers and fast detectors for triggering. The trigger system consists of a hardware-based first-level trigger and a software-based high-level trigger (HLT)  [16] , which reconstructs the event in a manner similar to that performed offline. Data for this analysis were recorded during a special running period in 2017 in which the mean number of pp interactions per beam crossing was two. Events were recorded using triggers that require a muon at the HLT stage with p T larger than 4 GeV in coincidence with various triggers designed to select highmultiplicity events  [17]. The latter included requirements on the transverse energy in the calorimeter, and the number of space points recorded in the silicon microstrip detector, and the number of charged-particle tracks reconstructed by the HLT.
The v 2 of muons from bottom decays is consistent with zero in the entire N rec ch range of the measurement and has no discernible p T dependence. In contrast, the v 2 of muons from charm decays is nonzero at lower p T but consistent with zero at higher p T within the sizeable uncertainties. It also shows no significant N rec ch dependence within the uncertainties. In summary, a measurement of elliptic flow coefficients for heavy-flavor decay muons in pp collisions at 13 TeV is presented, including a separation between charm and bottom contributions. The measurement uses a dataset corresponding to an integrated luminosity of 150 pb -1 recorded by the ATLAS experiment at the LHC. The inclusive heavy-flavor muon v 2 values are not dependent on N rec ch in the range 60-120 and show a clear decrease with p T from 4 to 7 GeV.
acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW 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 and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRT, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russia Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America.
In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; ERC, ERDF, Horizon 2020, Marie Skłodowska-Curie Actions and COST, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; CERCA Programme Generalitat de Catalunya and PROMETEO Programme Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom.
Dynamic Light Scattering (DLS) and Zeta Potential Determination DLS measurements were performed with a Malvern Zetasizer Nano ZS (Malvern Instruments Ltd. , Worcestershire, UK) equipped with a 633 nm He-Ne laser and operating at an angle of 173 •. The software used to collect and analyze the data was the Dispersion Technology Software (DTS) version 5. 10 from Malvern. 600 µL of each sample was measured in low volume semi-micro disposable sizing cuvettes (Fisher Scientific, USA) with a path length of 10 mm. Triplicate measurements were made at a position of 4. 65 mm from the cuvette wall with an automatic attenuator. For each sample, 15 runs of 10 s were performed. The size distribution, the Z-average diameter and the polydispersity index (PDI) were obtained from the autocorrelation function using the "general purpose mode" for all nanoparticle samples.
MRI Phantoms MR images of tubes containing PBS, AuNP-Gd and AuNP-Gd-BBN were acquired on a BioSpec 9. 4 T horizontal magnet (94/21 USR, Bruker BioSpin, Wissembourg, France) equipped with B-GA06S gradient system (950 mT/m maximal strength and 35 mm inner diameter) and Paravision 5. 0 software (Bruker BioSpin, Wissembourg). Images were acquired using rapid spin echo sequence (RARE sequence, factor 8) with TE = 10 ms; 30 ms; 50 ms; 70 ms; and 90 ms and TR = 2. 5 s; 1. 5 s; 1. 0 s; 0. 8 s; 0. 5 s; and 0. 2 s. The resolution was 156 × 156 µm 2 with a matrix of 256 ×256. Slice thickness was 1. 0 mm. T 1 and T 2 maps were generated with Paravision software, and T1 and T2 values were calculated from these maps. The Gd 3+ concentrations were assessed by ICP-OES.
The absorbance at 570 nm was measured using a plate spectrophotometer (Power Wave Xs, Bio-Tek, Winooski, VT, USA). Cell viability was presented as a percentage of untreated cells and non-irradiated cells (control cells) considered as 100%. Data represent the mean ± SD of four independent experiments. GraphPad Prism 7. 0 (GraphPad Software, Inc. , La Jolla, California, USA) was used for the statistical analysis. A comparison of values was performed with a non-parametric t test with Welch's correction; p-values < 0. 05 were used to define statistical significance.
In vivo bioluminescence imaging Primary tumor growth and metastasis development were monitored weekly by in vivo bioluminescence imaging (BLI) for 6 weeks after tumor cell implantation. Five minutes before imaging, awake mice received an intraperitoneal injection of 150 µg/g of D-luciferin (Promega, France),were then anesthetized (isoflurane 4% for induction and 1. 5% thereafter) and placed in the BLI system (IVIS Kinetic, PerkinElmer, USA), first on the left side and then in supine position. Semiquantitative data were obtained from bioluminescence images by drawing regions of interest on the liver area. Based on the bioluminescence signal from the liver, mice were distributed into two groups: early stage (n=12): BLI signal < 7. 7×10 6 ph/s; advanced stage (n = 12): BLI signal > 2. 4×10 7 ph/s.
Contrast agent fluorescence and photoacoustic detectability The dilutions of each contrast agent were prepared in PBS. Ten microliters drops of contrast agent at each concentration (n = 3) and 10-µL drops of PBS were then placed on a piece of parafilm, and fluorescence imaging was performed using the Fluobeam800 (Fluoptics, France) (laser excitation: 780 nm; emission: 820 nm long-pass filter). The fluorescence signal was quantified using Wasabi software (Hamamatsu photonics, Japan) and expressed as Relative Light Units normalized by exposure time (RLU/ms). Multispectral PAI was performed with the VevoLAZR (Fujifilm, Visualsonics Inc. , Canada) to characterize the ICG and Angiostamp800 photoacoustic spectra. Samples of various contrast agent concentrations (n = 3) were placed into a 1 mm 3 silicon cassette and submitted to spectroscopic PAI from 680 to 970 nm with a 1 nm step.
[9]. Then, 1, 24 and 48 hours after ICG injection and 24 hours after Angiostamp800 injection, whole-body 2D fluorescence imaging was performed before 3D PAI (at 800 nm) was carried out on the whole liver and 2D multispectral PAI (from 680 to 970 nm, 5 nm step) was implemented on a metastasisbearing tissue slice. Spectral unmixing analyses were performed using VevoLab software according to  [11].
Combined fDOT and microCT imaging One hour after ICG injection or 24 hours after Angiostamp800 injection, anesthetized mice (isoflurane/air: 2%) were placed in a homemade mobile animal holder for bimodal microCT/fDOT imaging. Three-dimensional fluorescence imaging was performed on the liver using the previously described fDOT system  [3]. MicroCT imaging was performed in line using the vivaCT40 system (Scanco Medical AG, Switzerland), whose acquisition parameters were 55 keV energy, 177 µA intensity, 300 ms integration time and 80 µm voxel isotropic resolution. MicroCT-and 3D-fluorescencereconstructed volumes were merged using ImageJ software. The total fluorescence signal in the volume of interest (liver) was quantified and expressed as RLU.
Acknowledgments Imaging systems used in this study were purchased thanks to  France Life Imaging  ( French program  " Investissement d'Avenir " grant;  "Infrastructure d'avenir en Biologie Sante ",  ANR-11-INBS-0006 ) and the  Contrat Plan Etat-Région Auvergne-Rhone-Alpes. We thank  Gisèle Froment ,  Didier Nègre  and  Caroline Costa  from the lentivectors production facility/ SFR BioSciences Gerland -Lyon Sud  ( UMS3444-US8 ) for providing viral particles for Luciferase transfection.
Metabolomic and RNA-Seq analyses Metabolomics and RNA-Seq analyses were performed as described by  Baldacci-Cresp et al. (2020). For the metabolomics analysis, roots from the CAD (modified) and the wild-type lines (n = 4) were frozen in liquid N 2 and ground. A total of 200 mg of root samples was extracted by two volumes of methanol. The RNA-Seq results were validated with RT-qPCR on a set of 6 genes, implicated in several different metabolic pathways. Fold changes of both methods were highly correlated (r 2 = 0. 98), confirming the robustness of the transcriptomic data. PT1, CYC063, and UBQ22 were used as reference genes  (Baldacci-Cresp et al. 2016 ). The complete RNA-Seq data was submitted to GEO (GSE112673).
GA is known to inhibit arbuscule formation  (Gutjahr 2014; Liao et al. 2018). Since the increase in GA signalling suggested by our transcriptomic dataset is correlated with the deficient mycorrhizal colonisation of the modified line, we may speculate that GA signalling is also negatively associated with ectomycorrhizal colonisation. This is consistent with a recent study showing that exogenous GA treatment of poplar roots impedes the ectomycorrhizal colonisation process  (Basso et al. 2020). DELLA proteins suppress GA signalling, as demonstrated in Medicago truncatula double della mutant  (Gutjahr 2014). Overall, it is expected that crosstalk among these different classes of phytohormones shapes the relation between ectomycorrhizal fungi and roots  (Pozo et al. 2015). Whether the reduced mycorrhizal fungi colonisation at the early stages of plant development affects the overall growth and development of their plant hosts in the field deserves further analysis.
I n a d d i t i o n , Potri. 006G221800 (encoding PtrMYB134, a positive and specific regulator of proanthocyanidin biosynthesis,  James et al. 2017) , is also upregulated in the modified line (Online resource 2). DFR is a key enzyme of the proanthocyanidin pathway, and its coding gene is strongly induced upon herbivory attack in aspen  (Peters and Constabel 2002)  and in poplar overexpressing MYB134  (Mellway et al. 2009). Mellway and colleagues also show that MYB134 upregulates the expression of 4-coumarate:CoA ligase, as observed in our transcriptomic dataset. All together, these data suggest that phenylpropanoids may be involved in resistance to nematode feeding, as observed for other plant defence mechanisms  (Dixon et al. 2002). Lectins are proteins showing nematicidal activity in several species  (Ali et al.
Magnetotellurics (MT) is a geophysical method that investigates the relationships among the different components of the natural electromagnetic field related to the geoelectric structure of the subsurface. Data can be contaminated by anthropic noise sources and suffer from transient noise to signal variations. Since the 80s, robust processing methods have been introduced to minimize the impact of noise on sounding quality. This paper presents Razorback, an open source Python library, implemented to handle, manipulate, and combine time series of synchronous data. This modular library allows users to plug in data prefilters and includes both M-estimator and bounded influence techniques, as well as a two-stage multiple remote reference. Validation of this library is performed on a real data set by comparing the results with those of an existing code.
, 2016). Existing standalone robust codes have specific input formats (time series of electric and magnetic fields, for example) that may be incompatible with Fourier data pre-filtering and that are controlled at a higher level by parameter files. Once the code is launched, tracking the influence of processing parameters in the framework of the code can be difficult. At present, MT processing faces the need for integration and requires an approach to conveniently combine the available techniques as the steps of a full process. We suggest that an easy-to-use open source library would greatly help integrate and experiment with the well-established, the new and the forthcoming techniques. Thus, we implemented the library in Python, a modern programming language that allows modular codes and that is being increasingly adopted in scientific computing.
A similar approach has already be taken by the MTPY library  (Krieger and Peacock, 2014; Kirkby et al. , 2019)  that assists with MT data processing, analysis, modeling, visualization and interpretation while the focus of the present library is the actual processing of MT data. The Razorback library is constructed over elementary components that, when combined, allows for a simple modular implementation of classical weighted least squares and robust TF techniques, such as the M-estimator and bounded influence estimator for MT. The library aims to allow the user to be in control of data transformation at each step of the processing. In MT robust processing, it implies accessing robust Fourier transforms of electric and magnetic fields in the time-frequency representation (i. e. , view of electric and magnetic signals, initially taken to be a functions of time represented over both time and frequency) to track which ones are rejected/down-weighted during pre-filtering and during the robust TF estimation procedure.
In this paper, we first recall the basics of MT TF theory. Next, we define the features, functions and objects provided by the Razorback library. Then, MT data processing examples are shown for validation. Finally, we show two uses of the standard package with higher level functions: (a) performing processing with all possible combinations of synchronous remote reference stations, and (b) performing time lapse processing by subdividing continuous data in consecutive and overlapping window portions. The Razorback library is licensed under GPL v3. 0 and is available at https://github. com/BRGM/razorback. Jupyter notebooks for data handling and MT robust processing are available at https://github. com/BRGM/razorback/blob/doc/ docs/source/tutorials/.
Single Site Least-Squares Estimate Under the Gauss-Markov conditions on the noise (zero mean, homoscedasticity, i. e. , all the data don't have the same finite variance, and no correlation), the ordinary least squares (OLS) operator O OLS can be applied to impedance estimation for a single site (SS) dataset: z i = b H b -1 b H e i. ( 4 ) The accuracy of the OLS estimates can be compromised because (i) data rarely satisfy the homoscedastic assumption due to transient noise sources and (ii) the electromagnetic field data deviate from the plane wave assumption. When a small portion of the dataset is corrupted by (i) and (ii), the OLS method can still provide a reliable estimate. However, correlated EM noise that may exists between the local electric and magnetic fields can lead to strongly biased estimates.
Remote Reference Least-Squares Estimate The remote reference (RR) method introduces an additional magnetic field (b r ) from a distant second site to minimize the impact of noise. The RR method requires uncorrelated noise between the RR site and the local station. The RR-OLS impedance estimate is given by z i = b H r b -1 b H r e i. (5) MT data can still be affected by correlated noise between the RR and the local station, whose impact can be reduced with several RR stations.
Two-Stage Remote Reference Estimate Chave  and Thomson (2004)  proposed a generalization of the RR method to multiple RR data sets. The principle is to use a set of q RR horizontal magnetic fields, collected in the array Q ∈ C N×q. The local magnetic field is linked to Q by b = QW + ǫ, (6) where W ∈ C q×2 is a TF between local and remote magnetic field data. The predicted local magnetic field b is expressed as b = Q W, with W = O(b, Q), (7) where O denotes the operator used to estimate the TF W. The impedance can then be estimated by using the predicted local magnetic field b rather than the local magnetic field b: z q i = O(e i , b) (8) This formulation for multiple RR impedance estimates (Equations 7 and 8) will be referred as the two-stage RR estimate. Chave and Jones (2012)  demonstrated that when only one RR (q = 2) and the OLS are used (O = O OLS ), the two-stage RR estimate is equivalent to the classical RR OLS estimate.
Then, for each site, we extract a sub Inventory, produce the adapted SignalSet and estimate the TF. The data volume of an entire survey exceeding the available computer memory is not rare. This would prevent fully using the Inventory object. Razorback overcomes this limitation thanks to the Dask library  (Dask Development Team, 2016). By storing the time data in a dask. array object rather than a classical (Numpy) array, the memory cost of the Inventory of an entire survey becomes negligible. The time data files are only loaded when needed during the computation of the Fourier coefficients, and then they are unloaded. When we use the dask. array, the memory footprint of the processing no longer depends on the size of the survey. A jupyter notebook dedicated to data handling is provided by the authors and available at https://github.
Computing Fourier Coefficients The SyncSignal and SignalSet objects provide the method fourier_coefficients(freq, Nper, overlap, window) to compute the Fourier coefficients of time data. Here, freq is the frequency of interest f k , Nper is the number of 1/f k periods, overlap is the overlapping coefficient 0 < c < 1, and window is an object coding the data taper window. The window argument must be a function that takes the size of the window (a positive integer) and returns a discrete data taper (an array of the given size). When a calibration has been provided to the SyncSignal or SignalSet object, the calibration value at the given frequency is integrated in the resulting Fourier coefficients. More precisely, the returned value is the Fourier coefficient computed from the raw time value divided by the calibration value.
Razorback provides the slepian_window(half_bandwidth) function that builds the window argument corresponding to the Slepian data taper with a given half bandwidth. The following code shows how to compute the Fourier coefficients from a given signal: As shown, two values are returned, coeffs and winfo. coeffs contains the Fourier coefficients of the different channels of signal. winfo summarizes information on the sliding window: the number of windows, the size of the discrete window and the index shift between consecutive windows. Note that this code works for SyncSignal and SignalSet objects. However, a SignalSet object can hold multiple runs, possibly at different sampling rates. In that case, coeffs contains the Fourier coefficients of all the runs, and winfo describes the sliding window on each run.
In the following, we show how the function transfer_function is used to perform the least squares estimator (4), the M-estimator (  14 ) and the bounded influence estimator (19). We consider two arrays of complex values and size 2 × N k , E and B, that contain the Fourier coefficients of the two electric field components and those of the magnetic field components. Computing the least squares impedance estimate (4) with the function transfer_function is simply done by using the default parameter values: from razorback. mestimator import transfer_function Z_lsq, rixd = transfer_function(E, B) For the M-estimate, we need to specify the weights argument according to (  14 ). Razorback provides implementations of the Huber weighting function (  12 ) and the Thomson weighting function (  13 ). The code is written as follows: To ease the use of the M-estimator and bounded influence methods, Razorback provides helpers for the corresponding weighting function sequences.
Helper Function for Impedance Estimate Section 3. 3 shows how to use the low-level function transfer_function to compute an impedance estimate for given data Fourier coefficients with different algorithms. At a higher level, Razorback provides the helper function impedance that integrates the different algorithms, the use of a pre-filter and the computation of the Fourier coefficients. It aims to provide a simple yet fully controlled use of the methods described in section 2. The function signature is impedance(data, l_freq, weights=(None,), prefilter=None, fourier_opts=None, remote=None, remote_weights=None, remote_prefilter=None, tag_elec='E', tag_mag='B'). The first two arguments are the SignalSet object containing all the relevant channels and the list of the frequencies to investigate.
The other arguments are optional and are used for customizing the processing. The weights and prefilter arguments correspond to those described in section 3. 3. The fourier_opts argument corresponds to the options to pass to the fourier_coefficients method described in section 3. 2. Its default value is dict(Nper=8, overlap=. 71, window=slepian_window(4)). The remote, remote_weights and remote_prefilter arguments are relative to the two-stage RR method defined in equation (  8 ) and illustrated in section 3. 3. To activate the RR method, one must provide the tag gathering the remote channels in data to the remote argument. The remote_weights and remote_prefilter arguments are used to customize the first stage of the method, and the default value None indicates to use the corresponding settings for the second stage (weights and prefilter).