dataesr/scientific-paragraphs-categorization huggingface dataset:
Term rewriting is a simple, yet expressive model of computation, which finds direct applications in specification and programming languages (many of which embody rewrite rules, pattern matching, and abstract data types), but also indirect applications, e. g. , to express the semantics of data types or concurrent processes, to specify program transformations, to perform computer-aided verification, etc. The Rewrite Engines Competition (REC) was created under the aegis of the Workshop on Rewriting Logic and its Applications (WRLA) to serve three main goals: (i) being a forum in which tool developers and potential users of term rewrite engines can share experience; (ii) bringing together the various language features and implementation techniques used for term rewriting; and (iii) comparing the available term rewriting languages and tools in their common features.
Introduction When searching Google for "rewrite engine", most of the references are about Apache web servers and rewrite engines for URLs. Such engines perform string rewriting, which is a particular case of term rewriting  [1, 3] , a very general model of computation based on the repeated application of simplification rules. Despite its simplicity, term rewriting has shown itself a suitable paradigm for expressing fundamental concepts of logics, mathematics, and computer science (e. g. , concurrency, communication, interaction, etc. ). Beyond such theoretical aspects, the ideas of term rewriting influenced the design of specification and programming languages, many of which incorporate algebraic terms and rewrite rules. Software implementations of term rewriting have been developed including, of course, rewrite engines, but also a large variety of tools for compiler construction, program transformation, and formal verification by theorem proving or model checking.
ocaml. org × Opal (OCS)  [23]  http://github. com/TU-Berlin/opal × Rascal  [25]  http://www. rascal-mpl. org × Scala  [22]  http://www. scala-lang. org × SML (MLton)  [21]  http://www. mlton. org × SML (SML/NJ)  [21]  http://www. smlnj. org × Stratego/XT  [5]  http://www. metaborg. org × × × TermWare  [11]  http://gradsoft. ua/index_eng. html × Tom  [2]  http://tom. loria. fr × × × TXL  [8]  http://txl. ca ×.
6, which was oriented perpendicular to the 561nm light sheet. Images were captured using a sCMOS Camera (Orca-Flash4. 0) synchronized with the z-stage moving the sample through the light sheet. The ultramicroscope is managed by Micro-manager software and z-stacks of images were taken every 2µm. The images stacks are fused using the alpha-blending method with a home-made ImageJ macro (Rasband, W. S. , ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, http://imagej. nih. gov/ij/, 1997e2012).
Carotid blood pressure: C57/BL6 mice were anaesthetized as described above, a catheter was placed into the left carotid, connected to a pressure transducer and recorded using a Acqknowledge 881 (MP100WS) software (Biopac System, Inc). A tunnel or cuff electrode were placed under the vagus nerve or the splenic respectively and different patent of stimulation were delivered starting by the lowest instensity. Mean blood pressure and heart rate were analyses using Biopac data acquisition Acqknowledge 881 (MP100WS) software (Biopac System, Inc).
For the evaluation of incidence, mice were considered to have arthritis if the clinical arthritis score was at least at 1 point for three consecutive days. Flow Cytometry: Single-cell suspensions were stained with anti-CD11b (clone M1/70, BD Biosciences), anti-CD11c (HL3, BD Biosciences), anti-MHCII (M5/114,15,2, ebioscience), anti-F4/80 (BM8, ebioscience), anti-Ly6C (AL-21, BD Biosciences). Dead cells were excluded using 7-AAD staining. Data were acquired on a LSRII (BD) flow cytometer and analyzed using the FlowJo software. Statistics: CIA progression was plotted using Kaplan-Meier's curves and differences between groups were estimated using the log-rank test. Normality of sample distribution was assessed using the Kolmogorov-Smirnov test. For comparison between two groups, statistical significance was assessed using unpaired t-test.
These vascular artifacts can lead to overestimating the perfusion. No data exists in the literature about placental blood transit time. The use of a λ (tissue-to-blood partitioning coefficient ) of 0. 9  [6, 37]  and a T1 blood of 1600 ms  [34]  was based on other literature studies, but these two values are unknown and this could also be a limitation of our study. This choice may have affected the results by causing a possible systematic error of the absolute value of PBF but does not affect its relative value and the variability of the results. Some other technical issues related to small animal use must be considered. We were forced to use a single slice sequence to reduce the time of acquisition. It is a limitation of our study because only a part of the volume of the placenta is assessed, and the slice may contain amniotic fluid or another structural feature.
20 in bivariate analysis were included in the multivariable model. Then a final model was built with age, education level, BCVA, and axial length. A manual backward procedure was applied with all variables associated with SFCT (P values < 0. 10). Associations were expressed as b (SE). For all analyses, the tests were two-sided and the results were considered significant when P values were less than 0. 05. Analyses were performed using SAS software (version 9. 4; SAS institute, Inc. , Cary, NC, USA).
Acknowledgments The authors thank  Sandrine Daniel  for her precious skills in data management for the Montrachet study. Supported by an  interregional grant  ( Programme Hospitalier de Recherche Clinique ) and the  Regional Council of Burgundy ; by  INRA ,  CNRS ,  Université de Bourgogne, Regional Council of Burgundy France (PARI Agrale 1) ,  FEDER (European Funding for Regional Economic Development) ; and a  French government  grant managed by the  French National Research Agency (ANR)  under the '' Investissements d'Avenir'' program ,  ANR-11-LABX-0021-01-LipS-TIC Labex.
Methods We searched MEDLINE, Embase, and the Cochrane Library database using the key terms "aortic valve stenosis" and "longitudinal strain" between 2005 and 2017 without language restriction. The protocol of this individual participant data meta-analysis was validated by the Research & Innovation Committee of the European Association of Cardiovascular Imaging (EACVI) and the study was conducted on behalf of all members of the Committee. The PRISMA statement(  14 ) was followed to conduct the individual participant data meta-analysis.
Selection of studies A first selection of the studies was based on the title and on the abstract. The full articles of all selected studies were then consulted in order to verify all pre-specified inclusion criteria. The selection of the studies was performed simultaneously during specific meeting (JM, BC and ED). The flow chart illustrating the selection of the studies process is reported in Figure  1. Great care was taken to avoid inclusion of various studies based on the same cohort population in order to avoid redundancy in the meta-analysis. Finally, all corresponding authors and/or first, second or last authors of the paper were contacted by email in order to propose them to participate to the meta-analysis. Responding authors were invited to share a short anonymized database including a limited number of variables.
Statistical analysis Extraneous data was removed from the database and units of continuous variables were standardized and continuous variables were dichotomized. Descriptive analysis was performed and mean ± standard deviation or proportion was reported. The distribution of LVGLS was compared according to each included study using one-way analysis of variance. A univariate Cox proportional hazards model was used to derive, for each study, the hazard ratio (HR), standard error and 95% of confidence interval (95%CI) related to LVGLS (as continuous variables) and occurrence of death. Log transformation was performed and inverse variances as weights were then calculated for each study. The meta-analysis was performed using random effects models and forest plots were generated to express the pooled effect.
Results A total of 10 studies, including 1 067 asymptomatic patients with LVEF >50% were used for the present individual participant data meta-analysis. The dataset was completed for LVGLS and outcome data. There was 0. 8% of missing values for LVEF (i. e. patients with LVEF >50% but without exact value). The selected studies are summarized in Table  1 , the description of the population is reported in Table  2. The median LVGLS was 16. 2% (from 5. 6% to 30. 1%). A LVGLS>13. 7% was observed in 75% of patients and less than 15% of patients had LVGLS>20% (i. e. preserved LV longitudinal function). In patients with severe AS (i. e. indexed aortic valve area [AVAi] <0. 6cm²/m²), the median LVGLS was 16. 3% (from 6% to 30. 1%). The distribution of LVGLS according to selected studies is reported in Figure  2.
PATIENTS AND METHODS Adults patients with an A1AT deficiency (A1ATD) and followed in our respiratory unit were enrolled prospectively in this study. Patients with an acute respiratory decompensation were excluded from the study. Twenty-eight patients PiZZ were included in this study. Four patients (2 with PiSZ and 2 with PiZ null) were screened but excluded because the risk of liver involvement is unclear in this setting. Informed consent was obtained for all patients. This study was approved by the Ethical Review Board of South East IV (CPP Sud-Est IV) on 7 th April 2011, ID RCB Number 2011-A00264-37 and was registered as NCT01455298 on https://clinicaltrials. gov. On the same day, all patients underwent a physical examination (with determination of Body Mass Index (BMI) and daily alcohol consumption), a biochemical profiling (which AST, ALT, GGT, ALP, INR, platelet count), an ultrasound (with determination of hepatomegaly, steatosis, dysmorphy and splenomegaly) and a transient elastography (Fibroscan®).
This should occur at large times for ∆ = 1, but also if we consider the regime where one simultaneously takes the limit of large time and ∆ → 1. In such situations we conjecture that the quantum effects as well as the lattice effects will become weaker and weaker, and that the classical (anisotropic) LL description should become quantitatively accurate when compared to the XXZ problem. This conjecture is supported by the data presented below, where we compare the numerical solutions of the (classical) LL equations to numerical results obtained for the (quantum) XXZ spin chain with ∆ close to 1. Of course the short-time dynamics of the XXZ model is not classical, and we should therefore expect a quantitative agreement between the lattice quantum problem and the classical LL model only for quantities that are independent of the short-distance properties of the initial domain wall.
Another important consequence of the lattice is the existence of a maximum Lieb-Robinson velocity in the quantum chain, whereas arbitrarily high velocities can in principle be observed in the continuum limit. Weak quantum fluctuations will also be present at any finite time, since the energy density is never strictly zero for t < ∞. Analyzing in detail the quantum corrections to the classical dynamics is beyond the scope of this paper, but the numerical data presented in this study show that many observables in the XXZ chain behave almost classically at long times.
Ω z (r,t) r/t Δ=0. 95 t=200 t=400 t=800 GHD -0. 4 -0. 2 0 0. 2 0. 4 -0. 4 -0. 2 0 0. 2 0. 4 Ω z (r,t) and <S z > r/t Δ=0. 99 t=100 t=200 t=400 t=800 XXZ t=100 GHD Figure  3 : Ω z (r, t) from the solution of the classical LL equation, as a function of the rescaled position r/t for ∆ = 0. 8, ∆ = 0. 9, ∆ = 0. 95 and ∆ = 0. 99 and for different times from t = 100 to t = 800. The black lines, labeled GHD, represent the long-time limit of the profile for the XXZ chain, S z = -r 2t 1 -∆ 2 -1/2 = -r 2t (v XXZ ) -1 [19] , as predicted by GHD. In the panels associated to ∆ = 0. 8, 9 and ∆ = 0. 99, the black crosses represent tDMRG data for the XXZ chain. While the emergence of a linear profile is quite easily understood for the LL model (Sec. 4. 1), the fact that such a simple profile emerges from such a complicated model as the XXZ chain (for a generic value of ∆) and from the the GHD equations is quite remarkable.
Data The application and validation of the methods for bias correction have been achieved using observational SST and SIC data from the Program for Climate Model Diagnosis and Intercomparison (PCMDI) that are generally used as boundary conditions for Atmospheric Model Intercomparison Project (AMIP) experiments  (Taylor et al. , 2000) , called "PCMDI obs. " or "observations" in this paper. The AOGCM's historical and projected sea surface conditions come from CMIP5 simulations  (Taylor et al. , 2012). Only the first ensemble members of the historical and the Representative Concentration Pathway (RCP;  Moss et al. , 2010)  4. 5 and 8. 5 simulations have been considered. Most methods have been tested using CNRM-CM5, IPSL-CM5A-LR and HadGEM-ES coupled GCM. Data from NorESM1-M, MIROC-ESM, EC-EARTH and CCSM4 have also been used as analogue candidates in the analogue method for sea ice.
The double criterion on both SIE and SIA was introduced in order to distinguish cases in which the total SIE in a sector is similar but the average SIC is very different (and vice versa). In order to avoid issues introduced by different land masks between AOGCM and PCMDI data, we filled land grid points with sea ice using a nearest neighbour method and masked all the grid points with the same land mask built with land fraction from PCMDI data in order to compute SIEs and SIAs for each region with the same reference. Analogues are attributed without taking into account the month of the analogue candidate in the library. This allows, for instance, for the attribution of a summer sea-ice map from present observations for a future winter month reconstructed sea-ice field. For each quantile (q), month (m) and sector (s), this procedure yields a hemispheric SIC field SIC opt (i, q, m, s) that minimises the cost function for the given sector, month and quantile.
Real-case application Here, we present the application of the anomaly and the quantile-quantile methods in a real-case application. For this application, we use SST from the PCMDI observation data set over 1971-2000, from IPSL-CM5A-LR and CNRM-CM5 historical simulation over the same period, and the RCP8. 5 scenario over 2071-2100. Histograms of the frequency distribution of SST for different regions of the world (Weddell Sea, central Pacific and North Atlantic) have been plotted in order to compare frequency distributions in the observations, in the GCM historical and future simulations, and in the estimated bias-corrected future SST using the quantile-quantile and anomaly methods (Fig. 4 ). In this figure, we can appreciate the change in mean and variance between the GCM historical simulation and the GCM future scenario and between the PCMDI observations and the biascorrected SST scenario.
Perfect model test In this section, we present the results of the application of the perfect model test for the three methods for the bias correction of SIC. The term "perfect model test" is not absolutely pertinent for the evaluation of the look-up table method, as we first computed LUTs using SST and SIC from an AOGCM historical simulation. Then, we used the SST of the climate change projection from the same AOGCM and re- trieved SIC with the help of the previously computed LUT. An example of computed LUT using data from the historical simulation of CNRM-CM5 can be seen in the Appendix (Fig. B1 ). It is noteworthy that this new LUT is significantly different from the one using PCMDI observations (Fig. 1 ). Even though the use of this LUT for the perfect model test instead of LUTs computed using observed SST and SIC over the AMIP period can be discussed, the use of LUT computed using observations would necessarily produce a poorer result for the reconstruction of SIC of the AOGCM scenario in a perfect model test.
Real-case application We applied the three bias-correction methods using PCMDI SIC data from the 1971-2000 period, as well as the IPSL-CM5A-LR and CNRM-CM5 historical data over the same period and data from the RCP4. 5 and RCP8. 5 scenarios from 2071-2100, in order to obtain future bias-corrected SIC. The reliability of the methods is evaluated by comparing the change in mean and variance between the observations and the bias-corrected projected SICs with the corresponding changes in the original AOGCM scenario with respect to the historical simulation. We consider here that an ideal method should apply the same statistical changes to observed sea ice as the one present in the climate change projection used to derive climate change signal. In Fig. 7 , the bias-corrected mean SIC change is plotted against the corresponding change in mean SIC in the AOGCM scenario used to determine the climate change signal.
As a result, the reconstructed SST time series has the chronology of the AOGCM projected scenario. A2 Quantile-quantile method This method has been proposed and described in  Ashfaq et al. (2011). It consists of adding, for each grid point and each calendar month's quantile in the observations, the corresponding quantile change in the GCM data set, i. e. the difference between the maximum SST in the projected scenario and in the historical simulation, between the second highest SSTs in the two simulations and so on for each ranked SST quantile. However, unlike  Ashfaq et al. (2011) , we did not create a new SST field for the present by replacing SST from the GCM in the historical period with its corresponding quantile in the observations, but we directly added the quantile change to the corresponding quantile of the observational time series (Fig.
Acknowledgements. This study was funded by the  Agence Nationale de la Recherche  through contracts  ANR-14-CE01-0001-01  ( ASUMA ) and  ANR-15-CE01-0003  ( APRES3 ). We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups participating in CMIP5 for producing and making available their model output. For CMIP the  U. S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison  provides coordinating support and led the development of software infrastructure in partnership with the  Global Organization for Earth System Science Portals. Rein Haarsma  was funded through the  PRIMAVERA  project under grant agreement  641727  in the European Commission's  Horizon 2020 research programme.
Code and data availability. The FORTRAN code enabling the generation of bias-corrected future SST and SIC using CMIP5 scenarios and PCMDI data as input is publicly available for each method via https transfer (https://mycore. core-cloud. net/index. php/s/80x0Te7CQ0BowNG, last access: 9 January 2019) or using the following DOI: https://doi. org/10. 17605/OSF. IO/EFUY2 (Beaumet  and  Krinner, 2018a). Bias-corrected future CMIP5 scenarios (RCP4. 5 and 8. 5) realised within the framework of this study (IPSL-CM5A-LR and CNRM-CM5) are available as well (https://mycore. core-cloud. net/index. php/s/Q1cIsS71Mo4vGrG, last access: 9 January 2019) or by using the DOI https://doi. org/10. 17605/OSF. IO/GMH8C (Beaumet and Krinner,  2018b).
Identification of Raters and Reviewers We identified volunteers for this project by polling the full committee. There were 2 groups of volunteers. The first group identified, categorized, and ranked the domains and requirements for both autologous and allogeneic HCT, and the second group reviewed these data and established a consensus. The 15 HCT physicians who participated (raters, n = 10; reviewers n = 5) had between 4 and 40 years of experience in autologous and/or allogeneic HCT procedures. They represented pediatric and adult programs, were mostly from academic centers, and were from distinct global regions (Austria, Brazil, France, Germany, India, Italy, Japan, Morocco, Saudi Arabia, and the United States). Raters and reviewers from Brazil, Germany, India, Saudi Arabia, and the United States were directly involved in the development of new transplantation centers.
Participation in clinical trials may also depend on whether the center is accredited by national or international agencies, such as the Foundation for the Accreditation of Cellular Therapy (FACT) and the Joint Accreditation Committee ISCT-Europe & EBMT (JACIE). For other centers, working through the accreditation process is important to standardize all the processes associated with HCT and improve quality. However, given the demanding nature of formal accreditation, the timing and necessity for approval need to be balanced with the cost and time investment for establishing an HCT program. A commitment to work toward accreditation should be part of the aspirational goals of any new program. Another important activity is the development of a process to monitor and understand HCT outcomes. This is done by registering data from all HCTs with national or international outcomes databases.
Statistical analysis Statistical analyses were performed using SPSS v. 23. 0 (SPSS, Chicago, IL). Continuous variables are summarized as mean ±standard deviation following the central limit theorem for the total population. Categorical variables are presented as number (%). Normality was tested using the Shapiro-Wilk test. Comparisons between groups were performed using one-way variance analysis for continuous variables and Chi-square test for categorical data. Pearson correlation coefficients (r) were used to express the correlation between hemodynamic or echocardiographic indices. Survival estimates were based on the Kaplan-Meier method and compared by log-rank statistics. Patients who underwent heart transplant were right-censored on the transplant day and considered as "alive". Logistic regression analyses were performed on preoperative variables to identify correlates of the primary endpoint (<30 days RVAD need) or secondary endpoints (<30 days clinical RHF excluding those requiring RVAD or composite endpoint of RVAD need or RHF) as all events occurred within 3 weeks of LVAD implantation, with a majority (77% of RVAD) occurring within 72 hours.
M. Amsallem received a  Young Investigator Seed Grant  from the  Vera Moulton Wall Center at Stanford  and is supported by a public grant overseen by the  French National Research Agency  as part of the  second Investissement d'Avenir program  ( ANR-15-RHUS-0002 ). M. Aymami  received a Grant from the  French Federation of Cardiology. None of the sponsors participated in the study design, collecting, analyzing, and interpreting the data, writing the report, or deciding whether and where to submit the report for publication. The authors would like to thank the  Stanford Cardiovascular Institute  and the  Vera Moulton Wall Center of Pulmonary Hypertension at Stanford  for their support. M. Amsallem received a  Young Investigator Seed Grant  from the  Vera Moulton Wall Center at Stanford  and is supported by a public grant overseen by the  French National Research Agency  as part of the  second Investissement d'Avenir program  ( ANR-15-RHUS-0002 ).
II. C. Molecular Dynamic (MD) simulation of the NP thermal expansion The energy deposited by the impinging SHI into the NP is rapidly transferred from the electronic to the atomic subsystem in the form of heat (see section II. B). Thus, MD simulation is used to estimate the thermal-expansion velocity of gold spherical NPs in vacuum with a diameter in the range 4 to 10 nm when the temperature is increased from 300 K up to a temperature (1500 K) greater than the melting point. Dataset is then extrapolated to larger NP sizes up to 30 nm. Here, Au-Au interactions are described using an empirical n-body potential  [25]  of the tight-binding type  [26] [27]  that satisfactorily reproduces their physical properties. The simulation cell is cubic and large enough to follow the expansion of one spherical NP.