dataesr/scientific-paragraphs-categorization huggingface dataset:
chem. agilent. com). NMR Pipe37 and CcpNmr Analysis38 were used for NMR data processing and analysis, respectively. 3D  13  C and 15 N NOESY-HSQC experiments with mixing times of 120 ms were recorded on a Bruker AVANCE III 950 MHz equipped with a TCI probe. In this case, the data was acquired and processed with TOPSPIN 3. 1 (Bruker, Germany). Resonance assignments were obtained from the analysis of standard NMR experiments as previously described35. , where Δδ H and Δδ N are the observed 1 H and  15  N chemical shift changes between the two experimental conditions. For the calcium-free state (in the presence of EGTA), only the conformer for which 94% of backbone resonances were assigned was considered. The secondary structure content of PulGp conformers in the calcium-free and bound states were estimated based on the backbone chemical shifts with the TALOS-N software39.
NMR structure calculation We determined the structure of PulGp in the calcium bound state by performing several cycles of calculation with ARIA 2. 3 software40 coupled to CNS 1. 2 software41, making use of the standard torsion angle/Cartesian simulating annealing protocol. Each cycle consisted of automatic NOESY assignment and structure calculations with 9 iterations with default parameters. In the last iteration 200 structures were calculated and further refined in an explicit water box42. The 15 lowest energy structures exhibiting no NOE restraint violations > 0. 5 Å and no dihedral angle violations > 5° were selected as the final ensemble. Distance restraints were derived from 3D  13  C and 15 N NOESY-HSQC experiments, by making use of the almost complete PulGp resonances assignment previously reported35.
The coordination scheme was set up as described for calcium-bound X-ray structures of homologous major pseudopilins10 and supported by the PulGp-calcium/EGTA CSP results. The geometry of the calcium coordination was maintained by imposing fixed distances between the calcium ion and the above-mentioned oxygen atoms. Proline residues 75 and 103 were defined in cis configuration based on their backbone chemical shifts and predictions using Promega software43. The structure ensemble was visualized and inspected with PyMOL44 and the quality was evaluated with PROCHECK-NMR45 and the PSVS server46. The structure atomic coordinates and restraints used in the calculation were deposited in the Protein Data Bank (PDB id: 5O2Y). We estimated the global rotation correlation time (τc) of the protein after the measurement of  15  N relaxation parameters assuming an isotropic tumbling model and using the program Tensor247 as described previously48.
CryoEM data collection and image processing The PulG sample (3μL, double mutant H106C-W129C) was applied to glow-discharged lacey carbon grids (TED PELLA, Inc. , 300 mesh). Then the grids were plunged frozen using a Vitrobot Mark IV (FEI, Inc. ), and subsequently imaged in a Titan Krios at 300keV with a Falcon II direct electron detector (pixel size 1. 05 Å /pixel). A total of 1,819 images, each of which was from a total exposure of 2 seconds dose-fractionated into seven chunks, were collected and motion corrected using MotionCorr49. Program CTFFIND350 was used for determining the defocus and astigmatism. Images with poor CTF estimation as well as defocus > 3μm were discarded. The SPIDER software package51 was used for most other operations. The CTF was corrected by multiplying the images from the first two chunks (containing a dose of ~ 20 electrons/ Å 2 ) with the theoretical CTF, which is a Wiener filter in the limit of a very poor signal-to-noise ratio (SNR).
Model building and refinement The PulGp NMR structure was docked in the cryo-EM map of PulG H106CW129C by rigidbody docking with the program Situs53. Next, we generated a homology model of PulG Nterminal α-helical residues (T2-V20) with the software Modeller 54 based on the structure of the Pseudomonas PAK pilin (PDB 1OQW) as template. This model of the PulG Nterminal helix was rigidly docked in the remaining density of the cryo-EM map (not occupied by PulGp) with Situs53 (residues 25-26 were not included since they are not ordered in the NMR ensemble). A full-length model of PulG was then constructed with Modeller54 by connecting the docked PulGp and N-terminal helix with an extended linker (V21-G26) and adding F1. Side-chains of H106 and W129 were replaced by a Cysteine side-chain and a methyl group was added to the N-terminal Phe (N-methylphenylalanine).
Acknowledgements This work was funded by the  Institut Pasteur , the  Centre National de la Recherche Scientifique (CNRS) , the  French Agence Nationale de la Recherche  ( ANR-14-CE09-0004 ), the  European Union   FP7-IDEAS-ERC 294809  (to M. Nilges) and the  NIH   R35GM122510  (to E. H. E. ). We thank  Léa Khoury  for help in sample preparation, the  Plateforme de Biophysique Moléculaire of Institut Pasteur  and  Bruno Baron  for assistance in CD experiments. We are grateful to  Muriel Delepierre  and  Daniel Ladant  for support and interest in this work. We acknowledge  Nelly Morellet  and financial support from the  TGIR-RMN-THC Fr3050 CNRS.
The irreversible illness-death model describes the pathway from an initial state to an absorbing state either directly or through an intermediate state. This model is frequently used in medical applications where the intermediate state represents illness and the absorbing state represents death. In many studies, disease onset times are not known exactly. This happens for example if the disease status of a patient can only be assessed at follow-up visits. In this situation the disease onset times are interval-censored. This article presents the SmoothHazard package for R. It implements algorithms for simultaneously fitting regression models to the three transition intensities of an illness-death model where the transition times to the intermediate state may be interval-censored and all the event times can be right-censored.
Furthermore, both the process of visit times and the observation of the time of the transition into the absorbing state are usually right-censored, i. e. , limited to the individual follow-up period of the subjects. This yields a rather complex general observational pattern, because for a subject who died without being diagnosed as diseased at earlier visit times, it may or it may not be possible to determine retrospectively if and when the subject became diseased between the last visit time and the time of death. The SmoothHazard package  (Touraine, Joly, and Gerds 2017)  provides estimates of the baseline transition intensities and of covariate effects when the data fall into one of the 6 cases that are displayed in Figure  2. Thus, the case of left-truncated event times (delayed entry) is covered, as well as the case where for some subjects the transition time into the intermediate state is observed exactly and for others it is interval-censored.
Comparison to other packages If the exact transition times of an illness-death model are observed, standard procedures can be used to estimate transition intensities, regression coefficients and functionals thereof  (Putter, Fiocco, and Geskus 2007). In particular, in R (R Core Team 2017) the packages survival  (Therneau and Grambsch 2000; Therneau 2017 ) and rms  (Harrell 2017 ) estimate the regression coefficients using the Cox partial likelihood  (Cox 1975)  without the need to model the baseline intensities. Besides, the package simPH  (Gandrud 2015)  is useful to simulate and plot quantities of interest and their uncertainty for coefficient estimates from Cox models (even interactive and nonlinear effects). Moreover, a number of packages facilitate the use of multi-state models and illness-death models.
The packages etm  (Allignol, Schumacher, and Beyersmann 2011 ) and mstate (de Wreede, Fiocco, and Putter 2010 , 2011; Putter et al. 2007 ) can be used to estimate transition probabilities for the Aalen-Johansen estimator but the etm package does not account for the influence of covariates. The p3state. msm package (Meira-Machado and Roca-Pardiñas 2011) obtains non-Markov estimates for the transition probabilities. The TPmsm  (Araújo, Meira-Machado, and Roca-Pardiñas 2014)  package implements several estimators for the transition probabilities, including the Aalen-Johansen estimator and estimators that are consistent even without Markov assumption or in case of dependent censoring. However, when transition times to the intermediate event are interval-censored, it is then generally not possible to arrive at consistent estimates with the software provided by the packages listed above.
Indeed, a common approach for handling subjects who died with unknown disease status consists of artificially ending their follow-up at the last time they were seen without disease and subsequently treating them as right-censored. However, this approach can lead to a systematic bias in the estimates of transition intensities and of regression coefficients  (Joly, Commenges, Helmer, and Letenneur 2002; Leffondré, Touraine, Helmer, and Joly 2013). The bias will be especially pronounced if the risk of death is higher for diseased subjects than the risk of death for disease-free subjects. To our knowledge, there is only one package that focus on fitting illness-death models to interval-censored data, the coxinterval package  (Boruvka and Cook 2015)  that implements a sieve estimator for the Cox regression model.
The msm package  (Jackson 2011 ) is able to fit Markov multi-state models to panel data where the status of the subjects is known at a finite series of inspection times. As a special case the setting includes the illness-death model and it can be used with interval-censored disease times and exact death times. However, in this package the likelihood is calculated using the Kolmogorov differential equations that relate the transition probabilities and the transition intensities and in order for there to be an analytic solution a time-homogeneity assumption is made where all transition intensities are constant or piecewise-constant between two successive observation times. The SmoothHazard package fits also survival models to data that might be interval-censored. However, other packages can be used in this case.
The available packages include the ICsurv package (McMahan and Wang 2014) that implements a semi-parametric proportional hazard regression model, the intcox package that implements an algorithm for extending the Cox regression model  (Pan 1999) , the MIICD package  (Delord 2016 ) that implements a multiple imputation approach to Cox regression and the coarseDataTools package that implements the parametric accelerated failure time model  (Reich, Lessler, Cummings, and Brookmeyer 2009; Reich, Lessler, and Azman 2016). The interval package  (Fay and Shaw 2010)  implements tests for comparing survival distributions for interval-censored data.
A fitted illness-death model as produced by idm can be used to calculate predictions with: • S3 predict method for 'idm' objects: for estimating transition probabilities, cumulative event probabilities and life expectancies for a given set of covariates. In this paper, we focus on the main goal of the package which is fitting illness-death models to interval-censored data. Section 2 presents the model and the likelihood. Section 3 presents the estimation methods. Section 4 briefly presents predictions that can be made in an illnessdeath model. Section 5 provides some examples illustrating SmoothHazard. We discuss limitations and further extensions in Section 6.
Paquid study In order to illustrate the functionality of the package we provide a random subset containing data from 1000 subjects that were enrolled in the Paquid study  (Letenneur, Gilleron, Commenges, Helmer, Orgogozo, and Dartigues 1999) , a large cohort study on mental and physical aging. R> data("Paq1000", package = "SmoothHazard") The population consists of subjects aged 65 years and older living in Southwestern France. The event of interest is dementia and death without dementia is a competing risk. Furthermore, the time to dementia onset is interval-censored between the diagnostic visit and the previous one and demented subjects are at risk of death. Thus, subjects who died without being diagnosed as demented at their last visit may have become demented between last visit and death.
The default convergence criteria are 10 -5 , 10 -5 and 10 -3 , respectively and can be changed by means of the eps argument. We now illustrate how to fit the illness-death model to the Paq1000 data set, based on interval-censored dementia times and exact death times. In the following call, a Weibull parametrization is used for the three baseline transition intensities and we include two covariates on the transition to dementia, one covariate on the transition from no dementia to death and no covariates on the transition from dementia to death. Note that in case of missing formula12 argument the covariates on the 1 → 2 transition are taken to be the same as the ones specified in the formula02 argument. The hazard ratios HR (e coef ) have the usual interpretation, as in a parametric Cox regression model.
Discussion In this article, we have described the methods implemented in the R package SmoothHazard for fitting illness-death models to interval-censored transition times to the intermediate state and exact transition times to the absorbing state. Note that the package also fits simple survival models (two-state models) and also models where the transition times are rightcensored but not interval-censored. We have also explained and illustrated the actual use of SmoothHazard and note that several extensions are in the development phase. One extension is a model which assumes equality or proportionality between the two transition intensities α 02 and α 12. Another is a model which assumes the same covariate effects for these transition intensities. At the moment, the illness-death model implemented in the package assumes a Markov process.
By acquiring a multi-diffusion time CHARMED data set it is possible to accurately estimate the axon diameter distribution function with the AxCaliber framework. In AxCaliber, unlike CHARMED, water diffusion is measured exactly perpendicular to the long axis of the fibers (this is a prerequisite of the model). It was found that by using a gamma function (with only 2 free parameters) it is possible to adequately estimate the axon diameter distribution function by AxCaliber  79. The AxCaliber framework was verified on excised samples of optic and sciatic nerves. These two nerve samples have very different axon diameter distribution functions (Figure  2 ). The AxCaliber diameter distribution functions were in good agreement with the histological ones (Figure  2 )  79. In addition, AxCaliber was implemented to study the morphology of the corpus callosum (in-vivo).
Conclusion and Future Direction Epicardial substrate ablation for BrS is effective and a welcome addition to the therapeutic armamentarium. We hypothesize that a certain subset of patients with BrS could be treated with catheter ablation procedures without the need of an ICD. This is the subset of patients with BrS who had no overlapping syndrome i. e combined BrS and early repolarization syndromes and could be treated with extensive substrate ablation alone, provided that the procedure yields normalization of the right precordial ECG at the higher ICS lead positioning after sodium channel blocker challenge. To test this hypothesis, a multicenter randomized study, Ablation in Brugada Syndrome for the Prevention of VF Episodes (BRAVE study, ClinicalTrials identifier NCT02704416), has been launched.
The SECHIBA module of the ORCHIDEE land surface model describes the exchanges of water and energy between the surface and the atmosphere. In the present paper, the adjoint semi-generator software called YAO was used as a framework to implement a 4D-VAR assimilation scheme of observations in SECHIBA. The objective was to deliver the adjoint model of SECHIBA (SECHIBA-YAO) obtained with YAO to provide an opportunity for scientists and end users to perform their own assimilation. SECHIBA-YAO allows the control of the 11 most influential internal parameters of the soil water content, by observing the land surface temperature or remote sensing data such as the brightness temperature. The paper presents the fundamental principles of the 4D-VAR assimilation, the semi-generator software YAO and a large number of experiments showing the accuracy of the adjoint code in different conditions (sites, PFTs, seasons).
In the present study, we focused on the SECHIBA module  (Ducoudré et al. , 1993) , which is part of the ORCHIDEE land surface model dedicated to the resolution of the surface energy and water budgets. Our objective was to test the ability of 4D-VAR to estimate a set of its inner parameters. A dedicated software (called SECHIBA-YAO) was developed by using the adjoint semi-generator software called YAO developed at LOCEAN-IPSL  (Nardi et al. , 2009). YAO serves as a framework to design and implement dynamical models, helping to generate the adjoint of the model, which permits one to compute the model gradients. SECHIBA-YAO provides an opportunity to control the most influent internal parameters of SECHIBA by assimilating LST (land surface temperature) observations. At a given location and for specific soil and climate conditions, twin experiments of assimilation have been executed.
These twin experiments conducted on actual sites were used to demonstrate the accuracy and usefulness of the code and the potential of 4D-VAR when dealing with LST assimilation. This paper is structured as follows. In Sect. 2, model and data used to illustrate the capabilities of the SECHIBA-YAO are detailed. In Sect. 3, fundamentals of variational data assimilation are presented. In addition, principles of YAO and of its associated modular graph formalism are shown. The principle of the computation of the adjoint with YAO is provided. The implementation of SECHIBA-YAO and the details of the experiments that prove the efficiency of the 4D-VAR assimilation are also given in Sect. 3. Sensitivity experiments and simple twin experiments at two FLUXNET locations are presented in Sect. 4. These experiments illustrate the convenience of YAO to optimize control parameters.
Models and data ORCHIDEE is a land surface model developed at the Institut Pierre Simon Laplace (IPSL) in France. ORCHIDEE is a mechanistic dynamic global vegetation model  (Krinner et al. , 2005)  representing the continental biosphere and its different biophysical processes. It is part of the IPSL earth system model  (Dufresne et al. , 2013)  and is composed of three modules: SECHIBA, STOMATE and LPJ. The version used in this work corresponds to version 1. 2. 6, released on 22 April 2010. SECHIBA computes the water and energy budgets at the biosphere-atmosphere interface, as well as the gross primary production (GPP); STOMATE  (Friedlingstein et al. , 1999 ) is a biogeochemical model which represents the processes related to the carbon cycle, such as carbon dynamics, the allocation of photosynthesis respiration and growth maintenance, heterotrophic respiration and phenology, and finally, LPJ  (Sitch et al.
7975. 10 11 0. 2. (2) In the following, the capabilities of the 4D-VAR are demonstrated in a series of assimilation experiments using the data provided by the FLUXNET network  (Baldocchi et al. , 2001). FLUXNET is a network coordinating regional and global analysis of observations from micrometeorological tower sites. The flux tower sites use eddy covariance methods  (Aubinet et al. , 2012)  to measure the exchange of carbon dioxide (CO 2 ), water vapor, and energy between terrestrial ecosystems and the atmosphere. SECHIBA-YAO can be run using other data as long as the same inputs needed to operate SECHIBA are given. Measurement towers sprang up around the world, grouped into regional networks. The data from all networks are accessible to the scientific community via the FLUXNET website (http://www.
The second part represents the distance between the observations and the model estimates. B is the covariance error matrix of k b and R i is the covariance error matrix of y o at time t i. The objective of this work is to show the capacity of 4D-VAR to help determine the value of the principal inner parameters k of SECHIBA and the initial conditions for surface water content. The present distributed software allows the reader to do his or her own experiments using synthetic or actual data. When the observations are synthetic (produced by the model itself), no transfer functions from the estimation to the observation are needed, and H is taken as the identity matrix. If actual data are used, a specific H is used that transforms the soil temperature into brightness temperature (see section Model and Data).
YAO Variational data assimilation requires the computation of the adjoint code of the direct model, which is a heavy and complex task, especially for a large model such as SECHIBA. Usually, the adjoint code is computed with the help of specific softwares (automatic differentiators) (e. g. ,  Bischof et al. , 1997; Giering and Kaminski, 2003; Hascoët and Pascual, 2004). These softwares are appropriate for the differentiation of large codes, but their use will be optimal only under specific coding conventions and a good level of modularity of the codes  (Talagrand, 1991). Moreover, manual optimization of the produced code is often necessary. Therefore, in many practical cases the automatic production of code will not be totally optimal in terms of flexibility (e. g. , when the direct model is updated frequently, one has to re-differentiate the whole code).
, 2009 , for more details on the "backward" algorithm). This property is the basis of the semi-automatic adjoint computation by YAO. An implementation of a variational assimilation procedure with YAO follows the structure represented in Fig. 3. The YAO compiler builds an executable file following the scheme presented in Fig. 3. This file is independent of the assimilation instructions. The executable file reads these instructions from an instruction file. Due to the graph structure of the model and its adjoint, it is easy to modify the model and its adjoint, e. g. , by updating some adequate modules; one can systematically obtain the updated global direct model and the global adjoint. As mentioned in the Introduction, this paper gives access to a compiled version of SECHIBA-YAO and allows one to perform some assimilation experiments related to the control of the 10 most influent internal parameters of SECHIBA by observing the land surface temperature.
Development of SECHIBA-YAO The implementation of SECHIBA in YAO starts with the definition of the modular graph describing the dynamics of the model (see Appendix A). Elementary processes and interconnections between modules are defined in order to represent the computation flow in the model. The modular graph was built as follows. -Every component of the original code was carefully studied line by line directly. -A list of inputs and outputs for each subroutine was made for every routine of SECHIBA. This permits one to know exactly the information flow in the model. -A second zoom in the subroutines was made in order to understand the internal dynamics of the code. This is the last step in the modular graph definition. When studying the subroutines, their complexity was reduced by breaking the different steps into simpler elements.
The idea is to have a scalable code. Uncoupled modules give more independence when changing part of the model. Cohesive modules help to understand the model. -The original six subroutines in the SECHIBA-Fortran code are split into 130 modules by the SECHIBA-YAO modular graph, corresponding to every process modeled by SECHIBA and to a number of transitional modules serving as auxiliary computing. -It is important to mention that every variable and subroutine name was kept as in the original model. If a user or developer of SECHIBA-Fortran sees the implementation in YAO, he will find his way easily.