dataesr/scientific-paragraphs-categorization huggingface dataset:
Patients with overlap syndrome associating AIH and primary biliary cholangitis (PBC) as defined by Chazouillères et al. [12]  were also included. Data collected from the medical files included: age, gender, other autoimmune diseases, identification of a triggering factor, biological tests including serum hepatic tests, autoantibodies profile and serum γ-globulin or IgG concentration at diagnosis and at treatment withdrawal, histopathological features at presentation and at the end of first line treatment if available, therapeutic modalities, time to remission and duration of biochemical remission, time to relapse after treatment withdrawal. Patient's records were anonymized and de-identified prior to analysis. Database was constituted in accordance with the reference methodology MR004 of the Commission Nationale de l'Informatique et des Libertés (n°2206749, 13/09/2018).
Patient characteristics A total of 302 patients were identified from databases. Two hundred and forty were excluded from the study because of missing data due to lack of regular follow-up in participating centers (n=60), no response to first line treatment (n=58), responders still on first line treatment without second liver biopsy (n=107), type 2 HAI (n=5), no biopsy at diagnosis (n=4), absence of treatment (n=6) (Fig. 1 ). Table  1  shows the baseline characteristics of the study population at diagnosis. Associated autoimmune diseases were coeliac disease (n=1), Gougerot-Sjögren syndrome (n=3), Crohn's disease (n=1), ulcerative colitis (n=1), Biermer's disease (n=2), rheumatoid arthritis (n=2), lupus (n=2), autoimmune thyroiditis (n=4), immune thrombocytopenic purpura (n=1), autoimmune hemolytic anemia (n=1), vitiligo (n=1).
Methods Cohort, clinical phenotyping FTLD cases were collected between 2016 and 2018 (within the IFGC phase-III project [https://ifgcsite. wordpress. com/ongoing-projects/]). The samples were recruited by clinicians and research groups who are part of the IFGC network and based in Italy, Spain, Germany, the Netherlands, Belgium, UK, Sweden, Norway, Slovenia, and USA (Supplementary Table  1 : https://doi. org/10. 5522/04/12418157). Patients were diagnosed at each contributing site (Supplementary Table  2:  https://doi. org/10. 5522/04/12418157) in a harmonised fashion according to international consensus criteria such as the Neary et  [c. f. 27, 28   , see Supplementary Materials and Methods and Supplementary Figure  3 : https://doi. org/10. 5522/04/12418157) data available (Figure  1A ).
F. is supported by  ONLUS Lamezia Terme ; C. G. is supported by grants from  JPND Prefrontals Swedish Research Council (VR)   529-2014-7504 ,  Swedish Research Council  (VR)  2015-02926 ,  Swedish Research Council (VR)   2018-02754 ,  Swedish FTD Initiative-Schörling Foundation ,  Swedish Brain Foundation ,  Swedish Alzheimer Foundation ,  Stockholm County Council ALF ,  Karolinska Institutet Doctoral Funding and StratNeuro, Swedish Demensfonden ; M. G. is supported by the  NIH  (grant number  AG054519 ); M. G. , E. S. , J. T. and V. V. D. are supported by the  NIH  (grant number  AG017586 ), and  P30 AG10124  and  U01 AG062418 ; J. H. and P. A. L. are supported by a  MRC   Programme grant  ( MR/N026004/1 ); J. H. is supported by the  UK Dementia Research Institute  which receives its funding from  DRI Ltd , funded by the  UK Medical Research Council, Alzheimer's Society and Alzheimer's Research UK, Wellcome Trust  (award number  202903/Z/16/Z ),  Dolby Family Fund, National Institute for Health Research.
Data Collection We collected the usual demographic, clinical, biological, management and outcome data from the patients' records. Prolonged immobilization was considered if signs of compression (redness, blisters, pressure ulcers) were reported by the physician when examining the patient's skin. Serum and urinary myoglobin concentrations were not collected as not routinely measured in our ICU. When several data were reported, the most critical values close to the ICU admission were considered.
DIRAC is a freely distributed general-purpose program system for 1-, 2-and 4component relativistic molecular calculations at the level of Hartree-Fock, Kohn-Sham (including range-separated theory), multiconfigurational self-consistent-field, multireference configuration interaction, coupled cluster and electron propagator theory. At the self-consistent-field level a highly original scheme, based on quaternion algebra, is implemented for the treatment of both spatial and time reversal symmetry. DIRAC features a very general module for the calculation of molecular properties that to a large extent may be defined by the user and further analyzed through a powerful visualization module. It allows the inclusion of environmental effects through three different classes of increasingly sophisticated embedding approaches: the implicit solvation polarizable continuum model, the explicit polarizable embedding, and frozen density embedding models.
I. INTRODUCTION DIRAC is a general-purpose program system for relativistic molecular calculations and is named in honor of P. A. M. Dirac (Program for Atomic and Molecular Direct Iterative Relativistic All-electron Calculations), who formulated 1 his celebrated relativistic wave equation for the electron in 1928. The beginnings of the DIRAC code can be traced back to the 4-component relativistic Hartree-Fock code written by Trond Saue during his Master thesis, defended at the University of Oslo, Norway, in 1991. The original code stored all integrals, provided by the HERMIT code, 2 on disk, but during Saue's Ph. D. thesis, defended in 1996, the code was extended to direct Self-Consistent Field (SCF) with integral screening  3  and a highly original symmetry scheme based on quaternion algebra.
In relativistic calculations the integrals are generally complex, in contrast to the nonrelativistic domain, and contain fewer zero elements, since spin symmetry is lost. The DIRAC code features several electronic Hamiltonians, allowing molecular electronic structure calculations at the 4-, 2-and 1-component level. 4-component relativistic calculations are sometimes referred to as 'fully relativistic' in contrast to 'quasirelativistic' 2component calculations. However, a fully relativistic two-electron interaction, which would contain magnetic interactions and effects of retardation in addition to electrostatics, is not readily available in closed form, rendering this terminology somewhat misleading. The default Hamiltonian of DIRAC is the 4-component Dirac-Coulomb Hamiltonian, using the Simple Coulombic correction,  8  which replaces the expensive calculation of twoelectron integrals over small component basis functions by an energy correction.
Self-consistent field (SCF) calculations At the core of DIRAC is an SCF module allowing both Hartree-Fock (HF)  3  and Kohn-Sham (KS)  32  calculations. These calculations are Kramers-restricted and use a symmetry scheme based on quaternion algebra which automatically provides maximum point group and time-reversal symmetry reduction of the computational effort. 4 In nonrelativistic quantum chemistry codes, spin-restricted open-shell SCF calculations employ Configuration State Functions (CSFs) |S, M S of well-defined spin symmetry. However, in the relativistic domain spin symmetry is lost, and so the use of CSFs would require linear combinations of Slater determinants adapted to combined spin and spatial symmetry, which is a challenge for a general molecular code. We have therefore instead opted for the average-of-configuration Hartree-Fock method  33  for open-shell systems.
The program is complemented by a utility program that can convert the MO integrals to formats used by other major quantum chemistry programs, such as the FCIDUMP format,  49  thus facilitating the interfacing  50  of DIRAC to other electron correlation implementations, such as MRCC,  51  or even to quantum computers. With respect to the latter, a 4-component relativistic quantum algorithm was reported in Ref. 52. More recently, DIRAC has been interfaced to the electronic structure package OpenFermion through a Python interface,  53  thus allowing the calculation of energies and energy derivatives on a quantum computer 54 using either the full Dirac-Coulomb Hamiltonian or the Lévy-Leblond Hamiltonian. The implementation has been revised several times over the years to account for changes in computer hardware architectures.
3] [64]  This implementation allows for the calculation of expectation values of one-electron property operators over the CI wave functions. The more recent KR-CI module is a string-based Hamiltonian-direct configuration interaction (CI) program that uses Dirac Kramers pairs from either a closed-or an open-shell calculation in a relativistic two-and four-component formalism exploiting double pointgroup symmetry. KR-CI is parallelized using MPI in a scalable way where the CI vectors are distributed over the nodes, thus enabling use of the aggregate memory on common computing clusters. 65, 66 There are two choices for the CI kernel: LUCIAREL or GASCIP. The LUCIAREL kernel  61, 67  is a relativistic generalization of the earlier LUCIA code by Olsen. 68 It is capable of doing efficient CI computations at arbitrary excitation level, FCI, SDCI, RASCI, and MRCI, all of which are subsets of the Generalized Active Space (GAS) CI.
Based on the original complete open-shell implementation (GOSCIP) of MOLFDIR that is used to obtain state energies after an average-of-configuration Hartree-Fock calculation a more general and efficient GASCIP (Generalized Active Space CI Program) module was originally written by Thyssen and Jensen for KR-MCSCF and later parallelized by Jensen. This specialized CI implementation is primarily used for KR-MCSCF and ESR calculations,  77  but it is also available for KR-CI calculations. Another separate implementation is the spin-free version of LUCIAREL, LUCITA, which fully exploits spin and boson symmetry. LUCITA will consequently be faster for spin-free CI calculations than KR-CI using the LUCIAREL kernel. LUCITA has also been parallelized with MPI,  78  in the same way as KR-CI.
C. Molecular properties A common framework for the calculation of molecular properties is response theory. A less economical, but computationally easier, approach is to use a finite-field approach. Implementations for both strategies are available in DIRAC. The property module of DIRAC has been written in a very general manner, allowing the user to define 4-component property operators, benefiting from the extensive integral menu of the HERMIT module. 2  For convenience, a number of properties are predefined, as shown in Table  I.
73 Electron propagator The Algebraic Diagrammatic Construction (ADC) is an efficient, size-extensive post-Hartree-Fock method, which can be used to obtain molecular properties. With DIRAC the calculation of single 141 and double 142 ionization as well as electronic excitation  143, 144  spectra using the RELADC and POLPRP modules are possible. Decay widths of electronic decay processes can be obtained by the FanoADC-Stieltjes method. 145  The ionization spectra can be obtained at the level of ADC(2), ADC(2x) as well as ADC(3) plus constant diagrams, while the electronic excitation spectra are available at ADC(2) and ADC(2x) levels of accuracy. Technically, the ADC implementation and the RELCCSD code share much of their infrastructure.
Inside the cavity, the permittivity is that of vacuum (ε = 1), while outside the permittivity assumes the value appropriate for the environment being modeled. For example, in the case of water the experimental value ε = 78. 39 would be used. The electrostatic polarization is represented as an apparent surface charge (ASC), which is the solution to the integral formulation of the Poisson equation. The coupling with the QM code at the SCF level of theory is achieved by augmenting the usual Fock operator with an ASC-dependent environment polarization operator. This results in minimal, localized changes to the SCF cycle. The implementation in DIRAC is based on the PCMSolver library,  148, 149  which provides a well-defined interface to a stand-alone computational backend. The PCM method is available for mean-field (Hartree-Fock and Kohn-Sham DFT) wavefunctions.
120 It is also possible to localize molecular orbitals, which is favourable for bonding analysis. 169 e visualization module in DIRAC makes it possible to export densities and their derivatives, as well as other quantities (such as property densities obtained from response calculations) to third-party visualization software commonly used by the theoretical chemistry community such as Molden,  172, 173  as well as by less known analysis tools such as the Topology Toolkit (TTK),  174  with which we can perform a wide range of topological analyses, including atoms-in-molecules (AIM)  175  with densities obtained with Hartree-Fock, DFT and CCSD wavefunctions. DIRAC can export such data in Gaussian cube file format, or over a custom grid. DIRAC has been extensively used for the visualization of property densities, in particular magnetically induced currents.
176, 177 More recently, shielding densities have been investigated in order to gain insight into the performance of FDE for such NMR properties. 163, 166  an illustration of the visualization module, we start from the observation of Kaupp et al. 178  that the spin-orbit contribution to the shielding σ(H β ) of the β-hydrogen of iodoethane follows closely the Karplus curve of the indirect spin-spin coupling constant K(H β , I) as a function of the H-C-C-I dihedral angle. The DIRAC program makes it possible to isolate spin-free and spin-orbit contributions to magnetic properties; 10 this has allowed us to show that this connection is manifest at the level of the corresponding property densities (Figure  2 ).
F. Programming details and installation The source code consists mostly of Fortran 77 and Fortran 90 code, but some modules are written in C (exchange-correlation functional derivatives using symbolic differentiation, pre-Fortran-90 memory management) and C++ (exchange-correlation functional derivatives using automatic differentiation, polarizable continuum model). Python is used for the powerful code launcher pam, which has replaced the previous launcher written in Bash. The code base is under version control using Git and hosted on a GitLab repository server. The main development line as well as release branches are write-protected and all changes to these are automatically tested and undergo code review. For integration tests we use the Runtest library,  179  and we run the test set both nightly as well as before each merge to the main code development branch.
Since the focus in chemistry is definitely on the positive-energy solutions, the relativistic energy scale is aligned with the non-relativistic one through the substitution E → E -m e c 2 , whereupon the limit is taken as lim c→∞ cψ S = lim c→∞ 1 2m e 1 + E -V 2m e c 2 (σ • p) ψ L = 1 2m e (σ • p) ψ L. (7) In practice one may choose between 1-and 2-component basis functions for 4-component relativistic molecular calculations. The latter choice allows the straightforward realization of restricted kinetic balance, 188 hence a 1:1 ratio of large and small component basis functions, but requires on the other hand a dedicated integration module. In DIRAC we opted  3  for Cartesian GTOs G α ijk (r) = N x i y j z k exp[-αr 2 ]; i + j + k = (8) since this gave immediate access to integrals of the HERMIT integral module, 2 where the extensive menu of one-electron integrals boosted functionality in terms of molecular properties.
B. SCF module The SCF module has some unique features that will be described in the following. In matrix form the HF/KS equations read F c = Scε (9) where F and S are the Fock/KS and overlap matrices, respectively, and c refers to expansion coefficients. Before diagonalization the equations are transformed to an orthonormal basis F c = cε; F = V † F V ; c = V c; V † SV = I. (10) As a simple example we may take the Dirac equation in a finite basis,   V LL cΠ LS cΠ SL V SS -2m e c 2 S SS     c L c S   =   S LL 0 0 S SS     c L c S   E. (11) After orthonormalization it reads   Ṽ LL c ΠLS c ΠSL Ṽ SS -2m e c 2 ĨSS     cL cS   =   ĨLL 0 0 ĨSS     cL cS   E (12) DIRAC employs canonical orthonormalization  203  which allows the elimination of linear dependencies. However, the orthonormalization step is overloaded: 1.
C. Symmetry considerations The DIRAC code can handle symmetries corresponding to D 2h and subgroups (denoted binary groups) as well as linear (and atomic) supersymmetry. At the SCF level DIRAC employs a unique quaternion symmetry scheme which combines time reversal and spatial symmetry. 4 A particularity of this scheme is that symmetry reductions due to spatial symmetry are translated into a reduction of algebra, from quaternion down to complex and possibly real algebra. This leads to a classification of the binary groups as: • Quaternion groups: C 1 , C i • Complex groups: C 2 , C s , C 2h • Real groups: D 2 , C 2v , D 2h At the SCF level DIRAC works with the irreducible co-representations obtained by combining the above spatial symmetry groups with time reversal symmetry. 4 A source of confusion for DIRAC users is that occupations are given for each irreducible co-representation at the SCF level.
IV. CONCLUSIONS DIRAC is one of the earliest codes for 4-component relativistic molecular calculations and the very first to feature exact 2-component (X2C) relativistic calculations. 22 It is not the fastest such code around, but is presently hard to beat in terms of functionality. This stems in part from the fact that the code has been written with generality in mind. There is a wide range of Hamiltonians, and most program modules are available for all of them. The SCF module allows Kramers-restricted HF and KS calculations using an innovative symmetry scheme based on quaternion algebra. In some situations, though, for instance in SCF calculations of magnetic properties, unrestricted calculations are desirable in order to capture spin polarization. A number of molecular properties, such as electric field gradients, 107 parity-violation in chiral molecules,  119  nuclear spin-rotation constants  211, 212  and rotational g-tensors  115  were first studied in a 4-component relativistic framework with DIRAC.
The freedom of users to define their own properties combined with the availability of properties up to third order means that there are many new properties waiting to be explored. Such properties may be further analyzed through the powerful visualization module. Another strength of DIRAC is the large selection of wave function-based correlation methods, including MRCI, CCSD(T), FSCCSD, EOM-CCSD, ADC and MCSCF. As already mentioned, the latter allowed a detailed study of the emblematic U 2 molecule, demonstrating that spin-orbit interaction reduces the bond order from five  213  to four. 81 Methods implemented in DIRAC that account for more dynamic correlation have, combined with experiment, provided reference values for properties such as nuclear quadrupole moments,  214, 215  hyperfine structure constants  216  and Mössbauer isomer shifts.
217 DIRAC also provides theoretical input for spectroscopic tests of fundamental physics, both within the Standard Model of elementary particles 218 as well as tests of Beyond Standard Model (BSM) theories which give rise to electric dipole moments of fermions. 135, 137, 219  recent years, DIRAC has been extended to include several models for large environments: PCM, PE and FDE, which opens new perspectives. For instance, recently EOM-CC was combined with FDE to calculate ionization energies of halide ions in droplets modelled by 50 water molecules. 164  believe it is safe to say that DIRAC is a reference in the domain of 2-and 4-component relativistic molecular calculations and that it will remain so in the foreseeable future. In 2015 DIRAC was one of 13 scientific software suites chosen for adaption to the SUMMIT supercomputer  220  at the Oak Ridge Leadership Computing Facility (OLCF).
torials and annotated examples which provide an excellent starting point for users exploring a new code functionality or entering a new field. H. Distribution and user supportThe program is distributed in source code form under a custom open-use license. Traditionally we have distributed the code to the community upon request but starting with the DIRAC18 release 183  we have switched to distributing the source code and collecting download metrics via the Zenodo service. 184 We plan to transition to an open source license (GNU Lesser General Public License) in the near future to encourage contributions and simplify derivative work based on the DIRAC package. User support is provided on a best-effort basis using Google Groups, with presently 360 subscribers. Deliberate efforts in community building are also reflected in the social media presence.
ACKNOWLEDGMENTS TS would like to thanks his former advisors Knut Faegri jr. and the late  Odd Gropen  for putting him on an exciting track. LV acknowledges support of the  Dutch Research Council (NWO)  for this research via various programs. He also likes to thank his former advisors Patrick Aerts and  Wim Nieuwpoort  for introducing him to the wonderful world of relativistic quantum chemistry. ASPG acknowledges support from the  CNRS Institute of Physics (INP) ,  PIA   ANR  project  CaPPA ( ANR-11-LABX-0005-01 ), I-SITE  ULNE  project  OVERSEE  ( ANR-16-IDEX-0004 ), the  French Ministry of Higher Education and Research, region Hauts de France council and European Regional Development Fund (ERDF)  project  CPER CLIMIBIO. MI acknowledges the support of the  Slovak Research and Development Agency and the Scientific Grant Agency ,  APVV-15-0105  and  VEGA 1/0562/20 , respectively.
This research used resources of a High Performance Computing Center of the  Matej Bel University in Banska Bystrica using the HPC infrastructure acquired in projects ITMS 26230120002 and 26210120002 (Slovak infrastructure for high performance computing ) supported by the  Research and Development Operational Programme  funded by the  ERDF. RDR acknowledges partial support by the  Research Council of Norway  through its  Centres of Excellence scheme , project number  262695  and through its  Mobility Grant scheme , project number  261873. MO acknowledges support of the  Polish National Science Centre  ( 2016/23/D/ST4/ 03217 ). IAA acknowledges support from  CONICET  by grant  PIP 112-20130100361  and FONCYT by grant  PICT 2016-2936. JMHO acknowledges financial support from the  Research Council of Norway  through its  Centres of Excellence  scheme (Project ID: 262695).
, 2005)  and also some contribution of black carbon (BC). The particles are often light absorbing, giving rise to the semi-direct effect  (Hansen et al. , 1997). The exact characteristics, however, vary from case to case  (Petters et al. , 2009) , and, while freshly emitted particles tend to exhibit a range of  values, they often converge after atmospheric aging  (Engelhart et al. , 2012). Even though  of fresh fire particles is relatively low, some observational studies suggested fire-induced increases in CCN  (Bougiatioti et al. , 2016; Mallet et al. , 2017; Wu et al. , 2017). That is, large numbers of aerosol particles from forest fires were observed to contribute to cloud droplet formation. Rosenfeld (1999)  used TRMM satellite data to find a suppression of precipitation caused by biomass burning aerosols over Indonesia, as well as a resultant increase in cloud heights.