dataesr/scientific-paragraphs-categorization huggingface dataset:
Using RASSCF, larger active spaces were possible, but technical complications did not allow a proper RASPT2 program to be developed. Ultimately, an approximate form of RASPT2 started being used experimentally  79  , and it was accepted into Molcas. A benchmark study by Sauri et al. 80  showed the general usability of the approach. Among the large number of more recent applications, we can mention the chromium dimer study 81 as a recent example of the capability of the plain RASSCF/RASPT2. A number of other examples can be found in the papers  [3] [4] [5]  and in other contributions in this paper.
B. Magnetic properties of mono-and poly-nuclear compounds In recent years, the [Open]Molcas package has been successfully employed for describing magnetic properties of mono-and polynuclear compounds containing transition metals and/or lanthanides and actinides. In this section, the main functions allowing these studies are reviewed alongside with some results. Practical examples how to run such calculations are given in Supplementary Information. In all these studieswith the exception of the last example, which rested on DMRG-SCF/MPSSI/SINGLE ANISO calculations  47  -the computational methodology employed was based on the RASSCF/RASPT2/RASSI/SINGLE ANISO + POLY ANISO calculations. A more detailed description of the capabilities of [Open]Molcas for magnetic properties can be found in references 4 and 5.
The last term in Eq. 5 is the hyperfine interaction, where Ĩ is the spin of the nucleus, A is the parameter describing the interaction between nuclear spin and the ground electronic spin. It is common to extract parameters for Eq. 5 by fitting experimental EPR spectra. It is noted here that the general form of Spin Hamiltonian equations may include tensorial terms This is the author's peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset. Comparison between the experimentally fitted parameters of the Spin Hamiltonians and the ab initio extracted parameters represents an active research area. In MOLCAS, two implementations exist for the computation of the g-tensor. One implementation is done inside the rassi module (EPRG) allowing the computation of the g-tensor for a Kramers Doublet  74.
The algorithm was implemented in the poly aniso software and is available as a module in the MOLCAS package and also as a stand-alone program. Figure  3  shows a particular example of application of the above described computational approach for the investigation of the magnetic properties of Dy 3 triangles  122. Recently, the projection of exchange interaction on products of irreducible tensor operators was implemented in the poly aniso. This function allows a full derivation of all parameters of the multipolar magnetic exchange, in a way similar to the extraction of the crystal field. The feature was first reported in Ref. 5.
Molecular dynamics An alternative way to explore the potential energy surface (PES) is to run molecular dynamics (MD) simulations. In this type of simulation the molecular system of interest is evolving in time and its kinetic energy is explicitly taken into account. This is in contrast to a geometry optimization or other static calculations like the minimum energy path (MEP) where the kinetic energy is zero. As a result, MD allows to overcome small barriers and reaches regions of the PES which are otherwise not accessible. This is accomplished by the dynamix module in [Open]Molcas that solves Newton's equations of motion, to propagate the system in time. In order to start an MD simulation the user needs to provide the initial conditions, which are the nuclear coordinates and velocities. Based on the selected electronic structure method and basis set, forces are computed which are required to extract the acceleration to solve Newton's equations of motion.
The dynamix module works with any elec-This is the author's peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset. tronic structure method in [Open]Molcas. 4] [135] [136] [137] [138] [139] [140] [141]  Photochemical processes start in the electronic excited state and then pass non-radiatively through a conical intersection (Fig. 5 ) to the ground state, while chemiluminescent processes behave the other way around. The non-radiative transition is modelled by a trajectory surface hopping algorithm 142 that works for multiconfigurational methods in [Open]Molcas. The simulation of these events provides a molecular level insight into the rearrangements responsible for the excited state relaxation.
PLEASE CITE THIS ARTICLE AS Furthermore, the effect of external forces can also be included. 136 The MD can be combined with other modules in [Open]Molcas such as espf  143  to run QM/MM molecular dynamics in complex environments. One example is a study of the photoisomerization in the proteorhodopsin protein (Fig. 5 ) by Borin et al. 144  Using the input file (see SI) a QM/MM trajectory was calculated at the CASSCF level of theory. Starting from the Franck-Condon point, the trajectory evolved towards the conical intersection within 227 fs. While this conformational change did not require much space in the tight protein binding pocket, the completion of the trans-to-cis isomerization was hindered specifically by the interaction of the C14 of the retinal and the amino acid Tyr200. It was found that this is due to a twist around the C13 = C14 and C15 = N in opposite directions.
Hence, it was deduced that tyrosine 200 is a critical amino acid for a successful isomerization. In addition to the native dynamix module, [Open]Molcas is interfaced to several standalone molecular dynamics programs. One option is SHARC  145  , that provides the possibility to include the effect of an external electric field and transitions between state of different spin multiplicity  146. Another option is COBRAMM,  147  that, with its combined quantum mechanical/molecular mechanical (QM/MM) framework and surface hopping scheme, allows to simulate ground and excited state reactions in vacuum, explicit solvent environments, or complex macromolecular systems (e. g. , biopolymers), including non-adiabatic molecular dynamics and the underlying transient spectral signatures. A more sophisticated option is explicitly considering the nuclear wave function and simulating quantum molecular dynamics.
189, 190 Computational details are available in these references and the SI of the present article. Valence and different groups of core-hole states are ideally converged in separate, state-averaged RASSCF calculations by either exploiting symmetry and/or the HEXS keyword. The rassi module then produces spin-orbit coupled manifolds and transition dipoles (and other multipoles) needed to generate the XANES spectrum. The eXatomic [Open]Molcas-NBO interface was used in Ref. 189  to rationalise the measured high-resolution An M 4,5 XANES spectra of AnO 2+ 2 (An = U, Np and Pu) molecules  194  in terms of the An-ligand bonding. The spectra (see Figure  3  in Ref. 189) feature a main peak assigned to transitions from An 3d mainly to non-bonding (φ, δ) 5f orbitals, and a satellite at high energy due to excitations from 3d into ligand-An 5f σ * u orbitals.
IV. CONCLUSIONS AND OUTLOOK ON FUTURE DEVELOPMENT The change of the development model in MOLCAS code (from a code made by a closed community to an opensource project) unleashed a rapid development of new features and simplified the integration with other computational codes. The [Open]Molcas open-source code is in many aspects a legacy code. For it to adapt to future soft-and hardware environments it need substantial rewriting and modifications. In particular the use of a multitude of different files and old algorithms makes efficient parallelization impossible. Near-future extensions of the capabilities of the QC-Maquis module to further enable DMRG-related techniques are broad. Some work will focus on exploiting the current capabilities of [Open]Molcas to implement efficiently the time-dependent CASSCF (TD-CASSCF) algorithm  [200] [201] [202].
Thanks to the implementation of the time-dependent DMRG algorithm within the QCMaquis package  203  , active spaces with about 20-30 orbitals could be targeted. This would pave the route toward ab initio simulations of time-resolved spectra of complex molecules, including metal complexes. Moreover, we plan to extend the traditional SA-RASSCFbased algorithms designed to simulate X-ray absorption spectra  192  to DMRG within the QCMaquis setting. In this way, L-edge absorption spectra of large metal complexes could be simulated by combining the SA formulation of DMRG  44  to optimize a large number of excited states, and the MPSSI algorithm 47 to properly account for spin-orbit effects. Further developments of QCMaquis in OpenMolcas will also concentrate on the improvement of (parallelized) traditional multiconfigurational methods within the MPS/MPO framework including i) DMRG-SCF, ii) DMRG-NEVPT2 and DMRG-CASPT2, and iii) DMRG-MC-srDFT  204.
It is also planned to streamline and clear the use of external libraries such that it will be possible to enhance the performance of QCMaquis by a factor of more than ten. New approaches for analyzing the most important orbitals or even most important individual excitations will be developed and polished. The possibility to verify an active space selection by the usage of DMRG codes in combination with graphical user interfaces (e. g. Auto-CAS) will convert multiconfigurational calculations into automated routine and will allow to integrate these calculations into existing automated workflows. Development of the dynamix module will include an improvement of the sampling procedure to generate initial conditions for molecular dynamics simulations. Further, the trajectory surface hopping algorithm will be extended to be used with the CASPT2 method.
The [Open]Molcas environment is constantly evolving, as demonstrated by the varied possible features here described. A common denominator, and an excellence of the program suite, is definitely its capacity of treating complex molecular systems at the highest possible level of theory, both as a stand-alone and as interfaced to other computational codes. Furthermore, constant development ensures the possibility of employing [Open]Molcas to reproduce, rationalize and predict experimental data obtained employing the latest spectroscopical techniques.
3, 0. 9, 1. 5, 3. 0, 4. 5, 6. 4, and 8. 0 keV. The PSF image at each position and energy is described by shapelets  (Refregier 2003) , that is, by a linear image decomposition into a series of differently shaped basis functions of characteristic scales. The shapelet description is a convenient way to compress the PSF information over a few coefficients. Two different scale parameters with individual shapelet coefficients are used in order to reproduce the complex behavior of the PSF core on small scales and the PSF wings on large scales. Each of the seven eROSITA mirror modules are made out of 54 nested Wolter-I type  (Wolter 1952 ) shells and have their individual PSF measurements. However, in the current implemen-tation of the X-ray telescope simulator (Sect. 2. 4), the PSFs of all seven modules are assumed to be the same, using only the shapelet reconstruction of flight module number 2.
Source detection and characterization The standard technique when source catalogs are created is to split source detection and characterization because different optimized software packages perform better on the individual tasks. After the initial detection, a maximum likelihood (ML) source characterization is used to separate extended and pointlike sources, based on the value and the significance of the extent  (Vikhlinin et al. 1998; Burenin et al. 2007; Pacaud et al. 2016; Clerc et al. 2018). The approach of splitting detection and characterization is also implemented in the standard eROSITA data-processing pipeline based on the eROSITA Science Analysis Software System (eSASS) 2. The forward-fitting routine employed by the ML fitting ensures the best sensitivity toward detecting an object with the assumed characteristics.
We follow this path because the incorporation of in-orbit PSF calibration data into the software analysis has proven to be time consuming in our experience. Similar to Chandra and XMM-Newton, the off-axis degradation of the eROSITA PSF is driven by the fact that the detector plane is out of focus. Thus, we can directly apply our experience with developing the source detection algorithm for Chandra and XMM-Newton to eROSITA. However, the eROSITA maximum degradation in terms of the half-energy width is 20%  (Predehl et al. 2010) , which is lower than for XMM-Newton and far lower compared to Chandra. The eROSITA PSF does not have a core, but a typical survey half-energy width of 28 at 1 keV. In scanning mode, the eROSITA PSF is roughly uniform across sky tiles. The detector pixel size corresponds to 9.
Both predicted β-model count rates are independent of PSF redistribution effects. We used the X-ray spectral-fitting program XSPEC  (Arnaud 1996)  as well as the temperature guess to calculate the conversion factor of count rate to flux (λ RF ) by dividing the model flux of a partially absorbed APEC model (see Sect. 2. 3) with unity normalization by the corresponding APEC-model count rate. The conversion factors of count rate to flux range between (6. 45-7. 65) × 10 -13 erg count -1 cm -2. The cluster flux limit is derived according to f 500,lim = λ RF • C det T exp • R(0 , r 500 ) R(1 , 4 ). ( 2 ) Using the redshift, we calculated the conversion factor of count rate to luminosity (λ RL ) by shifting the desired rest-frame energy band (0. 5-2 keV) to the observed one. This is in order to correct for the fact that the energies of detected photons in a given passband are (1 + z) times lower than in the cluster restframe.
For a smaller extent, the flux threshold increases because the surface brightness profiles decline faster, such that there are fewer counts in the outskirts. For a larger extent, the flat inner core of the beta model profile causes more photons to lie beyond 4 and 16. This also causes the crossing of the scales around 2. As expected, the flux threshold decreases with increasing net exposure time. Figure  5  shows the total count limit of clusters on the two considered angular scales as a function of redshift. Toward low redshift, increasingly larger statistics are required to detect a cluster because the angular extent increases. This emphasises the challenge for eROSITA to securely detect very nearby extended sources. We used the Python packages COLOSSUS  (Diemer 2018 ) and Astropy  (Astropy Collaboration et al.
2013 , 2018)  to calculate the differential number of galaxy clusters per square degree at a given redshift by integrating the cluster mass function (dn/dM,  Tinker et al. 2008)   (5) The lower integration limit, M lim (z), corresponds to the cluster mass limit at the corresponding redshift, and we set the upper limit, M max , to 10 16 M , above which the contribution of the mass function to the integral is negligible. Figure  6  shows the differential number of galaxy clusters per square degree as a function of redshift for the three final eROSITA survey fields. We computed the total number of clusters in a given survey area A s detected by eROSITA according to N = A s M max M lim (z) z max 0 dn dM dV dz dMdz. ( 6 ) For the performance verification (PV) phase of eROSITA, a program to reach the average equatorial depth of the final survey on a smaller patch of the sky is planned, the eROSITA Final Equatorial-Depth Survey (eFEDS).
Simulated field This section demonstrates the performance of the source detection based on wavelet decomposition and characterization on a simulated equatorial eROSITA survey field. It serves as an exemplification of the method, and the final adjustments and finetuning of the pipeline need to be made on real eROSITA data. We simulated the field as described in Sect. 2 and processed the output of the simulator using a preliminary version of the eSASS package (User release of 2018 April 20).
The final extended source catalog was compiled by exploring the output parameter space (detection likelihood, extent parameter, and extent likelihood) and by determining appropriate classification thresholds, for instance, to distinguish point-like and extended sources or reduce contamination. This resembles the approach used in  Clerc et al. (2018)  to characterize extended sources that are detected by a sliding-cell algorithm, which scans the X-ray image with a sliding square box of different sizes and weights the counts in the detection box with a β-model kernel. This method is a modified version of a sliding-cell and ML fitting adapted for the XMM-Newton Science Analysis Software. Valtchanov et al. (2001)  compared the performances of several source detection algorithms and found serious drawbacks of this method for the analysis of extended sources because a relatively large number of spurious detections are made and extended sources are split.
Purpose of the study: Work addiction risk is a growing public health concern with potential deleterious health-related outcomes. Perception of work (job demands and job control) may play a major role in provoking the risk of work addiction in employees. We aimed to explore the link between work addiction risk and health-related outcomes using the framework of job-demand-control model. Methods: Data were collected from 187 out of 1580 (11. 8%) French workers who agreed to participate in a cross-sectional study using the WittyFit software online platform. The self-administered questionnaires were the Job Content Questionnaire by Karasek, the Work Addiction Risk Test, the Hospital Anxiety and Depression scale and socio-demographics. Data Analysis: Statistical analyses were performed using the Stata software (version 13).
Study Methodology This epidemiological study used a cross-sectional observational and descriptive research design. Participants were recruited through workers using the WittyFit online platform (https://wittyfit. com/) implemented in several French companies  [40, 41]. WittyFit is a web platform that aims to improve well-being at work. WittyFit has been designed in partnership with the University Hospital of Clermont-Ferrand in France. The National Commission for Data Protection and Liberties (CNIL), and the South-East VI ethics committee (clinicaltrials. gov identifying number NCT02596737) approved the study. Participants had no relationship with researchers. Workers through the WittyFit platform received an online questionnaire, and they had the opportunity to read the instructions and participate in the study voluntarily without further incitation from their companies or from WittyFit.
Statistics The calculation of the number of subjects required for our study was carried out a priori in accordance with the literature  [53]  and considered the lack of any study connecting the perception of work (Karasek's model) and work addiction risk on a heterogeneous population. The sample size estimation was performed in order to highlight an absolute difference between "active" and "passive" workers around 20% on the prevalence of high risk of work addiction (ranging from 30% to 50% within the active quadrant of Karasek's model), for a two-sided type I error at 5% and a statistical power greater than 90% (See Figure  S1 ). Statistical analyses were performed using the Stata software (version 13, StataCorp, College Station, USA). Continuous data were expressed as mean and standard deviation or median [interquartile range] according to the statistical distribution.
(Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms.
Successfully pooling lifespan data was recently shown in a study combining 18 datasets to derive age trends of brain structure  (Pomponio et al. 2020 ). This contrasts with meta-analysis where summary statistics are combined and weighted from data that is analyzed at each site  (van Erp et al. 2019). MRI data from a large sample (n = 16,683) of participants aged 1 to 90 years was included. We investigated subcortical volumes and regional cortical surface area and thickness. Our first aim was to replicate previous findings of greater male variability in brain structure in a substantially larger sample. Based on prior studies  (Forde et al. 2020; Ritchie et al. 2018; Wierenga et al. 2018 Wierenga et al. , 2019) )  and reports of somewhat greater genetic effect on surface area than thickness  (Eyler et al.
| Participants The datasets analyzed in the present study were from the Lifespan working group within the ENIGMA Consortium  (Jahanshad and Thompson 2016). There were 78 independent samples with MRI data, in total including 16,683 (7,966 males) healthy participants aged 1-90 years from diverse ethnic backgrounds (see detailed descriptions at the cohort level in Table  1 ). Samples were drawn from the general population or were healthy controls in clinical studies. Screening procedures and the eligibility criteria (e. g. head trauma, neurological history) may be found in Supplemental Table  1. Participants in each cohort gave written informed consent at the local sites. Furthermore, at each site local research ethics committees or Institutional Review Boards gave approval for the data collection, and all local institutional review boards permitted the use of extracted measures of the completely anonymized data that were used in the present study.
| Imaging data acquisition and processing For definition of all brain measures, whole-brain T1-weighted anatomical scan were included. Detailed information on scanner model and image acquisition parameters for each site can be found in Supplemental Table  1. T1 weighted scans were processed at the cohort level, where subcortical segmentation and cortical parcellation were performed by running the T1-weighted images in FreeSurfer using versions 4. 1, 5. 1, 5. 3 or 6. 0 (see Supplemental Table  1  for specifications per site). This software suite is well validated and widely used, and documented and freely available online (surfer. nmr. mgh. harvard. edu). The technical details of the automated reconstruction scheme are described elsewhere  (Dale, Fischl, and Sereno 1999; Fischl et al. 1999 Fischl et al.
| Statistical analysis Statistical analyses were performed using R Statistical Software. The complete scripts are available in the Appendix. In brief, we first adjusted all brain structure variables for cohort, field strength and FreeSurfer version effects. As age ranges differed for each cohort this was done in two steps: initially, a linear model was used to account for cohort effects and non-linear age effects, using a third-degree polynomial function. Next, random forest regression modelling  (Breiman 2001 ) was used to additionally account for field strength and FreeSurfer version. See Supplemental Figure  1  for adjusted values. This was implemented in the R package randomForest, which can accommodate models with interactions and non-linear effects.