dataesr/scientific-paragraphs-categorization huggingface dataset:
CHEOPS transmission Fig. 12. Spectral energy distribution of the planet WASP-189 b. The high-resolution synthetic SED computed with HELIOS  (Malik et al. 2017 (Malik et al. , 2019) )  is represented in light blue and assumes the irradiating power is fully absorbed and not redistributed (A B = ε = 0). The dark blue line is the HELIOS data averaged over bins of 5 nm. The blackbody profile of the maximum dayside temperature T day, max is shown in orange. We note that accounting for emission lines in the planetary spectrum leads to a different SED with respect to the black-body approximation, with more energy in the blue end. This affects the flux measured in the CHEOPS passband (black dotted line), with the dayside of WASP-189 b appearing slightly brighter. for the planet but accounting for a cooler and oblate star.
Photometric precision The photometric performance of CHEOPS can be evaluated from the residual light curve after correcting for all systematic effects, the stellar activity and removing the planetary model (bottom panel of Fig. 7 ). The noise level in the residuals is evaluated over a given duration by computing the error on the mean of all data points in a time window of the considered duration. The window is moved across the whole time series to estimate the noise at different moments of the observation. The median of the noise levels computed for a given duration gives us a precise estimate of the photometric performances of CHEOPS. We compute the noise levels using five different methods, which will not be detailed here as it goes beyond the scope of this work; however, all give consistent results.
Acknowledgements. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  FOUR ACES , grant agreement No. 724427 ). It has also been carried out in the frame of the  National Centre for Competence in Research "PlanetS"  supported by the  Swiss National Science Foundation (SNSF). A. De. acknowledges the financial support of the  SNSF. M. J. Ho. and  Y. Al. acknowledge the support of the  Swiss National Fund  under grant  200020_172746. M. Le. acknowledges support from the  Swiss National Science Foundation  under grant No. PCEFP2_194576. S. Sa. has received funding from the  European Research Council (ERC)  under the European Unions  Horizon 2020 research and innovation programme  (project  STAREX , grant agreement No.
De. is supported in the form of work contract ( DL 57/2016/CP1364/CT0004 ) funded by national funds through FCT. B. -O. De. acknowledges support from the  Swiss National Science Foundation  ( PP00P2-190080 ). L. M. Se. gratefully acknowledges financial support from the  CRT foundation  under Grant No. 2018. 2323"Gaseous or rocky?Unveiling the nature of small worlds". S. G. So. acknowledges support from  FCT  through  FCT  contract No. CEECIND/00826/2018  and  POPH/FSE (EC). T. G. Wi. and  A. C. Ca. acknowledge support from  STFC  consolidated grant numbers  ST/R000824/1  and  ST/V000861/1 , and  UKSA  grant  ST/R003203/1. R. Al. acknowledges support from the  Spanish Ministry of Science and Innovation  and the  European Regional Development Fund through grants  ESP2016-80435-C2-1-R ,  ESP2016-80435-C2-2-R, PGC2018-098153-B-C33 ,  PGC2018-098153-B-C31 ,  ESP2017-87676-C5-1-R , MDM-2017-0737  Unidad de Excelencia Maria de Maeztu-Centro de Astrobiologí-a (INTA-CSIC) , as well as the support of the  Generalitat de Catalunya/CERCA  programme.
The MOC activities have been supported by the  ESA  contract No. 4000124370. S. C. C. Ba. acknowledges support from FCT through FCT contracts No. IF/01312/2014/CP1215/CT0004. X. Bo. acknowledges his role as ESA-appointed CHEOPS science team member. M. De. acknowledges support by the  CNES. The Belgian participation to CHEOPS has been supported by the  Belgian Federal Science Policy Office (BELSPO)  in the framework of the  PRODEX Program , and by the  University of Liège  through an  ARC grant for Concerted Research Actions  financed by the  Wallonia-Brussels Federation. L. De. is an F. R. S. -FNRS Postdoctoral Researcher. M. Fr. gratefully acknowledges the support of the  Swedish National Space Agency  ( DNR 65 /19 ,  174/18 ). D. Ga. gratefully acknowledges financial support from the  CRT foundation  under Grant No.
2018. 2323"Gaseous or rocky?Unveiling the nature of small worlds". M. Gi. is an F. R. S. -FNRS Senior Research Associate. K. G. Is. is the  ESA   CHEOPS  Project Scientist and is responsible for the  ESA CHEOPS Guest Observers Programme. She does not participate in, or contribute to, the definition of the  Guaranteed Time Programme  of the CHEOPS mission through which observations described in this paper have been taken, nor to any aspect of target selection for the programme. J. La. acknowledges granted access to the HPC resources of MesoPSL financed by the  Region Ile de France  and the project  Equip@Meso  (reference  ANR-10-EQPX-29-01 ) of the programme Investissements d'Avenir supervised by the  Agence Nationale pour la Recherche. P. Ma. acknowledges support from  STFC  research grant number  ST/M001040/1.
Fibrosis is assessed from the quanti cation of the collagen proportionate area (CPA) corresponding to the ratio of the stained collagen area versus the area of the prospected eld of liver section. However, some limitations apply to most of available computerized image analyses such as: i) a number of miscellaneous manual interventions are required; these may involve operator-subjectivity; ii) CPA is determined mainly from selected regions of interest and whole tissue section analysis is not systematically performed, which may lead to bias given the heterogenous distribution of collagen bers across the lobules and the liver section. To overcome these limitations, we developed an automated computerized image analysis software (MorphoQuant™, Biocellvia, France) which allows an accurate, reliable, fast and reproducible analysis of NAFLD/NASH histological features in rodent models and humans regardless of the operator from whole slide images  [4, 15].
452 µm/pixel and 40X: 0. 226 µm/pixel). At 20X, CPA was determined within an average time of 12. 5 sec per section (n = 385 sections). Repeatability of automated CPA quanti cation was assessed by repeatedly running the software: a standard error of 0 ± 0 pixel was obtained over 5 reiterations, demonstrating 100% repeatability (data not shown). CPA values were equivalent whether computed on images scanned at either 20X or 40X magni cation (Fig. 1C, 2C ) for the two mouse models (r² = 0. 9989, p < 0. 0001; r² = 0. 9985, p < 0. 0001 for CCl4 and HF-CDAA, respectively).
Automated CPA Quanti cation is Highly Correlated with Fibrosis Staging In clinics, liver brosis is currently graded using brosis staging systems such as the standardized NASH CRN  [10] , or Ishak score  [9] , depending on the underlying chronic liver disease. Correlations between automated CPA and brosis scores were determined to check whether liver brosis could be apprehended by CPA. Considering CCl 4 mouse model, a high correlation was found between CPA data and Ishak stages (r = 0. 9705, p < 0. 0001) (Fig. 3E ). In HF-CDAA-treated mice, brosis was staged using Ishak modi ed system as well as using the NASH CRN score, which is more relevant to the distribution of collagen bers in this model (Table  1 ). High correlations were also found between CPA data and brosis modi ed Ishak stages (r = 0.
Liver Histology and Fibrosis Staging samples from two different lobes (harvested similarly to hydroxyproline samples in standardized fashion) were formalin-xed and mounted in para n-embedded blocks. Three 5-µm sections apart from 50 µm each per animal were used and stained with PSR as described before  [15]. PSR-stained liver sections were then scanned at 20x (0. 452 µm/pixel) and 40x (0. 226 µm/pixel) using a NanoZoomer-SQ and their whole slide image (WSI) captured using the NDP. view 2 software (both from Hamamatsu Corporation, Japan). WSI were scored blindly for brosis according to the NASH CRN System  [10]  and/or Ishak score  [9]  by a hepatologist (IL) trained to histological assessment of liver diseases in preclinical rodent models. CCl 4 causes centrilobular injury and thus brosis initiates in pericentral zones.
Automated Quanti cation of Liver Fibrosis The automated quanti cation of liver brosis was carried out by means of MorphoQuant™ software (Biocellvia, France) from the same WSI used presently for histological scoring. Tissue area of the entire liver section was automatically delineated followed by an accurate segmentation of the PSR-stained collagen bers as described before  [14, 15]. Liver brosis was expressed as collagen proportion area (CPA) corresponding to the ratio of the area of PSR-stained collagen bers to the area of the entire liver section. The analysis was conducted at the two different high magni cations, 20X and 40X, to investigate the impact of the magni cation on the software performances. The reproducibility of the automated quanti cation of liver brosis was tested on the whole set of slides by repeatedly running the software 5 times.
Statistical Analysis Data were expressed as mean ± standard deviation. For CPA analysis, a mean per subject was calculated from the 3 sections. For the longitudinal analysis of liver brosis in the two investigated models, statistical differences between groups were analyzed using a one-way ANOVA followed by Dunnett's multiple comparison post-hoc test (GraphPad Prism 9. 0; GraphPad Software, Inc. La Jolla, CA). Linear regression analysis was then performed to compare CPA assessment at 20X and 40X magni cations. Statistical analysis was performed using the non-parametric Spearman correlation coe cient to estimate the correlations between automated CPA and HYP assay on one hand, and CPA and brosis scores on the other hand. A p-value <0. 05 was considered statistically signi cant. To further characterize the automated CPA quanti cation as an endpoint measurement of liver brosis, correlation rates of histopathological scoring with CPA or HYP were compared using the one-sided Fisher's Z transformation, considering histopathological scoring data as references  [47].
INTRODUCTION Incoherent techniques for treating excited states have been the primary driver for the surge of photoexcitation studies in the last couple of decades. These techniques approximate the nuclear part of the molecular wave function as ensembles of classical systems, with each element independent of the others. 2] [3]  Newton-X (www. newtonx. org) is an open-source software platform tailored for these two types of simulations. The Newton-X development started in 2005 based on surface hopping codes developed by Granucci et al. 4  The first paper describing Newton-X (version 0. 13) was published in 2007. 5 Seven years later, a second paper reported the new developments in the program, at that point, still in version 1. 4. 6 ince then, other 8 years have passed, and Newton-X has branched into four stand-alone software packages (Figure  1 ): Initcond for nuclear ensemble simulations, Newton-X classical series (CS), Newton-X new series (NS), both for dynamics, and Ulamdyn for data analysis.
Now that the Newton-X CS has reached version 3, it is an adequate time to synthesize the software's status in a new publication, crediting many new coauthors who joined the software development. This paper focuses on Initcond and Newton-X CS. However, whenever we mention Newton-X without specifying Received: August 4, 2022 Figure  1. Newton-X platform. Newton-X is branched into four standalone programs: Initcond for nuclear ensembles, spectra, and initial conditions; Newton-X CS and NS for dynamics; and Ulamdyn for data analysis, including unsupervised ML. the series, the feature in question should be valid for both CS and NS. Newton-X NS's core features are discussed in Section 7. Ulamdyn is introduced in Section 6. 3. When Newton-X was first released in 2007, it was one of the first publicly available software for surface hopping and nuclear ensembles using quantum-chemical methods.
(A few earlier surface hopping codes were available, like those implemented in ANT,  7  CPMD,  8  or MOPAC. 4 ) Since then, many other options have appeared,  [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19]  each excelling in specific niches. Newton-X remains relevant by providing a complete set of tools for all steps of the nonadiabatic dynamics research, from spectrum simulations to advanced data analysis. Thus, it became a platform for external developments,  [20] [21] [22] [23] [24]  which are eventually included in the public version. Moreover, Newton-X counts on an active developers' basis, implementing new methodologies to extend the domain of applicability of standard approaches. In recent years, Newton-X became the host of novel methods for nonadiabatic couplings without wave functions,  25  zero-point-energy (ZPE) leakage corrections,  26  and nonadiabatic dynamics induced by incoherent light,  27  on complex-valued potential energy surfaces,  28  and including multiple chromophores.
PROGRAM ARCHITECTURE Newton-X is primarily focused on atomistic simulations, meaning that molecules are described as nuclei and electrons treated within the Born-Oppenheimer approximation, with nonadiabatic effects added as a correction. In principle, neither dynamics nor nuclear ensembles require model Hamiltonians or precomputed, fitted potential energy surfaces. The programs are tailored to evaluate on the fly the electronic properties at specific nuclear coordinates determined by dynamics or probability distribution functions. The electronic structure calculations are carried out by thirdparty computational chemistry programs interfaced with Newton-X. Newton-X CS uses Perl scripts to call such interfaces, execute the third-party programs, read their outputs, and use their results to propagate dynamics, compute nonadiabatic couplings, and simulate spectra.
119 Nevertheless, once the ML model is ready, it allows semiquantitatively predicting the nonadiabatic dynamics at much lower computational costs than with a quantum-chemical method. 120 raining and predicting nonadiabatic coupling vectors demand special attention because of their narrowly peaked functional features and arbitrary phases. 116 Alternatively, Newton-X allows computing couplings with TD-BA,  25  which, as discussed in Section 3. 6, only depends on the excitation energies and their time derivatives. 6. 3. Data Analysis and Unsupervised ML. Newton-X has native programs to perform basic statistical analysis of the simulated trajectories. They deliver averages and standard deviations over trajectories for energies, populations, and geometric parameters as a function of time. These statistical programs are left in Newton-X CS as a legacy feature.
They have been replaced by Ulamdyn (www. ulamdyn. com), an independent analysis open-source program also developed by the Newton-X team. Ulamdyn is not only more efficient than the native statistical codes but it has several additional functionalities. In particular, it extends the analysis to a broader set of statistical descriptors, delivering information about the data distribution's central tendency, skewness, and sharpness, as well as the accuracy measure of the estimated mean via bootstrap simulation (see an example in Figure  4 ). As a major feature, Ulamdyn provides a complete integrated pipeline starting from the data curation to perform advanced unsupervised learning analysis of the molecular dynamics data. Several dimensionality-reduction and data-clustering algorithms were implemented for this task based on the popular Python package Scikit-Learn (scikit-learn.
NEWTON-X FUTURE Newton-X was initially developed in a context where the electronic structure calculations were much more computationally expensive than integrating the EOM. For this reason, the Newton-X architecture has privileged modularity over efficiency. Thus, a new interface could be quickly built at the price of intensive disk reading and writing. In the end, the time spent on quantum chemical calculations more than compensated for this computational overhead. This situation changed with the development of extremely fast parameterized quantum chemical methods (such as TD-DFTB) and the advent of ML potentials. Now, computational overheads may have a significant impact on the costs. To tackle this issue, we are developing Newton-X NS, which aims at extreme computational efficiency and better data management standards.
Newton-X NS will be presented in a separate paper in the future, but we will highlight some features here. The development of Newton-X NS aims at efficency. More precisely, we bear three goals in mind: (1) maximize computational performance (optimizing, in particular, the input/output operations), (2) deliver data management standards that allow for easier sharing and more streamlined data production, and (3) keep the entry barrier for new developers as low as possible. The first goal leads us to switch from a collection of Perl scripts to a monolithic Fortran code, allowing direct data exchange between the different parts of the code through memory, not text files. This redesign effectively reduces the time spent in read/write operations, which is particularly useful when using built-in analytical models.
In those cases, no external electronic structure code is invoked, and the dynamics can be up to 2000 times faster than with Newton-X CS. 124  Having a monolithic code also helps with the second goal. We chose the H5MD file format  125  based on the Hierarchical Data Format 5 (HDF5) framework. Our monolithic implementation allows us to open the interface only once, and we can write the outcome of the dynamics to disk at one specific point in the code. In that way, we can be sure that the data written is always consistent. This data format is widely supported and implemented in several quantum-chemistry software (such as LAMMPS,  126  Molcas,  127  and MLatom  101  ), so we can benefit from the tools already developed for tasks such as visualization of the dynamics (VMD 128 plugin). The HDF5 library also binds to popular scripting languages such as Python or Julia.
To keep the low entry barrier to code development, we offer a clear and up-to-date developer documentation using automatic tools [FORD (github. com/Fortran-FOSS-Programmers/ford), for instance] to generate the documentation directly from the comments inside the code. We follow guidelines inspired by object-oriented programming practice, where each step of the computation is performed in its own module with a clearly defined entry point. Finally, all parameters used in the dynamics, as well as the default values and descriptions, are defined in a collection of easy-to-read YAML files, which a Perl driver parses to generate a proper input file for the Fortran program based on the user's defined parameters. This input management helps to keep inputs consistent from version to version, as changes are transparent to the end user.
Quantifying gene-specific oDNA loss patterns across eukaryotes To quantitatively explore the features predicting oDNA gene retention, we first define a retention index for a given oDNA gene, measuring its propensity to be retained in oDNA. To this end, we acquired data on organelle gene content across eukaryotes, using 10,328 whole mtDNA and 5,176 whole ptDNA sequences from NCBI. We curated these data with two different approaches, resembling supervised and unsupervised philosophies, to form consistent records of gene presence/absence by species (see STAR Methods). The supervised approach (manual assignment of ambiguous gene records to a chosen gene label) and the unsupervised approach (all-against-all BLAST comparison of every gene record from the organelle genome database) agreed tightly (Figure  S1 ).
Viridiplantae, making up the majority of samples in the dataset, display a very large number of variations on a general structural theme, with many ptDNA gene sets shared by dozens of species. More reduced ptDNA gene sets are found in parasitic taxa, with the smallest found in vestigial plastids like apicoplasts in Apicomplexa. Several ribosomal subunits and ndhb are among the most retained in ptDNA, with a second tier involving many ndh, psa, psb, and atp genes retained in around half our species. Least retained ptDNA genes include other members of psa, psb, rps, and rpl.
Cross-organelle symmetry in the prediction of gene retention by hydrophobicity and GC content To facilitate the principled investigation of different existing and new hypotheses on the features shaping oDNA gene retention, we proceed with a Bayesian model selection approach applied across oDNA. Here, the power of combinations of diverse molecular features to predict gene retention patterns are explored using the large-scale dataset alone, with no prior favoring of one hypothesis over another. To this end, we compiled a set of quanti-tative properties of genes and their protein products, linked to evolutionary hypotheses about the mechanisms shaping oDNA gene retention  (Johnston and Williams, 2016). These included gene length and GC content, statistics of encoding and codon usage, protein hydrophobicity, molecular weight, energy requirements for production, average carboxyl and amino pK a values for amino acid residues, and others (Figure  S3 ).
Our quantitative estimates for each feature were averages over a taxonomically diverse sampling of eukaryotic records (see STAR Methods). We used Bayesian model selection to ask which of these properties were most likely to be included in a linear model predicting the retention index of each gene. Following  Johnston and Williams (2016) , this approach identifies likely predictors with quantified uncertainty, while acting without prior favoring of any given hypotheses and automatically guarding against overfitting and the appearance of correlated predictors providing redundant information. In both mtDNA and ptDNA datasets, models where high hydrophobicity and high GC content predict high gene retention were strongly favored (Figure  2A ). The model with the highest posterior probability that featured neither of these predictors instead featured amino pK a and assembly energy, with a posterior probability of 3.
We next tested the capacity of models involving this combination of features to predict the retention index of oDNA genes in an unseen dataset. We split mtDNA and ptDNA gene sets into 50:50 training and test sets, trained linear models involving hydrophobicity and GC content using the training data, and examined their performance in predicting retention index in the independent test set. Average Spearman correlations were r = 0:64 and r = 0:62 for training mt and pt sets, respectively, and r = 0:63 and r = 0:60 for test mt and pt sets, respectively (Figure  2B ). Correlations were higher still (r>0:7) when only subunits of core bioenergetic complexes were considered (Table  S1 ). Following our hypothesis that the same features predict retention in the two organelle types, we also performed cross-organelle experiments.
We found, not unexpectedly given previous work, that hydrophobicity consistently appeared in all the model structures with highest posterior probability (Figure  3C ). More surprisingly, high-probability predictive models also featured the relatively under-examined feature of carboxyl pK a , in combination with hydrophobicity. Their appearance together in a Bayesian model selection framework suggests that they provide independent information on gene encoding, despite a correlation (albeit rather weak) between the features (Figure  S3 ). We next asked if these features could predict the encoding compartment of genes in an unseen dataset. To this end, we constructed GLMs predicting encoding compartment using hydrophobicity and carboxyl pK a. Here, we consider each species in our dataset separately to avoid having to account for phylogenetic correlations between species-these models then give species-level predictions about encoding compartment.