dataesr/scientific-paragraphs-categorization huggingface dataset:
Construct the graph of evolution of structures The bonding patterns of the identified conformer and the respective reference are compared. For the bonds present in the new conformer, but not in the reference, we consider that the bond is formed (marked with a '+') and, respectively, for the bonds present in the reference and not in the new conformer, the bond is considered broken (and marked with a '-'). Based on the changes in the bonding patterns, the differences between each conformer and the reference structure are stored in the database. In addition, we extract the energy values from the input file (the XYZ file that contains the structures S 1 , S 2 , S 3. , S n ), and calculate the relative energy difference of the conformer through comparison with the reference. For the purposes of the current study, we consider the minimum energy value from the isomers' list as the representative energy value assigned to the unique configuration for further analysis on the database.
Correlation analysis of the structural features and reactivity Histogram plots in Figure  5  were made to illustrate how the combinations of different structural features are associated with the possibility of the formation of alternative stable configurations from a given pre-catalyst. These plots summarize the frequency of appearance of species with relative stabilities in the [0, -40] kcal. mol -1 with respect to the given structural/compositional features. Figure  5  presents correlations between (a) the R 1 groups and other structural features including catalyst backbone, (b) R 2 groups, (c) coordination number (CN) of the metal (Mn), as well as (d) the energetics of the alternative species (∆E) and (e) the adducts (X) type. The number of explored species in the database having specific features with different combinations of R 1 with other parameters (backbone, R    The formation of stable alternative species with 5-and 6-coordinated Mn centers is respectively correlated with the presence of t Bu and CF 3 moieties at R 1 (Figure  5.
Finding the most frequent classes of interactions: K-mode clustering In the next step towards implementing data-driven analysis of large databases of homogeneous catalyst structures, we have leveraged on labeled data organized based on automated exploration of the chemical space of the complete catalyst library. We have enumerated the global distinct types of interactions leading to alternative stable species and subsequently done clustering to find the most frequent modes. Clustering, in general, is an unsupervised learning method whose task is to divide the population or data points into a certain number of groups, such that data points belonging to the same group are similar to each other and dissimilar to the data points in the other groups. It is basically a collection of objects based on similarity and dissimilarity between them.
K-mode clustering 50 is one of the unsupervised machine learning algorithms that is used to cluster categorical variables. Here, we used the kmodes 0. 12. 2 50 library for categorical clustering based on the reactivities of the observed species. The results of the clustering analysis are summarized in Figure  6  by presenting the relative energy diagrams of the alternative configurations discovered for each of the backbone type and classified according to the interaction type realized in them. Analyzing the nature of interactions identified in Figure  6 , we can draw insights into the new reactivity of Mn(I) pincers and their correlations with the type of backbone and modifications to the ligand backbones at R 1 and R 2 positions. Filtering the new transformations in the database of events has led to three different sets of reactions: • Decoordination of the Mn-D (D = C, N, P) bonds • Nucleophilic attack on the carbonyl ligands.
Nucleophilic attack on carbonyl ligands Nucleophilic attack and insertion into the carbonyls have been observed for all the basic adducts (OH, OCH 3 and O t Bu) in the database. This is in line with our previous hypothesis on the potential relevance of such a reactivity for the base-assisted deactivation paths of Mn catalysts stabilized by carbonyl CO ligands. 20, 53, 54  Let us consider in more details the revealed reactivity patterns by focusing on the Mn-CNC complex family. Figure  7  summarizes the results of the automated analysis and shows the optimized structures of the most stable species identified for each class (denoted with the roman numbers in Figure  7 ). The energy values for the observed structures are plotted on the Y-axis against the set of reactivities observed for catalyst structures with the CNC backbone shown on the X-axis.
For example, such activation procedures allow [Mn(CO) 5 (CF 3 )] compound with the strongest Mn-RF bond to initiate the radical polymerization of vinylidene fluoride (CH 2 =CF 2 ) to produce poly(vinylidene fluoride). 71  The migration of the CF 3 moiety to the Mn center identified by the current automated algorithm is thus in line with the previous experimental investigations on the related chemistries and suggests new avenues to expand this field by utilizing CF 3 -modified ligand scaffolds and secondary transformations of the respective Mn catalysts. A method is introduced for the automated exploration of reactivities of extended databases of transition metal catalysts. The proposed workflow is designed to tackle the key challenges for bias-free mechanistic explorations on large databases of catalysts, namely: I) the automated exploration of the chemical space around each catalyst given specific structural features and (II) the automated analysis of results from such chemical datasets and provision of design rules for catalyst with improved performances or new reactivity.
To address these challenges, we have extended the application of our previously developed ReNeGate method for bias-free chemical space exploration to databases of synthetic organometallic catalysts. We implemented an analysis procedure to identify the classes of reactivity patterns within specific catalyst groups in the large organometallic datasets. Our procedure applied to an extended series of representative Mn(I) pincer complexes revealed new correlations between the structural and reactive features, pointing to new channels for catalyst transformation under the reaction conditions. Specifically, we have identified different hemilability behavior for catalyst with CNC, PNN, PNP and SNS backbones with a data-driven approach. Understanding hemilability is important because it affects the energy changes associated with pre-activation and regeneration steps in the catalytic process, and in turn, influences the coordination sphere and geometry of the complex.
In addition, two new classes of reactivities, namely, nucleophilic attack on carbonyl ligands and migration of CF 3 moieties have been identified through high-throughput virtual screening on the databases. Such a biasfree high-throughput virtual screening on the systematically designed structures opens new opportunities for the design of high-performance catalysts, as well as an accelerated method for exploring new reactivity patterns. • Exploration results leading to decoordination of Mn-N ligands for catalysts with different backbones • Exploration results for attack by NHC moieties of the CNC pincer on the carbonyl ligands (Mn-C interactions) • DFT-optimized structures for explored species(XYZ) • Dataset including structure IDs, structural features, energetics and reactivities(xlsx).
Acknowledgement E. A. P. and A. H acknowledge the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation Program  (grant agreement no. 725686 ) for financial support. This work was sponsored by  NWO Domain Science  for the use of the national computer facilities. M. -P. G. and S. B. thank  Prof. D. Barth  for fruitful discussions.
Data and Software availability HiREX code is publicly available at: https://github. com/ahashemiche/HiREX. Database produced based on ReNeGate explorations on ChemSpaX structures is included in the repository. 576 ChemSpaX structures for catalysts with CNC, PNN, PNP and SNS backbones required to reproduce the reaction networks are included in the input files directory. Scripts used to run explorations and DFT optimizations are included in the input files. HiREX code for the analysis of the database is included in the repository. Further details on using ReNeGaTe and ChemSpaX packages are included in the original repositories: ReNeGate : https://github. com/ahashemiche/ReNeGate ChemSpaX: https://github. com/EPiCs-group/chemspax.
Plain Language Summary The H 2 O and CO 2 ices that form at the surface on Mars play an important role in the exchange between the atmosphere and the surface of the planet. While most of the ice is located within the two polar caps that grew and shrink seasonally, ice is also known to condensate as surface frost during the night and sublimate during the day. This nighttime surface frost deposition can be observed even at equatorial latitudes. In this paper we use data from the Emirates Mars Infrared Spectrometer onboard the Emirates Mars Mission to detect the H 2 O and CO 2 ices at the surface of the planet at all local times over almost one Martian Year, which allows us to monitor both the seasonal and diurnal evolution of the distribution of ices at the surface of Mars. We observe that nighttime CO 2 frost forms at equatorial latitudes in the second half of the night to disappear at sunrise around the Martian equinoxes.
Supporting Information: Supporting Information may be found in the online version of this article. (Piqueux et al. , 2015) , it is of interest to monitor the evolution of both the Seasonal North Polar Cap (SNPC) and Seasonal South Polar Cap (SSPC). In this paper, we use for the first time data from the Emirates Mars Mission to map and monitor the evolution of the Martian polar caps along with the nighttime surface CO 2 frost at low latitudes. First, we describe in Section 2 the data set and methods used in this study for the detection and mapping of the surface ices. Then, Section 3 presents the seasonal and diurnal monitoring of the polar caps and the midlatitude nighttime CO 2 frost. Finally, Section 4 summarizes the main points of the study.
Data Set The Emirates Mars InfraRed Spectrometer (EMIRS) instrument onboard the Emirates Mars Mission (EMM) "Hope" probe is a Fourier Transform Infrared spectrometer that is observing the Martian surface and atmosphere between 6 and 100 μm with a selectable spectral resolution of 5 cm -1 or 10 cm -1 from February 2021  (Amiri et al. , 2022; Edwards et al. , 2021). The unique orbit of EMM allows EMIRS to observe the whole Martian surface across all local times in ∼4 orbits, which corresponds to ∼5° of L s , or 10 Earth days. For every daytime EMIRS observation, high-resolution UV-visible images of the full Martian disk are acquired immediately before by the Emirates eXploration Imager (EXI) instrument (A. R. Jones et al. , 2021). The coordination of these two instruments allows a direct comparison between the two datasets.
With a pixel size typically between 100 and 300 km, it is important to consider the spatial extent of each pixel to compute accurate maps, especially for the study of the polar regions where the emission angles are high. However, although polygons of the pixels footprints as computed by the SPICE kernels (C. H. Acton, 1996; C. Acton et al. , 2018)  are provided to users, their use to generate maps is not straightforward and significantly time-consuming. Thus, we have developed a new Python module called "SPiP" (Spacecraft Pixel footprint Projection) that generates an approximation of the pixel footprints projected on a regular longitude/latitude map using 3D trigonometry and assuming (for now) a spherical planet  (Stcherbinine, 2023). In this study, we use the Martian surface temperature previously retrieved using a multiple-step algorithm applied on a large portion of the EMIRS spectra between 7.
First, we gather all the surface temperature values retrieved from EMIRS that meet the data filtering conditions described in Section 2. 1 over 4 consecutive EMM orbits to have a full spatial coverage for all local times. Then, we compute surface temperature maps for bins of 1 hr of local time with a spatial resolution of 0. 5° × 0. 5° by (a) projection of the pixel footprints, (b) linear interpolation of the data over the entire longitude/latitude grid with scipy. interpolate. griddata (Virtanen et al. , 2020) , and (c) Gaussian smoothing with a standard deviation σ = 5. We also compute for each map the Gaussian kernel-density estimation (KDE) of our data for each map via scipy. stats. gaussian_kde (Virtanen et al. , 2020) , and we flag as "low data density" all the pixels of the final maps associated with a KDE lower than 5 × 10 -6 , this value has been tuned experimentally by comparing with the pixel footprints maps.
Local Time CO 2 Ice Maps Similarly to what is done for the diurnally stable ice maps (see Section 2. 2), we select all the EMIRS surface temperature values previously retrieved  (Smith et al. , 2022)  over 4 consecutive orbits, and compare them to the freezing temperature of CO 2 (T CO2 ) computed for each pixel according to Clapeyron's law  (Piqueux et al. , 2016) : ln 𝑃𝑃 = 𝛼𝛼 - 𝛽𝛽 𝑇𝑇CO 2 (1) with α = 23. 3494, β = 3182. 48, and P the CO 2 partial pressure taken as 96% of the total surface pressure (in mbar in Equation  1 )  (Piqueux et al. , 2016). As P cannot be reliably retrieved from EMIRS data alone, it is taken from the Mars Climate Database (MCD)  (Forget et al. , 1999; Millour et al. , 2018)  and computed to match the observational parameters of each EMIRS observation  (Smith et al.
Data Availability Statement The SPiP module is freely available on GitHub at https://github. com/NAU-PIXEL/spip (Stcherbinine, 2023). Data from the Emirates Mars Mission (EMM) are freely and publicly available on the EMM Science Data Center (SDC, http://sdc. emiratesmarsmission. ae). This location is designated as the primary repository for all data products produced by the EMM team and is designated as long-term repository as required by the UAE Space Agency. The data available (http://sdc. emiratesmarsmission. ae/data) include ancillary spacecraft data, instrument telemetry, Level 1 (raw instrument data) to Level 3 (derived science products), quicklook products, and data users guides (https://sdc. emiratesmarsmission. ae/documentation) to assist in the analysis of the data. Following the creation of a free login, all EMM data are searchable via parameters such as product file name, solar longitude, acquisition time, sub-spacecraft latitude & longitude, instrument, data product level, etc.
Anti-NMDAR encephalitis has been associated with multiple antigenic triggers (i. e. , ovarian teratomas, prodromal viral infections) but whether geographic, climatic, and environmental factors might influence disease risk has not been explored yet. We performed a systematic review and a meta-analysis of all published papers reporting the incidence of anti-NMDAR encephalitis in a definite country or region. We performed several multivariate spatial autocorrelation analyses to analyze the spatial variations in the incidence of anti-NMDA encephalitis depending on its geographical localization and temperature. Finally, we performed seasonal analyses in two original datasets from France and Greece and assessed the impact of temperature using an exposure-lagresponse model in the French dataset. The reported incidence of anti-NMDAR encephalitis varied considerably among studies and countries, being higher in Oceania and South America (0.
Genetic factors such as HLA profile (HLA-I B*07:02 in European patients and HLA-II DRB1*16:02 in Chinese populations) have also been suggested to modulate disease risk  [8, 9] , supporting the hypothesis that, similarly to several other autoimmune disorders, both genetic and environmental factors may concur to disease pathogenesis  [10]. The possible impact of environmental factors such as latitude, sun exposure, and air pollution has not yet been evaluated in autoimmune encephalitis, although they have shown to heavily influence the risk and disease activity of multiple sclerosis  [11, 12] , another immune-mediated disorder affecting the central nervous system. Herein, we performed a systematic review and a meta-analysis of the literature, including unpublished datasets from four additional countries, to assess the incidence of anti-NMDAR encephalitis in different countries, searching for elements suggesting an influence from geographic, climatic, and environmental factors.
Materials and Methods The literature review was conducted and reported following PRISMA statements  [13]. The PubMed (https://pubmed. ncbi. nlm. nih. gov/, accesed on 15 January 2020) and Google Scholar (https://scholar. google. com/, accessed on 15 January 2020) research was performed between 20 December 2019 and 15 January 2020 using the keywords 'autoimmune encephalitis' and 'NMDA encephalitis' in combination with each of the 177 country names included in the ISO list of world countries (Supplementary Table  S1 ). No restriction was applied concerning language or year of publication. Two investigators (AA, GB) independently reviewed the articles retrieved from the research, extracting relevant information using a standardized data extraction sheet, as recommended by quality standards for reporting meta-analyses of observational studies in epidemiology  [14].
The assessments performed separately by the two investigators were then cross-checked and, if any disagreement arose, a third reviewer (DP) was consulted to achieve a final decision. To be included in the meta-analysis, studies needed to provide the number of incident cases or crude and/or age-specific incidence estimates for anti-NMDAR encephalitis, study period, and referring population. Studies reporting incidence estimates inferred from a subset of patients not representative of the whole anti-NMDAR encephalitis population (i. e. , concerning intensive care unit or epileptic patients) were excluded. The list of the 68 studies  [9,  included in the meta-analysis is given in the Supplementary Data. Literature data were complemented by unpublished data on the incidence of NMDAR encephalitis in Colombia, Chile, France, and Greece, collected by coauthors actively working in these countries.
This allowed having information on the incidence of anti-NMDAR encephalitis in three countries, with no literature data available (Colombia, Chile, and Greece). This study was approved by the local ethics committee of the Pitié Salpêtrière Hospital and informed consent was waived (reference CPP SUD-EST II). Two unpublished datasets of patients diagnosed with "definite" anti-NMDAR encephalitis according to the 2016 criteria  [83] , one from Greece and one from France, were used as exploratory datasets for additional analyses on seasonal and climatic trends. Individual data on Greek patients' dataset were collected retrospectively during the period 2010-2019 from two diagnostic neuroimmune laboratories considered as nation-wide referral centers. Individual data on French patients were drawn from the database of the National Reference Centre for Paraneoplastic Neurological Syndromes and refer to the period 2008-2018.
Seasonal and Monthly Trends After comparing the accuracy of different seasonal and non-seasonal models, we used the X13-Seasonal Extraction in Autoregressive Integrated Moving Average (ARIMA) Time-Series (SEATS)-ARIMA algorithm [91] in the French dataset, and the Seasonal and Trend decomposition using locally weighted running line smoother (LOESS), STL  [92]  in the Greek dataset, to assess temporal trends (Supplementary Methods). We compared average monthly counts of anti-NMDAR encephalitis, using a Quasi-Poisson regression, with the number of cases as the outcome, month as the sole predictor (with February as baseline), and the log of the population as an offset (Supplementary Methods). All statistical analyses were performed using the software "R" (version 4. 0. 1). The threshold for statistical significance was p < 0.
Spatial and Temporal Analyses on the French and Greek Dataset To better assess the impact of some climatic variables on the incidence of anti-NMDAR encephalitis, we analyzed two unpublished cohorts, one from France (n = 329, 328 with age available) and one from Greece (n = 57). The crude and standardized incidences of anti-NMDAR encephalitis in the French and Greek datasets are provided in Supplementary Tables  S3  and S4. We represented the female/male ratio according to a five-year interval in two datasets with a higher proportion of females. Female predominance was stronger in younger patients (Supplementary Figure  S14 ). The frequency of the different tumors associated with anti-NMDAR encephalitis is described in Supplementary Figure  S15. These two datasets were used for temporal distribution analyses.
In the French dataset, the median number of cases was 2 per month, with a recurrent lower number of cases in February and a recurrent higher number of cases in June and August (Figure  5 , panel C). In the Greek dataset, where the median number of cases was 1 per month, this trend was far less evident (Figure  5 , panel D). To better circumstantiate monthly variations, we performed a Quasi-Poisson regression using February as the month of reference. The Quasi-Poisson regression disclosed a significant higher number of cases in June in both countries, with a relative risk (RR) of 2. 1 (95% CI 1. 2-4) and p = 0. 02 in France and a RR of 2. 3 (1. 2-4. 7) and p = 0. 02 in the Greek dataset.
In the French dataset, the median number of cases was 2 per month, with a recurrent lower number of cases in February and a recurrent higher number of cases in June and August (Figure  5 , panel C). In the Greek dataset, where the median number of cases was 1 per month, this trend was far less evident (Figure  5 , panel D). To better circumstantiate monthly variations, we performed a Quasi-Poisson regression using February as the month of reference. The Quasi-Poisson regression disclosed a significant higher number of cases in June in both countries, with a relative risk (RR) of 2. 1 (95% CI 1. 2-4) and p = 0. 02 in France and a RR of 2. 3 (1. 2-4. 7) and p = 0. 02 in the Greek dataset. These findings prompted us to perform more in-depth seasonal analyses using the ARIMA model (French dataset) and the STL model (Greek dataset), details are provided in the Supplementary Methods.
These models confirmed the existence of a seasonal trend in both countries, with a recurrent peak of cases during summer (Figure  5 , panel E and F). The same models disclosed a progressive increase in the number of cases of anti-NMDAR encephalitis diagnosed in France over the study interval (2008-2018) (Figure  5 , panel E)  Based on the observation that the incidence of anti-NMDAR encephalitis seemed to increase during warm months, we used the French dataset to perform temperature analyses. Tlag-he impact of temperature on the incidence of anti-NMDAR encephalitis was assessed using a lag-response association model. Both in the overall dataset (n = 328) and findings prompted us to perform more in-depth seasonal analyses using the ARIMA model (French dataset) and the STL model (Greek dataset), details are provided in the Supplementary Methods.