dataesr/scientific-paragraphs-categorization huggingface dataset:
Patient Population From a multi-center international database (collaborative database of Peritoneal Surface Oncology Group International (PSOGI) and BIG-RENAPE working groups(9)), the "CRS plus HIPEC" group represented patients with PM treated with CRS and HIPEC. The "CRS alone" patients with PM treated with CRS but without HIPEC were retrieved from hospital database of the Institut de Cancérologie de Lorraine. Ethics approval was obtained from the participating institutions through their institutional review boards or through the chairpersons of their ethics committees. The inclusion criteria were patients with primary or advanced peritoneal carcinomatosis of endometrial origin, giving consent to the procedure and without contraindications either to CRS alone or HIPEC. Patients with pre-operative extra-abdominal metastasis, unresectable disease or lack of tness for the procedure were excluded.
A total of 44 patients in the "CRS plus HIPEC" group and 90 patients in the "CRS alone" ful lled inclusion and exclusion criteria. In order to control the potential confounding factors, patients of "CRS plus HIPEC" and "CRS alone" groups were 1:1 matched by the global optimal algorithm (18) based on propensity score. The exact matching was performed on three criteria: age at diagnosis (+/-10 years), histological type (endometrioid vs adenocarcinoma vs other carcinosarcoma), and year of surgery (+/-5 ans); the propensity score was computed by a multivariate logistic regression with group as dependant parameter and all patients and clinical characteristics as independent parameters. The main clinical data were collected retrospectively from patients treated for peritoneal carcinomatosis. Age, histological type, tumor histology, peritoneal cancer index (PCI), surgical procedure, HIPEC techniques and drugs, completeness of cytoreduction (CC) score, data regarding primary treatment, chemotherapy, postoperative complications according to the common terminology criteria for adverse events (CTCAE) v3.
2A  and 3A, B ). This appears to be independent of Ser618 phosphorylation and this is also evident in the crystal structure where this residue is not involved on interface interactions (PDB ID 5FM5)  [31, 32]. Interestingly, the myomesin domain My5 is not visible in the crystal structure of the myomesin My4-My5/obscurin complex, indicating that the My5 domain is rather flexible. The handshake arrangement of the myomesin and obscurin within the complex provides a stability and establishes a tight connection within the sarcomere as evidenced by a significant mechanical stability of the complex of about 135 pN force  [32]  (Fig. 3A, B ). Further, a crystal structure of myomesin domain My5 (PDB ID 6ZVA) shows a dimerization of this domain which is compatible with the My4-My5/obscurin complex (Fig.
3A, C ), although no publication is yet associated with this structure. The current structural picture of this region is enriched by a crystal structure of a relatively strong complex between the N-terminal Ig domain of obscurin or Obsl1 with the titin C-terminal Ig domain m10  [33, 34]. In conclusion, current data suggest that myomesin-1 forms a ternary complex with obscurin and titin at the M4/M4' lines with multiple interaction points. Still, these interactions are not completely understood, not only in terms of regulation but also in terms of architecture: the latest data show dimerization interfaces both on domains My4 and My5, therefore it is plausible to assume that two filaments could run parallel towards the M1 line. More recent results using size exclusion chromatography and small angle X-ray scattering (SAXS) indicate a dynamic concentration dependent conformation of the My4-My5/Obscurin complex  [32]  which could be compatible with the aforementioned dimeric structure of domain My5 (PDB ID 6ZVA).
Altogether, the current reported interactions and structural details render myomesin-1 as the central part of a multiprotein assembly important for the M-band architecture and function. There are still several unanswered questions and further work is required to address them focusing both in structural and cell biology. The most obvious discrepancy highlighted in this review, is the oligomerization state of the myomesin-1 filaments since a potential dimerization on the M4/M4' lines would result a tetrameric assembly on the C-terminus. Could it be possible to have a myomesin filament bundling at the C-terminus able to support the observed complexes at the central part of the molecule? Although previous work on the domains My9-My13 show a single antiparallel dimer by negative stain EM and solution X-ray scattering  [22, 39] , the crystal structures revealed an additional conserved interface on domain My13 "hidden" under crystallographic symmetry (PDB IDs 2Y25, 2R15) (Fig.
This is rather surprising since sequence alignment reveals conserved residues in the dimerization interface (Fig. 4B ). We further compared the AlphaFold  [52]  predicted model of the single domain Mp13 (model AF-P54296-F1) with the known structure of myomesin-1 domain My13 (PDB ID 2R15). Superposition of Mp13 onto the dimeric My13 showed no conflicting residues that could disrupt the dimerization interface; we also observed a high conservation of the hydrogen bonds and the salt bridges that are present in the My13 dimeric structure (Fig. 4B, C ). Given the high sequence similarity between the two proteins, the most plausible scenario is that the unique C-terminus of myomesin-2 could be involved in a still unknown interaction, that either disrupts the interface in an auto-inhibitory fashion, or interacts with another yet unknown protein that regulates the dimerization of myomesin-2 or simply interferes with the previous experiments  [11].
Myomesin variants In terms of pathogenicity, a continuously increasing number of single point variants is emerging over the past years to all myomesin proteins that can potentially link them to disease (Table  1 , Fig. 2 ). Nevertheless, due to the lack of sufficient knowledge and functional studies of myomesin-1 and myomesin-2 in myocardial cells, their biological functions and their role in heart diseases have not yet been clarified. No variant was reported as pathological in the clinical databases such as OMIM (https:// www. omim. org) or ClinVar (https:// www. ncbi. nlm. nih. gov/ clinv ar/), they are considered as likely benign or variants of unknown significance (VUS). The encoding genes MYOM1, MYOM2 and MYOM3 have more functional genetic variations than expected based on the apparently neutral variations found in the genes (https:// gnomad.
broad insti tute. org). They are classified as tolerant to functional genetic variations in contrast to most of the genes responsible for Mendelian diseases. Since myomesins are large proteins with repetitive elements, it is not surprising that numerous benign or of unknown significance variants were reported in population databases from exome or genome sequencing, as in the case for titin gene, TTN  [61]. Many of them are probably frequent or rare benign variants, as well as errors of sequencing due to the homology of the repeated domains. Nevertheless, due to the importance of variants in diverse sarcomeric proteins leading to skeletal and cardiac muscles, we can hypothesize that M-band components could also contribute to the genetics of these muscle disorders, possibly as major effectors in some very rare cases or as oligogenic components or susceptibility genes in others.
Secondly, several missense variants have been described in cohorts of patients with hypertrophic (HCM), dilated (DCM) or restrictive (RCM) cardiomyopathies, often late-onset pathologies considered with 50% of genetic components and oligogenic components. The first identified variant, p. Val1490Ile, in MYOM1 screened as a candidate gene, was transmitted from a mother to her two sons, all affected by HCM  [65]. This change occurred on the Ig domain My12 and it was reported to affect the protein properties  [65]. Given however the available structural information on the domain (PDB IDs 2Y25 and 2R15), we can observe no involvement of the Val1490 to the dimerization of the protein or any interaction with the connecting My12-My13 α-helix. In addition, we can only assume minimal effects on the biophysical properties of domain My12 since Val1490 targets the core of the domain on a position where Methionine is also a possible substitution based on the sequence conservation of β-barrel domains  [22, [66] [67] [68].
This contribution describes and discusses the main climatological characteristics (climatology, variability and trends) and the main limitations of this dataset. We provide a short overview of the scientific applications using this reanalysis in various scientific fields related to meteorological conditions and the snow cover in mountain areas. An evaluation of the skill of S2M is also displayed, in particular through comparison to 665 independent in situ snow depth observations. Further, we describe the technical handling of this open-access dataset,.
, 2016; Morin et al. , 2021). However, in this article we only focus on the original model chain addressing mountain regions of France. This paper introduces the latest version of the SCM reanalysis, now referred to as SAFRAN-SURFEX/ISBA-Crocus-MEPRA (S2M). This new version differs from the previous one  (Durand et al. , 2009a, b)  by its temporal extent (15 more years, now spanning 1958-2021), its extension to Corsica and the Pyrenees in addition to the French Alps, and an update of the observations and models involved. For example, Crocus is now fully embedded as a snow cover model of the ISBA land surface model within the SURFEX interface  (Lafaysse et al. , 2013; Masson et al. , 2013). However, the major innovation is that this new dataset is now freely available (see Sect. 3 for the dataset description and Sect.
6 for access information) for scientific applications using the AERIS portal (https://doi. org/10. 25326/37#v2020. 2). The first part of this paper describes the input data and model chain used to produce this reanalysis as well as the simulated variables and details on the practical access to the reanalysis dataset. The last section gives an overview of the possible uses of this dataset, with an emphasis on its three main dimensions  (spatial, temporal and altitudinal). It also presents an objective assessment of the limitations of this dataset in terms of climate trends, which have never been published until now in spite of its large use in the French scientific community for climate applications. Last, an evaluation of the reanalysis performance is given in terms of total snow depth by comparison to independent observations.
, 2011; Tarasova et al. , 2016; Garavaglia et al. , 2017) , consistently with the pioneer introduction of snow cover units by  Seidel et al. (1983)  and  Ehrler et al. (1997)  for snow remote sensing. This concept was also cho-sen by  Durand et al. (1999)  for the operational application of SAFRAN-Crocus modeling in support of avalanche hazard forecasting. Consistently, the S2M reanalysis results from simulations performed over elementary areas specifically designed to represent the main drivers of the spatial variability in mountain ranges called "massifs" (shapefiles of the different massifs are included in the dataset, and a glimpse of the geographic division of the three areas can be seen in Fig. 2 ). A massif is a conceptual object corresponding to a mountainous area (of about 1000 km 2 on average) over which the meteorological conditions are considered homogeneous at a given elevation.
However, users must be aware that a significant small-scale spatial variability of snow cover remains unresolved by this approach (e. g. ,  Weber et al. , 2020). In addition to simulations provided on the massif geometry, simulations are also performed over a set of 665 observation stations. These sites have been selected to be higher than 600 m a. s. l. and to provide snow depth measurements. The selected sites cover the three domains of the reanalysis with 435 observations sites in the Alps, 208 in the Pyrenees and 22 in Corsica. Each site is characterized by its altitude, slope and aspect. A shapefile containing all the station information is provided with the dataset. These simulations result from an interpolation between the nearest elevation bands of the corresponding massif and a projection of the direct solar radiation according to the slope and aspect of the station, the time during the day, and the information about solar masks from surrounding topography.
Since 1 August 2017, 24 h cumulated ARPEGE precipitation fields are used as precipitation guess, and the chronology of precipitation is taken from 6 h ARPEGE precipitation. This change is due to the improvements in sim-ulated precipitation and their availability at a 6 h time resolution, which increases the consistency of the analysis and meaningfulness of the computation of the phase of precipitation. However, it is not possible to extend this approach back in time, using the current NWP model output, since 6 h resolution precipitation data in ARPEGE are only available since 2017.
The stabilization (for 2 m temperature: Fig. 4a ) or decrease (for precipitation: Fig. 4b ) of the number of observations for the last 5 years of the period is due to a cost reduction in some manual measurements and automatic networks. The rapid increase of 2 m temperature observations (Fig. 4a ) starting from the beginning of the 1990s can be explained by the development of automatic networks providing hourly observations in mountain areas, thus adding many more observations than daily precipitation observations. It is an important source of temporal heterogeneity in the dataset (see Sect. 5. 2). These figures also show that the three areas have different observation network densities; in particular, there are significantly more observations in the French Alps than in the Pyrenees although these two areas have a similar number of massifs.
The observation network at these elevations in the French Alps is dense enough for the assimilation algorithm to reject some apparently spurious observations, whereas at higher elevations and in the two other domains most available observations are usually used in the analysis system. At low elevations (below 600 m) many observations from outside the massif limits are used, including observations from distant flat areas. For this new version of the S2M reanalysis, a more complete set of observations than the previous one  (Durand et al. , 2009a)  was used, using all observations available in the Météo-France database.
Other parametrizations allow simulating the main other physical processes: metamorphism, compaction and liquid water percolation. The model was initially developed and described by  Brun et al. (1989 Brun et al. ( , 1992)). The most-up-to-date description of the model was published by  Vionnet et al. (2012). A multiphysical version was developed by  Lafaysse et al. (2017)  to quantify uncertainties associated with the empirical parametrizations. The physical options of Crocus used in the S2M dataset correspond to the default configuration as defined in  Lafaysse et al. (2017)  except for turbulent fluxes (RI2 option).
S2M dataset description The S2M reanalysis spans the time period from 1 August 1958 at 06:00 UTC until 1 August 2021 at 06:00 UTC. The atmospheric (FORCING. nc) and snow cover (PRO. nc) datasets are each stored in 63 annual NetCDF files for each mountainous area (French Alps, Pyrenees and Corsica). Two kinds of simulation are available: one on flat terrain only and one taking slopes into account by projecting the incoming radiation variables according to the slope value and its aspect.
Metadata The S2M dataset consists of two shapefiles containing all information concerning the geometry of the simulation (for both massifs and stations geometries) and NetCDF files containing the simulated data. Simulated variables have two main dimensions: a temporal one ("time", the number of days from the previous 1 August) and the total number of simulation points ("Number_of_points"). Geometry variables that can be used to retrieve specific sub-data from the full yearly files are summarized in Table  2. For example, to get the snow depth simulation over a south heading slope, 40 • steep in massif number 1 at 2400 m a. s. l. elevation, select the following characteristics for variable "DSN_T_ISBA": -massif_num = 1, -slope = 40, -ZS = 2400, -aspect = 180. A practical code in Python is provided in the Supplement to get the simulated snow depth evolution over a south heading slope, 40 • steep in massif number 1 at 2400 m a.
Snow cover and soil variables Files containing snow cover and soil variables include data at a daily time resolution (with state variables provided at 06:00 UTC). The list of the output snow cover and soil diagnostics is given in Table  4. Note that some diagnostics have a high diurnal variability (snow surface temperature, stability indices, etc. ). A higher temporal resolution may be provided in a future version of this dataset if we receive expressions of interest. The detailed vertical profiles of the prognostic variables of the snowpack are not provided because it would represent too big a data volume at this spatial extent. Tables 5 and 6 provide a list of the variables relevant to the surface energy balance (with or without snow on the ground). Mechanical variables computed by the MEPRA model are given in Table  7  and provide estimates of the stability of the snowpack.
Meteorological and snow cover climatology and inter-annual variability from the S2M reanalysis The S2M reanalysis provides a comprehensive appraisal of the climatology and inter-annual variability of meteorological and snow cover conditions in the French Alps, Pyrenees and Corsica. Here we introduce examples on how the data can be exploited, enabling exploration of the dataset across its three main dimensions (time, horizontal dimension -massif -and elevations), using the French Alps as an example and focusing on the five indicators as follows: air temperature at 2 m (seasonal and annual mean values), total precipitation (seasonal and annual cumulative values), fraction of solid precipitation (seasonal and November-April mean values), mean snow depth (seasonal and November-April mean values), and snow cover duration (number of days with a non-zero simulated snow depth).
8  shows the yearly anomalies of the average snow depth in winter at 1800 m a. s. l. against the reference 1961-1990 (shown in Fig. 7 ). This visualization highlights the strong inter-annual variability of the winter snow cover, with fluctuations that can be greater than 100 % of the mean value over the reference period. It also highlights the spatial variability for one given year with anomalies that can be positive for some massifs and negative for other massifs. Figure  9  explores the vertical dimension of the dataset, as well as the simulated trends for the different seasons of the year. It compares the mean of the different variables over the Alps for each season at different elevations over the periods Soil temperature TG1 and TG4 K 0. 5 and 8 cm Liquid water content WG1 m 3 m -3 0.
Data and methods Among the local simulations described in Sect. 2. 1, a specific evaluation of the simulated air temperature at 2 m and precipitation was made using homogenized series of monthly observations across the French Alps (mostly located at midelevations) between 1960 and 2012. These series provide observations of monthly mean of daily maximum (14 stations) and minimum (9 stations) 2 m temperatures as well as monthly precipitation (43 stations) and have been homogenized using the HOMER software  (Mestre et al. , 2013). However, it is important to consider that the corresponding raw daily observations are assimilated in the S2M reanalysis described in this paper and therefore do not constitute a fully independent evaluation dataset. Consequently, another reanalysis was performed after removing these observations from the assimilation process for an independent evaluation.
4 • C lower than the absolute daily maximum. The 0. 2 • C gap between the minimum and maximum 2 m temperatures shifts highlights that the daily 2 m temperature evolution is not a perfect sinusoid and presents a day-night asymmetry  (De Saintignon et al. , 1990) : a typical daily 2 m temperature evolution curve for a clear-sky day exhibits a narrow maximum and flat minimum. Thus, the skill of the mean simulated 2 m temperature cannot be strictly assessed from the skill of the average of minimum and maximum 2 m temperatures. The available evaluation dataset does not allow us to provide an equivalent evaluation of hourly 2 m temperatures or even daily mean 2 m temperatures. Thus the 2 m temperature evaluation consists of direct comparison between (hourly) simulated and (absolute) observed minimum and maximum 2 m temperatures.
Using a large number of snow depth observations partly mitigates the effect of the spatial variability and the evaluation focuses on the last four decades of the reanalysis since very few snow depth observations are available before 1983. We computed two scores for each observation station, mean deviation and RMSD, by taking into account data from 1 October to 30 June of each year. Figure  13  provides an overview of the ability of the system to simulate snow depth values, with respect to observations, depending on the elevation. This figure compares the simulated and observed snow depth values on the 665 evaluation sites, grouped by elevation range. Figure  13  shows no strong systematic deviation, with a confidence interval of the median deviation always covering both positive and negative values, which means that the number of sites with a positive deviation is not significantly different from the number of sites with a negative deviation.
Main assets and known uses The S2M reanalysis makes it possible to take into account meteorological and snow cover conditions for any time between 1958 and 2021 in the French high-elevation mountain regions. It supersedes the SCM reanalysis, which has been developed by  Durand et al. (2009a)  in the early 2000s. SCM and then S2M have been used to address a wide range of applications in various scientific domains. The S2M dataset was used as a reference for evaluating snow cover simulations driven by the NWP model AROME  (Queno et al. , 2016; Vionnet et al. , 2019). SCM and S2M reanalyses have been used in a number of studies addressing glacier mass balance in the French Alps  (Gerbaux et al. , 2005; Réveillet et al. , 2018; Bolibar et al. , 2020; Peyaud et al. , 2020)  or hydrological simulation of alpine mountainous catchments  (Lafaysse et al.
, 2011). Pellarin et al. (2016)  used S2M soil thermal state and the overlying snow cover to investigate the potential of L-band satellite measurements to improve soil moisture retrievals. Although climate trends may be questionable, the S2M dataset provides a robust climatological baseline for mountain regions. It has been used as a reference for adjusting climate change projections with statistical downscaling techniques  (Lafaysse et al. , 2014; Verfaillie et al. , 2017). This method leads to the provision of meteorological forcing driving files corresponding to future climate time series on the same geometry and data format as S2M reanalysis forcing files, enabling homogeneous postprocessing including running SURFEX/ISBA-Crocus for natural  (Verfaillie et al. , 2018)  and managed  (Spandre et al.