dataesr/scientific-paragraphs-categorization huggingface dataset:
Summary and future possibilities There was not much of the development for the implementation of τ lepton decays embedded in TAUOLA library. The new version of the program implementation was archived and finally published  [9]. The status of associated projects: TAUOLA universal interface and TauSpinner was reviewed. Also new results for the high-precision version of PHOTOS for QED radiative corrections in decays, were presented, in particular algorithm for emission of additional light lepton pairs was supported with documented tests. Some details of presentation of the TAUOLA general-purpose C++ interface was given and its applications were shown. Use of weighted events was demonstrated to be useful for studies of LHC phenomenology in domain of τ lepton polarization observables for precision tests of Standard Model as well as for Higgs boson CP parity.
, 2015). Furthermore, time-limited (typically, 1-2 months) measurement campaigns demonstrated that primary fine aerosols are mainly influenced there by traffic emissions all over the year and residential wood burning during cold seasons, while secondary aerosols originate from both local production and regional transport  (Sciare et al. , 2011; Crippa et al. , 2013a, b; Petit et al. , 2014; Srivastava et al. , 2018b). Furthermore, such a background site can be considered representative of air quality at a regional scale, including neighboring northwestern countries  (Bressi et al. , 2013 (Bressi et al. , , 2014)). In the present study, main OA factors were identified and quantified from seasonal PMF analyses (total 25 seasons) on the 6-year ACSM datasets, with the objective of understanding sources and long-term temporal trends of these factors.
Major submicron aerosol chemical species, i. e. , OA, nitrate, sulfate, ammonium, and chloride, were measured using a quadrupole ACSM. These measurements were performed continuously, always using the same instrument. Over the whole investigated period, the data capture is of about 87 %, and missing data correspond to two field campaigns performed elsewhere (in fall 2012 and March 2013) and to a few technical breakdown and maintenance periods. Briefly, fine aerosols are sampled into the ACSM system through a 100 µm diameter critical orifice mounted at the inlet of the PM 1 aerodynamic lens  (Liu et al. , 2007; Ng et al. , 2011b). Then, submicron aerosol particles are impacted and vaporized at a temperature (T ) of about 600 • C and detected using electron impact (70 eV) ionization mass spectrometry.
The ACSM was operated at a time resolution of about 30 min with a scan rate of 0. 2 s amu -1 from m/z 12 to 150 amu (atomic mass unit). Coarse particles were removed upstream using an URG Corporation cyclone separator (with the size cutoff diameter of 2. 5 µm). Calibrations of the detector response factor were performed regularly (typically every 6 months) using ammonium nitrate solutions  (Ng et al. , 2011b; Freney et al. , 2019). The 1. 4 default value was used for the OA relative ion efficiency for the whole dataset  (Canagaratna et al. , 2007). The composition-dependent collection efficiency correction recommended by  Middlebrook et al. (2012)  was applied to the whole ACSM data used here. The accuracy of these ACSM measurements and the overall good working conditions of the instrument were verified through the participation in the ACTRIS ACSM intercomparison exercises that took place at SIRTA in November-December 2013  (Crenn et al.
, 2015; Fröhlich et al. , 2015b)  and March-April 2016  (Freney et al. , 2019, and Fig. S1  in the Supplement). Co-located multiwavelength Aethalometer (Magee Scientific) datasets were also available for the purpose of the study, providing complementary information on equivalent black carbon (eBC) concentrations and sources. Two Aethalometers were used successively: from November 2011 to February 2013 (AE31 model) and then from March 2013 to March 2018 (AE33 model). Both instruments measure aerosol light attenuation at seven wavelengths, i. e. , 370, 470, 520, 590, 660, 880 and 950 nm. The detailed descriptions of the AE31 operation at SIRTA and Aethalometer data analysis can be found in  Petit et al. (2015). The AE33 is an advanced Aethalometer version, which allows for better assessment and compensation of the filter-loading effect using two simultaneous light attenuation measurements performed at different rates of particle accumulation onto the filter tape  (Drinovec et al.
Results obtained from these comparisons with filter-based measurements supported the validity of the datasets used in the present study. Co-located measurements of nitric oxide (NO) and nitrogen dioxide (NO 2 ) were performed with a NO 2 /NO/NO x analyzer (model T200UP, Teledyne API, USA). Data mea-surements were used to further constrain traffic-related OA sources. The meteorological parameters, including temperature (T ), relative humidity (RH), wind speed (WS), wind direction (WD), boundary layer height (BLH) and precipitation, were obtained from the main SIRTA ground-based meteorological station (located at about 4 km northeast of the aerosol monitoring site). It should be noted that the BLH data were achieved in combining a diagnostic of the surface stability from high-frequency sonic anemometer measurements and light detection and ranging (lidar) attenuated backscatter gradients from aerosols and clouds  (Pettie et al.
PMF analysis The OA source apportionment was performed using the PMF algorithm  (Paatero and Tapper, 1994). Organic concentration and error matrices were exported from the ACSM Local software (v 1. 5. 11. 2). Only m/z values ranging from 13 to 100 were applied in the PMF analysis due to larger uncertainties for larger m/z ions and large interferences of naphthalene (m/z 128) signals  (Sun et al. , 2012). Down-weighting of the m/z 44 group of ions for the PMF model analysis was performed following procedures implemented in the ACSM Local software and following the data treatment strategy proposed by  Ulbrich et al. (2009). When using PMF, it may be difficult to distinguish between factors with similar spectral profiles, especially for ACSM datasets, which are associated with larger uncertainties compared to AMS  (Sun et al.
, 2013a, b; Fröhlich et al. , 2015b). Mass spectra obtained from these studies were used here as references to constrain POA factors because of the prior knowledge of source information as constraint. Conversely, mass spectral profiles of possible OOA factors were left unconstrained. It should be noted that  Crippa et al. (2013a)  resolved up to three different types of OOA factors and/or a marine OA (MOA) factor when combining HR-ToF-AMS and proton-transferreaction mass spectrometer (PTR-MS) datasets obtained during a summer and a winter campaign at SIRTA. OOA factor profiles may differ with time, notably due to seasonal variations of several parameters such as meteorological conditions, photochemistry, atmospheric lifetime, air mass origin and/or gaseous precursor origins. In order to better account for such variability, individual PMF analyses were performed on a 3-month basis, i.
e. , winter (December-January-February), spring (March-April-May), summer (June-July-August), and fall (September-October-November), with a total number of 25 different PMF runs (7 for winters and 6 for each of the other seasons). November 2011 and March 2018 data were included in the winter 2011-2012 and winter 2017-2018 analyses, respectively. To evaluate the influence of the chosen temporal PMF window (i. e. , time duration of data used in ME-2 runs) on the seasonal ME-2 model results, different time frames  (i. e. , 15, 30, 60, 90 and 120 d)  were tested. As shown in Fig. S4  (with 2017 winter data as an example), the excellent consistency of those results from different scenarios suggests a very limited influence of the given PMF windows on determining the outputs of ME-2 analyses. To better assess the variations in primary and secondary OA in different seasons over the 6-year period and to allow for some degrees of freedom within the model runs, the main OA factors, including both POA factors (HOA and BBOA) and two SOA factors (a less oxidized OOA (LO-OOA) and a more oxidized OOA (MO-OOA)), were calculated as the average of 50 convergent ME-2 runs with the a values varying from 0 to 0.
Trend analysis The multiyear trends of monthly mean OA factors and total OA, as well as other chemical components (including eBC wb , eBC ff , nitrate, sulfate and total PM 1 ), were analyzed using the Mann-Kendall (MK) trend test  (Mann, 1945). The trend slope was calculated using Theil-Sen estimator  (Sen, 1968). Before performing the MK trend test, the normality and seasonality of the OA factors were examined, respectively. The normality of the mass concentrations of the OA factors was examined by the Shapiro-Wilk normality test  (Shapiro and Wilk, 1965). As a result of the Shapiro-Wilk normality test, all datasets of the mass concentrations of the four OA factors were not normally distributed cases. The MK test associated with Sen's estimator of slope is insensitive to outliers, while it is not appropriate for the chosen dataset with significant seasonality.
The Kruskal-Wallis test  (Kruskal and Wallis, 1952)  was performed to evaluate the seasonality of monthly average datasets at the 5 % significance level. If the seasonality of the data is insignificant, the MK test was used for the trend analysis, while the seasonal MK test was then applied for the data with significant seasonality. In addition, to further compare the differences between the MK test and the seasonal MK test in our trend analysis, both methods have been applied for all datasets (see Table  S1  in the Supplement). The trend computation was performed here using a R trend package  (Pohlert, 2018).
(3): PSCF (i,j ) = m ij n ij • w ij , (3) where m ij is the total number of selected trajectory endpoints (i, j ) associated with receptor concentrations of PMF factors higher than the threshold value, and n ij is the total number of back-trajectory endpoints at each grid cell (i, j ). The 75th percentile of each OA factor during the entire study was used as the threshold value to calculate m ij. To reduce uncertainty caused by small n ij values for the PSCF modeling, an arbitrary weighting function (w ij ) was applied  (Waked et al. , 2014). To minimize the influence of some trajectories on the possible pathways of air mass transport, observed data points associated with low-wind-speed conditions (WS < 4 m s -1 ) were filtered out. In addition, observed data points at SIRTA during the period with any hourly precipitation events (precipitation > 0 mm) were removed to reduce influence of wet deposition on ambient aerosol concentrations.
In these tests, POA reference mass spectra determined by  Fröhlich et al. (2015b)  were employed as anchor profiles (with a values ranging from 0 to 0. 4 with steps of 0. 05). PMF solutions with a factor number ranging from 3 to 6 were investigated on ACSM datasets corresponding to different seasons of different years (December 2011-February 2012, March-May 2015, June-August 2017, September-November 2017, December 2017-February 2018). Results obtained from these preliminary individual PMF runs showed very good consistency between them with two unconstrained OOA factors -MO-OOA and LO-OOA -always appearing in the 4-factor (with constrained HOA and BBOA factor) and 5-factor (with constrained HOA, BBOA and COA factor) solutions. Conversely, 3-and 6-factor PMF analyses generally led to unsatisfactory solutions.
Altogether, it could then be concluded that the constrained COA-like aerosols at SIRTA were primarily linked with wood burning emissions, while pure cooking aerosols were probably present loadings that were too low to be properly quantified within the present study. This assumption is consistent with conclusions drawn by other studies performed at SIRTA, e. g. , based on an online (ACSM) dataset  (Petit et al. , 2014)  and a combining PMF method using online (ACSM) and offline (4 h filter sampling) datasets  (Srivastava et al. , 2019) , as well as other studies showing that the COA factor could not be solely attributed to cooking aerosols (e. g. ,  Freutel et al. , 2013; Dall'Osto et al. , 2015). Therefore, the 4-factor solution, including two constrained POA factors (BBOA and HOA) and two unconstrained factors, was chosen here as the "best estimate" for the PMF runs performed over the long-term dataset.
BBOA presents an overall good correlation (r 2 = 0. 50) with eBC wb (Fig. S11c ), suggesting important influence of wood burning emissions on this factor production. Based on the filter-based dataset, primary OC (POC) and secondary OC (SOC) were calculated using a method of OC-to-EC mass ratio (see Fig. S12 ). Overall, POA (sum of HOA and BBOA) versus POC (r 2 = 0. 47) and SOA (sum of LO-OOA and MO-OOA) versus SOC (r 2 = 0. 38) have acceptable correlations during the entire filter measurement period. Thus, all of these comparison results could additionally support our "best estimation" for selecting such 4-factor PMF solution across the entire period.
However, such trend analyses should be performed on longer datasets for a much better evaluation of the pollution control strategies (both on road transport and residential heating emissions) in the Paris region. MO-OOA shows a significant decreasing trend (p < 0. 05) with a Sen's slope of -175 ng m -3 per year. Considering the overwhelming secondary origin of this factor, this significant decreasing trend may be partially explained by an overall reduction of anthropogenic VOCs emissions (-13 %) in France  (CITEPA, 2018)  and even in a larger spatial scale, e. g. , the western European region, during 2012-2017. LO-OOA presents no significant trend (with p > 0. 05). As discussed above, higher LO-OOA loadings may be linked to BSOA formation, especially at summertime. The stability of LO-OOA concentrations over time may be linked to limited changes in biogenic VOC emissions and/or in relevant oxidant concentrations that control the SOA burden in the atmosphere.
LO-OOA displayed a pronounced seasonal cycle, with highest contributions to total OA in summer Figure  10. Maps for potential source origins of regional transport that may contribute to SOA (including MO-OOA and LO-OOA) burdens at SIRTA. Observed data points with wind speed (less than 4 m s -1 ) and in the presence of precipitation events are filtered for the PSCF calculation. The solid black point in each plot represents the location of the sampling site. (50 %-66 %) and lowest ones in winter (12 %-19 %). Enhanced LO-OOA production during the warm season was assessed to be mainly driven by biogenic SOA formation. This factor showed no significant long-term trend (p > 0. 05) for the studied period. MO-OOA presented a higher contribution to OA in wintertime (35 %-51 %) and springtime (32 %-62 %) than during the rest of the year.
Acknowledgements. This work greatly benefited from discussions within the  COLOSSAL COST action   CA16109. Yunjiang Zhang  acknowledges the  China Scholarship Council  for a PhD scholarship. Financial support. This research has been supported by the  EU-FP7  and  H2020   ACTRIS  projects (grant nos. 262254  and  654109 ), by the  National Center for Scientific Research (CNRS) , by the  French alternatives energies and Atomic Energy Commission (CEA) , and by the  French ministry of Environment  through its funding to the reference laboratory for air quality monitoring (LCSQA). Review statement. This paper was edited by  Eleanor Browne  and reviewed by two anonymous referees.
In this paper, we investigate the potential of using a probabilistic modelling approach in the prospect of ocean colour data assimilation. The main objective of the study is to assess the benefits of using error covariances based on an explicit simulation of model uncertainties. The relevance of this approach is evaluated by considering 3D observational updates of the ensemble (one update at one time step) performed every 5 10 days (over one year) using the statistics of a North Atlantic coupled NEMO/PISCES stochastic ensemble simulation involving 60 members, as previously described in  Garnier et al. (2016). In this experiment, SeaWIFS ocean colour data are used to update the ensemble with a low rank ensemble Kalman Filter analysis scheme. The non-Gaussian behaviour of the model variables is taken into account using anamorphic transformations.
35 Considering today's fundamental needs for estimating stocks of carbon and marine resources in a climate change context, a synthesis between these different sources of information including their respective accuracy is required to reduce uncertainties on the representation of the marine biogeochemistry (and in particular the primary production). The generation of synthetic products describing the biogeochemical ocean state is one of the challenging 40 goals of the Copernicus Marine Environment Monitoring Service (CMEMS). One major issue is to develop a capacity to routinely assimilate ocean colour data into coupled physical-biogeochemical models, and thereof deliver real-time and reanalysis products of the global ocean biogeochemistry. Following the seminal modelling approach proposed by  Garnier et al.
80 To achieve this goal, we first use the probabilistic North Atlantic 1/4 o configuration of the NEMO-PISCES coupled model (hereinafter NATL025-PISCES) fully described in  Garnier et al. (2016)  to generate a 60-members prior ensemble simulation. Then, using high resolution SeaWiFS ocean colour data, we compute a sequence of 5-day ensemble updates to investigate if a low rank ensemble Kalman filter analysis scheme is able to reduce the model uncertainty. In particular we will verify if the analysis improves the 85 statistical consistency of the ensemble. I order to investigate the predictability of the system, some of the biogeochemical updates are thereafter used as initial conditions to produce ensemble forecasts. The objective is to assess whether the information brought by the observations is preserved with time during the forecast, which is a necessary condition prior to data assimilation.
220 (1/24 • ) version of the SeaWiFS data set-up in the ESA (European Spatial Agency) project OC-CCI (Ocean Colour Climate Change Initiative). These data are accessible through the MyOcean-Copernicus web site (http://marine. copernicus. eu). In addition, 1/12 • MERIS-Envisat ocean colour observations are used to perform statistical assessments. In the analysis scheme described in section 3. 1. 1, we suppose 1) a gaussian distribution of observa-225 tion errors and 2) that the observation are spatially uncorrelated. Consequently, R is a diagonal matrix characterized by a 30% observation error. In order to take into account the non-gaussianity of chlorophyll distributions, the observations y 0 and their associated standard deviations are also evaluated into the anamorphosis space defined by the ensemble simulation.
Background: The approach of building large collections of gene sets and then systematically testing hypotheses across these collections is a powerful tool in functional genomics, both in the pathway analysis of omics data and to uncover the polygenic effects associated with complex diseases in genome-wide association study. The Molecular Signatures Database includes collections of oncogenic and immunologic signatures enabling researchers to compare transcriptional datasets across hundreds of previous studies and leading to important insights in these fields, but such a resource does not currently exist for neuromuscular research. In previous work, we have shown the utility of gene set approaches to understand muscle cell physiology and pathology. Methods: Following a systematic survey of public muscle data, we passed gene expression profiles from 4305 samples through a robust pre-processing and standardized data analysis pipeline.
Gene set analysis tools and gene set collections A commonly used approach to pathway analysis is functional enrichment testing. In this approach, the gene set is considered to represent a biological function, and statistical tools are applied to test how this function behaves within the omics dataset under study. Functional enrichment tools often use a standard statistical method called Fisher's exact test or the hypergeometric test, which evaluates whether the proportion of genes in a results list that also belongs to a given gene set is greater than expected by chance. An example of such a tool is EnrichR, which applies hypergeometric testing across a large variety of gene set collections relating to pathways, ontologies, transcriptional regulation, disease, and other biological themes  [10].
A widely used collection of gene sets is the Molecular Signatures Database, MSigDB  [19, 20] , which is divided into eight major collections. These include curated gene sets from pathway databases such as KEGG  [21] , the Gene Ontology (GO)  [22] , and genes with shared regulatory motifs or chromosomal positions. Importantly, in the context of the present work, MSigDB also includes collections of genes having shared dysregulation in cancer or immunologic gene expression studies. These last two collections, 'oncogenic signatures' and 'immunologic signatures' have been created by systematic analyses of relevant datasets from the Gene Expression Omnibus (GEO)  [23] , enabling researchers to compare later transcriptional datasets across hundreds of previous studies  [24] , and thereby contributing to leading publications in their field  [25, 26].
Gene sets for functional genomics in the study of skeletal muscle tissue and neuromuscular pathology Functional genomics is integral to the current study of skeletal muscle tissue and neuromuscular pathology, as evidenced by the vast quantities of omics data now generated by researchers in this field. Considering RNA expression alone, a simple search of 'skeletal AND muscle' in the ArrayExpress database returns more than 1000 separate experimental studies of gene/transcript and microRNA expression  [27]. These studies frequently make use of pathway analysis approaches, but this is done without the aid of gene set collections of the subject-specific type that have been beneficial to researchers in the fields of oncology and immunology. In prior work, we have extracted gene sets from published muscle gene expression data and applied these for analytical purposes in three previous studies:  (1)  In a study of human myotubes from old compared to young subjects, we found a similar profile of dysregulation to that observed in previous gene expression studies of myoblast differentiation, despite that the fusion index of old myotubes was unaffected-this led to the discovery of a failure of re-quiescence in elderly myoblast cultures, and the identification of SPRY1 methylation as an underlying mechanism, with resultant loss of the stem cell pool having a potential role in sarcopenia  [28].
Differential expression analysis and gene mapping We used the 'limma' package  [42, 43]  for differential expression analysis. We included into gene sets all genes with Benjamini-Hochberg FDR adjusted p value < 0. 05 for each experimental comparison, up to a maximum of 300 genes, taking the top 300 after ranking by the significance of differential expression (Limma's B statistic). Before the eBayes step, we also removed 25% of the genes that had the lowest average expression values. To map Ensembl gene IDs to gene symbols, we used Ensembl BioMart  [44]. We extracted the required information from GRCh38. p5 assembly for human and GRCm38. p4 assembly for mouse. Following the standard of MSigDB, standard gene names were used that are approved by the Human Gene Nomenclature Committee (HGNC) and Mouse Genome Informatics (MGI) groups.
Batch effect correction For batch effect identification and correction, we used the surrogate variable analysis (sva) algorithm  [45]  from the 'SVA' Bioconductor package  [46]. We used the 'leek' method to detect the number of surrogate variables, if present, but we also set a limit of up to two surrogate variables to avoid overcorrecting the data. The sva algorithm found and corrected technical variation in 256 out of a total of 578 experiments.