dataesr/scientific-paragraphs-categorization huggingface dataset:
Lemmatization Our hypotheses are specified with respect to the structure of the lexicon and make no clear predictions with respect to whether inflected forms differ in concreteness. The Brysbaert concreteness rating study included some inflected forms, which show only small differences in concreteness (e. g. , dog 4. 85, dogs 5; eye 4. 9, eyes 4. 85). Here, we lemmatized all forms using the texstem package version 0. 1. 4 (Rinker 2018) , thus getting rid of inflectional morphology (averaging over the different concreteness values). This is not only motivated based on the small differences in concreteness ratings, but also because it facilitates merging and comparison across datasets (e. g. , the countability lexicon considers lemmas), and because it allows us to avoid violating the independence assumption of standard regression (i.
Statistical analysis All analyses were performed with R version 4. 1. 1 (R Core Team 2019) and the tidyverse package version 1. 3. 1  (Wickham et al. 2019). The car package version 3. 0. 11 (Fox and Weisberg 2018)  was used to compute variance inflation factors to assess collinearity. All datasets and code are made publicly available in the following Open Science Framework repository: https://osf. io/rej6b/. We performed a series of regression analyses, each time with concreteness as the main response variable. We first consider each linguistic predictor in isolation before we perform a simultaneous regression analysis with all predictors. One reason for performing separate regression analyses first is the presence of missing values for different words in the different data sets, as there are only few words The linguistic dimensions of concrete and abstract concepts having data for all variables.
03) than words with suffix (M = 2. 77, SD = 0. 89). The corresponding linear model (t = 32. 80, df = 11,164, p < 0. 0001) described 9% of the variance in concreteness ratings. Again, the weighted regression performed even better, describing 13% of the variance. Figure  2  shows the result broken up by the most frequent suffixes (corresponding to the suffixes listed in Table  1 ). This shows that  Reilly and Kean's (2007)  claim that words with suffixes are on average more abstract clearly needs to be qualified. While we have replicated this finding in our previous analysis for a larger dataset than they consider (including not only just nouns), Figure  2  shows that there are vast differences in the average concreteness of particular suffixes. In fact, words with the suffix -er (as in worker, dreamer, swindler, pensioner) had similar average concreteness (M = 3.
Countability For nouns only, we investigated the distinction between mass and count nouns, using data from the Bochum English Countability Lexicon  (Kiss et al. 2016). The BELC database is organized around senses (see Section 2. 3), rather than word forms. To match this data to the concreteness ratings, which do not separate senses, we decided to analyse only the subset of those nouns for which all senses were either count or mass without exception. This dataset included a total of 3,599 count nouns and 801 mass nouns. As seen in Figure  4 , count nouns were on average more concrete (M = 3. 78, SD = 0. 91), and mass nouns relatively more abstract (M = 2. 76, SD = 0. 95) (t = 28. 74, df = 4,398, p < 0. 0001), and described 16% of the variance in concreteness ratings. As before, the weighted regression penalizing high-SD words described even more variance, 19%.
Etymology We processed etymologies for a total of 287,341 unique word senses from the Oxford English Dictionary (OED). For sequences of borrowing (e. g. , French < Latin), The linguistic dimensions of concrete and abstract concepts we used the proximate source (in this case, French) rather than the ultimate source (in this case, Latin), as it is the language that is most directly tied to the contact history of English. Of course, distinct word senses can have distinct etymological histories: typically, from the perspective of historical linguistics and OED, this is homonymy, with the same word form resulting from different origins. To match the OED data to the data from the concreteness rating study (which does not distinguish between word senses, as discussed above), we collapsed etymologies across distinct entries using a majority vote criterion (i.
2021). Second, researchers using word-based concreteness datasets should balance for linguistic factors when selecting stimuli for empirical studies of conceptual concreteness. For example, experiments that use nouns as stimuli should take into consideration the distinction between count and mass nouns, given that they tend to be characterized by different degrees of concreteness. Moreover, although much empirical research currently focuses on nouns, it would be important to verify whether and how its results generalize to other lexical categories. More generally, we would like our study to raise awareness of the fact that concreteness ratings are associated with words, rather than with concepts directly. It could be the case that speakers who provided the concreteness ratings, faced with a long list of words to judge during the data collection, developed a strategy to perform the task less effortfully.
Acknowledgements: We thank the anonymous reviewers and members of the audience at the  UK Cognitive Linguistics Conference  ( UKCL 2020 ,  University of Birmingham ) for helpful comments and suggestions. Bodo Winter  was supported by the  UKRI   Future Leaders Fellowship   MR/T040505/1. This paper is the result of the close collaboration of the three authors. Francesca Strik Lievers is responsible for writing Sections 1 and 2,  Bodo Winter  is responsible for writing Sections 3 and 4, Section 5 has been written together by the three authors.
2D examination was performed with the use of CTAn software in order to assess the diameter of cranial defects. For each defect, a 2 mm area was measured, as this was the size of the initial cranial defect. The assessed parameters include defect circularity and geometry aspect ratio, which depend on the defect diameter differences and the volume content of the newly formed bone tissue in the measured area (BV/TV). The peri-implantation tissue area was selected as the 3 mm diameter surface around the implantation site (without new bone tissue).
Statistical Analysis Results obtained during in vitro and in vivo studies were presented as the mean with standard deviation (±SD), derived from at least three technical repetitions of the detailed analysis. The statistical calculations were made with parametric assays: t-Student test or One-way analysis of variance with Dunnett's post hoc test. GraphPad Software (Prism 8. 20, San Diego, CA, USA) was used for testing and graphical data presentation. Prior to the analysis, the normality of the population data was evaluated with Shapiro-Wilk test. In turn, the equality of variances was assessed by Levene's test (STATISTICA 10. 0 software, StatSoft, Inc. , Statistica for Windows, Tulsa, OK, USA. In the comparative analysis, differences described with a probability of p < 0. 05 were considered significant.
Acknowledgments Financial support from the  National Science Centre  over the course of the realisation of the project Harmonia 10 titled New, two-stage scaffolds based on calcium nanoapatite (nHAP) incorporated with iron nanotoxides ( Fe 2 O 3 /Fe 3 O 4  ) as a function of controlled release of miRNA in a static magnetic field for the regeneration of bone fractures in osteoporotic patients (Grant No. UMO 2017/26/M/NZ5/01184 ) is gratefully acknowledged. The publication is financed under the Leading Research Groups support project from the subsidy increased for the period 2020-2025 in the amount of 2% of the subsidy referred to Art. 387 (3) of the Law of 20 July 2018 on Higher Education and Science, obtained in 2019. We also thank  Klaudia Marcinkowska  and  Mateusz Sikora  for their help during in vitro studies and  Professor Zbigniew J.
Also, spatial variability at a country scale is currently unknown and requires homogeneous sampling and analysis methodologies for all filters and time series. For a comprehensive investigation of the intrinsic OPs of various PM sources, we built up an extensive dataset of about 1700 samples from 14 sites consisting of 15 year-round time series of observations over continental France, collected during research programs conducted between 2013 and 2018. In each of these samples, we concurrently measured the OP with the DTT and AA assays together with an extensive chemical characterization allowing PM source apportionment using a harmonized PMF approach  (Weber et al. , 2019). Then, we apportioned the OP measured by the DTT and AA assays to the emission sources using a multilinear regression approach following  Weber et al.
Site descriptions The selected sites had to fulfill three conditions: (1) a yearly sampling period, (2) the required chemical analysis to perform a harmonized PMF analysis and (3) enough filter surface left to assess the OP measurements. A total of 14 sites were included in this study (one being sampled twice at a 5year interval) taken from different research programs. These sites reflect the diversity of typology we could encounter in western Europe: urban (NGT, TAL, AIX, MRS-5av, NIC), urban Alpine valley (GRE-cb, GRE-fr, VIF, CHAM, MNZ, PAS), industrial (PdB) and traffic (RBX, STG-cle) (see Table S1 in the Supplement) covering different areas of France (Fig. 1 ). We can note, however, the absence of remote or rural sites in our current dataset. The air quality at all of these sites is operated by the local air quality agencies (Atmo Sud, Atmo Auvergne Rhône-Alpes, Atmo Nouvelle Aquitaine, and Atmo Hauts de France), and most of them are part of the so-called CARA program  (Favez et al.
Methodological background The PM mass source apportionment for the 15 yearly series was conducted using the U. S. Environmental Protection Agency (US-EPA) EPA PMF 5. 0 software (US EPA, 2017) with the ME-2 solver from  Paatero (1999). Briefly, the PMF was introduced by  Paatero and Tapper (1994)  and is now one of the most common approaches used for PM sourceapportionment studies  (Hopke et al. , 2020; Karagulian et al. , 2015; Belis et al. , 2020). It aims at solving the receptor model equation X = G • F, ( 1 ) where X is the n × m observation matrix, G is the n × p contribution matrix and F is the p × m factor profile matrix (or source, despite some factors not being proper emission sources but possibly reflecting secondary processes), with n the number of samples, m the number of measured chemical species and p the number of profiles.
PMF setup Some of the PMF analyses included in this paper have been performed during previous programs, namely, SOURCES (http://pmsources. u-ga. fr, last access: 27 July 2021,  Weber et al. , 2019) , DECOMBIO  (Chevrier, 2016; Chevrier et al. , 2016) , or MobilAir (https://mobilair. univ-grenoble-alpes. fr/, last access: 27 July 2021,  Borlaza et al. , 2021a). In order to get comparable PM source profiles from a common set of input species and constraints in the model, PMF analyses have been run with a harmonized methodology, as previously reported  (Weber et al. , 2019). Then, the PMF of MNZ and PAS were run specifically for this study, and the ones of MobilAir (GRE-fr_2017, GRE-cb and VIF) are the "classic" runs in  Borlaza et al. (2021a). The other ones (AIX, CHAM, GRE-fr_2013, MRS-5av, NGT, NIC, PdB, RBX, STG-cle and TAL) directly come from the SOURCES program  (Weber et al.
, 2019). The input species were slightly different from one study to another but always included carbonaceous compounds (OC and EC), ions (SO 2- 4 , NO - 3 , Cl -, NH + 4 , K + , Mg 2+ , and Ca 2+ ), organic compounds (levoglucosan, mannosan, arabitol and mannitol -the latter two summed and referred to as polyols) and MSA -and a set of trace metals for a total of about 30 species. The list of metals used within the PMF analysis was not the same for each of the sites due to overly low concentrations (lower than the quantification limit) on some filters leading to a very low signal-to-noise ratio (see Table  S2 ). The uncertainties were estimated following the method proposed by  Gianini et al. (2012)  and were tripled if the signal-to-noise ratio was below 2 (classified as "weak" in the PMF software).
The intercept was not forced to zero on purpose. Indeed, if the system is well constrained, the intercept should spontaneously be close to zero, and conversely a non-zero intercept would point out missing explanatory variables. A weighted least-square regression (WLS) was finally used to consider the uncertainties of the OP measurements. The uncertainties of the coefficients β given by the MLR were estimated by bootstrapping the solutions 500 times, randomly selecting 70 % of the samples each time to account for possible remaining extreme events or seasonal variations of the intrinsic OP per source. The uncertainty of the PMF result G was however not considered because the EPA PMF software only returns to the user the uncertainties associated with the profile matrix F (see  Weber et al. , 2019 , for a firstorder estimation of the G uncertainties).
OP results and seasonality The 15 time series for both OP DTT and OP AA at each site are presented on the website (http://getopstandop. u-ga. fr/results?component=rd_ts, last access: 27 July 2021). A monthly aggregated view is given in Fig. 2  for OP DTT v and OP AA v , respectively. As the dataset covered complete full years, including the influence of different PM sources with different seasonal activities, the results obtained are representative of spatio-temporal patterns of the OPs at least over France and probably over large parts of western Europe. As reported previously by  Calas et al. (2018 Calas et al. ( , 2019)) , we observed a seasonality of both OP DTT v and OP AA v , with higher OP values during the colder months (October-March) compared to the warmer months (April-September).
Intrinsic OP of the main PMF sources Even if the models reproduce the observations correctly, this does not guarantee that the geochemical meaning extracted is the same for each of the models; i. e. , the intrinsic OPs of the sources may completely differ from site to site. The question is then to identify whether a given source contributes similarly to the OP at all the sites. In other words, do all the models extract any general geochemical information relative to the OP? Figure  3  presents the intrinsic OP DTT and OP AA for the selected subset of sources in terms of median and interquartile range (Q1 and Q3) to investigate the general information given by the different model. The values of mean and standard deviation and details per station for all sources are given in Tables  S5-S7. For most of the sources identified, positive intrinsic OP values were observed considering the whole dataset.
Biomass burning The variability of the biomass burning intrinsic OP is somewhat site-dependent, with a low uncertainty within a given site but with slightly different intrinsic OPs between sites. This suggests that the variability is not linked to uncertainties of the MLR but may be due to actual local variations of the chemical composition of this profile. This result contrasts with the fact that the biomass burning was identified as displaying relatively homogeneous chemical profiles between site profiles, with PD < 0. 1 and SID < 0. 7 (Fig. S2 ). Actually, the variability of OP-intrinsic values may be related to species that are not directly measured in our dataset. That is, polycyclic aromatic hydrocarbons (PAHs), oxy-PAHs, OH-PAHs, nitro-PAHs and especially polyaromatic quinones could not be measured at each of the sites used in the present study.
Mineral dust The mineral dust displays a rather low variability when considering all datasets, but a deeper analysis showed two groups of sites: AIX-RBX-VIF vs. all other sites. The first group presents a high intrinsic OP for both assays (around 0. 25 nmol min -1 µg -1 OP DTT and 0. 16 nmol min -1 µg -1 from OP AA ), whereas sites from the second group display halved (around 0. 1 nmol min -1 µg -1 for OP DTT ) or almost null (OP AA ) intrinsic OPs. A first conclusion is that 80 % of the sites present a common intrinsic OP for the dust source, hence the low IQR observed for this source. This factor apportions some OC * but most of the Ca 2+ , Al, Co, Cs, Mn and Sr. Then, none of these species appear to present intrinsic redox activity for the AA assay. It is not clear however whether the DTT is reacting with the organic species, with the metals, or both.
Contribution of the sources to the OPs The relative importance of the contributions of the sources to the total PM 10 OPs is weighted by their different intrinsic OPs. The question, therefore, is to what extent the contributions of the sources to the OPs differ from their contributions to the PM mass concentration. In this part, we present an aggregated view of the seasonal contribution of the sources to the OP in Fig. 4  and the daily contribution in Figs. 5  and 6 , considering all sites. Details per sites are presented in the website provided herewith as the Supplement. We would like to stress here that our dataset included an important proportion of Alpine sites as well as urban sites. Then, the extrapolation to the whole of France or to other regions of western Europe should be done cautiously.
The skewness of the distribution is not surprising as some high-PM 10 events (i. e. , a short time event in the dataset) were present in our measurements. This is also specifically anticipated in Alpine areas (CHAM, PAS, MNZ, VIF, GRE-cb, GRE-fr_2013 and GRE-fr_2017), where the frequent development of atmospheric thermal inversion layers in winter is causing increased pollutant concentrations. We observed (Fig. 5 ) a redistribution of the daily mean contribution sources' rank between the PM 10 mass, OP DTT  and OP AA v similarly to the monthly mean contribution discussed above. The biomass burning source, being an important contributor to the PM 10 mass, contributes also significantly to both OP and is ranked as the first contributor to both OP DTT v and OP AA v mean daily contribution (mean 0.
The addition of a covariation term or even the use of nonlinear regression may be the next step to better explain the OP of the sources  (Borlaza et al. , 2021b). Moreover, if the intrinsic OP results from the MLR can be extrapolated to any given site with a similar regional background to the urbanized area used in this study, the source contribution extrapolation should be taken cautiously since our dataset displays an over-representation of the Alpine sites with regard to the whole of France.
Conclusions To our knowledge, this study gathers the most important database of OP samples, with concomitant observations of chemistry analysis, source apportionment through PMF, and the measure of two OP assays (DTT and AA) for 15 yearly time series over France spanning between 2013 and 2018 for a total of > 1700 samples. We demonstrated that source apportionment of OP through a "simple" multilinear regression without any constraint on the coefficient provides good statistical results and can explain the observed OP DTT v and OP AA v. -The intrinsic OPs of the main regional sources present values in the same range at each site, especially for the primary road traffic, biomass burning, nitrate-rich, dust, and sulfate-rich PMF factors. Biogenic and MSA-rich factors present higher discrepancy according to the site together with the highest uncertainties at each site.
Acknowledgements. This work was partially funded by  ANSES  for  OP measurements  ( ExPOSURE program , grant  2016-CRD-31 ), an  IDEX UGA  grant for innovation  2017   ROS-ONLINE  and  CDP IDEX UGA MOBILAIR  ( ANR-15-IDEX-02 ). It was also supported by the  French Ministry of Environment  as part of the national reference  laboratory for air quality monitoring  (LCSQA, program CARA), for some of the chemical analysis related to the GREfr, TAL, RBX, STG-cle, MRS-5av and NIC stations. The study in CHAM, MNZ and PAS was funded by  ADEME  and  PRIME-QUAL within the   DECOMBIO program  ( 1362C0028 ). The studies in GRE-cb and VIF were funded by the  UGA IDEX MobilAir program  ( ANR-19-CE34-0002-01 ) and the  ADEME QAMECS program  ( 1262c0011 ). Regional monitoring networks (namely,  Atmo AuRA ,  Atmo Sud ,  Atmo HdF ,  Atmo NA  and  Atmo GE)  financially contributed to sampling and/or chemical analyses for samples from their respective sites.
The PhD of Samuël Weber was funded by a grant from  ENS Paris. This study was also supported by direct funding by  IGE  (technician salary), the  LEFE CHAT  (program  863353 : " Le PO comme proxy de l'impact sanitaire" ), and  LABEX OSUG @2020  ( ANR-10-LABX-56 ) (for funding of analytical instruments). The authors wish to thank all the numerous people (who could not be listed exhaustively here) from the different laboratories (IGE and Air-O-Sol analytical platform, Ineris and PTAL analytical platform of EDYTEM) and from the regional air quality monitoring networks listed as co-author affiliations, who actively participated in filter sampling and/or analysis. The  Institute Mines Telecom (IMT-Lille Douai , PI:  Laurent Y. Alleman ) and the  Laboratoire des Sciences du Climat et de l'Environnement (LSCE , PI:  Nicolas Bonnaire ) are also warmly thanked for their contribution to chemical analysis of samples collected at RBX, NGT TAL and  STG-Cle.
Financial support. This research has been supported by the  Agence Nationale de Sécurité Sanitaire de l'Alimentation, de l 'Environnement et du Travail  (grant no. 2016-CRD-31 ), the  Université Grenoble Alpes  (grant nos. ROS-ONLINE  and  ANR-19CE34-0002-01 ), the  Agence Nationale de la Recherche  (grant nos. ANR-15-IDEX-02  and  ANR-10-LABX-56 ), and the  Agence de l'Environnement et de la Maîtrise de l'Energie  (grant nos. 1362C0028  and  1262C0011 ). Review statement. This paper was edited by  Manabu Shiraiwa  and reviewed by two anonymous referees.
S. Weber et al. :  Harmonized source apportionment of OP in 15 sites Code and data availability. Data available on request at gaelle. uzu@ird. fr. Website code available at https://gricad-gitlab. univ-grenoble-alpes. fr/pmall/app_op (last access: 27 July 2021) (commit fb99ed42e3f704464e88deca9548b74128fd7b24)  (Weber, 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-21-11353-2021-supplement.
Demographic and clinical data Seventy-two patients with insomnia disorder (66. 67% female), aged from 20 to 64 (M = 43. 64, SD = 12. 528) were recruited. Patients presenting with sleep-onset insomnia (N = 51) did not differ on demographic and clinical variables from patients with sleep-maintenance insomnia (N = 21). Among the sleep-onset insomnia participants (N = 51), only five presented with sleep-onset complaints only, reflected by a score on item 1a of the ISI >1 and zero on the other items (i. e. , 1b and 1c); forty-six patients in the sleep-onset group were actually mixed, as they presented with sleep-maintenance complaints as well (i. e. , scores >0 on items 1b and 1c). Since sleep-onset difficulties distinguished the two insomnia groups, for the sake of simplicity, the group with sleeponset difficulties was labeled 'sleep-onset insomnia', whereas the group presenting with sleep-maintenance difficulties in the absence of sleep-onset complaints was labeled 'sleep-maintenance insomnia'.
All participants were unmedicated except for 4. 17% of the sample, whom were under hypnotic drugs. Forty-nine patients with BD in a hypomanic episode (M = 40. 82 years, SD = 13. 39) and 99 healthy subjects (M = 26. 68 years, SD = 7. 62) were recruited. Healthy controls were significantly younger than participants from the clinical groups. The betweengroup comparisons presented hereafter were similar when age adjustments were performed. Detailed demographic and clinical data are presented in Table  1.