dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, this study also investigates the monthly trends (averaged over 22 datasets) and corresponding variabilities (IQR/median ratio) across sites for key ions in the commonly resolved OA factors. While these key ions barely show monthly trends in HOA, COA, MO-OOA, and Total OOA, BBOA key ion (f 60 and f 73 ) intensities increase during the cold seasons due to the abundance of biomass burning sources and the lower reactivity of levoglucosan. The increased f 43 of LO-OOA during warm seasons is most likely due to enhanced biogenic SOA formation. In terms of spatial variabilities, key ions for HOA and COA factors show a small IQR/median ratio with a range of 0. 06-0. 27 due to both factor contributions being consistent (if present), as reported in previous studies. However, the key ions for the BBOA factor show a relatively larger spatial variability with the IQR/median ratio ranging from 0.
20 to 0. 39, which suggests potentially different combustion conditions, type of woods, etc. , contribute to this variability in the real-world scenarios. In addition, the f 43 intensities in MO-OOA and LO-OOA show large spatial variability, with the IQR/median ratio ranging from 0. 45 to 0. 83 and 0. 39 to 0. 60, respectively. The f 44 in LO-OOA has a large IQR/median ratio range of 0. 38-0. 94, but the f 44 in MO-OOA is rather less variable across sites with a relatively small IQR/median ratio range of 0. 16-0. 34. This is expected, since OOA factors are never constrained combined and complex ageing processes are affected by different locations and meteorological conditions. OOA is the dominant contributor to OA, but ACSM data alone is insufficient for its apportionment to specific sources and formation mechanisms.
Normalised spatial variations for key ions using the interquartile range (IQR) divided by the medians of average monthly intensities across the 22 datasets. ± 0. 03 0. 20 ± 0. 03 0. 16 ± 0. 07 0. 09 ± 0. 05 0. 34 ± 0. 03 0. 26 ± 0. 06 0. 26 ± 0. 05 0. 64 ± 0. 13 0. 61 ± 0. 16 0. 49 ± 0. 08 0. 40 ± 0. 05 0. 34 ± 0. 05 conditions can readily cause the accumulation of pollutants. In particular, HOA (traffic emissions) is a non-negligible OA source with a rather consistent contribution across different stations (10. 7 ± 3. 8%). Six urban sites display a significant COA factor (i. e. , two Athens datasets, Zürich, London, Barcelona, and Marseille) with an overall average contribution to OA of 14. 5 ± 2. 5%. Moreover, most of the datasets show a resolved BBOA component (except Hyytiälä, Puy de Dôme, and Helsinki) with important contributions to OA (annual average:12.
Acknowledgement This work would not have been possible without the following contributions: All co-authors were supported by the cost action of Chemical On-Line cOmpoSition and Source Apportionment of fine aerosol (COLOSSAL,  CA16109 ). The  Paul Scherrer Institute (PSI ) team acknowledges the support of the  Swiss Data Science Centre (SDSC)  and the  Swiss Federal Office of Environment (FOEN)  within the Project Aurora. GC and AT were supported by a  COST  related project of the  Swiss National Science Foundation , Source apportionment using longterm Aerosol Mass Spectrometry and  Aethalometer Measurements  (SAMSAM, IZCOZ0_177063), as well as the  EU Horizon 2020 Framework Programme  via the  Research Infrastructures Services Reinforcing Air Quality Monitoring Capacities in European Urban & Industrial AreaS  ( RI-URBANS) project  ( GA-101036245 ), the  ERA-PLANET projects SMURBS and iCUPE  (grant agreement no.
689443 ). LP and SA were supported by  ACTRIS  (grant nos. ACTRIS (262254) and  ACTRIS-2 (654109) ). HC, VR, and JFB acknowledge financial support from the  Labex CaPPA project , which is funded by the  French National Research Agency (ANR)  through the  PIA (Programme d'Investissement d'Avenir)  under contract  ANR-11-LABX-0005-01 , and the  CLIMIBIO  project, both financed by the  Regional Council "Hauts-de-France " and the  European Regional Development Fund (ERDF). In addition, The  ATOLL (Lille), Longchamp (Marseille) , and SIRTA (Paris region) platforms are part of the so-called LCSQA- CARA program , co-funded by the  French Ministry of Environment. JV, CM, LM were supported by a grant of the  Romanian Ministry of Research, Innovation and Digitalization, CNCS -UEFISCDI , project number  PN-III-P1-1.
1-TE-2019-0340 ,within  PNCDI III  and by  Romanian National Core Program  contract  18N/2019. European Union's  Horizon 2020 research and innovation programme  (project  FORCeS  under grant agreement No  821205 ). Consolidator grant  INTERGRATE No 865799  and starting grant  COALA No 638703 ). RL, PP, JS and PV were supported by  ICPF CAS  acknowledges financial support from the  MEYS's of the Czech Republic  within the  INTER-EXCELLENCE INTERCOST program  under grant agreements No. LTC18068  and by under grant  Actris   CZ LM2018122 , and by the  GACR  under grant  P209/19/06110Y. IDAEA-CSIC acknowledges financial support from  Generalitat de Catalunya  ( AGAUR 2017 SGR41 ), the  Spanish Ministry of Science and Innovation  through  CAIAC  project ( PID2019-108990RB-I00 ) and  FEDER  funds, through  EQC2018-004598-P.
IDAEA-CSIC is a Centre of Excellence Severo Ochoa  ( Spanish Ministry of Science and Innovation , Project  CEX2018-000794-S ). The  Cyprus Atmospheric Observatory  team acknowledges funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  (under grant agreement no. 856612 ) and the  Cyprus Government. Operational program Competitiveness, Entrepreneurship and Innovation (RESEARCH-CREATE-INNOVATE)  project code:  T1EDK-03437. KRD acknowledges support by the  SNSF Ambizione  grant  PZPGP2_201992. The LCE team acknowledges financial support from the  PACA region  ( PRISM  project; grant n •  2017_08809 ). TROPOS has been supported by the  German Federal Environment Ministry (BMU)  grants  F&E 370343200  (German title: " Erfassung der Zahl feiner und ultrafeiner Partikel in der Außenluft "),2008-2010, and  F&E 71143232  (German title: " Trendanalysen gesundheitsgefährdender Fein-und Ultrafeinstaubfraktionen unter Nutzung der im German Ultrafine Aerosol Network (GUAN) ermittelten Immissionsdaten durch Fortführung und Interpretation der Messreihen "), 2012-2014.
KS and ASk were supported by the subsidy from the  Ministry of Science and Higher Education , grant numbers:  16. 16. 220. 842B02 ,  16. 16. 210. 476  and by the  EU  Project  POWR. 03. 02. 00-00-I004/16. EF, JEP and OF gratefully acknowledge  CNRS-INSU  for supporting measurements performed at SIRTA and  CO-PDD , within the long-term monitoring aerosol program SNO-CLAP, both of which are components of the  ACTRIS French Research Instructure , and whose data is hosted at the AERIS data center (https://www. aeris-data. fr/). LS gratefully acknowledges  DIM Qi 2 research network  for financial support of her doctoral work. National University of Ireland Galway work was supported by the  EPA   Research Programme 2021-2030 ,  AEROSOURCE  Project ( 2016-CCRP-MS-31 ). The  EPA   Research Programme is a Government of Ireland initiative  funded by the  Department of Environment , Climate and Communications.
GM acknowledges the support of the  Slovenian Research Agency  (grant no. P1-0385 ). NOA team (IS, AB,  NM (Nikolaos Mihalopoulos) ) acknowledges support by the " PANhellenic infrastructure for Atmospheric Composition and climatE change " ( MIS 5021516 ) which is implemented under the Action " Reinforcement of the Research and Innovation Infrastructure ", funded by the  Operational Programme "Competitiveness, Entrepreneurship and Innovation"  ( NSRF 2014-2020 ) and co-financed by Greece and the  European Union  ( European Regional Development Fund ). JS and MP acknowledge the support of the  European Union 's  Horizon 2020 Research and Innovation Programme  (under grant agreement no. 856612 ) and of the  Cyprus Government. Moreover, we also want to thank  Krista Luoma  for  Hyytiälä aethalometer data.
, 2013)  in its inclusion of a new package of parameterisations for turbulence, convection and clouds. The NEMO ocean model comprises three components, i. e. ocean dynamics (NEMO-OPA), sea-ice dynamics and thermodynamics (NEMO-LIM), and marine biogeochemistry (NEMO-PISCES). All of these ocean components have been updated from IPSL-CM5A-LR to IPSL-CM6A-LR, from version 3. 2 to version 3. 6 of NEMO  (Madec et al. , 2017; Rousset et al. , 2015; Aumont et al. , 2015) , with the addition of a nonlinear free surface, a parameterisation of mixing in the mixed layer due to submesoscale processes, an energy-constrained parameterisation of mixing due to internal tides for NEMO-OPA and a new multicategory halother-   Séférian et al. (2020). These two versions from the IPSL Climate Model family qualify as Earth system models as they include carbon cycle components for the land biosphere (ORCHIDEE) and the ocean (NEMO-PISCES).
It is urgent as well to verify the sensitivity of nitrogen fixation with more mechanistic models of N fixation (such as those of  Pahlow et al. , 2013 , or Inomura et al. , 2018). ), N fixation (mmolN m -2 yr -1 ), surface NO 3 concentrations (mmol m -3 ) and surface PO 4 concentrations (mmol m -3 ). The upper panels show NPP based on remote-sensing observations with the vertically generalized production model (VGPM) algorithm  (Behrenfeld et al. , 2005) , N fixation from  Luo et al. (2014)  updated by  Landolfi et al. (2018) , and NO 3 and PO 4 as provided in the World Ocean Atlas database  (Garcia et al. , 2013). The other panels show model-data anomalies averaged over the period 1986-2005. Table  2. Root-mean-square errors (RMSEs) for NPP (mgC m -2 d -1 ), N fixation (Nfix, mmolN m -2 yr -1 ), and NO 3 and PO 4 surface concentrations (mmol m -3 ) for all PISCES versions used here against observations (NPP based on remote-sensing -  Behrenfeld et al.
, 2005 ; N fixation from  Luo et al. , 2014 , updated by Landolfi et al. , 2018; and NO 3 and PO 4  as provided in the World Ocean Atlas database -  Garcia et al. , 2013). All model estimates are for  1986-2005. Model version NPP (mgC m -2 d -1 ) Nfix (mmolN m -2 yr -1 ) NO 3 (mmol m -3 ) PO 4 (mmol m.
Acknowledgements. The  CMIP6  project at IPSL used the HPC resources of TGCC under the allocations  2016-A0030107732 ,  2017-R0040110492  and  2018-R0040110492  (project  gencmip6 ) provided by  GENCI  ( Grand Équipement National de Calcul Intensif ). This study benefited from the  ESPRI (Ensemble de Services Pour la Recherche  à l'IPSL) computing and data centre (https://mesocentre. ipsl. fr, last access: July 2022), which is supported by  CNRS ,  Sorbonne Université ,  École Polytechnique  and  CNES  and through national and international grants. All authors acknowledge support from the  French ANR  project  CIGOEF  (grant  ANR-17-CE32-0008-01 ). The authors also acknowledge support from the  European Union 's  Horizon 2020 research and innovation programmes CRESCENDO  (grant agreement no.
641816 ),  COMFORT  (grant agreement no. 820989 ),  4C  (grant agreement no. 821003 ) and  ESM2025  (grant agreement no. 101003536 ). Laurent Bopp received funding from the  Chaire de Recherche ENS-Chanel. AT received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 724289 ). Financial support. This research has been supported by the  Agence Nationale de la Recherche (CIGOEF  (grant no. ANR-17-CE32-0008-01 )) and the  Horizon Europe framework programme, Horizon Europe Climate, Energy and Mobility (CRESCENDO  (grant no. 641816 ),  COMFORT  (grant no. 820989 ),  4C  (grant no. 821003 ),  ESM2025  (grant no. 101003536 ) and  BYONIC  (grant no. 724289 )). Review statement. This paper was edited by  Carolin Löscher  and reviewed by two anonymous referees.
Mining the P. notatum Floral Transcriptome for Homologs of Rice AUX/IAA Members We queried a previously available P. notatum floral transcriptome  [42]  using the sequences of 31 AUX/IAA rice proteins (OsIAA1-31)  [43]  and identified 33 unique transcripts (isotigs) that assembled into 23 isogroups (1 to 4 isotigs per group) (Table  S1 , pages 1 and 2). The alignments of the predicted amino acid sequences (ranging 155-387 aa and 16. 44-41. 05 kDa) revealed that all of them contained the four conserved Aux/IAA domains (PF02309, CL0072), including: domain I (D/ELXLXL), responsible for target genes repression and recruitment of the TOPLESS (TPL) corepressor; domain II (GWPPV) or "degron", conferring instability and causing rapid degradation of Aux/IAA when interacting with F-box TIR1/AFB proteins; and C-terminal domains III and IV, involved in homo and/or heterodimerization with Aux/IAA proteins and/or with ARFs, respectively  [43]  (Figure  S1a, b ).
Moreover, a search in the recently delivered P. notatum sense/antisense Illumina TruSeq/HiSeq floral transcriptomes  [41]  detected several transcripts (i. e. , TRpn_30859, TRpn_56938, TRpn_91884, TRpn_120368, TRpn_179711 and TRpn_197389) with homology to AUX/IAA sequences targeted by antisense reads (see  Podio et al. 2021 , Supplementary Table  S4 )  [41]. Based on these data, we mapped the sense and antisense reads expressed in the sexual and apomictic floral transcriptomes (GenBank GIUR00000000. 1) over PnIAA30. The assay revealed homogeneous coverage along the transcript with the sense reads derived from both sexual and apomictic genotypes (Figure  6a (ii,iii)) and partial coverage of the antisense reads in both samples, overlapping exons and splice junctions (Figure  6a (iv-vi)). Interestingly, the 5 region of the transcript appeared targeted by antisense reads derived only from the sexual genotype (Figure  6a(v) ).
In Silico Analysis of Putative PnIAA30 Interactors In order to detect PnIAA30 interactors expressed during sexuality and apomixis, we carried out an in silico analysis by combining the use of the STRING protein-protein interactions database  [49]  and the P. notatum quantitative floral transcriptome  [41]. First, we used STRING to search for interactors of IAA30 and IAA16 in rice (Oryza sativa L. ) and Arabidopsis, respectively. Then, we identified their homologs in the P. notatum floral transcriptome and searched for their expression patterns. The initial search in the rice database showed significant interactions (scores values > 0. 600) between OsIAA30 and orthologs to Arabidopsis ARFs (ARF1, ARF3, ARF5, ARF6, ARF7), TRANSPORT INHIBITOR RESPONSE 1-like (TIR1), Protein AUXIN SIGNALING F-BOX 2 (AFB2) and the PEPTIDYL-PROLYL CIS-TRANS ISOMERASE FK506 (Figure  7 ; Table  S3 , page 1).
Heatmaps of the transcriptomic profile across reproductive developmental stages for the PnARF1 and PnARF5 illustrate the difference between the auxin-dependent transcriptional landscape in sexual and apomictic plants (Figure  8 ). These results show that several genes related to the IAA30 function are differentially regulated during sexual and apomictic development in P. notatum. S3. Each node represents all the proteins produced by a single, protein-coding gene locus. Types of interaction between proteins are indicated by colors (see STRING database references).
simplex, which shows down-regulation in ovules  [45, 62]. Moreover, in a recent work, the antisense down-regulation of the TGS1-like gene, which is expressed in nucellar tissues of sexual individuals, induced the formation of an aposporous-like embryo in the species  [63]. Finally, to disclose possible molecular pathways associated with PnIAA30 downregulation during the apomictic development, an integrative analysis of differentially expressed interactors was carried out by using the STRING database  [49]  and the quantitative sense and antisense floral transcriptome reported by  Podio et al. (2021)   [41]. As the biological material used for the transcriptome construction originated from two highly heterozygous tetraploid individuals, the presence of several allelic variants derived from each genotype, also paralogs and/or splicing variants were expected.
Vegetative replicates of each plant were grown in pots at the Instituto de Investigaciones en Ciencias Agrarias de Rosario (IICAR, CONICET-UNR), Zavalla, Argentina, and maintained in neighboring plots to guarantee identical growing environmental conditions. 4. 2. Identification and Analysis of Transcripts Homologous to Rice AUX/IAA Genes P. notatum transcripts with homology to rice Aux/IAA genes expressed during the sexual and apomictic developments were retrieved from the reference floral transcriptome of the species available at the NCBI BioProject PRJNA330955  [42]. Amino-acid sequences of the 31 rice Aux/IAA proteins (OsIAA1-31)  [44]  were obtained for the Rice Genome Annotation Project (http://rice. plantbiology. msu. edu/, accessed on 16 September 2020)  [67]  and used as queries in TBLASTN searches (https://blast.
ncbi. nlm. nih. gov/Blast. cgi, accessed on 16 September 2020) against a global assembly (NCBI TSA 330955) containing reads from sexual and apomictic samples reported by  Ortiz et al. (2017)   [42]. Putative homologous sequences were defined using the following threshold values: score ≥ 100; e-value < 10 -5 and; ID > 50%. The predicted proteins were checked for containing the conserved AUX/IAA domain (Family PF02309) using the Pfam database (http://pfam. xfam. org/, accessed on 20 September 2020). The molecular weight and isoelectric point (pI) for each sequence were obtained using the Expasy Compute pI/Mw online tool (https://web. expasy. org/compute_pi/, accessed on 20 September 2020). The putative gene structure of the relevant P. notatum transcript was reconstructed with the SLPING (www.
1) and the reference (β-tubulin; NCBI GFMI02021278 and GPDH NCBI GFMI02003604. 1) genes were designed with Primer 3 v. 0. 4. 0 (http://bioinfo. ut. ee/primer3-0. 4. 0/primer3/, accessed on 23 November 2020)  [71]  (Table  S4 ). PnIAA30 primers specificity was examined by in silico PCR amplifications using all identified transcripts carrying the AUX/IAA motive as templates with the online tool http://insilico. ehu. eus/PCR, accessed on 25 November 2020. In each cDNA sample, PCR amplification of the target and the reference genes was carried out, including non-transcribed RNA (minus RT) and water (negative) controls to assess cDNA purity (Figure  S7 ). Additionally, PCR amplification of the target and the GDPH gene with primers flanking two adjacent exons was routinely performed to control the absence of DNA contamination in cDNA preparations (Figure  S7 ).
Validation of PnIAA30 Antisense RNA Expression Sense and antisense reads available from the P. notatum TSA assembly GenBank GIUR00000000. 1  [41]  were mapped to the PnIAA30 transcript using Hisat2  [74] , and the read coverage was visualized using the IGV v2. 3. 90 (Integrative Genomics Viewer, University of California, Oakland, CA, USA)  [75]  software (http://software. broadinstitute. org/software/igv/, accessed on 10 February 2022) with the option window size 1. Then, we validated the presence of antisense reads by strand-specific cDNA synthesis and PCR amplification according to the protocol reported by  Ho et al. (2010)   [76]. Briefly, PnIAA30 antisense strand-specific cDNA was synthesized from RNA samples derived from sexual (Q4188) and apomictic (Q4117) genotypes extracted at anthesis using the ImProm-II Reverse Transcriptase (Promega) with the antisense-specific primer IAA30_probeF (F1) (Table  S4 ) following the recommendations of the supplier.
Identification of Putative PnIAA30 Interactors and In Silico Expression Analysis The STRING database (https://string-db. org/, accessed on 10 June 2021)  [49]  was used to infer protein-protein interactors for both OsIAA30 and its Arabidopsis ortholog, AtIAA16. Then, P. notatum transcripts encoding for interactor's homologs were identified by TBLASTN searches on the annotated stage-specific quantitative flower transcriptome of the species (NCBI BioProject: 511813; accession PRJNA511813)  [41]. The expression patterns of the P. notatum transcripts were recovered from the differential analysis reported by  Podio et al. (2021)   [41]. The heat maps of transcript expression were built with the HEATMAPPER (http://www. heatmapper. ca/, accessed on 7 December 2020) online tool  [77].
The eOSCE application Organizing large-scale eOSCEs on Zoom requires programming several Zoom meetings and sending them to both students and teachers before the exam. We initiated a collaboration with Zoom in San Jose, California to develop an 'eOSCE application' to mass create Zoom meetings from a commaseparated values (CSV) file. It also retrieved the URLs, IDs, and passwords for these meetings so that they could be shared with eOSCE participants. The eOSCE application was broken down into three parts: (1) a server part in Node. js using the Zoom™ JSON Web Token application programming interface (JWT API), (2) a '/ admin' web interface using React for administration, and (3) a 'user' web interface using React and the Zoom™ web software development kit (SDK) to participate in Zoom videoconferences.
Background and Objectives: The femoral neck system (FNS) is a new minimally invasive internal fixation system for femoral neck fractures (FNFs), but its use has not been reported in adolescents. The aim of this study was to compare the clinical and radiographic outcomes of displaced FNF in adolescents treated with FNS or a cannulated compression screw (CCS). Materials and Methods: A retrospective study of 58 consecutive patients with displaced FNF treated surgically was performed; overall, 28 patients underwent FNS and 30 CCS fixation. Sex, age at injury, type of fracture, associated lesions, duration of surgery, radiation exposure, and blood loss were collected from the hospital database. The clinical and radiographic results, as well as complications, were recorded and compared. Results: The patients were followed up for 16.
Statistical Analysis Data were analyzed using the IBM SPSS statistical package version 22. 0 (IBM Corporation, Armonk, NY, USA). The Shapiro-Wilk test was first used to determine whether the data fit a normal distribution. Age, time from injury to surgical treatment, perioperative blood loss, and consolidation time were normally distributed and expressed as the mean, range, and standard deviation, and two independent sample T tests were used for comparisons between the two types of treatments. Categorical parameters are expressed as frequencies and percentages. Statistical analysis was performed using the chi-square test or Medicina 2022, 58, 999 5 of 10 Fisher's exact test for categorical variables. Statistical significance was defined by p values of p < 0. 05.
Soualhi et al. [25]  proposed a procedure based on fuzzy logic and artificial neural network (ANN) to estimate the internal resistance and capacitance of SCs. Roman et al. [26]  also proposed a machine learning Model based on ANN to estimate the capacitance of electrochemical capacitors at high temperature. ANN and fuzzy logic can be a powerful technique to provide a robust identification for systems that are subjected to uncertainties  [27]. However, these techniques usually require long and tedious offline tests for each new component to have enough training data  [26, 28]. In addition, they usually require a high computation time and complexity  [29]. In the present study, we aim to develop an identification method that can be applied to each new SC model and that requires only voltage and current measurements (no need for a database).