dataesr/scientific-paragraphs-categorization huggingface dataset:
Filaments are a ubiquitous morphological feature of the molecular interstellar medium and are identified as sites of star formation. In recent years, more than 100 large-scale filaments (with a length > 10 pc) have been observed in the inner Milky Way. As they appear linked to Galactic dynamics, studying those structures represents an opportunity to link kiloparsec-scale phenomena to the physics of star formation, which operates on much smaller scales. In this letter, we use newly acquired Outer Galaxy High Resolution Survey (OGHReS) 12 CO(2-1) data to demonstrate that a significant number of large-scale filaments are present in the outer Galaxy as well. The 37 filaments identified appear tightly associated with inter-arm regions. In addition, their masses and linear masses are, on average, one order of magnitude lower than similar-sized molecular filaments located in the inner Galaxy, showing that Milky Way dynamics is able to create very elongated features in spite of the lower gas supply in the Galactic outskirts.
(2017)  considers only the longest structures (with a length > 55 pc). In this aspect, we have a comparable number of filaments (ten objects with a length ∼ 40 -144 pc; see Fig. 1  and Appendix C), despite having searched for them in roughly a third of the Galactic area surveyed by  Du et al. (2017). Additionally, our search focuses on only the most prominent structures and does not consider all LSFs present in OGHReS SD1 data as we have included only connected and isolated structures at V LSR > 40 km s -1. Indeed, we have also observed LSFs within the 'local' gas (at V LSR ∼ 0 km s -1 ), objects with a length and AR below our assumed thresholds, and velocity coherent fragments of filaments that dendrograms alone are not able to merge into a single structure. The 37 OGLSFs represent a lower limit of the actual number of filaments present in the survey.
We might expect to find at least ∼ 120 similar properties of LSFs in the full 100 deg 2 span by the full OGHReS data, once the survey is completed. A full catalogue of all filamentary structures present within OGHReS data will be presented in a future work. This would be comparable to the current number of LSFs observed in the inner Galaxy (approximately 70 objects), whose properties we use here for comparison. Those inner Galaxy large-scale filaments (hereafter IGLSFs) have been found by different studies and have been named according to their appearance or identification methods (see Appendix B. 2 for more details). The properties of the IGLSFs that we use here have been re-elaborated by  Zucker et al. (2018) and Zhang et al. (2019) , with the addition of the filaments from  Mattern et al.
1) where the mean molecular weight of the molecular hydrogen is µ H 2 = 2. 8, m p is the proton mass (i. e. the mass of the hydrogen atom), and ∆x and ∆y represent the pixel size. Benedettini et al. (2021)  used three methods to derive the mass of the clouds in the outer Galaxy (for 220 < l < 240 deg) for the Forgotten Quadrant Survey (FQS), finding that they all give very similar mass estimates. They calculated N(H 2 ) from the 12 CO(1-0) emission, from the 13 CO(1-0) column density, and from the farinfrared dust emission. This study is particularly relevant for our purposes since the FQS observed the molecular medium of the outer Milky Way in a Galactic region that will also be covered by the full OGHReS data once the survey is completed. For our OGLSFs, we chose to calculate the molecular gas mass from 12 CO(2-1) intensity assuming a 12 CO-to-H 2 conversion factor X CO.
Acknowledgements. The authors thank the anonymous referee for the feedback that helped improving the clearness of the paper. DC acknowledges support by the  German Deutsche Forschungsgemeinschaft ,  DFG  project number  SFB956A. DC thanks  Miaomiao Zhang  for the help and the useful discussion regarding the inner Galaxy filament properties. SL acknowledges financial support from  INAF  through the grant  Fondi mainstream 'Heritage  of the current revolution in star formation: the Star-forming filamentary Structures in our Galaxy' This research made use of Astropy, 1 a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013, 2018); matplotlib (Hunter 2007); numpy and scipy (Virtanen et al. 2020).
A. 3 Dataset Details We used the MNIST dataset version 3. 0. The baseline comparison values offered with our experiments were optimized similarly to the above, for completeness we report everything here. The MLP model had three hidden ReLu layers of size 100 each trained with Adam, a learning rate ∈ {0. 1, 0. 01, 0. 001} and a batch size of 256 for 100 epochs. Complete test and train evaluation was performed after every epoch, and in the absence of a bound, the model and epoch with lowest train linear loss was selected. For PBGNet we choose the values of hyperparameters from within these values giving the least bound value. Note that, unlike in the original paper, we do not allow the hidden size to vary {∈ 10, 50, 100}, and we use the entire MNIST training set as we do not need a validation set.
While attempting to train a three hidden layer network, we also searched through the hyperparameter settings with a batch size of 64 as in the original, but after this failed, we returned to the original batch size of 256 with Adam. All experiments were performed using the code from the original paper, available at https://github. com/gletarte/dichotomize-and-generalize. Since we were unable to train a multiple-hidden-layer network through the PBGNet algorithm, for this model only we explored different numbers of hidden layers ∈ {1, 2, 3}.
| Participants and recruitment procedure The present study included the datasets from ten countries that have validated the FCV-19S in their respective countries. A short sampling description is given herewith, details can be found in the original papers  (Abad et al. , 2020; Broche-Pérez et al. , 2020; Chang, Hou, et al. , 2020; Harper et al. , 2020; Mailliez et al. , 2021; Masuyama et al. , 2020; Pakpour, Griffiths, Chang, et al. , 2020; Sakib et al. , 2020; Soraci et al. , 2020; Winter et al. , 2020). More specifically, all the participants used in the present study were recruited through convenience sampling. Some were recruited using online surveys and some were recruited using paper-based (offline) surveys because most of the validations were carried out independently by different research teams and the respective teams had different resources in the different countries.
Given the huge difference in sample sizes (27 "other" gender, 7,723 male gender, and 8,363 female gender), carrying out invariance testing on such a small sample size would be problematic. A model with structural equation modelling (SEM) was then constructed to examine the associations between age, gender, and fear of COVID-19. In the SEM model, young to middle-aged adults aged between 18 and 60 years and being male were reference groups. All the statistical analyses were performed using SPSS 24. 0 (IBM corp. ), WINSTEPS 4. 1. 0 (winst eps. com), and lavaan package (https:// lavaan. ugent. be/tutor ial/index. html) in the R software.
| D ISCUSS I ON In order to respond to the need of assessing mental health difficulties and associated behaviors (e. g. problematic use of the internet, suicidal thoughts, sleep problems, psychological distress and panic buying) among different populations during the COVID-19 pandemic  (Holmes et al. , 2020; Lin, 2020; Pramukti et al. , 2020; Taylor et al. , 2020) , the present study used datasets from ten countries to evaluate the measurement invariance and other psychometric properties of the Fear of COVID-19 Scale  (FCV-19S; Ahorsu, Lin, Imani, et al. , 2020; Ahorsu, Lin, & Pakpour, 2020). With the use of CFA, multigroup CFA, and Rasch analysis, the psychometric properties of the FCV-19S were reaffirmed to be satisfactory and consistent with prior findings  (Alyami et al. , 2020; Pakpour, Griffiths, & Lin, et al.
First, all the data used in the present study were collected using convenience sampling methods. Therefore, the representativeness of the participants in each ethnic population is low. Also, the characteristics between the ten ethnic populations were not directly comparable and the comparisons of their FCV-19S scores are somewhat biased. Finally, given the importance of LGBT+ community, it is important to assess whether the FCV-19S is invariant across "other" gender compared to male and female genders. Unfortunately, the present sample had too few participants of "other" gender to reliably calculate measurement invariance in this group. Future studies are, therefore, needed to recruit a large enough sample size of other gender to provide robust analysis on this important gender variant.
able" differently to individuals in other countries when evaluating their fear of COVID-19. A similar pattern was observed among children as compared with the other two age groups. Items F1 (loading = 0. 472) and F2 (loading = 0. 462) were the lowest among the seven FCV-19S items. Therefore, children when compared to the other two age groups appear to interpret the words "afraid" and "uncomfortable" less when considering fear of COVID-19. In relation to gender, it appeared that both genders considered all the seven FCV-19S items as of equal and strong importance when evaluating their fear of COVID-19. The major strength of the present study is the use of crosscountry data with a large sample size. The large sample size across different ethnic groups ensures the robustness of the dataset. Therefore, the present findings support the use of FCV-19S in these ten countries.
Protein identification and quantification Relative intensity-based label-free quantification (LFQ) was processed using the MaxLFQ algorithm  (Cox et al. , 2014)  from the freely available MaxQuant computational proteomics platform  (Cox and Mann, 2008). Spectra were searched against a Drosophila melanogaster database (UniProt Proteome reference, date 2017. 08; 21982 entries). The false discovery rate (FDR) at the peptide and protein levels were set to 1% and determined by searching a reverse database. For protein grouping, all proteins that could not be distinguished based on their identified peptides were assembled into a single entry according to the MaxQuant rules. The statistical analysis was done with Perseus program (version 1. 5. 1. 6) from the MaxQuant environment (www. maxquant. org).
Beforehand, the three housekeeping genes (Rpl32, RPlII40 and Act42A) were compared to verify absence of any variation between samples (not shown). Transcript levels were first normalized to the chosen housekeeping gene RPI32; and then to the control group. DDCq method was used to estimate relative amounts using the Bio-RAD CFX Maestro software. Further statistical analyses were performed by unpaired t-tests by using the qbasePLUS software version 2 (Biogazelle).
Toll-2=18W= Dm01841837_s1 (position 5228 / GenBank NM_057466, amplicon = 152bp) Toll-6= Dm01822826_s1 (position 5259 / GenBank NM_001259836, amplicon = 109bp) RPl32: House-keeping gene reference: Dm02151827_g1: (exon 2-3 boundary, 377 / GenBank NM_001144655, amplicon = 72bp) RPII140: House-keeping gene: Dm02134593_g1 (exon 2-3 boundary, 2347 / GenBank NM_001300394, amplicon = 78bp) Act42A: House-keeping gene: Dm02362162_g1 (one exon, 1439 / GenBank NM_078901, amplicon = 108bp) RT-qPCR conditions were as follows: 50 for 2min; 95 for 10min; 40 cycles [95 C for 15 s and 60 C for 1min].
ACKNOWLEDGMENTS We thank all members of the Lecuit team,  B. Aigouy  ( IBDM, France ) for stimulating discussions during the course of this project, the IBDM imaging facility for microscopy assistance, FlyBase for maintaining curated databases and the  Bloomington Stock Center  for providing fly stocks. We thank  T. Langenhan  and  N. Scholz  ( Leipzig, Germany ) for sharing information about Cirl and fly reagents, and for stimulating discussions. We thank  B. Aigouy  ( IBDM, France ) and  P. Villoutreix  ( Centuri, France ) for developing the method for quantifying clone smoothness in wing discs. We thank  B. Habermann  ( IBDM, France ) for her valuable guidance on performing pairwise alignment between Toll-8 and FLRTs. We are grateful to  M. Ludwig  ( Birmingham, UK ),  P. F. Lenne  ( IBDM, France ), E.
univ-amu. fr)supported by  IBISA  ( Infrastructures Biologie Santeet Agronomie ),  Plateforme Technologique Aix-Marseille , the  Canceropole PACA , the  Provence-Alpes-Cote d'Azur Region , the  Institut Paoli-Calmettes  and the  Centre de Recherche en Cancerologie de Marseille. We acknowledge the  France-BioImaging  infrastructure supported by the  French National Research Agency  ( ANR-10-INBS-04-01 , Investments for the future).
melanogaster NCBI database (Table  S1 ), indicating that several proteins were identified in each spot, especially those of high molecular weights for which the resolving power of the gel is lower. In Table  S1 , all identified proteins were ranked according to their Mascot score, which gives an idea of the relative abundance of each protein in this protein set (see Mat. and Meth. ). Three hits (post-translational modification ubiquitin, viral proteins Polyprotein and Gypsy) absent from the D. melanogaster genome were therefore not retained as D. melanogaster proteins, and two proteins (HDC13314, HDC12021), although described previously  (Hild et al. , 2003) , did not have corresponding annotated genes in Flybase. Among the 10 most abundant proteins, various cytoskeletal components (such as different actin and tubulin) and chaperones (Hsc-70) were predominant, but PPO3 had the second highest score confirming its high abundance in these cells.
A second PPO identification of (rank 17) corresponded to a chimeric cDNA sequence between the PPO3 and PPO2 ones in the database  (Asada et al. , 2003)  (Fig. S1 ) and therefore had no corresponding gene in the D. melanogaster genome. However, some of the identified peptides were specific to PPO2 (Fig. S1 ), indicating its presence in lamellocytes, although in a lower amount than PPO3 (see also below). The third D. melanogaster PPO, PPO1, was also identified but with a very low score (score 50, rank 219). Other known markers of lamellocytes were identified: the beta-PS myospheroid integrin and two alpha integrins, alphaPS4 (ItgaPS4) and alphaPS5 (ItgaPS5), whose genes are overexpressed in Hop Tum-l lamellocytes  (Jung et al. , 2005; Irving et al. , 2001; Crozatier et al. , 2004). This list also contains many known integrin-associated Proteins (IAPs) such as GP93, talin, vinculin, and fermitin  (Klapholz and Brown, 2017; Maartens et al.
A good indication that we have extracted/enriched a subset of proteins from the plasma membrane was the high score for integrins, membrane ATPases, membrane receptors and transporters, and the presence of GPI-anchored proteins. Based on demonstrated or predicted cellular localization from the Uniprot database (http://www. uniprot. org), Flybase comments or the literature, ~34% (116/344) of proteins were tagged for PM or secretion (Table  3 ). It is noteworthy that many of the identified proteins may also be present transiently at the membrane level, such as proteins involved in membrane proteins or vesicular trafficking/recycling, or forming complexes with integral membrane proteins such as PM-associated cytoskeletal proteins like the different forms of actin involved in cell spreading (filopodia/lamellipodia)  (Röper et al.
)  (Charroux and Royet, 2009; Fogerty et al. , 1994; Nelson et al. , 1994; Kramerova et al. , 2003; Freeman et al. , 2003; Paladi and Tepass, 2004). This suggests that, if present, their number/amount of proteins was very low. We therefore considered the proteomics dataset (Tables  S1  and S2 ) as lamellocyte proteins and used them to analyze more specific networks and functional pathways that may be important in these cell functions.
The most enriched pathways are related to endocytosis, phagosomes, amino acid biosynthesis and metabolic pathways (see Table  S3 ). Note that most of the proteins in the phagosome pathway are also involved in endoplasmic and cytoskeletal functions  (Stuart et al. , 2007; Li et al. , 2010). The list of unique tags can also be used in the String program (https://string-db. org/) to construct interaction networks. Considering only the experimental evidence in the String program, we observed several clusters (Fig. S5 ): the most intense ones are formed by ribosomal proteins and cytoskeletal proteins, then a fuzzier cluster includes TCP proteins linked to proteins in a Rab cluster, the integrin cluster, and some IAPS, which are themselves linked to the Vacuolar ATPases cluster. Other protein clusters described above are also visible (such as Ykt6, Gos28, Vamp7, etc.
Cell surface measurements The surface distributions of hemocytes from hemolymph and Percoll gradient fractions were estimated using the Image J software (https://i magej. nih. gov/ij/). Digital images of actin-labeled cells were taken with the axioplan Z1 microscope (Zeiss), contrasted, and the cells transformed into ROI to measure their surface area. Cells were grouped into 6 classes according to their surface area (25 < 100 μm 2 ; 100 < 250 μm 2 ; 250 < 400 μm 2 ; 400 < 625 μm 2 ; 625 <900 μm 2 ; >900 μm 2 ).
Mass spectrometry Protein identification was performed by LC-MS/MS (Q-orbitrap mass spectrometer, Q-Exactive, Thermo-Fischer Scientific). Gel spots and excised bands were treated with trypsin (10 ng/μl; Promega) to generate the protein fragments. Peaklists were generated with Proteome Discoverer 2. 0 in mgf format. Data analysis was performed with Mascot 2. 3 software (http://www. matrixscience. com) using the NCB D. melanogaster nonredundant database (https://www. ncbi. nlm. nih. gov; March 27th, 2017). Mascot Mudpit scoring parameters were used (sum of score above threshold of significant peptide matches plus the average threshold of these matches). The significance threshold was set to p < 0. 05, a maximum number of hits fixed to AUTO (to display all of the hits that have a protein score exceeding the average identity threshold score for an individual peptide match).
An automatic search of the decoy database was done. The analysis was performed with a mass tolerance of 20 mDa for fragment ions and 10 ppm for parent ions. The carbamidomethylation of cysteines and the oxidation of methionines shown as variable modification for calculation of peptide masses. The maximum number of missed cuts by trypsin was set at 5 in case of biotinylated proteins and to 2 otherwise. Residual modification of peptides by cleavable biotin was considered a described  (Markoutsa et al. , 2014). Additional data with details on the identified peptide (m/z, charge, error, amino acid sequence, modifications, identity and homology scores, expectation value, unique peptide) as well as details on the identified proteins (protein family number, coverage, misscleavages, number of peptides, number of significant peptides, proteins matching the same set of peptides) are given in Tables  S1  and S2  (see raw data sheet).
GO analysis For each identified protein, its name, gene number and information were retrieved from Flybase (http://flybase. org) or from the literature. The list of gene identifications was transformed into protein IDs (https://david. ncifcrf. gov/content. jsp?file=conversion. html) for use in GO analysis software (Panther GO; http://pantherdb. org), String v11 (https://string-db. org) and ShinyGO v0. 51 (http://bioinformatics. sdstat e. edu/go/; with the different available options). The Venn diagram was made online (http://bioinformatics. psb. ugent. be).
Acknowledgments We are grateful to  C. Rebuf  for his technical help. We also thank the microscopy platform of  ISA-INRAE of Sophia Antipolis  for assistance with cell imaging and the " Plate-forme d'Infectiologie Expérimentale " (PFIE;  INRA-Nouzilly)  for the production of rabbit antibodies. We thank  Dr I. Ando  ( Institute of Genetics, Biological Research Center of the Hungarian Academy of Sciences Genetics, Hungary ) for discussion and kindly providing the mouse L1 anti-Attila monoclonal antibody.
Funding This work received support from the  Department of Plant Health and Environment (SPE)  from the  French National Research Institute for Agriculture, Food and Environment (INRAE) , and the  European Union 's  Seventh Framework Program  for research, technological development and demonstration, under grant agreement No  613678  (DROPSA) and by the  French Government (National Research Agency, ANR)  through the  "Investments for the Future" programs LABEX SIGNALIFE   ANR-11-LABX-0028-01  and  IDEX  UCAJedi  ANR-15-IDEX-01. B. W. PhD was funded by the  DROPSA program.
Satellite data Several remote sensing datasets were exploited using SPASSO (Software Package for an Adaptive Satellitebased Sampling for Ocean campaigns, https://spasso. mio. osupytheas. fr/, last access: 2 October 2020). Altimetry data were obtained from the AVISO regional Mediterranean product. Delayed-time L3 maps of SST sat (Mediterranean Sea -ultra-high-resolution L3 SST reprocessed;  Nardelli et al. , 2013; Pisano et al. , 2016)  and chl sat a (Mediterranean Sea reprocessed surface chlorophyll concentration from multisatellite observations) were retrieved for the period of the cruise, on regular grids of 0. 01 × 0. 01 resolution for SST sat and of 1 × 1 km resolution for chl sat a from CMEMS -Copernicus Marine Environment Monitoring Service (http: //marine. copernicus. eu/, last access: 10 November 2020).
, 2020). The particle trajectories and final positions were analyzed to determine the main source areas (http://www. obs-vlfr. fr/proof/ php/PEACETIME/peacetime. php, last access: 10 September 2021). Once the special issue is completed, the PEACE-TIME biogeochemical dataset will be available in SEA-NOE at https://www. seanoe. org/data/00645/75747/ (last access: 10 September 2021). 3 Results.
Acknowledgements. This study is a contribution to the  PEACE-TIME  project (http://peacetime-project. org; last access: 10 September 2021), a joint initiative of the MERMEX and ChArMEx components supported by  CNRS-INSU ,  IFREMER ,  CEA  and  Météo-France  as part of the decadal program MISTRALS coordinated by  INSU. PEACETIME was endorsed as a process study by GEOTRACES and is also a contribution to the international IMBER and SOLAS programs. (PEACETIME cruise, https://doi. org/10. 17600/17000300 ). Chief scientists and crew are thanked. Jean-Luc Fuda  is thanked for his help on CTD calibration. We thank  Dominique Lefèvre ,  Olivier Grosso  and  Gérald Grégori  ( MIO ) for the setup and handling (DL) of the automated and continuous sea water surface sampling system. We also thank  Dominique Lefèvre  and  Thibault Wagener  for regular checks on the CytoSense flow cytometer during the cruise.
[29] , 16 OTUs from the 16 species of the mock community were obtained when applying a 95% threshold. The taxonomic annotation was performed by BLAST against the SSURef SILVA database NR108  [45]  that was extended with lacustrine DNA reads originating from previous studies  [44, [46] [47] [48]. This database is provided with the PANAM2 pipeline (https://github. com/panammeb/PANAM2 (accessed on 7 January 2021)). For blank corrections, we removed a total of 1326 OTUs seen at relative abundances >10% higher in the blanks than in molecular inventories from sediment samples. We removed all the OTUs affiliated to metazoans, embryophytes, fungi (except chytrids), Cercozoa nuclear, Cryptophyta nucleomorph and unclassified eukaryota in order to consider only microbial eukaryotes in our analysis (Supplementary Table  S2 ).