dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, it would be interesting to apply the algorithms used on this small dataset to a larger one. We may also test other data augmentation techniques to seek the most appropriate ones  [58]. Futhermore, in order to assess the prediction accuracy of bioactivity for a new compound, it is important to know if this compound has a chemical structure that is similar to the ones used in the training set. For this, we could use the "applicability domain" approach  [59]  as a tool to define the chemical space of a ML model. If we refer to the long term objective of this work which is to link the molecular structure to in vivo toxicity, we could think about using the approach we have used as an intermediate step, and also train ML techniques to go from in vitro data to the prediction of in vivo effects.
Sample collection Samples of the wedge clam Donax trunculus were collected in 2010 and 2011 in the context of increased mollusc mortality in different wild beds of the French Atlantic coast (Fig. 1 ). Mortality events were reported by fishermen to the competent authority and the national network for surveillance and monitoring of mollusc health, called REPAMO (REseau de PAthologie des MOllusques), who recorded mortality cases and performed analysis on representative samples associated with the mortality cases. For each mortality case, data were collected from fishermen by face to face interviews using a questionnaire that helped to explore the different factors which could be involved in the mortality events. In 2010, 15 individuals per sample were collected in three beds and in 2011, 30 individuals of two beds were sampled (Fig.
In order to amplify a longer 18S fragment, 13 different primer pair associations using previous primers and primers developed by Abbott et al. [17]  and Hartikainen et al. In order to detect potential co-infection, fifteen PCR products (three from each location and three from archived material) targeting the 18S region were cloned using the TOPO TA cloning kit (Invitrogen, Waltham, Massachusetts, USA), according to the manufacturer's recommendations, and positive clones were then selected for plasmid DNA purification by FastPlasmid® Min (Eppendorf, Hamburg, Germany). Plasmid DNA suspensions and PCR products targeting the ITS1-5. 8S-ITS2 region were bi-directionally sequenced using the Big Dye V3 sequencing kit (Applied Biosystem, Waltham, Massachusetts, USA). The resulting sequences were deposited in the GenBank database under accession numbers KY923792-KY923807 for partial 18S region and KY923808-KY923809 for ITS1-5.
Spain (KF5480485), and Mikrocytos boweri (KF297352. 1). Alignments were performed using ClustalW  [39]  in MEGA 6 with default parameters. The average length of the 25 18S gene sequences in the final alignment was 328 bp. For the nine ITS sequences it was 535 bp. The alignments were manually checked for alignment gaps and missing data in nucleotide positions. Evolutionary divergence over sequence pairs between groups of sequences was estimated using the Maximum Composite Likelihood model  [40]. The rate variation among sites was modelled with a gamma distribution (shape parameter = 1). Differences in the composition bias among sequences were considered in evolutionary comparisons  [41]. For 18S gene sequences, the analysis involved 25 nucleotide sequences. All positions containing gaps and missing data were eliminated, resulting in a total of 253 positions in the final dataset.
For ITS1-5. 8S-ITS2 sequences, the analysis involved 9 nucleotide sequences. Analysis was carried out on the complete array ITS1-5. 8S-ITS2 and on each gene. All positions containing gaps and missing data were eliminated, leaving a total of 344, 82, 118 and 142 positions in the final dataset for ITS1-5. 8S-ITS2, ITS1, 5. 8S and ITS2, respectively. Evolutionary analyses were conducted using MEGA 6  [38]. Prior to the phylogenetic analysis of sequences, the program jModelTest 0. 1. 1  [42]  was used to select the best fitting substitution model according to the corrected Akaike information criterion (AIC)  [43]. A total of 88 candidate models, including models with equal/unequal base frequencies, with/without a proportion of invariable sites (+I), and with/without rate variation among sites (+G) were tested.
In total, 36 putative 18S rDNA sequences of 263 bp were obtained. These sequences shared 74. 5-79% and 78-83% similarity with Mikrocytos mackini (HM563060. 1) and M. boweri (KF297353), respectively. Clones obtained from the same PCR products did not necessarily have the same sequences and 16 distinct sequences could be identified and were submitted to GenBank (KY923792-KY923807). The alignment of these 16 sequences revealed point nucleotide substitutions, sometimes between clones obtained from the same PCR products. Additionally, sequences obtained from the Audierne Bay area presented major polymorphic regions in comparison with sequences from other areas (Additional file 1: Figure  S1 ).
8S-ITS2 region. Six sequences of 370 bp were aligned, revealing two distinct sequences: one for parasites from Audierne Bay and one for parasites from Douarnenez Bay. These sequences showed 9. 2 ± 2. 03% divergence (Table  3 ) and were deposited in GenBank under the accession numbers KY923808 and KY923809, respectively. Phylogenetic analyses performed on the entire ITS1-5. 8S-ITS2 array confirmed the results obtained from the 18S rRNA gene region, the existence of two distinct species of Mikrocytos in Donax trunculus in France. However, contrary to the analysis performed on the 18S rRNA gene, the species detected in Audierne Bay appears distinct but closer to Mikrocytos sp. from Spain (KF548045) than from the other species in D. trunculus (Fig. 6 ). Phylogenetic analyses were subsequently carried out for each region separately: ITS1, 5.
Available ITS and 18S gene sequences from Mikrocytos spp. and Paramikrocytos canceri Hartikainen, Stentiford, Bateman, Berney, Feist, Longshaw, Okamura, Stone, Ward, Wood & Bass, 2014 were downloaded from GenBank and included in the phylogenetic analyses together with the sequences obtained in the present study. 18S gene sequences from GenBank included: Mikrocytos mackini (HM563060. 1, AF477623. 1), Mikrocytos sp. from British Columbia (HM563061. 1, DQ237912. 1), Mikrocytos sp. from Spain (KF548052. 1, KF548051. 1), Mikrocytos boweri (KF297353. 1), Mikrocytos mimicus (KP164508. 1) and Paramikrocytos canceri (KJ150292). rDNA ITS sequences from GenBank included: Mikrocytos mackini (HM563060. 1), Mikrocytos sp. from.
85°N, 4. 35°W). Type-material: Fixed tissues (foot and mantle, accession No. 11/089/06, 11/089/25 and 11/089/27) infected with Mikrocytos donaxi n. sp. have been deposited at the OIE Reference Laboratory for infection with Mikrocytos mackini: Pacific Biological Station, Aquatic Animal Health Section, 3190 Hammond Bay Road, Nanaimo, British Columbia V9T 6 N7, Canada. Representative DNA sequences: Nucleotide sequences of partial 18S DNA region have been submitted in the GenBank database under the accession numbers.
( 47. 47 85°N, 4. 35°W). Type-material: Fixed tissues (foot and mantle, accession No. 11/089/06, 11/089/25 and 11/089/27) infected with Mikrocytos donaxi n. sp. have been deposited at the OIE Reference Laboratory for infection with Mikrocytos mackini: Pacific Biological Station, Aquatic Animal Health Section, 3190 Hammond Bay Road, Nanaimo, British Columbia V9T 6 N7, Canada. Representative DNA sequences: Nucleotide sequences of partial 18S DNA region have been submitted in the GenBank database under the accession numbers.
Luminex CD4 + T cells, isolated from spleen, were enriched by positive selection using magnetic beads (Miltenyi Biotec) and cocultured with bone marrow derived DCs (BM-DC) as previously described  (30). CD4 + T cells (2 × 10 5 cells/well) were put in culture with 5 × 10 4 DCs loaded or not (negative control) with S1+S2+S3 for 24 and 48 h at 37 • C, 5% CO 2. At 24 or 48 h after culture, supernatant was collected and ProcartaPlex Mouse Th1/Th2 Cytokine Panel (11 plex, eBioscience) was used to measure cytokines according to manufacturer's indications. The data was obtained using Bio-Plex R 200 (Biorad).
Flow Cytometry Antibodies for surface staining against CD8 (53-6. 7) (Ozyme), CD4 (RM4-5), CD3 (145-2C11), CD44 (IM7), CD62L (MEL-14), KLRG1 (2F1), PD-1 (J43) (eBioscience) were used. For intracellular staining, samples were fixed/permeabilized (Foxp3/Transcription Factor Staining Buffer Set, eBioscience), and stained with FoxP3 (FJK-16s; eBioscience) and Granzyme B (GrB) (GB11; Ozyme) antibodies. Isotype controls were used as negative controls. Acquisitions were performed on a LSRII (BD Biosciences). The data were analyzed using FlowJo software (TreeStar).
Production of the cr1 ccP22-30 his 6 Variants in insect cells The CR1 CCP22-30 His6 fragment, its deletion variants ΔCCP22-23, ΔCCP26-30, ΔCCP25-26, and fragments CCP25-26 and CCP24-25 were produced in Trichoplusia ni insect cells, purified, characterized, and quantified as described in Ref. (26). The CCP24-25 His6 plasmid was generated by site-directed mutagenesis (Quickchange XLII, Agilent) from the template pNT-Bac-CR1 CCP22-30 His6, using an optimized procedure for large insertion/deletion  (30). Purification of CCP24-25 on a Ni NTA column (His-select, Sigma Aldrich), was achieved as described previously  (26). The concentration of the purified CR1 CCP24-25 variant was estimated using the absorption coefficient A1%,1 cm at 280 nm of 10. 5 calculated using the PROTPARAM program on the Expasy Server 1  , and an experimental molecular weight determined by MALDI mass spectrometry of 18,500 Da.
5 and 14. 1. surface Plasmon resonance (sPr) analyses and Data evaluation Multiple cycle interaction analyses and competition experiments were performed on a BIAcore 3000 instrument (GE Healthcare). Recombinant soluble CR1 (sCR1), CR1 CCP22-30, and its size variants were covalently immobilized on CM5 sensor chips in 10 mM HEPES, 150 mM NaCl, 0. 005% surfactant P20, pH 7. 4 (HBS-P) using the amine coupling chemistry according to the manufacturer's instructions (GE Healthcare). The protein ligands were diluted in 10 mM sodium acetate, pH 4. 2 at 25 µg/ml (sCR1), 20 µg/ml (CR1 CCP22-30), and 5 µg/ml (deletion variants and bimodular CCP fragments). Binding was measured at a flow rate of 20 µl/min in HBS-P containing 3 mM EDTA for MBL and in 50 mM triethanolamine-HCl (TEA), 150 mM NaCl, 1 mM CaCl2, 0.
2 software (GE Healthcare). Buffer blanks were subtracted from the data sets used for kinetic analysis (double referencing). The apparent equilibrium dissociation constants (KD) were calculated from the ratio of the dissociation and association rate constants (kd/ka). Chi 2  values were below 7 in all cases. Single cycle interaction analyses were performed on a T200 instrument (GE Healthcare). CR1 CCP22-30 and its deletion fragment ΔCCP24-25 were diluted to 20 µg/ml in 10 mM sodium acetate, pH 4. 2, and immobilized on a CM5 (series S) sensor chip (GE Healthcare) in HBS-P using the amine coupling chemistry according to the manufacturer's instructions (GE Healthcare). The reference surface was prepared using the same procedure except that the protein solution was replaced by buffer. The immobilization levels for CR1 CCP22-30 and ΔCCP24-25 were, respectively, 3,000 and 2,200 RU.
analysis of small-angle X-ray scattering (saXs) Small-angle X-ray scattering experiments were performed at the European Synchrotron Radiation Facility (ESRF, Grenoble) beamline BM29, using a Pilatus 1M detector, and an X-ray energy of 12. 5 keV. A gel filtration was performed immediately prior to the experiment using a S200 10/300 column (GE Healthcare), 10 mM TRIS, 150 mM NaCl, pH 8. 0 in the presence of 1 mM dithiothreitol (DTT) to protect the sample from radiation damage and limit the aggregation. The distance between detector and sample was 2. 87 m, covering a q-range of 0. 025-5/nm and the sample temperature was set to 4°C. For each dataset, 10 measurements of 1 s exposure were recorded on a 50 µl sample injected into the capillary. Three concentrations of the CCP24-25 fragment were used (serial dilutions: 5, 2.
5, 1. 25 mg/ml). Images were radially integrated, averaged, and buffer subtracted using the beamline data processing pipeline  (31). As some aggregation was observed in the highest concentration even in the presence of 1 mM DTT, the final SAXS curve has been obtained by merging the high angle and low angle regions from the 5 and 1. 25 mg/ml, respectively, using the PRIMUS software from the ATSAS package  (32). Low resolution ab initio envelopes were calculated using DAMMIF and also GASBOR, to make use of good data quality in the high angle region. An intitial model of CCP24-25 was obtained by homology modeling using the server ALLOSMOD  (33)  and the crystal structure of the CRRY complement receptor (2xrb) as starting template. Two N-glycans were modeled at positions Asn 1534 and Asn 1540 according to the prediction of the NetNGlyc server.
The overall ab initio envelopes, calculated by the GASBOR software, show two elongated blobs with a global boomerang shape (Figures  6D-E ). Temptatively, one could attribute the larger blob to the CCP24 which is slightly bigger in size and also bears two N-glycans. The molecular weight estimate from the ab initio envelope is ~18. 1 kDa which is in good agreement with the experimental value (18. 5 kDa). Rigid body modeling using the program CORAL resulted in an extended conformation with the bend point located between the two CCP modules and a good fit to the experimental data (χ = 1. 2) that could be superposed on the ab initio envelope (Figures  6C-E ). location of the cr1-Binding sites within c1q In order to identify the region of C1q that is involved in CR1 interaction, the comparative binding of C1q collagen stalks (CLF), C1q globular heads (GR), and whole C1q to CR1 CCP22-30 was performed by SPR.
aUThOr cOnTriBUTiOns VR, NT, and CG designed the study; MJ, GC, GF, and IB performed the research; MJ, GC, VR, CG, and NT analyzed the data; VR, NT, and CG wrote the manuscript; all authors revised and approved the final version of the manuscript. acKnOWleDgMenTs This work used the platforms of the Grenoble Instruct-ERIC center (ISBG; UMS 3518 CNRS-CEA-UGA-EMBL) with support from FRISBI (ANR-10-INSB-05-02) and GRAL (ANR-10-LABX-49-01) within the Grenoble Partnership for Structural Biology (PSB). The authors thank Luca Signor for assistance and access to the mass spectrometry facility as well as Martha Brennich and other local contacts and staff for assistance and access to the ESRF BM29 beamline. This work was supported by grants from the French National Research Agency (ANR-09-PIRI-0021 and ANR16-CE11-0019).
We study the radial acceleration relation (RAR) for early-type galaxies (ETGs) in the SDSS MaNGA MPL5 dataset. The complete ETG sample show a slightly offset RAR from the relation reported by  McGaugh et al. (2016)  at the low-acceleration end; we find that the deviation is due to the fact that the slow rotators show a systematically higher acceleration relation than the McGaugh's RAR, while the fast rotators show a consistent acceleration relation to McGaugh's RAR. There is a 1σ significant difference between the acceleration relations of the fast and slow rotators, suggesting that the acceleration relation correlates with the galactic spins, and that the slow rotators may have a different mass distribution compared with fast rotators and late-type galaxies. We suspect that the acceleration relation deviation of slow rotators may be attributed to more galaxy merger events, which would disrupt the original spins and correlated distributions of baryons and dark matter orbits in galaxies.
2006). We search the NASA-Sloan Atlas catalog 1 (NSA v1 0 1,  Blanton et al. 2011 ) to obtain the galactic K-corrected rest-frame magnitudes, colors (e. g. , NUV-r, gr), r-band Sérsic indices n, and stellar masses M. The ellipticities are obtained by using the find galaxy software  (Cappellari et al. 2006 ) to fit their r-band images. Since we are interested in whether the acceleration relation changes with galactic spins, we calculate the dimensionless spin parameter λ defined in  Emsellem et al. (2007)  to distinguish fast and slow rotators, i. e. , λ = Σ Np i=1 F i R i |V s,i | Σ Np i=1 F i R i V 2 s,i + σ 2 s,i , (1) where F i is the flux inside the ith bin, R i is the distance of the ith bin to the galaxy center, and V s,i and σ s,i are the mean stellar velocity and dispersion in the ith bin, respectively.
g tot from the dynamical modelling We perform JAM for all the sample galaxies. The modelling allows for anisotropy in the second-velocity-moments, and provides accurate (∼ 10% -18%) and unbiased estimates of the total mass distribution as tested in  Li et al. (2016). We first use the Multi-Gaussian Expansion (MGE) method  (Emsellem et al. 1994 ) with the fitting algorithm and Python software 2  by  Cappellari (2002)  to fit the SDSS r-band image, and then deproject the surface brightness to obtain the luminosity density by assuming an inclination. The deprojected stellar luminosity density is used as the tracer density in the modelling. Similar to the equation (2) in  Poci, Cappellari, & McDermid (2017) , we assume that the total mass density is axisymmetric and follows ρ tot (l) = ρ s l l s γ 1 2 + 1 2 l l s -γ-3 , (2) where z-axis and R denote the symmetric axis and radius in the zplane, respectively, and l = R 2 + z 2 /q 2 is the elliptical radius; q, l s , and ρ s are the intrinsic axis ratio, scale radius, and density at the scale radius, respectively; γ is the inner slope.
ACKNOWLEDGMENTS YR thanks  Thomas H. Puzia ,  Dandan Xu ,  Zheng Zheng ,  Yougang Wang ,  Yan-Mei Chen ,  Xiao-Lin Yu ,  Zhou Fan ,  Jin-Yuan Liao ,  Shuangpeng Sun  for their help and discussions, and acknowledges supports from  CAS-CONICYT  postdoctoral fellowship No. 16004  and  NSFC  grant No. 11703037. LG acknowledges support from  NSFC  grants (nos. 11133003  and  11425312 ). THP acknowledges support in form of the  FONDECYT Regular  Project No. 1161817  and by the  BASAL Center for Astrophysics and Associated Technologies  ( PFB-06 ). JP acknowledges support from the  National Basic Research Program of China  (program 973 under grant No. 2015CB857001 ) and  NSFC  under grant No. 11573030. This research made use of  Marvin , a core Python package and web framework 4 for MaNGA data, developed by  Brian Cherinka ,  José Sánchez-Gallego , and  Brett Andrews.
Sampling Samples were taken from the personal collections of the co-authors of this paper, or borrowed from the Queensland Museum (Australia) or the Australian National Insect Collection, Canberra. Details of the samples included in the analysis are presented in Table  S1. Most specimens sampled in the field were placed directly into ethanol for storage. On average, specimens spent 3. 5 years in ethanol before being processed (maximum storage time in alcohol = 34 years). Two specimens were critical point dried 25 or 34 years ago. UCE data for three specimens were retrieved from a previous study  (Branstetter et al. 2017a.
Raw data cleaning The analytical workflow is summarized in figure S1. In the next paragraph, chosen parameter values (different from default value) are provided between parentheses. Quality control checks were performed on raw sequence data with FastQC v. 0. 11. 2  (Andrews 2010). Quality filtering and adapter trimming were performed with Trimmomatic-0. 36  (Bolger et al. 2014)  (LEADING:20 TRAILING:20 SLIDINGWINDOW:4:20 MINLEN:180, with PrefixPE/1= AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGA TCT and PrefixPE/2 = CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTC CGATCT). Overlapping reads were merged using FLASH-1. 2. 11 (-M 300)  (Magoc & Salzberg 2011). Demultiplexing was performed using a bash custom script (no mismatch in barcode sequences was allowed, additional file 4).
The final dataset (25%-complete matrix; i. e. at least 25 taxa on the 99 should have a sequence to keep the locus in the analysis) comprised 1,139 UCEs and 340,286bp (missing data = 47. 0 %; parsimony informative sites = 72. 3 %; GC-content = 42. 6%). Specimens retrieved from a previous study  (Branstetter et al. 2017a ) that were either represented by empirical data (Euplectrus sp. ) or UCEs extracted from published genomes (Copidosoma floridanum and Trichogramma pretiosum) displayed a number of UCEs comparable to what was obtained for other specimens. Their placement in the trees (Figures 2; S3-S5) was in accordance with their morphology. Whatever the method used (supermatrix approaches versus coalescent-based summary methods), all families, except for Aphelinidae, were recovered as monophyletic with high support.
ACKNOWLEDGEMENTS We are grateful to  Nicole Fisher  ( CSIRO, National Museum of Australia, Canberra ),  Chris Burwell ,  Christine Lambkin  and  Susan Wright  ( Queensland Museum, Brisbane, Australia ) for facilitating access to samples and to the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees, France  for providing computing resources. AC thanks  Lisa Schlée ,  Philippe Cruaud  and  Maya  for their patience in the field and  Jean-Pierre Rossi  for his help with R. AG thanks  V. Fursov  ( Schmalhausen Institute of Zoology, Ukraine ) for his help with specimen identification. This work is part of a large  NSF  project (Award# 1555808 ) led by  John Heraty  ( UC Riverside USA ) that attempts to solve the phylogeny of Chalcidoidea with NGS approaches, and was partly funded by the  ANR  project  TriPTIC  ( ANR-14-CE18-0002 ) led by JYR and the  SPE department of the INRA (French National Agronomic Institute).
Child maltreatment remains a major health threat globally that requires the understanding of socioeconomic and cultural contexts to craft effective interventions. However, little is known about research agendas globally and the development of knowledge-producing networks in this field of study. This study aims to explore the bibliometric overview on child maltreatment publications to understand their growth from 1916 to 2018. Data from the Web of Science Core Collection were collected in May 2018. Only research articles and reviews written in the English language were included, with no restrictions by publication date. We analyzed publication years, number of papers, journals, authors, keywords and countries, and presented the countries collaboration and co-occurrence keywords analysis. From 1916From   to 2018From  , 47,090 papers (53.
Introduction The World Health Organization has defined child maltreatment as: "all forms of physical and/or emotional ill-treatment, sexual abuse, neglect or negligent treatment or commercial or other exploitation, resulting in actual or potential harm to the child's health, survival, development or dignity in the context of a relationship of responsibility, trust or power"  [1]. This global health issue has existed since ancient civilization  [2]. However, finding a full or accurate definition of this social problem is a continuing challenge to researchers, institutions and even governments. Thus, the aims and scope of each paper in this topic are different because of the database  [3]. Due to the various definitions, as well as the paper's objectives, we decided to choose the World Health Organization (WHO) definition on child maltreatment.
Search Strategy A cross-sectional study was designed to analyze the bibliography related to child abuse. The Web of Science was selected as the database. Although there were many definitions of child abuse  [20] , after reviewing some papers, we decided to choose the WHO's conceptual definition of child maltreatment which also helped us to construct the search strategy [1]. To make sure that most of the results would be covered, we also added "child trafficking" and "child grooming" in our search query  [21, 22]. The search query was built by using the combination of words related to "child maltreatment": child, maltreatment, abuse, violence, sexual, emotional, physical, trafficking, grooming (full strategy in Table  S1 ) via the category "Topic" on the Web of Science. In our study, we have used the following terms interchangeably: child abuse and neglect, child maltreatment, child violence or child abuse.