dataesr/scientific-paragraphs-categorization huggingface dataset:
The qPCR reactions were performed in duplicate using a Bio-Rad detection system. Amplifications were carried out in a volume of 25 μl containing 12. 5 μl of SYBR green supermix (BioRad), 0. 4 μM of forward and reverse primer and 1 μl cDNA in milliQ water. Cycling conditions were: denaturation at 95˚C for 3 minutes, followed by 45 cycles of denaturation (95˚C for 10 s) and annealing/elongation (temperatures in S3 Table) for 30 s. A melt curve analysis was performed for every reaction to verify amplicon specificity. IQ5 Real-Time PCR detection system software (BioRad) was used for data analysis. A no template control was also run in duplicate with each plate as a negative control. Expression levels were normalized by using the average expression levels of the reference genes taking into account the PCR efficiencies per gene product.
Transcriptome analysis Canine Gene Expression Microarrays V1 (Agilent Technologies, Belgium) representing 42,034 canine 60-mer probes in a 4x44K layout were used. The experiment was carried out in dye swap set-up in 18 Labrador retriever dogs (N: n = 4, HC: n = 5, HCH: n = 4, HCCH: n = 5, random samples per group). RNA amplifications and labeling were performed  [44]  on an automated system (Caliper Life Sciences NV/SA, Belgium) with 3 μg total RNA from each sample. Hybridizations were done on a HS4800PRO system supplemented with QuadChambers (Tecan Benelux B. V. B. A. , Giessen, The Netherlands) using 1000 ng labeled cRNA per channel  [45]. Hybridized slides were scanned on an Agilent scanner (G2565BA) at 100% laser power, 100% PMT. After automated data extraction using Imagene 8. 0 (BioDiscovery), printtip Loess normalization was performed  [46]  on mean spot-intensities.
Dye-bias was corrected based on a within-set estimate  [47]. Gene enrichment based on biological profiles and pathways was performed using the MetaCore program (GeneGo, Thomson Reuters, St. Joseph, MI, USA) by ranking the significant ontology and pathways dominant in each stage of disease progression. All data have been deposited in NCBI's Gene Expression Omnibus  [48]  and are accessible through GEO Series accession number GSE86932 (http://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSE86932).
(ref DMM. ) Multiple human and animal studies indicate that oxidative stress is one of the most important deleterious effects of excess copper, leading to hepatocellular injury  [58] [59] [60] [61] [62] [63] [64] [65]. However, recent studies in fibroblasts from ATP7A mouse mutants and liver samples from ATP7B knock-out mice, did not detect convincing evidence of oxidative stress caused by excess copper  [66, 67]. From our data we observe only modest evidence for oxidative stress in the HC group exists; differential mRNA expression of oxidative stress-responsive transcription factors and genes was identified with qPCR and microarray analysis, including MT, Syk, MAP kinases, NF-κB family, MAT1A and GSTP1. However, in the present study the possible presence of oxidative stress was only evaluated on mRNA level.
The SFG signals (the ssp and psp signals at Ω vis = 0°, the ppp and spp signals at Ω vis = 90°, where the first letter s/p stands for the polarization state of SFG signal, the second letter stands for the polarization state of visible light, and the third letter stands for the polarization state of IR light) were separated by a polarization splitter (PS). The separated SFG signals passed through Glan polarizers (GP1, GP2), half-wave plates (HWP1, HWP2), notch filters (F1, F2), and focusing ■ ASSOCIATED CONTENT * S Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10. 1021/acsomega. 7b01214. Details about the molecular structure of thymidine and tilt angle analysis procedures of SFG data (PDF).
Seven samples of HRV-A in the URTI patients could not be genotyped precisely (Table  1 ). The detection rate of HRV-A21 was higher in the severe CAP patients (7. 9%, n = 3) than in the non-severe CAP (0. 9%, n = 1) or the URTI patients (1. 7%, n = 5) (P = 0. 047 by Fisher's exact test). Taken together, these data show that prevalence of HRV-A21 correlates with a spectrum of illness from mild URTIs to severe CAP. Unexpectedly, HRV-A21 was most frequency (9 of 42, 21. 4%) detected as the only pathogen present in an infection among the identified HRV genotypes. As only three genome sequences of HRV-A21 were available in GenBank and little was reported on its clinical manifestation, we focused on the characterization of A21 infections.
Phylogeny, coding polymorphisms of HRV-A21 strains, and viral neutralizing activity. Complete genomic sequences were obtained from all nine HRV-A21-positive cases. Phylogenetic analysis showed that the HRV-A21 strains identified in this study were closely related to the three available reference sequences from GenBank, including the prototype strain (ATCC VR-1131; FJ445121) and two strains identified in the USA in 2000 (p1295_s3993_2000; JQ747747) and 2010 (p1177_sR3307_2010; JN837693) (Fig. 1A ), but they formed a distinct clade in the phylogenetic trees of complete viral genome and VP1 gene), indicating the presence of variants of the newly isolated HRV-A21 strains. Multiple alignment analyses among the HRV-A21 strains in this study showed that the nucleotide (nt) identities of the genomes were 99.
These SNPs were located in the VP1, VP2, VP3, 3C, and 3D gene regions (see Supplementary Table  S1 ). Two SNPs switched between minor and major alleles in serial RMH123 viral strains, 3135 (aa 841 in VP1 from E to K), and 5740 (aa 1709 in 3D from C to Y) (reference strain RMH001/2013KM576764). The aa site E841 was the specific site mutation in all the HRV-A21 strains identified in this study. No specific site mutations were found in sequences obtained from RMH123 and RMH144, the two dead cases. Reads that mapped to known pathogens were also found but at lower level. Sequence data corresponding to Acinetobacter baumannii (23. 9% and 13. 3%) and human herpesvirus (HSV)-1 (6. 5% and 0. 4%) of the microbial species were detected in RMH123 on days 4 and 6, respectively. In RMH001, on day 3 after the onset of symptoms, reads corresponding to Escherichia coli, Haemophilus influenza, and Streptococcus pneumoniae accounted for 1.
Metagenomic assay showed that infection with HRV-A21 is strongly associated with sporadic severe CAP in adults. Respiratory infections with HRV-A21 were scarcely recognized clinically prior to this study. Only three whole HRV-A21 genome sequences were available in GenBank: ATCC VR-1131 (prototype strain), and two sequences obtained directly from clinical patients collected in 2000 and 2010. The only other case of HRV-A21-associated severe respiratory disease was reported in France in 2010, in which one child with pneumonia tested positive for HRV-A21, but only partial VP4/VP2 gene sequences (420 bp) were deposit in GenBank (accession number HE589784)  11. Our findings on the clinical manifestation and virological features of this genotype provide insights into the pathogenesis of HRVs and new knowledge on the roles of different HRV genotypes in ARTIs, which is informative and necessary to improve the management of ARTIs.
were determined by phylogenic analysis using the amplified gene sequences compared to the reference HRV genotype sequence deposited in GenBank and by MEGA 5. 1 using the neighbor-joining method with Kimura's two-parameter model and 1,000 bootstrap pseudo-replicates  30. The HRV viral loads in the respiratory samples were determined using an HRV 5′ -UTR quantitative real-time RT-PCR assay and standard curve analysis. The standard RNA transcript was amplified in vitro from the plasmid containing gene fragments using primers targeting UTR/VP4 29.
2. 3) with the parameters "Min Support 50, Min score 80, Top percent 10"  31. All reads assigned to the Enterovirus genus were retrieved and aligned to the consensus sequence of the reference strain, HRV-A21_ p1177_sR3307_2010 (GenBank accession number JN837693), using BWA (version 0. 7. 5, BWA-MEM algorithm) and GATK (version 2. 5. 2)  32, 33. Pileup files were generated by SAMtools, and the allele frequency was calculated as the proportion of reads presenting the allele amongst all reads mapped to this position  34. HRV-A21 genome amplification. Primers targeting the complete genome of HRV-A21 were designed according to the HRV-A21 sequences obtained from deep sequencing and used to amplify genomic sequences from other HRV-A21-positive samples (see Supplementary Table  S2 ). The 5′ and 3′ UTR sequences were determined via the RACE System (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's protocol.
Total RNA from respiratory specimens was converted to cDNA using combined random primers, oligo(dT) primers, and the SuperScript III reverse transcription system (Invitrogen, Carlsbad, CA). PCR was performed using the following conditions: 94 °C for 5 min, 40 cycles of amplification at 94 °C for 30 s, 50 °C for 45 s, and 72 °C for 90 s, with a terminal elongation step at 72 °C for 10 min. PCR products were sequenced directly and assembled manually through alignment to the reference strain (GenBank accession number KM576764). The whole genome and viral protein genes were used to construct phylogenetic trees in MEGA 5. 1 using the neighbor-joining method with Kimura's two-parameter model and 1,000 bootstrap pseudo-replicates  30. Virus isolation and neutralization assay. The HRV-A21 virus was isolated at 33 °C using a human cervical carcinoma cell line (H1-Hela; ATCC CRL-1958) from BALF samples of patient RMH001 collected on day 3 after the onset of symptoms.
Cytopathic effects could be visualized. A neutralization assay was then carried out by using the reference antiserum against the prototype HRV-A21 strain (ATCC VR-1131 AS/GP) and the HRV-A21 isolate in accordance with the World Health Organization (WHO) standard procedures for poliovirus  35. Nucleotide sequence accession numbers. The partial sequences obtained from genotyping were deposited in GenBank under the accession numbers: KR871703, KR871703, KR871714, KT229617, KT229619, KT229620, KU672654, KU859961, KU859962, KR873691 to KR873699, KR871693 to KR871699, KX384582 to KX384588, and KR871706 to KR871709. The GenBank accession numbers of the full HRV-A21 genomes obtained are KM576764 to KM576767, KR871674 to KR871677, and KR090587. The project number of the deep sequencing data is PRJNA335944.
Acknowledgements We would like to thank  Caroline Tapparel  and  Laurent Kaiser  ( University of Geneva and University Hospital of Geneva, Geneva, Switzerland ) for their critique of the manuscript and helpful discussions. This study was supported in part by the  National Major Science and Technology Project for the Control and Prevention of Major Infectious Diseases of China  ( 2012ZX10004-206 ,  2014ZX10004-001 ),  National Funds for Distinguished Young Scientists  ( 81225014 ), the  Program for Changjiang Program for Changjiang Scholars and Innovative Research Team in University  (IRT13007), the  Chinese National Health and Family Planning Commission  Grant ( 201302006-05 ) and  Fondation Mérieux. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Though scanty, the fossil record confirms that Saururaceae were widespread by the Paleogene. The fossil record is also in line with the latest molecular dating estimates of a magnoliid dataset. According to the dating analyses of  Massoni, Couvreur & Sauquet (2015b) , the divergence between the two clades of the Saururaceae (Anemopsis + Houttuynia vs. Gymnotheca + Saururus) was established at the latest by the Eocene (>45 Ma), and the modern genera (and disjunctions) by the late Miocene (>10 Ma; Table  3 ). Two nodes in the phylogenetic neighbourhood of the Saururaceae were constrained using fossil age priors: the Saururus (≥44. 3 Ma; 'safe' minimum constraint with reference to S. tuckerae) and Winteraceae root ages (=Canellales crown age; ≥ 126 Ma; Massoni,  Doyle & Sauquet, 2015). Here, we document fossil pollen from the middle Upper Cretaceous Eagle Formation (Fm) of Wyoming, western North America, that is very similar to those of extant Saururus and nearly identical to that of pollen recovered in situ from Saururus tuckerae from the Eocene of British Columbia.
On the backdrop of the dating estimates, S. aquilae could be the pollen produced by a potential precursor of all extant Saururaceae genera (hypothesis 1). Hypothesis 1 would fit also with the interpretation of the Cretaceous fossil wood described as Saururopsis nipponensis from Japan  (Stopes & Fujii, 1910). Although being more similar to wood of Saururus, Stopes and Fujii state that some features are reminiscent of Houttuynia, which belongs to the second lineage of extant Saururaceae (e. g. ,  Massoni, Forest & Sauquet, 2014) , and discussed the possibility that the wood comes from an ancestral member of the family. On the other hand, the tip ages are poorly constrained (likely too young) using  Massoni, Couvreur & Sauquet's (2015b)  dataset, who focussed on (much) deeper nodes. Hence, S.
aquilae sp. nov. could represent an early member of the Saururus-lineage (hypothesis 2). Notably, the age of S. aquilae sp. nov. is close to the lower boundary of the highest posterior density (HPD) intervals for the older age scenarios (Fig. 8 ;  Massoni, Couvreur & Sauquet, 2015a). Being interested in large-scale magnoliid processes,  Massoni, Couvreur & Sauquet (2015b)  did not use any age priors from within the Piperales subtree  (Massoni, Doyle & Sauquet, 2015)  and relied on relatively slow-evolving gene regions. It is a common observation that divergence ages towards the leaves of a tree tend to be (severely) underestimated in studies using large datasets when compared to focused studies that rely on ingroup constraints. Typically, the latter are in better agreement with the fossil record, as e.
Ticks are obligate hematophagous arthropods of significant importance to human and veterinary medicine. They transmit a vast array of pathogens, including bacteria, viruses, protozoa, and helminths. Most epidemiological data on ticks and tick-borne pathogens (TBPs) in the West Indies are limited to common livestock pathogens such as Ehrlichia ruminantium, Babesia spp. (i. e. , B. bovis and B. bigemina), and Anaplasma marginale, and less information is available on companion animal pathogens. Of note, human tick-borne diseases (TBDs) remain almost completely uncharacterized in the West Indies. Information on TBP presence in wildlife is also missing. Herein, we provide a comprehensive review of the ticks and TBPs affecting human and animal health in the Caribbean, and introduce the challenges associated with understanding TBD epidemiology and implementing successful TBD management in this region.
Experimental and natural data are however needed to confirm the role of these ticks in equine piroplasmosis epidemiology  (Wise et al. , 2013). The principal tick species suspected to be responsible for parasite transmission within the Caribbean is the one-host tick D. nitens. Generally, B. caballi is transstadially and transovarially transmitted in ticks which remain as infection reservoirs. T. equi parasites are transstadially or intrasstadially maintained in ticks, such that nymphs and adult ticks can then transmit this pathogen to their hosts  (Uilenberg, 2006; Scoles and Ueti, 2013). Even if these two parasites are considered endemic in most countries with tropical and subtropical climates, data concerning their importance and epidemiology within the Caribbean is limited. A previous study in Trinidad reported seroprevalences of 33.
, 2013 , 2015; Bonnet et al. , 2014; Moutailler et al. , 2016b). Despite the high-throughput capacity of NGS, a large portion of the generated data is unable to be analyzed, and the frequently short read length sometimes hinders de novo genome assembly and sequence analysis, especially in relation to species level microorganism identification. Morevover, NGS data is based on conventional homology-based sequence searches, and thus requires specific references. Current genomic reference databases are lacking in accuracy and richness, leading to a significant number of unassigned sequences. As this "black box" may include sequences of new or poorly characterized microorganisms, bioinformatic tools need to be developed in order to overcome such limitations. For example, the recently-developed Batch Learning Self-organizing Maps (BLSOMs) approach has enabled the characterization of bacterial communities from several tick species without the need for taxonomic reference information  (Nakao et al.
, 2016; Moutailler et al. , 2016a). High-throughput technologies thus represent a major improvement in surveillance methods, suitable for large-scale epidemiological studies, and which can be easily adapted for the monitoring of emerging pathogens from different areas of the world, such as the Caribbean. However, the increased performance of these methods should not conceal the increased risk of epidemiological mis-interpretation of data on pathogen transmission by ticks. Indeed, DNA or RNA detection alone does not conclusively establish that ticks can transmit pathogens and thus cause disease. Ticks harvested directly from the host may have only sampled pathogens present in the host while they themselves remain resistant to the infection and are unable to transmit these pathogens to other susceptible hosts  (Loftis et al.
Those specimens collected from hosts are often engorged and deformed, thus making morphological determination more difficult  (Zhang and Zhang, 2014). Under such circumstances, molecular tools can be useful in speciating ticks. DNA barcoding approaches have been successfully tested for tick molecular identification using reference databases. Owing to the consistency between morphological and molecular assignation for the majority of ticks, DNA barcoding is eminently suitable for taxonomy  (Zhang and Zhang, 2014). MALDI-TOF mass spectrometry for tick identification could also become a promising alternative tool as it only requires a single tick leg for the process  (Yssouf et al. , 2015). Thefore, the combined use of both morphological and molecular tick identification methods together is required to fully decipher the complexity of tick taxonomy.
CONCLUSIONS We have reviewed the currently available epidemiological literature on ticks and TBPs circulating in the Caribbean. The majority of reported and monitored TBPs in the Caribbean are those concerning pets and livestock, however, many records are outdated and are based on underperforming serological assays. This situation highlights the need to update epidemiological data concerning the diversity of TBPs circulating in the West Indies. Surprisingly, despite the widespread circulation of zoonotic TBPs, few human TBDs have been reported within the area. Most tick infestation reports in the West Indies have been associated with cattle and dogs. However, as the diagnosis of TBDs is challenging and requires a certain level of medical expertise and awareness, human infections might be under-reported in this region.
In addition, several zoonotic TBPs infecting dogs represent potential zoonotic risks that should be addressed. The use of innovative technologies such as high-throughput pathogen sequencing and detection has opened unparalleled capacities to unravel the diversity of TBPs circulating in the area. In particular, high-throughput real-time microfluidic PCR is a powerful tool for the simultaneous detection of up to 96 different pathogens in up to 96 samples, using nano volumes of samples. Additional advantages of this nanotechnology are the reduced level of expertise and shorter time required for sample preparation and data analysis compared to high-throughput sequencing. The application of such technologies for pathogen detection in largescale surveys is very likely to generate important contributions toward the epidemiology, prevention, and control of TBDs in the Caribbean.
ACKNOWLEDGMENTS This publication was funded by grants from  The French Agency for Food, Environmental and Occupational Health & Safety   CoVetLab grant  ( ANSES ) and the  French Agricultural Research and International Cooperation Organization (CIRAD). This work was performed under the framework of the  EurNegVec COST Action   TD1303. We thank the " Tiques et Maladies à Tiques (TMT)" group of the CNRS "Réseau Ecologie des Interactions Durables " for stimulating discussion and support.
Studying and protecting each and every living species on Earth is a major challenge of the 21 st century. Yet, most species remain unknown or unstudied, while others attract most of the public, scientific and government attention. Although known to be detrimental, this taxonomic bias continues to be pervasive in the scientific literature, but is still poorly studied and understood. Here, we used 626 million occurrences from the Global Biodiversity Information Facility (GBIF), the biggest biodiversity data portal, to characterize the taxonomic bias in biodiversity data. We also investigated how societal preferences and taxonomic research relate to biodiversity data gathering. For each species belonging to 24 taxonomic classes, we used the number of publications from Web of Science and the number of web pages from Bing searches to approximate research activity and societal preferences.
The 'taxonomic research' hypothesis implies that scientific reasons and limitations will lead and orientate biodiversity data gathering  20. Because of the intricate interactions between scientists, citizens and funding agencies, and their mixed influence  23  , the underlying mechanisms are unclear. Nevertheless, these hypotheses deserve to be explored and confronted at a global taxonomic scale. Moreover, the recent development of citizen science  24  may have increased the impact of societal preferences. Here, to investigate the relative impact of 'societal preferences' and 'taxonomic research' on biodiversity data, we used the number of webpages from Bing searches and the number of publications retrieved from Web of Science, as proxies, respectively (see Methods). The study of biodiversity is a daunting task -ca.
10 million species are estimated to inhabit the planet -that requires deploying a considerable workforce to gather and analyse biodiversity data  25. Fortunately, for ethical and scientific reasons  [26] [27] [28]  , data sharing practices and tools like the Global Biodiversity Information Facility (GBIF) were developed, facilitating access to species occurrence records. The GBIF portal was chosen because it hosts the biggest open access primary biodiversity database and, even though the big data paradigm does not mean that big datasets are devoid of flaws, they offer a significant potential for new and broad insights  29. Moreover, although open access primary biodiversity data are heterogeneous, resulting from the good will of contributors and not from a well-planned sampling protocol  30  , they reflect our knowledge and practices in the study of biodiversity.
Thus, they can be used to investigate taxonomic bias on a large geographical and taxonomic scale. Here, we aim to quantify taxonomic bias in biodiversity data using 626 million of GBIF-mediated occurrences covering 24 classes of organisms. After careful data validation procedures, we characterized biodiversity gaps, a necessary first step before trying to bridge these gaps  31. We did not assess the validity of GBIF mediated data, which is an issue that has already been raised and discussed repeatedly  [32] [33] [34] [35] [36]. Instead, we quantified taxonomic bias and imprecision (i. e. when an occurrence has been identified not at the species level but only at a higher taxonomic level) and related them to information provided in the occurrence records information (data origin, record date and locality).