dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequencing depth, accuracy, and error profiles First, we generated SMRT sequencing data from the wellcharacterized, lab-adapted infectious clone NL4-3 to determine sequence yields at various accuracy cutoffs. We binned sequences based on the quality scores provided by the Quiver algorithm  (Chin et al. 2013 ) and then plotted the empirical error rates (as measured against the known NL4-3 sequence) and read counts at the various filtering thresholds (Fig. 2 ). From a single SMRT cell, we obtained either 1) 10,000 sequences with a per-base error rate of one in two hundred, 2) 6,000 sequences with an error rate of one in four hundred, or 3) 1,800 sequences with an error rate of 1 in 1,000. See Supplementary Figure  S6  for further details about the relationship among filtering threshold, CCS yield, and empirical error rate.
PCR recombination Because high rates of PCR recombination could potentially diminish the importance of long reads, we performed a preliminary investigation into the PCR recombination rate of our amplification strategy. Input template material was derived from plasmid-encoded infectious molecular clones NL4-3 and CH040, and 2,000 DNA copies per isolate were mixed prior to amplification. All env-specific amplification was performed according to the protocol presented here. Because this experiment was performed later than the rest, sequencing was performed using the updated PacBio P6/C4 chemistry and 6-hour movie lengths. Although this change will yield better sequence quality (which we did not further investigate), it will not affect the PCR recombination rates, and so is not relevant to the data presented here.
We additionally filtered out a small proportion of reads that were only part env (including tandem repeats, which are often present at low frequencies in these datasets), but could confuse the recombination analysis. To count recombination events, each CCS read was aligned to a combination of both NL4-3 and CH040, and parameters for the HMM were estimated, likelihoods computed, and AIC-based model selection performed. In the 99 per cent accuracy condition, we identify 0. 87 per cent of the CCS reads as recombinants (Wilson's 95 per cent CI: 0. 72-1. 05), 0. 63 per cent as recombinants in the 99. 9 per cent accuracy condition (Wilson's 95 per cent CI: 0. 46-0. 85), and 0. 22 per cent as recombinants in the 99. 99 per cent condition (Wilson's 95 per cent CI: 0. 06-0. 79). The fact that the inferred recombination rate decreases as the accuracy cutoff becomes more stringent suggests that the HMM, which is extremely sensitive, has an appreciable false-positive rate at the higher noise levels.
Sequencing primary isolates Third, we successfully sequenced the amplified HIV env product from thirty-six samples from twelve individuals with primary infection (IAVI and SD-PIRC) on the PacBio RSII. We obtained a median of 8,244 CCS reads after quality filtering (using a 99 per cent expected accuracy threshold) (Table  1 ). Designing the analysis strategy for thousands of closely related full-length HIV env sequences presents multiple computational challenges. An often-used solution for NGS data is to pairwise align each read to a reference sequence to derive numerical metrics that can be compared; however, the high frequency and biological importance of insertions and deletions during the course of env evolution precludes this strategy, mainly because the homology of insertions relative to the reference would be lost.
Agreement with Sanger sequencing Twelve plasma samples we sequenced were collected longitudinally from a single HIV-1 subtype A infected donor. Clonal data derived from ten of the twelve time points were also available, comprising ten individual Sanger sequences for each time point (n ¼ 100). These clonal data were generated using an orthogonal method of bulk RT-PCR, amplicon cloning, and Sanger sequencing as previously described  (Richman et al. 2003). The 100 clonally derived Sanger sequences were intercalated into the HQCS read alignment provided by FLEA and a phylogeny was inferred using FastTree  (Price, Dehal, and Arkin 2010). The phylogeny, which had a very similar structure to the other three longitudinal donors presented in Figure  3 , also showed that each clone sequence had a close HQCS neighbor (Supplementary Fig.
Acknowledgements The following reagents were obtained through the  NIH AIDS Reagent Program, Division of AIDS, NIAID, NIH :  HIV-1s 92RW021 ,  92UG035 ,  93UG086  from The  UNAIDS Network  for HIV Isolation and Characterization; HIV-1 96ZM651 from  Dr. Feng Gao  and  Dr. Beatrice Hahn. We are grateful to  Kay Limoli  from  Monogram Biosciences  for assistance generating clonal env sequences. The  IAVI Protocol C Investigators  credited in this study are  Eric Hunter ,  Susan Allen ,  Etienne Karita ,  Julien Nyombayire ,  Rosine Ingabire ,  Jeannine Mukamuyango ,  Jean Bizimana ,  Gisele Umviligihozo  and  Jean-Nepo Nduwamungu.
Exposome-Explorer (http://exposome-explorer. iarc. fr) is the first database dedicated to biomarkers of exposure to environmental risk factors. It contains detailed information on the nature of biomarkers, their concentrations in various human biospecimens, the study population where measured and the analytical techniques used for measurement. It also contains correlations with external exposure measurements and data on biological reproducibility over time. The data in Exposome-Explorer was manually collected from peer-reviewed publications and organized to make it easily accessible through a web interface for in-depth analyses. The database and the web interface were developed using the Ruby on Rails framework. A total of 480 publications were analyzed and 10 510 concentration values in blood, urine and other biospecimens for 692 dietary and pollutant biomarkers were collected.
However, these methods are known to be error-prone and biased. Molecular biomarkers, on the other hand, are more direct and objective indicators of exposure. Indeed, biomarkers of exposure have been increasingly used since the early 1980s, thanks to rapid progress in analytical techniques and the establishment of large cohorts with extensive biospecimen collections. Biomarkers of exposure can be compounds present in the environment and absorbed in the gut after ingestion, inhaled in lungs, or absorbed through the skin. They can also be metabolic end-products derived from environmental compounds that were metabolized by the liver and other tissues, and the microbiota. They may also be macromolecular indicators of environmental effects (e. g. enzymes, proteins or RNA transcripts related to the status of a nutrient or toxic agent).
Over the past 30 years several hundred biomarkers of exposure have been measured and reported in blood, urine and other biospecimens in various populations. However, this information is scattered over hundreds of publications under many diverse titles and subject headings. This makes the identification of these biomarkers along with their comparative performance, their field of application and their concentration ranges in different populations difficult. Historically, most biomarkers of exposure have been measured individually using compound-specific assays. However, with D980 Nucleic Acids Research, 2017, Vol. 45, Database issue the emergence of various omics technologies there is an increasing tendency to characterize exposures more comprehensively. Indeed, modern mass spectrometry techniques now allow the measurement of thousands of compounds in blood, urine or other biospecimens in a single analytical run.
These developments are leading, increasingly, to the reporting of data from multiple markers of exposure. Modern omics technologies should also allow the translation into practice of the concept of the exposome (the totality of exposures of a particular individual over lifetime  (4, 5) ) and the development of exposome-wide association studies (EWAS)  (2, (6) (7) (8). These newly emerging trends in exposure science, combined with the growing volume of comprehensive exposure data being published, make the establishment of a centralized, online database on biomarkers of exposure particularly critical. To date, relatively little effort has been directed to collecting and organizing data on biomarkers of exposure. The ExpoCastDB database contains information on exposure to environmental chemicals such as PAHs, PCBs, nonylphenols, or pesticides (http://actor.
epa. gov/actor/faces/ExpoCastDB/Home. jsp). ExpoCastDB contains information on compound concentrations in various environmental matrices but very limited data in biospecimens. The Comparative Toxicogenomics Database (CTD) is the only online database containing a large number of concentration values in blood, urine and other biospecimens extracted from the scientific literature  (9). The CTD contains about 35 000 concentration values in various biospecimens for ∼250 organic and inorganic compounds. While ExpoCastDB and CTD are very useful and important databases, they contain only limited information on biomarkers of exposure. Here we describe Exposome-Explorer, the first database dedicated to exposure biomarkers. Exposome-Explorer consolidates the diffuse exposure biomarker data scattered throughout the literature.
DATA COLLECTION All data in Exposome-Explorer was compiled through extensive literature analysis along with manual curation and computer-assisted validation. Literature searches were conducted using the Web of Science (WOS). Only peer-reviewed publications describing original work with biomarker measurements in observational studies conducted in human populations were considered. Publications on intervention studies, analytical method development, associations of biomarker with biological status or disease, biomarkers of biological status (e. g. inflammation, oxidative stress, disease), or animal studies were not considered for this initial stage of development. Articles not available online were omitted. For pollutants, general rather than occupational exposures were prioritized. Every search included keywords commonly associated with biomarkers ('biomarker', 'metabolite', 'concentration', 'level', 'excretion', 'indice', 'indicator', 'exposure', 'biological monitoring') and biospecimens ('blood', 'serum', 'plasma', 'urine', 'adipose tissue', 'hair', 'adduct').
For dietary biomarkers, data on the correlations between food or food compound intake and biomarker concentrations were also collected. Citations were searched for according to several intake synonyms ('intake', 'consumption', 'diet', 'recall', 'questionnaire'), associations ('association', 'comparison', 'correlation', 'relation') and a variety of validation synonyms ('validation', 'validity', 'reliability', 'evaluation'). For pollutant biomarkers, data on biospecimen concentrations were collected. Citations were searched for by common pollutant chemical group, such as polycyclic aromatic hydrocarbons (PAH), polychlorinated biphenyls (PCB), polybrominated diphenyl ethers (PBDE), polybrominated biphenyls (PBB), polychlorinated dibenzodioxins/furans (PCDD/F), heterocyclic amines (HCA), phthalates and disinfection byproducts (DBP).
DATA COMPILATION A password protected annotation interface was used for the manual uploading of all data from scientific publications to Exposome-Explorer. This interface permits an efficient and consistent data annotation process. Through this annotation interface, the annotator is guided through successive steps to ensure comprehensive capture of the following information: • Publication with its bibliographic details (title, authors, year, journal, PubMed ID). Also included is the analytical method, the original measurement units as described in the publication, and specimen-specific concentration value adjustments (e. g. normalizing to 'creatinine' for urine, 'lipid' for blood, or a list of regression variables). Arithmetic or geometric mean, as well as median concentration values are compiled along with the minimum and maximum values, standard deviation, percentiles, confidence intervals, measurement size (number of subjects from which the concentration value was calculated), as well as the proportion of subjects for which the biomarker was detected.
Information on the inclusion or exclusion of zero values in the calculated concentration is also given. This was mostly done for pollutant biomonitoring studies where a chemical is often detected only in a small proportion of the studied population. Some authors used a threshold (e. g. 'detected in at least 30% samples'). Below this threshold, the geometric mean was not calculated and the authors considered the compound to be not detected in the studied population. In certain cases, the compound used to express the concentration is given if it is different from the compound or compound class being measured (e. g. 'polyphenols in urine expressed as gallic acid'). • Correlations. To compile data on the correlations between specific biomarker measurements and food or dietary compound intake, information was collected on the intake of different foods, food groups or dietary compounds together with the method used for dietary assessment (record or questionnaire, food composition database, food coverage and time period).
Food items in Exposome-Explorer are linked to the FooDB database (www. foodb. ca). For food groups, the list of individual foods considered to calculate the intake of that particular food group is indicated if described in the publication. For dietary compounds, the inclusion of dietary supplements in the calculation of their intake is indicated when mentioned in the publication. Correlation coefficients (Pearson's product moment or Spearman's rankorder) are collated together with p-value, confidence intervals, statistical significance ('yes'/'no'), and correlation size (number of measurements from which the correlation coefficient was calculated). Adjustment of intake or biomarker measurements prior to calculation of the correlation coefficient (e. g. 'energy intake by residual method' or 'creatinine') as well as a list of regression variables included in the calculation (e.
In some cases, withinand between-subject coefficient of variation (CV), as well as within-and between-subject variance (VAR) are also detailed in the publications. These data are also captured by Exposome-Explorer. Reproducibility size (number of measurements from which the reproducibility value was calculated) and the confidence interval are also indicated in the database. Exposome-Explorer's annotation interface uses a number of features to help complex, heterogeneous, literaturederived data to be easily and systematically translated into organized electronic data. Controlled vocabularies for compounds, foods, experimental methods, specimens, cohorts and units can be created and are fully documented in both the annotation interface and the database. This helps to avoid the inclusion of duplicate vocabulary items, such as different spellings or synonyms for a same item.
Hierarchical associations of populations, samples and measurements can be represented through the creation of parent-children relations. For instance, populations stratified into different sub-groups can be easily described, or the association of several samples taken at different collection times. All data uploaded to the database via the Exposome-Explorer annotation interface is automatically validated, thereby preventing the uploading of erroneous records. Error checks include the usual database integrity verifications such as the presence of mandatory fields, checks on input text size, checks on allowable values or the uniqueness of specific values. The error checking utilities also include applicationspecific consistency verifications such as 'correlations cannot be created between measurements of different subject groups' or 'measurement size cannot exceed subject group size'.
QUERYING THE DATABASE Exposome-Explorer's public user interface allows intuitive browsing and searching of almost any data type in the database (Figure  1 ). Data can be retrieved through the quick search functionality or through specific searches offered on the website's different menu pages. The 'Biomarkers' menu lists the biomarkers, biospecimens, analytical methods and cohorts documented in the database. Each item has its own summary page with both general details (name and classification) and all data related to this item in the database. This includes biomarker concentration values, correlation values, reproducibility values and publications. The 'Correlations' menu lists the correlation values between different food or dietary compound intakes and the corresponding biomarker measurements.
These correlations can be searched according to the food type, dietary compound, or biomarker. The data can be filtered according to a variety of parameters including the type of biospecimen or the method used to assess the dietary exposure. The 'Reproducibility' menu lists the biomarker reproducibility values, which can also be filtered according to biomarker classification (as well as other parameters) and ranked in decreasing or increasing order. The 'Data search' menu lists all biomarker concentration values and allows searches over all data fields. Searches by chemical structure are also possible, as well as browsing biomarker classes and subclasses. The 'Publications' menu lists all the publications in the database. The list can be filtered by title, authors, year or PubMed ID. Full-text for these publications can be accessed via PubMed links or direct publisher URLs.
For every annotated publication, the totality of collected data including the bibliographic information, detailed subject descriptions, and biomarker data (concentration values, dietary intake values, correlation values and reproducibility values) is displayed in a single page. Tables on Exposome-Explorer's different webpages can be easily adjusted to suit user-specific needs. They can be filtered and sorted on any column. Similarly, the number of displayed rows can be personalized. Several hidden columns are available and can be shown in order to provide more details for the default display. Every original value is listed with its corresponding bibliographic citation, making it possible to link each value back to its metadata. Default conversions from highly diverse to standardized units are possible while original values are preserved.
DATABASE IMPLEMENTATION Exposome-Explorer was developed using Ruby on Rails (http://rubyonrails. org/). Ruby on Rails is a web framework which employs the Model-View-Controller (MVC) design pattern. It allows the construction of robust, responsive and reliable web applications. The Exposome-Explorer data is stored in a MySQL relational database (http://www. mysql. com/). Hierarchical associations of records ('trees') are implemented as a nested set model inside a single database column of the tables using the Rails gem Ancestry (https://github. com/stefankroes/ancestry). Highly diverse units and their conversions are transparently handled with the Phys-Units library (https://github. com/masa16/ phys-units/), which is a Ruby implementation of the GNU Units software (http://www. gnu. org/software/units/).
DATA STATISTICS To date, data from a total of 480 publications have been entered into this first release of Exposome-Explorer. This includes 10 510 concentration values for 692 biomarker entries, among which 8,861 concentrations correspond to 488 'single' dietary and pollutant biomarkers. Approximately one third of these biomarkers are dietary biomarkers (Table 1). For dietary biomarkers, almost half of the concentrations are related to fatty acids, followed by carotenoids and polyphenols. With regard to pollutant biomarkers, about two thirds of the concentration data in Exposome-Explorer are related to PCBs and PBDEs, followed by PAHs, PCDD/Fs and phthalates. Most reported concentration measurements are from blood and urine (Table  1 ). Some concentration data are also available for other biospecimens such as hair, nails, adipose tissue or breast milk.
CONCLUSIONS AND FUTURE ENHANCEMENTS Exposome-Explorer currently contains the most complete and comprehensive information on exposure biomarkers ever compiled from peer-reviewed literature. It is also the first publicly available, web-enabled database specifically dedicated to exposure biomarkers. We believe it provides a good starting point for selecting markers of interest or for defining panels of biomarkers that can be used in exposome-wide association studies. Through Exposome-Explorer, biomarker concentrations can be compared in different cohorts or population groups at different levels of exposure (e. g. consumers and non-consumers of a particular food), or between different geographical areas. All of the database's manually curated information is fully linked with other online databases and with the original publications.
These features make it unique among all biomarker databases that we are aware of. The high granularity of the data in the database should allow users to conduct very diverse and advanced analyses or comparisons across publications. Looking both at the range of studied compounds and the number of corresponding studies, Exposome-Explorer also  Plans for future enhancements to Exposome-Explorer include its extension to other categories of exposures (nutritional status, pesticides, occupational exposures), the addition of other types of biomarkers (DNA and protein adducts), and the inclusion of more information characterizing biomarkers such as their half-life and other pharmacokinetic parameters. Plans are also being made to add putative or non-validated biomarkers identified in (pre)clinical studies, but never measured in populations.
A 'classification level' allows to distin- guish 'single' biomarkers corresponding to a single chem- ical entity from 'combined' biomarkers described in pub- lications as the sum of single chemical entities. Details on individual compounds considered to calculate this sum are collected. Identifiers and links to external databases such as CAS, PubChem (10), ChEBI (11), HMDB (12) and FooDB (www. foodb. ca) are provided. • Subject groups studied in the publication, and the populations to which they belong. Each population is named with a short informative summary sentence (e. g. 'Cases and controls in a case-control study on breast cancer'). Populations are subdivided into one ('All') or several (e. g. 'Soy consumers' and 'Soy non-consumers') subject groups according to different criteria (e.
Materials and method The spectral albedo of the surface is measured at Dome C (75 S, 123 E) with a home-made instrument presented in the first section. The second section details the construction and characterization of the light collectors (or fore optics) that are critical to achieve high quality measurements. The third section presents the data processing steps to derive spectral albedo from raw data and the last section addresses the retrieval of the surface specific area of superficial snow.
It could be improved in the future by exploiting the measured incident spectra to infer and better represent the actual atmospheric conditions. For the present study, we simply filtered out the data acquired in cloudy conditions. To detect such conditions, the incident spectrum is compared to a spectrum chosen as a reference when the sky was known to be clear  (2013-11-15  03:36 UTC close to the local zenith). Each spectrum is then scaled beforehand by the cosine of the solar zenith angle to account for the geometrical difference. When the scaled spectrum is more than 10% lower than the reference spectrum in the range 405 -550nm, we consider the sun as being partially obstructed and the data are discarded. Eventually the spectral albedo is calculated by taking the ratio between the upwelling and downwelling spectra.