dataesr/scientific-paragraphs-categorization huggingface dataset:
Metagenomic analyses We used kraken v0. 10. 6 63 in viral-ngs to analyse the metagenomic content of our pre-and post-capture libraries. First, we built a database that included the default kraken "full" database (containing all bacterial and viral whole genomes from RefSeq 64 as of October 2015). Additionally, we included the whole human genome (hg38), genomes from PlasmoDB 65 , sequences covering selected insect species (Aedes aegypti, Aedes albopictus, Anopheles albimanus, Anopheles gambiae, Anopheles quadrimaculatus, Culex pipiens, Culex quinquefasciatus, Culex tarsalis, Drosophila melanogaster, Varroa destructor) from GenBank , protozoa and fungi whole genomes from RefSeq, SILVA LTP 16 S rRNA sequences 66 , Uni-Vec vector sequences, ERCC spike-in sequences and the human pathogenic viral sequences that were used as input for the V ALL probe design.
The database we created and used is available in three parts. It can be downloaded at https://storage. googleapis. com/sabetipublic/meta dbs/kraken full-and-insects 20170602/[file] where [file] is: database. idx. lz4 (642 MB), database. kdb. lz4 (98 GB), and taxonomy. tar. lz4 (66 MB). For mock co-infection samples we ran kraken on all sequenced reads. To confirm that enrichment was successful, we calculated the proportion of all reads that were classified as of viral origin. To compare the relative frequencies of each virus pre-and post-capture with V ALL and V WAFR , we calculated the proportion of all viral reads that were classified as each of the 8 viral species. For this we used the cumulative number of reads assigned to each species-level taxon and its child clades, which we term "cumulative species counts".
Supplementary Figure  3  -Design of the V WAFR probe set. (a) Number of probes designed by CATCH for each dataset among all 89,990 probes in the V WAFR probe set. The total includes reverse complement probes, which were added to the design of V WAFR for synthesis. (b) Values of two parameters selected by CATCH for each dataset in the design of V WAFR : number of mismatches to tolerate in hybridization and length of the target fragment (in nt) on each side of the hybridized region assumed to be captured along with the hybridized region (cover extension). The label within each bubble is the number of datasets that were assigned a particular combination of values. Species included in our sample testing are labeled; for full list of parameter values, see Supplementary Table  1.
and β 2 = 1 100. We calculate s(d, θ d ) for each d over a grid of values of θ d before solving for {θ d } *. CATCH interpolates |s(d, θ d )| for non-computed values of θ d and rounds integral parameters in {θ d } * to integers while ensuring that |S({θ d } * )| ≤ N. The probe set pooled across datasets is then S({θ d } * ).
Acknowledgements We thank  S. Ye ,  C. Myhrvold ,  S. Weingarten-Gabbay ,  C. Freije ,  S. Schaffner , and other members of the  Sabeti Laboratory  for useful discussions and feedback on the manuscript;  B. Chak  for assistance with ethical approvals and compliance; and  Boca Biolistics , the  Florida Department of Health, Miami-Dade County Mosquito Control, Research Blood Components , the  Ragon Institute Cellular Immunology Database, and Brigham and Women's Hospital's Crimson Core  for support with samples. IAV samples were funded by  NIH NIAID  contract  HHSN272201400008C  to  J. A. R. This  project has been funded in whole or in part with Federal funds from the  National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services , under Grant Number  U19AI110818  to the  Broad Institute.
Data availability The full source code of CATCH is available at https://github. com/broadinstitute/catch under the terms of the MIT license. Sequences used as input for probe design (Supplementary Table  1 ), as well as the sequences of the designed probes, are available within that same repository. Viral genomes sequenced as part of this study will be deposited in NCBI GenBank  25  prior to publication under BioProject accession PRJNA431306.
= 1 for all d, β 1 = 1 , 11 and β 2 = 1 100. We calculate s(d, θ d ) for each d over a grid of values of θ d before solving for {θ d } *. CATCH interpolates |s(d, θ d )| for non-computed values of θ d and rounds integral parameters in {θ d } * to integers while ensuring that |S({θ d } * )| ≤ N. The probe set pooled across datasets is then S({θ d } * ).
CATCH's framework offers considerable flexibility in designing probes for various applications. For example, a user can customize the model of hybridization that CATCH uses to determine whether a candidate probe will hybridize to and capture a particular target sequence. Also, a user can design probe sets for capturing only a specified fraction of each target genome and, relatedly, for targeting regions of the genome that distinguish similar but distinct subtypes. CATCH also offers an option to blacklist sequences, e. g. , highly abundant ribosomal RNA sequences, so that output probes are unlikely to capture them. We implemented CATCH in a Python package that is publicly available at https://github. com/broadinstitute/catch.
Abstract. This study deals with the problem of identifying atmospheric data influenced by local emissions that can result in spikes in time series of greenhouse gases and longlived tracer measurements. We considered three spike detection methods known as coefficient of variation (COV), robust extraction of baseline signal (REBS) and standard deviation of the background (SD) to detect and filter positive spikes in continuous greenhouse gas time series from four monitoring stations representative of the European ICOS (Integrated Carbon Observation System) Research Infrastructure network. The results of the different methods are compared to each other and against a manual detection performed by station managers. Four stations were selected as test cases to apply the spike detection methods: a continental rural tower of 100 m height in eastern France (OPE), a high-mountain observatory in the south-west of France (PDM), a regional marine background site in Crete (FKL) and a marine cleanair background site in the Southern Hemisphere on Amsterdam Island (AMS).
This selection allows us to address spike detection problems in time series with different variability. Two years of continuous measurements of CO 2 , CH 4 and CO were analysed. All methods were found to be able to detect short-term spikes (lasting from a few seconds to a few minutes) in the time series. Analysis of the results of each method leads us to exclude the COV method due to the requirement to arbitrarily specify an a priori percentage of rejected data in the time series, which may over-or underestimate the actual number of spikes. The two other methods freely determine the number of spikes for a given set of parameters, and the values of these parameters were calibrated to provide the best match with spikes known to reflect local emissions episodes that are well documented by the station managers.
More than 96 % of the spikes manually identified by station managers were successfully detected both in the SD and the REBS methods after the best adjustment of parameter values. At PDM, measurements made by two analyzers located 200 m from each other allow us to confirm that the CH 4 spikes identified in one of the time series but not in the other correspond to a local source from a sewage treatment facility in one of the observatory buildings. From this experiment, we also found that the REBS method underestimates the number of positive anomalies in the CH 4 data caused by local sewage emissions. As a conclusion, we recommend the use of the SD method, which also appears to be the easiest one to implement in automatic data processing, used for the operational filtering of spikes in greenhouse gases time se-.
Measurement sites Amsterdam Island (AMS; 37 • 48 S, 77 • 32 E). This marine background station is operated since 1980 to monitor trends of trace gases in the southern hemispheric mid-latitude cleanair atmosphere. The observatory is located on the coast of a small island (55 km 2 ) covered by short grasslands, in the middle of the Indian Ocean 3000 km south-east of Madagascar. Measurements are performed at the Pointe Bénédicte site located north of the island, on the edge of a 55 m cliff above sea level. The air is sampled at the top of a 20 m high tower. The station contributes to the Global Atmospheric Watch program (WMO/GAW). The data used to feed the WMO/GAW database and estimate the long-term trends are filtered according to local wind measurements to avoid the influence of CO 2 emissions from the island itself  (Ramonet and Monfray, 1996).
Measurement methods The gas analyzers used at the four stations are cavity ringdown spectroscopy instruments (CRDS; O'Keefe and Deacon, 1988), namely Picarro/G2401 analyzers at FKL, OPE and PDM with CO 2 , CH 4 and CO and Picarro/G2301 at AMS with CO 2 and CH 4 (Table  1 ). The measurement protocols used at the four stations are similar and based on ICOS specifications (https://www. icos-ri. eu/documents/ ATCPublic). A calibration using four reference gases is performed every 3 to 4 weeks. Two more reference gases are analysed regularly for quality control purposes. The raw data (0. 2 to 0. 5 Hz) are transferred once per day to a central server and NRT datasets are available within 24 h. The NRT data processing  (Hazan et al. , 2016)  includes automatic filtering of raw data based on the physical parameters of the analyzers (e.
g. cavity temperature and pressure) and threshold values for rejection of outliers. This last filter aims to reject aberrant values from the NRT dataset. It may happen that it rejects an extreme but real event, for instance due to an urban pollution plume. In such cases, the data will be validated afterwards by the station manager. After the automatic processing, the station managers are invited to validate or invalidate data manually using a specific software developed by the ICOS Atmospheric Thematic Centre. For data manually flagged as invalid, a reason must be provided (e. g. leakage, maintenance, local traffic). This procedure does not ensure the systematic rejection of spikes in the data from local or regional processes. Meteorological measurements are also performed at the four stations with barometric pressure, temperature, wind speed, wind direction and relative humidity.
We also identified as contaminated data all data recorded 2 min before and after each contaminated data. The COV method is sensitive to the choice of threshold percentile. In the Supplement we illustrate in Fig. S2a  an example of spike detection using the COV method during a CO contamination episode known to be affected by a local fire. One important feature of the COV algorithm, compared to the other methods, is the a priori definition of the percentage of data to be filtered (threshold percentile), meaning that the number of spike data is not automatically detected.
Standard deviation of the background The SD method  (Drewnick et al. , 2012)  considers that a time series is a combination of a smooth signal superimposed with a fast variable signal. The variable signal component in our case is related to local emissions causing spikes. To determine the variability of background concentration levels we calculated the standard deviation (σ ) of data falling between the first and the third quartile of the entire dataset. A sensitivity test with various quantile ranges is presented in Sect. 3. 1. We then select the first available data point, called C unf (un-flagged data, example in the Supplement Fig. S2b ), assuming that it is not in a spike. The next data point in the time series C i is evaluated with respect to C unf ; spikes are defined by data values higher than a threshold defined as C unf plus an additive value: α × σ + √ n × σ (e.
Robust extraction of baseline signal The REBS method  (Ruckstuhl et al. , 2012 ) is a statistical method based on a local linear regression of the time series over a moving time window (characterized by a duration called the "bandwidth"), to account for the slow variability of the baseline signal, while outliers lying too far from the modelled baseline are iteratively discarded. The bandwidth h must be wide enough to allow for a sufficiently low fraction of outliers within h. The REBS code used here is based on the rfbaseline application developed in the IDPmisc package  (Locher, et al. , 2012)  in R software. It is a modified version of the robust baseline estimation method developed to delete baseline from chemical analytical spectra  (Ruckstuhl et al. , 2001). The REBS method was applied at the high-Alpine Jungfraujoch site (Switzerland, 3580 m a.
Both SD and REBS detect the same CO 2 spikes in both AN-1 and AN-2 time series (Fig. 4 ). For CH 4 , SD and REBS methods confirm the frequent contamination of the AN-1 time series since 2014 and show a good ability to detect the spikes, yet with significant differences regarding the percentage of data detected as contaminated. Considering that the AN-2 analyzer provides a less contaminated CH 4 time series, we have used this experiment to compare between the two methods and select which one performs better for CH 4 spikes at PDM. Figures  5  and 6  represent the CH 4 and CO 2 measurements of AN-1 and AN-2. For AN-2, CH 4 concentrations (black data point in Fig. 5 ) rarely exceed 1950 ppb, whereas for AN-1 it exceeds 2000 ppb (black data point) and occasionally reaches almost 2200 ppb. SD and REBS methods both detect all contaminated data that range between 1980 and 2200 ppb for AN-1.
The main difference between the automatic and the manual flagging methods are related to the lower part of the spikes. For 2861 data (CO < 400 ppb) flagged manually by the PI station, the SD method detects 2270 data points whereas the REBS method detects only 1799 data points. In fact, for moderate spikes the SD method selects 70 % of contaminated data according to the PI whereas the REBS method retrieves only 60 %. We have also calculated the number of events not considered by the manual flagging and considered by the automatic methods. For a total of 3402 data detected Table  6. Classification of the number of hours in which the SD method filtered at least 1 min data point for CO, CO 2 , and CH 4 at the four sites. The intervals represent the differences between filtered and the non-filtered time series averaged at a hourly scale in ppm for CO 2 and (ppb) for CO, and CH 4.
The SD method is found to be efficient and reliable for the purpose of spike detection. It has been proposed for operational implementation in the ICOS Atmospheric Thematic Centre Quality Control (ATC-QC) software to perform daily spike detection of the near-real-time dataset of continuous ICOS stations. The first step will be to run the SD method in a test mode over all ICOS stations and compare with manual detection when available in order to set optimal values of parameters. This analysis can be complemented with wind speed and direction data in order to possibly attribute spikes to fixed local sources.
Acknowledgements. Authors acknowledge the persons in charge of the monitoring programs at the four stations that are part of  SNO-RAMCES/ICOS-France  and supported by  CEA ,  CNRS ,  UVSQ ,  ANDRA ,  IPEV ,  P2OA/OMP , and the  University of Heraklion. We also thank the staff from  ATC  for the support in the data processing. This study was funded by the  European Commission  under the  EU Seventh Research Framework Programme through ICOS  (grant agreement no. 211574 ) and  ICOS-INWIRE  (grant agreement no. 313169 ). Edited by:  Huilin Chen Reviewed  by: two anonymous referees.
Two clusters (XLSSC 072 and XLSSC 029) are in the XXL 100 brightest galaxy cluster sample (XXL Paper II) and ten clusters (all C1s, 4 C2s -XLSSC 048, XLSSC 073, XLSSC 203, XLSSC 634 and 1 C3 -XLSSC 034) are in XXL Paper XX. The clusters in our Chandra snapshot programme that were not covered by archival data were observed with the ACIS-S configuration with an exposure time designed to give a significant detection of a point source contributing > 10% of the 0. 5 -2 keV band XXL flux for C1s and spectroscopically confirmed C2s and > 25% for other cluster candidates. A minimum exposure time of 10 ks was imposed on all observations. The snapshot observations were not designed to detect significant emission from the ICM, although a borderline significant detection was expected in some cases. For those clusters already covered by archival data, two were in the ACIS-S configuration and four in the ACIS-I configuration (see Table  1 ).
In some of the archived observations, the cluster fell relatively far from the optical axis, leading to a larger PSF than for an on-axis observation, which sometimes caused complications in the analysis (see Section 4). All 21 clusters in our sample were analysed with the ciao 1  4. 9 software package and caldb 2  version 4. 7. 4  (Fruscione et al. 2006 ). The level 1 event files were reprocessed using the chandra_repro tool following the standard data reduction threads 3. Periods of background flares were identified and removed using lightcurves analysed with the deflare tool. For observations taken in the ACIS-S configuration the cluster always fell on only the S3 chip, so a lightcurve was extracted from only the S3 chip. For the observations in the ACIS-I configuration a lightcurve was extracted from the four front illuminated (FI) chips, CCD_IDs I0-I3 (excluding any other chips in the observation).
In the following section we describe the detection and photometry of point sources in the Chandra data in or projected onto the cluster regions. We assume that all point sources detected are AGN, as AGN vastly outnumber any other contaminating point sources at this depth -the possibility that they could be X-ray bright stars is ∼3%  (Galametz et al. (2009) ,  Chiappetti et al. (2018)  -also known as XXL Paper XXVII). For several clusters, point sources were detected in these regions by the XXL pipeline and excluded from the XXL cluster flux measurements. Since the goal of our analysis is to estimate the effects of AGN that were unresolved by XMM we do not include the point sources that were detected by XXL in the main body of this paper. These sources are detailed in Table  A. 1.
Multi-wavelength data were used to assist the classification of these possible point sources, and details for each are given in Section 4. For the optical band we used the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) data for XXL-N 4. These images were taken with the wide field optical imaging camera MegaCam, a 340 Megapixel camera with a 1 by 1 field of view. For XLSSC 634 in XXL-S the image was taken from the BCS survey  (Desai et al. 2012)  with the Mosaic2 imager on the Blanco 4m telescope 5. For radio data we used the NRAO VLA Sky Survey  (Condon et al. 1998 ) and  Tasse et al. (2008)  for the XXL-N field and used Australia Telescope Compact Array (ATCA) data  (Smolčić et al. (2016)  -also known as XXL Paper XI, Butler et al. (  2017 ) -also known as XXL Paper XVIII) for the XXL-S field (for XLSSC 634).
Calculating Cluster Fluxes from the Chandra data The Chandra snapshot observations were optimised to detect significant point source contamination in the XXL clusters, and are not expected to be deep enough to measure detailed ICM properties. Nonetheless we attempted to place constraints on the ICM flux from the Chandra data. All of the point sources in the image were masked using a circle with a radius necessary to include 90% of the flux at 1 keV, and the flux from each cluster was estimated using srcflux. A 60 radius circle was used as the source region (consistent with the XXL flux measurements), and the background region used was an annulus with inner and outer radii of 120 and 180 respectively, as measured from the cluster centre. In some cases this background region went off chip and this was accounted for.
Conclusion We have analysed Chandra data for 21 clusters and cluster candidates that appear in the XMM-XXL survey catalogue in order to determine the extent of any contamination by unresolved point sources. Our main results are as follows: -In the 14 C1 and C2 clusters which form a complete sample with a defined selection function, we find that the majority have little or no contamination of their ICM fluxes by AGN. One C1 source appears to be an AGN that was misclassified as extended, but this source was detected at the extension parameter threshold, so represents a rare interloper rather than any broad problem in the classification scheme. Three or four of the nine C2 clusters are either AGN that were misclassified as extended sources, or else have ICM emission that is strongly contaminated by AGN emission.
Acknowledgements XXL is an international project based around an XMM Very Large Programme surveying two 25 deg 2 extragalactic fields at a depth of ∼ 6 x 10 -15 erg cm -2 s -1 in the 0. 5-2 keV band for point-like sources. The XXL website is http://irfu. cea. fr/xxl. Multi-band information and spectroscopic follow-up of the X-ray sources are obtained through a number of survey programmes, summarised at http://xxlmultiwave. pbworks. com. We thank Adam Mantz and Mauro Sereno for their useful comments on this work. FP and MERC acknowledge support by the German Aerospace Agency (DLR) with funds from the Ministry of Economy and Technology (BMWi) through grant 50 OR 1514. The Saclay group acknowledges long-term support from the Centre National d'Etudes Spatiales (CNES). This paper has also made use of observations obtained with the Canada-France-Hawaii Telescope (CFHT) which is operated by the National Research Council (NRC) of Canada, the Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique(CNRS) of France, and the University of Hawaii.
Acknowledgements B. E. T. would like to thank participants of the "disks17:  Confronting MHD Theories of Accretion Disks with Observations" program , held at the  Kavli Institute for Theoretical Physics (KITP) , for their feedback and useful comments on this project. Especially,  Alexandra Veledina  for advice given regarding the X-ray light curve analysis, and  Phil Charles  for his comments on the manuscript. B. E. T. , G. R. S. , and C. O. H. acknowledge support by  NSERC Discovery Grants , and C. O. H. by a  Discovery Accelerator Supplement. This research was supported in part by the  National Science Foundation  under Grant No. NSF   PHY-1125915 , via support for KITP. J. -P. L acknowledges support by the  Polish National Science Centre OPUS  grant  2015/19/B/ST9/01099. J. -P. L and G.
For each individual source in our sample, we use scripts from the WATCHDOG project, involving the rex script within the Heasoft Software Package (http://heasarc. nasa. gov/lheasoft/), to reduce and extract (mission-long) daily time-binned, background-subtracted light curves in the 2-10 keV band, from the PCA Std2 data available on that source in the WATCHDOG database. We have also compiled all available (http://astro. physics. ualberta. ca/WATCHDOG/download data). This tool compiles all the publicly available data from the MAXI archive (http://maxi. riken. jp/top/) in three standard bands (2 -4, 4 -10, 10 -20 keV),.
STEP 2: identifying relevant studies For each topic, three to five key concepts were defined using simple, short sentences associated with as many keywords as possible, some broader, some narrower. Two concepts were common to all reviews: vector-borne diseases and urban areas; as such, standardized lists of keywords were used across the consortium. For example, for the key concept of vector-borne diseases, we used keywords such as: vector-borne diseases; neglected tropical diseases; tropical infectious diseases; malaria; dengue; leishmaniasis; etc. Once the search strategy was finalized, the same exhaustive list of keywords was used to search all databases in titles and abstracts of papers. All complete search strategies for the VERDAS reviews are provided in appendices in each article. We defined which databases to search based on each team's access and the databases' relevance for the topic (see Table  3 ).