dataesr/scientific-paragraphs-categorization huggingface dataset:
We combined the results from 20 shufflings and plotted the results in the ROC-like curve shown in Fig. 1. For a false discovery rate between 0. 010 and 0. 015, VSEARCH is more accurate than USEARCH's latest version. For lower values, the three programs have similar accuracies. At higher false discovery rates, USEARCH version 8 has an advantage. The time to search the Rfam database as described above was measured. To avoid extremely short running times, 1,000 replicates of the datasets were used. USEARCH version 7 required on average 5 min 29 s for the search, USEARCH version 8 took 5 min 57 s, while VSEARCH took 5 min 26 s. Westcott & Schloss (2015)  have already carried out an evaluation of the clustering performance of VSEARCH. They tested the ability of several tools to assign OTUs for 16S rRNA sequences and ''demonstrated that for the greedy algorithms VSEARCH produced assignments that were comparable to those produced by USEARCH making VSEARCH a viable free and open source alternative to USEARCH''.
Clustering We independently evaluated the clustering accuracy of USEARCH and VSEARCH as described for Swarm  (Mahé et al. , 2014)  using two mock datasets, one with an even and one with uneven composition of 57 archaea and bacteria. The datasets were first dereplicated. Then the taxonomy of the unique sequences was assigned by a search against the set of rRNA reference sequences representing the species in the mock datasets, carried out with the usearch_global command of USEARCH. The sequences were shuffled randomly 10 times and clustering was performed at 20 different similarity levels ranging from 80% to 99% in steps of 1%. Clustering was carried out in two ways, first using the cluster_fast command that pre-sorts the sequences by length, and then using the cluster_smallmem command that simply processes the sequences in the user-supplied order (in that case, we supplied sequences ordered by decreasing abundance using the sortbysize command).
We then compared the clusters obtained to the assigned species and computed the recall, precision and the adjusted Rand index of the classifications. Recall measures to what extent amplicons assigned to the same species are grouped together in the same OTU (i. e. , not over-splitting). Precision measures to what extent amplicons in an OTU are assigned to the same species (i. e. , not over-grouping). The adjusted Rand index  (Rand, 1971; Hubert & Arabie, 1985)  summarises both precision and recall using a measure of agreement between two clusterings while adjusting for expected values by chance. The average values over the all shufflings are presented in Figs. 2  and 3  for the even and uneven datasets, respectively. For abundance-sorted sequences, the difference between VSEARCH and USEARCH version 8 is negligible.
The difference is larger for length-sorted sequences. When using length sorting, USEARCH 8 (as well as version 7 on the even dataset) shows better precision than VSEARCH for similarity levels below 93%. However, OTU delineation is usually performed at higher similarity values, typically 97%. In the case of our benchmark, overall accuracy as measured by the adjusted Rand index is maximised at 95-97% similarity. This is precisely the region where for length sorting at least VSEARCH outperforms USEARCH. The time used for clustering is shown in Fig. 4. The time used depended on the dataset, algorithm and clustering threshold. The USEARCH programs were in general 2-3 times faster than VSEARCH. In general the difference in speed was smaller for higher thresholds, especially at 99% similarity. Clustering with USEARCH using the fulldp option is an order of magnitude slower than VSEARCH, but contrary to our expectations it does not seem to improve accuracy.
Dereplication Measurements of dereplication speed were performed on the even and uneven datasets described earlier as well as on the BioMarKs dataset  (Logares et al. , 2014). For full-length dereplication (derep_fulllength) VSEARCH was about 40-50% faster than USEARCH version 7 and 50-70% faster than version 8 on all three datasets. All programs were approximately equally fast on prefix dereplication (derep_prefix) of the even and uneven datasets. However, prefix dereplication of the BioMarKs dataset was extremely slow with USEARCH. USEARCH version 7 used more than 4 min and version 8 more than 27 min, while VSEARCH used less than 4 s. The prefix dereplication algorithm used in USEARCH appears ineffective when dealing with short sequences. Removing the 811 sequences shorter than 200 bp out of the 312,503 sequences of the BioMarKs dataset reduces the running time of USEARCH version 7 and 8 down to just 5 and 6 s, respectively.
Chimera detection We evaluated the chimera detection accuracy of VSEARCH and USEARCH in two ways, first using a method similar to that performed for UCHIME, and then using a new chimera simulation procedure based on sequences from Greengenes  (DeSantis et al. , 2006)  and SILVA  (Quast et al. , 2013)  sequences. First we repeated the evaluation of the uchime_ref command described in the UCHIME paper  (Edgar et al. , 2011)  using the SIMM dataset downloaded from http://drive5. com/uchime/uchime_download. html. The dataset consists of 900 simulated chimeras that are approximately 250 bp long. The chimeras were generated from 2, 3 or 4 segments selected randomly from 86 original sequences and have similarities in the ranges 90-95%, 95-97% and 97-99% to the original sequences. They were either used unmodified or with 1-5% indels or 1-5% substitutions.
We assessed the performance of (i) the original open-source UCHIME version 4. 2 program, (ii) USEARCH version 7, (iii) USEARCH version 8, and (iv) VSEARCH. The results are shown in Table  1  and indicate that VSEARCH is superior to the other tools in almost all cases, and in particular when indels were added. The original UCHIME program was found to be quite, but also considerably slower than all the other tools. USEARCH was better than VSEARCH in only 4 out of 99 cases. Next, we tested reference-based (uchime_ref ) and de novo (uchime_denovo) chimera detection using sequences from the 2011 version of Greengenes downloaded from http://greengenes. lbl. gov/Download/Sequence_Data/Fasta_data_files/ and from version 106 (May 2011) of the SILVA database downloaded from https://www. arb-silva. de/no_ cache/download/archive/release_106/Exports/.
Sequences from the 16S rRNA V4 region was computationally extracted using the 515F (5 -GTGNCAGCMGCCGCGGTAA-3 ) and 806R (5 -GGACTACHVGGGTWTCTAAT-3 ) primers, and 8,000 reads were randomly selected from each database. PCR was simulated using a new simulation algorithm known as Simera  (Nichols & Quince, 2016 ) (available at https://github. com/bnichols1979/Simera) that includes amplification and creation of PCR artefacts like chimeras. We sampled 30,000 reads (-s 30,000) and generated 20,000 potential chimeras (-c 20,000). Defaults were used for other options to Simera. The output sequences were then fed into an Illumina MiSeq noise simulator  (Schirmer et al. , 2015)  ending up with 14,966 reads based on Greengenes and 14,952 reads based on SILVA, of which 1,262 and 1,640 reads contain chimeric sequences, respectively.
Next, the sequences were either clustered using the cluster_fast command at 97% identity or dereplicated. VSEARCH and USEARCH version 7 and 8 were run using the uchime_denovo command and then using the uchime_ref command with the Gold database downloaded from http://drive5. com/uchime/uchime_download. html as the reference database. To assess the performance, the results were sorted based on the chimera score, and then the ability to classify individual sequences correctly into chimeric and non-chimeric was plotted as ROC curves. The curves reflect the accuracy of classifying individual reads, not clusters, as abundances were taken into account. The plots in Figs. 5  and 6  show that de novo chimera detection performs better than reference-based detection, with the SILVA dataset in particular, but it does of course depend on the reference database used.
VSEARCH performs better than both versions of USEARCH for de novo chimera detection. For reference-based detection VSEARCH also performs better for the Greengenes Table  1  Chimera detection performance with the SIMM dataset. UCHIME (UC), USEARCH version 7 (U7) and 8 (U8), and VSEARCH (V) was run using the uchime_ref algorithm on the SIMM dataset that was originally also used to evaluate the UCHIME algorithm. Divergence is the percentage of similarity to the original sequences. Noise is either zero (-) or the percentage of indels (i1-i5) or substitutions (m1-5) added. The number of chimeras detected out of 100 of each type is shown. The best results in each category are shaded. dataset, while none of the programs work well with the SILVA dataset. Clustering at 97% appears to be more appropriate than dereplication.
Subsampling We evaluated the subsampling commands of USEARCH version 8 and VSEARCH to check if the results obtained correspond to those expected. We performed 10,000 random subsamplings of 5% of the 9. 5 million unique sequences in the TARA V9 dataset  (Karsenti et al. , 2011). To make this possible with the 32-bit USEARCH, we first downsampled the dataset once to 10% using VSEARCH and then randomly subsampled it again at 50% with either USEARCH or VSEARCH. Plots of the distribution of the abundance of the most abundant sequence in each subsampling are shown in Fig. 7. The highest amplicon abundance in the original dataset is 15,638,316. After the initial 10% subsampling, the highest abundance was 1,564,267. After the second subsampling, the top abundances should therefore have a distribution centred on a value of 782,133.
CONCLUSIONS VSEARCH supports almost all of the commands and options for nucleotide sequence analysis in USEARCH version 7 as well as several new features. It has a 64-bit design and handles large datasets virtually only limited by the amount of available memory. We have demonstrated that VSEARCH is in general more accurate than USEARCH when performing searching, clustering, chimera detection and subsampling. The accuracy is on a par with USEARCH for paired-end reads merging. VSEARCH is faster than USEARCH when performing dereplication and merging of paired-end reads, but slower for clustering and chimera detection. We will continue to improve the accuracy, speed and robustness of VSEARCH in the future, as well as adding new features.
Figure1Search accuracy on the RFAM v11 dataset. USEARCH version 7 (blue), USEARCH version 8 (orange) and VSEARCH (black) was run using the usearch_global command on subsets of the RFAM dataset to identify members of the same families. The plot shows the true positive rate (also known as the recall or sensitivity) as a function of the false discovery rate at varying sequence similarity levels. This curve is based on data from 20 shufflings of the dataset.
Figure6Chimera detection performance on the SILVA dataset shown with ROC curves. USEARCH version 7 (blue) and 8 (orange) and VSEARCH (black) was run using the uchime_denovo (A, B) and the uchime_ref (C, D) commands on simulated Illumina data based on the SILVA database that has either been clustered with a 97% identity threshold (using the cluster_fast command in USEARCH) (A, C) or dereplicated (using the derep_fulllength command in VSEARCH) (B, D).
and usearch_global. In addition, the following commands available in USEARCH version 8 have been implemented: fastq_eestats, fastx_revcomp, fastx_subsample and search_exact. VSEARCH additionally includes a few new commands that do not exist in USEARCH: cluster_size, fastq_convert, rereplicate and shuffle. Some USEARCH version 7 commands have not yet been implemented in VSEARCH. We have not prioritized commands related to amino acid sequences (findorfs), local alignment (allpairs_local, pairs_local, search_local, ublast ), brute-force search (search_global, pairs_global), UDB databases(makeudb_ublast, makeudb_usearch, udb2fasta, udbinfo,  udbstats), and the UPARSE pipeline (cluster_otus, uparse_ref ).
Data Availability The following information was supplied regarding data availability: VSEARCH is freely available at https://github. com/torognes/vsearch under a dual license, either the GNU General Public License version 3, or the BSD 2-clause license. Binaries are provided for x86-64 systems running GNU/Linux or OS X (10. 7 or higher). The repository with scripts and data for the evaluation is available at https://github. com/torognes/vsearcheval. Thanks to the work of several people, there is now a vsearch package in Debian and a vsearch package for Homebrew, as well as a Galaxy wrapper for VSEARCH in the Galaxy ToolShed.
The detection of astrophysical sources of these other messengers is very difficult due to the very small number of expected events and a large background contamination. A way to overcome this difficulty is to combine the detection of non-EM messengers with the EM signal, to provide a multi-messenger dataset. The two largest operating large-volume neutrino telescopes in the world are: the Northern hemisphere ANTARES telescope  [6] , located in the Mediterranean Sea, and the Southern hemisphere IceCube telescope  [7] , located in the South Pole ice. These telescopes are designed to search for high-energy cosmic neutrinos (E ν > 100 GeV) generated in extreme astrophysical sources. Each detector is able to monitor a full hemisphere of the sky (or even the whole sky if downgoing events are considered) with a high duty cycle.
Unprecedented in this domain, the ANTARES telescope is able to generate alerts within a few seconds after the neutrino detection, and provides a precision on the reconstructed direction to better than 0. 5 • at high energies (E > 1 TeV). The TAToO system is therefore well-suited to searching for rapid transients showing time variability at the minute scale. A similar program is running in IceCube  [17]  since 2008, but with larger alert generatoin delay. With this type of real-time analysis, one neutrino associated with a transient optical or X-ray counterpart would be a significant event. Since 2009, around 150 neutrino alerts have been sent to the optical telescope network. Among them, 42 alerts have had follow-up observations within one day after the neutrino detection. With the Swift-XRT follow-up, a total of 7 alerts have been studied.
Neutrino data set This dataset is composed of 42 alerts with an early optical follow-up and 7 alerts with an X-ray followup. Only one alert has an early follow-up with both optical and X-ray telescopes. The direction, time and energy of the alerts are reported in table  2. A total of 34 and 14 neutrinos have been triggered as high-energy and directional alerts respectively. These numbers are in agreement with expectations from Monte Carlo simulation assuming that they are of atmospheric origin. A skymap in galactic coordinates of the neutrino alert directions which have triggered the alert system since 2009 is displayed in figure  4. igure 4. Skymap in galactic coordinates of the candidates which triggered the alert system. Black triangles refer to the triggers which had an early optical follow-up, while red triangles show the triggers with no early observation.
) are then read by SCAMP  [30]  and compared with a reference catalogue to align the images. The photometric calibration is done with LePHARE  [31]. This program can compute theoretical magnitudes for different bands by using a reference photometric catalogue and a list of filter response curves. The photometric catalogue is provided by NOMAD  [32]  and contains magnitudes in B, V, R, J, H, K bands for the brightest stars in the field of the image. Knowing the charge-coupled device (CCD) response curve, magnitudes in the CCD band 1  can be computed. After calibration, one can select a reference image (REF) to perform the subtraction. This reference must be a very good quality image (in terms of limiting magnitude) with no or less signal than in the image to analyse. As we are looking for transient sources with a high variability on the minute/hour timescale, only images (IM) taken less than 24 hours after the neutrino trigger are analysed.
Therefore, a reference can be chosen among images taken several days or weeks after the alert. Some corrections are then applied to images (e. g. gaussian filter, flux normalisation) and subtraction IM-REF is made pixel by pixel using the imarith function from the IRAF package  [33]. Counterpart candidates are automatically selected among positive residual objects on the subtracted image if they satisfy one of the two following criteria: • New source: the candidate is detected in IM and not in REF. • Magnitude variation: the source in IM is at least 0. 5 magnitude brighter than in REF. All these candidates are then checked by eye and light curves are built for the interesting ones. Only candidates with at least two points in the light curve (i. e. candidates detected in at least two images), or one very bright point (i.
X-ray data analysis Each 4-tile observation with the Swift-XRT is automatically analysed at the UK Swift Science Data Centre. The first step of the analysis is to combine the 4 tiles into a single image and to apply the source detection algorithm which has been developed for the 1SXPS catalogue  [36]. It consists of filtering the data, creating exposure maps, locating and characterising sources. The source positions are generally determined using the on-board star trackers with an accuracy of 3. 5 arcsec  [37]. However it is sometimes possible to enhance the astrometry either by using stars in the field of view of the UV/optical telescope  [38, 39]  or by matching the detected XRT sources with the 2MASS cata- logue  [36]. The position with the smallest error is reported. A detection flag is then assigned to each detected source to determine the probability of the source being spurious.
The TAToO program offers an opportunity to test the GRB origin for each detected neutrino with the advantage of not depending on an underlying GRB model. The probability, P s , to detect a serendipitous GRB afterglow lying in the follow-up region for each neutrino alert is given by: P s (GRB, ν) = R GRB × p(ag|GRB) × Ω 4π × T obs (6. 1) where R GRB is the average GRB discovery rate (1000/yr) in the entire sky  [50]  and p(ag|GRB) is the probability of detecting the afterglow counterpart of a detected GRB. This probability is 0. 4 and 0. 5 for TAROT and ROTSE respectively  [51, 52] , and 0. 95 for Swift  [50]. The solid angle, Ω, viewed by the telescopes is given by Ω = 2π(1cos(θ)) with θ ∼ 1 • for TAROT and ROTSE, and θ ∼ 0. 2 • for the Swift-XRT. T obs is the exposure time of the observation of each neutrino alert.
Optical domain While small robotic telescopes, such as TAROT and ROTSE, are well-suited to quickly respond to ANTARES alerts, their diameters do not permit magnitudes deeper than R ∼ 18. 5 to be reached. This limits the ability of the TAToO program to investigate a GRB origin of the detected neutrinos to within only a few minutes after the ANTARES trigger. A one-metre-class robotic telescope is crucial to efficiently extend the optical follow-up of ANTARES alerts with stronger constraints on the neutrino/GRB association up to a few hours after the neutrino trigger. Since 2013, the Zadko telescope has been included in the optical telescope network  [53]. The Zadko telescope is a one metre fully robotic telescope located at the Gingin observatory in Western Australia, covering a field of view of about 0.
15 square degrees. This telescope is very interesting for the TAToO program as it is located in the area where most of the ANTARES neutrino alerts are immediately visible (figure  5 ). Thanks to its larger aperture, about 3 times larger than the TAROT and ROTSE telescopes, the limiting magnitude of the Zadko telescope with only 60 seconds of exposure is 1. 4 magnitudes deeper compared to the TAROT telescopes, with 180 seconds of exposure. This represents a gain of about a factor of 4 on the optical sensitivity. Due to its limited field of view, observations are composed of 7 tiles of 60 seconds exposure each, covering about 85 % of the neutrino alert error box. The best response of Zadko to an ANTARES alert achieved so far is about 70 seconds after the neutrino trigger. The combination of its reasonable response, its larger aperture and its good location will help to better constrain the origin of high-energy neutrinos.
In addition, five telescopes from the MASTER network  [54, 55]  have also joined the TAToO program since March 2015. They consist of pairs of fully robotic telescopes with a diameter of 0. 40 m covering a field of view of 8 square degrees per pair of telescopes. They are located in Russia and South Africa, and significantly increase the coverage of the TAToO telescope network. The first alert sendings with MASTER demonstrate the rapid response time to the TAToO alert of about 24 seconds after the neutrino trigger, which is roughly the same performance as TAROT. Thanks to this new partnership with MASTER, the very early optical follow-up (less than a minute) of ANTARES alerts will be considerably more efficient at constraining the origin of each neutrino. Finally, the addition of an infrared follow-up to TAToO alerts would be very helpful, since this part of the electromagnetic spectrum is less subject to dust absorption.
Conclusion Optical and X-ray follow-ups of the ANTARES neutrino alerts have been running stably since 2010 and mid 2013 respectively. Beginning 2015, 150 and 7 alerts have been sent to the robotic telescopes TAROT and ROTSE, and to the XRT telescope on board of the Swift satellite. The main advantage of the TAToO program is that it is able to send alerts within a few seconds after the neutrino detection with a precision better than 0. 5 • for high-energy neutrinos. Early follow-up has been performed for 42 alerts with delays as low as 17 seconds between the neutrino detection and the start of the acquisition of the first image. The image analysis has not yet discovered any transient sources associated with the selected high-energy neutrinos. Upper limits on the magnitude of possible transient sources have been derived.
Acknowledgments The authors acknowledge the financial support of the funding agencies:  Centre National de la Recherche Scientifique (CNRS) ,  Commissariat  à l'énergie atomique et aux énergies alternatives (CEA),  Commission Européenne  ( FEDER fund and Marie Curie Program ),  Région Ile-de-France (DIM-ACAV) ,  Région Alsace  (contrat CPER),  Région Provence-Alpes-Côte d'Azur, Département du Var and Ville de La Seyne-sur-Mer, France ;  Bundesministerium für Bildung und Forschung (BMBF) ,  Germany ;  Istituto Nazionale di Fisica Nucleare (INFN), Italy ;  Stichting voor Fundamenteel Onderzoek der Materie (FOM) ,  Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO) , the  Netherlands; Council of the President of the Russian Federation  for young scientists and leading scientific schools supporting grants,  Russia ;  National Authority for Scientific Research (ANCS) ,  Romania ;  Ministerio de Ciencia e Innovación (MICINN) ,  Prometeo of Generalitat Valenciana and MultiDark, Spain ;  Agence de l'Oriental and CNRST, Morocco.
The participating models differ in the availability of PM components and formation routes. For instance, EMEP, LOTOS-EUROS and RCG contain coarse-mode nitrate formation (produced by reaction of nitric acid with sea salt and dust), whereas the others do not. In CMAQ, additional anthropogenic dust is calculated as 90 % of unspecified PM coarse emissions and attributed to fugitive dust  (Binkowsky and Roselle, 2003). CAMx did not activate the parameterization of sea salt in this exercise. Based on the setup of models and completeness of datasets, an ensemble called ENS has been built based on   2006 , 2007 , 2008 , 2009 INERIS CHIMERE (Menut et al. , 2013 ) CHIM 2006 , 2007 , 2008 , 2009 HZG  CMAQ  (Byun and Schere, 2006; Matthias et al. , 2008 ) CMAQ 2006 , 2007 , 2008 , 2009 MSC-W -Met. NO EMEP (Simpson et al.