dataesr/scientific-paragraphs-categorization huggingface dataset:
The Waves data allocation, at minimum, will only support a few minutes of burst data per orbit, and even the higher allocations possible with the use of 70-m DSN stations increase this by only a factor or four or five. The primary objective for these data is to acquire observations on auroral field lines, those carrying auroral currents. It is expected that a variety of wave phenomena will be present on these field lines, as described above in Sect. 1, however, at the 60 km/s speed of Juno in the low altitude auroral region, auroral features could well be confined to very brief intervals. Further, the times for crossing such features will not be highly predictable. Given that a pass over one of Jupiter's poles can take on the order of an hour, it is not possible to simply record burst data for the entire passage.
Hence we have developed a simple event detection scheme which evaluates the integrated spectral intensity in some combination of the LFR-Lo and -Hi bands and assigns a quality index based on this spectral intensity. The intensity is recorded each second as burst waveforms are being transferred to the spacecraft data system. To predict approximate times of interest during a given polar pass, we can use a model magnetic field, the trajectory of the spacecraft, and models of Jupiter's polar aurora using, for example, a tool illustrated in Fig. 29. We can identify an interval of time during which Juno will likely be on field lines threading Jupiter's main auroral oval and collect waveform burst observations for that interval. For this interval we may choose to record 5 bins of one minute duration, each (as an example).
For example, if one can accurately target a time of interest, it is possible to record a commandable duration of time. This type of burst mode is planned for use at the equator crossing near periapsis, when it is possible Juno might be impacted by micron-sized dust particles from the ring system. The ring material should be located very close to the equator, so we can use a timed recording to acquire electric waveforms that will enable the detection of individual impacts and possibly even estimates of their mass using a pulse-height analysis as has been used with Voyager and Cassini plasma wave data  (Gurnett et al. 1983; Ye et al. 2016). In principle, it is possible to select from which receivers data are acquired in either of the burst types. For example, for bursts at the equator, perhaps just the LFR-Lo data from the electric sensor would be collected since this type of data is similar to that analyzed from Voyager and Cassini.
Waves Operational Modes Waves uses a set of Major Modes that are defined to configure the instrument to collect survey and burst data as described in the previous section. Each of these modes is defined in a table of configuration commands sometimes referred to as Instrument Expanded Blocks (IEBs) that are stored in the instrument and reside in memory for one or more spacecraft sequences. In Jupiter orbit, two sequences are expected to cover one of the 53-day orbits. In cruise, sequences nominally run 28 days.
Because of the additional data returned and the additional data handling involved, this diagnostic mode can return spectra no faster than once per 2 seconds and results in a greater telemetry rate than either of the other modes. It should be noted that by modifying the configurations, additional variations on these modes can be defined. Such changes would entail uploading different IEBs to the Waves memory for use. An example of an additional mode that might be created by a redefined IEB would be an Intermediate mode, for example, that returned a spectrum every 5 seconds instead of every 10 seconds. Burst Modes Burst modes that have been defined for Waves closely follow the two basic burst data products described in Sect. 6. 1. 1. Because of the need to focus limited telemetry on periods of interest in the auroral regions at Jupiter, the binning mode described above returns waveform data from each of the LFR channels, and the waveforms from the selectable HFR/HFWBR band that includes the local electron cyclotron frequency only for times deemed to be 'interesting'.
Since it is expected that a wide variety of wave phenomena covering a broad frequency range will be found on active auroral field lines, a rather simple algorithm identifying the largest broadband wave intensities has been implemented to capture such interesting times. Such a mode returns electric and magnetic waveforms in the band between 50 Hz and 20 kHz, and electric waveforms from 10 kHz to 150 kHz. Depending on the magnitude of the magnetic field, either the baseband HFWBR channel is selected (100 kHz to 3 MHz) or a 1-MHz wide band including f ce determined from onboard measurements of |B| is selected for waveforms. This mode is used with the high speed interface in a high speed binning mode with the Juno Command and Data Handling System (C&DH) in which a commandable number of burst bins of commandable length can be defined and quality factors determined within the Waves instrument are used to identify 'events'.
An identified event will result in storing data beginning somewhat before and after the event in one of the bins in the raw memory portion of the C&DH. Later events with higher quality indices (larger intensities) will overwrite bins with lower quality events. This process continues as long as the binning session is enabled. When the binning session is closed, the bins are formatted for downlink and moved into the framed telemetry portion of the C&DH memory. The Record Mode will be used to record burst mode data for targeting specific intervals of time for which there is a good understanding of the geometry for the desired phenomenon. The prime example is the jovigraphic equator crossing where the possibility of micronsized dust impacts exists. Since the equator crossing time is well defined and known well in advance, such a burst observation can be scheduled with a well-defined start and stop time designed to cover the expected latitudinal range of dust.
General Plan of Operations The general plan for Waves operations during an orbit are quite repetitive but are flexible to account for variations induced by the evolving orbit geometry, the availability of telemetry, or unforeseen circumstances. Note that the Juno planning cycle, called a science activity plan, runs for several weeks, with two per 53-day orbit. One of these runs from about perijove -24 hours to sometime around the following apojove. The second sequence continues from the end of the first and ends about 24 hours prior to the following perijove. The highest priority science is gained near perijove and it is expected that most or all of the data acquired near perijove will be downlinked within several days after perijove. As described above, Waves uses three major survey modes; one of these is expected to be operational at all times during the science phase.
Data Processing Juno Waves data processing is a largely automated process. A set of semi-automated processes acquire telemetry data from the Jet Propulsion Laboratory when available during and after a Deep Space Network (DSN) tracking pass. Two levels of data are generated by the processing pipeline. First, an Experiment Data Record (EDR) is generated that is uncalibrated and most closely represents the acquired measurements from the Waves instrument. This data set has been expanded from any compression that has been applied onboard, primarily for waveform measurements that may have been losslessly compressed. The instrument also uses a 9-bit floating point compression scheme for survey measurements. Juno employs a product accounting system that keeps track of data products that were transmitted, but not received properly.
Such products are re-transmitted so that missing data can be filled, minimizing data gaps. The resulting data set is a Committee on Data Management and Computation (CODMAC) Level 2 data set commonly referred to as an Experiment Data Record (EDR) (Committee on Data Management and Computation 1986). Once the Level 2 EDR exists, another semi-automated process is spawned which applies the most recent calibration and creates comma separated value (CSV) files of the low rate survey data. This is a full resolution, calibrated data set and is used for most science applications. Waveform data are calibrated for proper amplitude for a frequency well within the analysis band of the respective receiver and stored as a binary time series file for each receiver in units of electric or magnetic field amplitude.
In principle, it is possible to use the frequency response of each receiver to calibrate across each band for the waveform data, but this is only done for special studies as it requires a Fourier transform into frequency space and then back into a time series which, among other things, is an intensive process with little gain for most applications. The calibrated survey and waveform data products correspond to CODMAC Level 3 data sets. The frequency down-conversion process of the HFWBR function significantly alters the time domain signal received from the electric antenna  (Pickett et al. 2014 ) and thus only reconstructed spectra are designed to be archived. Original waveforms for burst data above 3 MHz are only present in raw form in the Level 2 dataset. Some additional data processing may be performed on the basic data described above to derive some basic parameters.
For example, for some time periods an electron density can be determined from the frequency of Langmuir waves in the solar wind, upper hybrid bands in the magnetosphere or the low-frequency cutoff of continuum radiation trapped in the magnetosphere  (Barnhart et al. 2009). Dust detection algorithms developed for Voyager and Cassini can be applied to Juno waveform observations to idenify and characterize dust grains, particularly near Jupiter's ring plane (cf. Ye et al. 2016). A vast portion of the Juno Waves data analysis software utilizes a tool known as autoplot as described at http://autoplot. org. While this is foremost a data visualization tool, it can also incorporate special scripts including analysis algorithms, functions involving Waves or other Juno data sets, etc.
Archiving The Juno Waves data are archived in the Planetary Data System's (PDS) Planetary Plasma Interactions Node. Two primary data sets have been defined for archive which correspond to the Level 2 EDRs (https://pds-ppi. igpp. ucla. edu/data/JNO-E_J_SS-WAV-2-EDR-V1. 0) and Level 3 full resolution calibrated data (https://pds-ppi. igpp. ucla. edu/data/JNO-E_J_SS-WAV-3-CDR-SRVFULL-V1. 0 and https://pds-ppi. igpp. ucla. edu/data/JNO-E_J_SS-WAV-3-CDR-BSTFULL-V1. 0) described above. The Level 2 dataset includes as-flown sequence information. The Level 3 datasets include full calibration files with documentation on how the telemetry data are converted to physical units. Here, the data set levels correspond to those in CODMAC (Committee on Data Management and Computation 1986). Since the archived data are those that are used by the Juno team for science analysis, most issues in the archived data sets are revealed through use well before they end up in the PDS.
The Waves data for the mission up to Jupiter Orbit Insertion are available through the PDS. The schedule for archiving orbital data is for data from two orbits to be archived within about 6 months from the point in time when all of the telemetry and ancillary data are available to the Waves team. In addition to the data files, themselves, all of the PDS required documentation is included. Browse data in the form of pre-computed frequency-time spectrograms are supplied as an easy means of finding events in the Waves archive without expending any programming effort to read the data files.
lanceolatum CYP26-1, CYP26-2, and CYP26-3. The PCR and RACE-PCR products were cloned into the pCRII-TOPO vector (Invitrogen, Cergy Pontoise, France) and sequenced on both strands for validation. The B. lanceolatum CYP26-1, CYP26-2, and CYP26-3 sequences were deposited in GenBank and their accession numbers are as follows: CYP26-1 (KX118106), CYP26-2 (KX118108), and CYP26-3 (KX118107). Furthermore, a 2291-bp piece of the B. lanceolatum Rootletin gene was amplified by PCR, cloned into the pGEM-T Easy vector (Promega, Charbonnières-les-Bains, France), and verified by sequencing on both strands (GenBank accession number: KX118111), before being used as a marker for in situ hybridization (ISH) experiments.
1  [94]  under the models selected by ProtTtest 3 (LG + I + G + F) and jModelTest v2. 1 (GTR + I + G). In addition, Bayesian Inference (BI) analyses were performed using the program MrBayes v3. 2. 6  [95]  and the same models. The robustness of each node was estimated by bootstrap analyses (in 1000 pseudoreplicates) using PhyML v3. 1 for the ML tree and by posterior probability for the BI tree, with two Monte Carlo Markov Chain (MCMC) analyses run independently for 100 million generations and trees sampled every 1000 generations. The burn-in was determined with Tracer v1. 6  [96] , and the average standard deviation of split frequencies remained at <0. 05 after the burn-in threshold. 10% and 50% of the trees were discarded, respectively, for the amino acid and nucleotide datasets. Consensus trees were visualized with Figtree v1.
Comparative dating analysis Based on the nucleotide dataset, the divergence dates of the CYP26 sequences were estimated with Beast v2. 4  [98]  using divergence times in million years ago (Mya) estimated with the calibration intervals proposed by Benton and colleagues and dos Reis and colleagues  [99, 100] : origin of Vertebrata (457,5-636,1 Mya), Euarchontoglires  (61, 6 Mya) , and Hemichordata (504,5-636,1 Mya) as well as the divergence between Holostei and Teleostei (250,0-331,1 Mya) and between Otocephala and Euteleostei (150,94-235 Mya). All calibration constraint sets were defined following a hard minimum, soft upper boundaries, and a lognormal prior  [98]. Monte Carlo Markov Chain (MCMC) analyses were run on the nucleotide dataset for 100 million generations with trees sampled every 1000 generations  [98].
Electrophoretic mobility shift assay (EMSA) experiments The B. lanceolatum RAR and RXR coding sequences were amplified by PCR and cloned into the pGEM-T Easy vector (Promega, Charbonnières-les-Bains, France). Subcloning into the pCS2+ vector  [102]  was performed using introduced EcoRI and XhoI restriction sites. Following verification by sequencing on both strands of the RAR-pCS2+ and RXR-pCS2+ constructs (GenBank accession numbers: B. lanceolatum RAR, KX118109; B. lanceolatum RXR, KX118110), RAR and RXR proteins were produced by in vitro translation using the TNT coupled reticulocyte lysate system (Promega, Charbonnières-les-Bains, France). Electrophoretic mobility shift assays (EMSAs) were performed as previously described  [69]  using 4 μl of each receptor synthesized in vitro and 30-50 × 10 3 CPM of double-stranded oligonucleotide probe end-labeled with (γ-32 P)ATP, incubated for 30 min on ice in a final volume of 20 μl of binding buffer: 20 mM Tris-HCl pH8, 50 mM KCl, 2 mM DTT, 25 mM MgCl 2 , 50 mM NaCl, 1 μg poly (dI-dC), and 10% glycerol.
Additional files Additional file 1: Accession numbers of the CYP26 and CYP51 subfamily sequences used to calculate the phylogenetic trees and to carry out the dating analyses. Species names and database information are provided for each sequence. (XLSX 12 kb) Additional file 2: Phylogenetic tree of the CYP26 subfamily, with CYP51 as outgroup. Tree calculated with the Bayesian Inference (BI) and Maximum Likelihood (ML) methods based on 15 vertebrate species (42 CYP26 sequences), 1 tunicate species (2 CYP26 sequences), 3 cephalochordate species (9 CYP26 sequences), 3 ambulacrarian species (6 CYP26 sequences), 5 lophotrochozoan species (12 CYP26 sequences), and 1 ecdysozoan species (2 CYP26 sequences) (for details see Additional file 1). The robustness of each node was assessed by posterior probability (for the BI phylogeny) and bootstrap (for the ML phylogeny) analyses, which are indicated at each node: posterior probabilities (ranging from 0 to 1)/bootstrap percentages (ranging from 0 to 100).
Acknowledgements The Observatoire Océanologique de Banyuls-sur-Mer in Banyuls-sur-Mer, France, graciously provided Branchiostoma lanceolatum adults and  Hector Escriva  kindly coordinated and supported the animal collections. This work was supported by the  Branchiostoma lanceolatum genome consortium  that provided access to the  Branchiostoma lanceolatum genome sequence. The authors are indebted to  Janssen Research & Development, a division of Janssen Pharmaceutica NV , for providing the CYP26 inhibitor. Furthermore, we would like to thank  Laurent Gilletta  and  Sophie Collet  for maintaining amphioxus adults,  Elisabeth Zieger  for help with confocal imaging and for commenting the manuscript,  Nicolas Robert  and  Ferdinand Marlétaz  for fruitful discussions as well as  François Lahaye  and  Guy Lhomond  for technical support.
Funding João E. Carvalho  is a FCT doctoral fellow ( SFRH/BD/86878/2012 ). The experiments reported in this study were financed by a grant from the  Agence Nationale de la Recherche  ( ANR-11-JSV2-002-01 ) and funds from the  Réseau André Picard  ( ANR-11-IDEX-0004-02 ,  Sorbonne Universities)  to  Michael Schubert.
The quantification of these events is done using paired-end (PE) sequencing. Meta3C reads can be used to perform a de novo assembly that will generate contigs reflecting the genetic content of the community, as well as the clustering and scaffolding steps that will provide a glimpse of the genomic structure of the population [reviewed by  Marbouty and Koszul (35) ]. Fortuitous hints have suggested that chromosomes and other kinds of DNA molecules, such as plasmids  (34) , could be identified from the meta3C data and assigned to their host cells. However, no large-scale exploration of the genomic structure of a truly natural complex community had been undertaken so far using this approach. Here, we investigated the ability of meta3C to bring new insights into the genomic structure of a natural and complex mammalian gut microbiota, including its phage-host interactions.
Translated coding sequences resulting from this assembly [594,648 open reading frames (ORFs) detected-MetaGeneMark;  (41) ] were analyzed at the taxon and functional levels using the metagenomics RAST (MG-RAST) pipeline (Materials and Methods)  (42). As expected from a gut metagenome, the major clades in the sample were Firmicutes (70%) and Bacteroidetes (15%) (Fig. 1B )  (43). An analysis of DNA sequences using the Kraken program (44) (Materials and Methods) confirmed these results with, also as expected, ~80% of the sequences not attributed to a specific genome  (43). Coding sequences were then annotated for essential genes, phages, and conjugative elements using repository databases  (14, 45, 46) , and the annotated contigs were then split into 1-kb fragments. This step has two objectives: first, to limit the impact of misassembly errors (such as chimeric contigs) arising during the assembly step, and second, to normalize the contact signal with respect to the influence of contig size on their representation during the segmentation of the network.
The pools of contigs generated for each CC were therefore screened using the CheckM pipeline for these markers  (49). Most assemblies had a marker gene content typical of what is expected from a single bacterial genome, although some of the largest communities contained multiple copies of marker genes, suggesting that they contained more than one genome (see below). The contigs from each of the 121 CCs were then scaffolded using the program GRAAL (Fig. 2B )  (30). Briefly, GRAAL exploits contacts between DNA regions to assess for their colinearity. The program progresses by successive iterations to converge toward the 1D genome structure that best accounts for the 3D data. For instance, the 3264 contigs present in CC #63 were reordered by GRAAL into a large, 3. 2-Mb scaffold (Fig. 2B ). These scaffolds can then be compared to chromosomal contact maps of single species, which have been described before and are schematically represented in Fig.
Phage assembly and analysis The annotation of the contigs contained in the small CCs revealed an enrichment in phage sequences, suggesting that some of these pools of contigs correspond to viral genomes. To further investigate these com-munities, we performed a new round of assemblies (Fig. 2A ) on these CCs (see Materials and Methods for details; no GRAAL scaffolding was performed at this stage). Contigs above 10 kb were annotated with a BLASTP search against two National Center for Biotechnology Information (NCBI) databases of viral sequences [Phage Orthologous Group (POG) and Viral databases; E < 10 -4 ; Materials and Methods] (57). Forty-three contigs ranging from 10 to 235 kb displayed at least one significant hit against the POG database (table  S3 ) and multiple hits against the Viral database (table  S3 ).
For instance, 11 putative encoded proteins from the 218 ORFs identified within the largest contig (235 kb, core129 contig0) presented a similarity with proteins from the POG database, including a genetic marker associated to the phiKZ giant phage family known to infect Pseudomonas aeruginosa (POG 3254) (table  S3  and fig. S7 )  (37, 38, 58). This contig's genomic organization is typical of phage genomes, with ORFs that are largely co-oriented and organized in sizable blocks encoded on the same strand (fig. S7 )  (28, 59). The contact map of this contig displays a circularization signal, as well as a skewed read coverage, suggesting that bidirectional replication is taking place (Fig. 3E ). This large contig was not present in its full length in the first assembly (95% of the sequence was contained within three large contigs), confirming the interest of our approach to assemble and scaffold metagenomes.
Contig annotations Putative coding sequences on the assembled contigs were determined using the MetaGeneMark v3. 26 software (41) and annotated using BLASTP v2. 2. 30 and two protein databases (ftp://ftp. ncbi. nlm. nih. gov/pub/kristensen/extendedPOGs-10/blastdb/ and ftp://ftp. ncbi. nih. gov/refseq/release/viral/), as well as published hidden Markov models (HMM) [CONJscan  (47)  and MultiMetaGenome  (48) ] and the HMMER software  (62). Positive hits (+1 positions) were then assigned to the processed contigs (500 bp to 1 kb). Sequences from contigs (>5 kb) recovered after the reassembly of small CCs were annotated using the same databases and HMM models. Among those sequences, the 43 contigs carrying at least one homolog contained in the POG database were considered to be a candidate phage contig  (57).
Assembly of CCs Contigs from each CC were used as an index to align all meta3C reads with Bowtie2 (mapping parameters: -local -sensitive, ambiguous matches allowed -parameters a-). When at least one member of the PE reads mapped onto one of these indexes, both read sequences were retained. Raw PE sequences recovered for each CC were quality-filtered (see above) and then processed using IDBA-UD v1. 0. 9 (same parameters as above) to generate a de novo assembly. For each CC, if the cumulated assembly size was larger than 500 kb, then all contigs above 500 bp were retrieved and processed by the scaffolding program GRAAL  (30). For assemblies smaller than 500 kb, which, for instance, can represent a poorly assembled chromosome because of low coverage, the resulting contigs were directly annotated (see above).
Phage-host prediction through CRISPR spacer analysis The pilecr v1. 06 program was used to screen the different assembled bacterial genomes and to identify 1575 CRSIPR spacers. The candidate phage contigs were then screened for the presence of these spacers using BLASTN v2. 2. 30 with short query parameters  (28, 29). Hits with E values lower than 0. 1 were retained and are displayed in fig. S9  and table  S4.