dataesr/scientific-paragraphs-categorization huggingface dataset:
9  (Li et al. 2009)  to remove sequences that mapped to the human genome build 38 (GRCh38) from NCBI and estimate the amount of host contamination. We used anvi'o v6. 2 and the contig snakemake workflow to compute the sequence metrics  (Köster and Rahmann 2012). Briefly, the workflow created a contigs database with 'anvi-gen-contigs-database', which used Prodigal v2. 6. 3  (Hyatt et al. 2010)  with the metagenome mode to identify open reading frames. It used 'anvirun-hmm' to detect the single-copy core genes from bacteria (n=71, modified from (Lee 2019)), archaea (n=76, (Lee 2019)), eukarya (n=83, http://merenlab. org/delmont-eukscgs), and ribosomal RNAs (n=12, modified from https://github. com/tseemann/barrnap). We used 'anvi-display-contigs-stats' to get the number of sequences, total length, N50, longest sequence, number of genes, number of single-copy core genes and ribosomal genes.
We used 'anvi-get-sequences-forhmm-hits' to recover 16S rRNA genes for each condition and used the Human Oral Microbiome Database (HOMD) online blast tool for taxonomic assignment. We assembled the long reads with Flye and the metagenomic option  (Kolmogorov et al. 2019) , which takes into account the uneven coverage nature of metagenomes. We created anvi'o contigs databases, as described above, with Flye's contigs to summarize the assembly metrics. enzyme buffer (includes MgCl2), 200 µM dNTP mix (ThermoFisher), and 0. 3 µM of each primer. We added approximately 10-25 ng template DNA to each PCR and ran a no-template control for each primer pair. Amplification conditions were: initial 94C, 3 minute denaturation step; 30 cycles of 94C for 30s, 57C for 45s, and 72C for 60s; final 2 minute extension at 72C.
The triplicate PCR reactions were pooled after amplification, visualized with the negative controls on a Caliper LabChipGX, and purified using Ampure followed by PicoGreen quantitation and Ampure size selection. Libraries were sequenced on an Illumina Miseq 250-cycle paired-end run. We used illumina-utils v2. 7  (Eren et al. 2013 ) for the quality filtering, following  (Minoche, Dohm, and Himmelbauer 2011)  recommendations, and the merging of the paired-end reads. We used Vsearch to remove chimeric sequences  (Rognes et al. 2016 ) and Minimum Entropy Decomposition (MED)  (Eren et al. 2015)  to cluster the merged reads into oligotypes. We assigned taxonomy using DADA2  (Callahan et al. 2016 ) and the Silva v132 non-redundant database  (Quast et al. 2013 ). Short-read metagenomic library preparation, sequencing and analysis.
Briefly, the workflow uses illumina-utils for quality filtering followed by a metagenomics assembly with IDBA-UD v1. 1. 3 (Peng et al. 2012 ) and generates contigs databases as described in the long-read sequence analysis section. The workflow also uses Bowtie2   For the short-read metagenomes, we used the taxonomy of the ribosomal gene S7 with the Genome Taxonomy Database (GTDB). We processed the 16S rRNA amplicons with the Minimum Entropy Decomposition (MED) algorithm and used Silva v132 to assign taxonomy.
Acknowledgements We thank  Nicole Robichaud  for technical help with short-read sequencing. This research was supported by the  Gastro-Intestinal Research Foundation , the  Simons Foundation  (# 687269 , AME). In addition, a  Helmsley Foundation  grant to LB, BJ, and AME supported FT, and an  NIH   NIDDK grant  ( RC2 DK122394 ) supported KL and AME.
Data availability Raw sequences for long-and short-read sequencing data for shotgun metagenomes and 16S rRNA gene amplicons are available under the BioProject PRJNA703035. We also made available FASTA files and anvi'o contigs databases for (1) assembled longread sequences at doi:10. 6084/m9. figshare. 14141228, (2) unassembled long-read sequences at doi:10. 6084/m9. figshare. 14141414, and (3) assembled shotgun metagenomes at doi:10. 6084/m9. figshare. 14141819. Supplementary Tables and Figures are also available via doi:10. 6084/m9. figshare. 14141918.
Context. The Radio and Plasma Waves (RPW) instrument on board Solar Orbiter has been operating nearly continuously since the launch in February 2020. The Time Domain Sampler (TDS) receiver of the RPW instrument is dedicated to waveform measurements of plasma waves and dust impact signatures in an intermediate frequency range from 0. 2 to 200 kHz. Aims. This article presents the first data from the RPW-TDS receiver and discusses the in-flight performance of the instrument and, in particular, the on-board wave and dust detection algorithm. We present the TDS data products and its scientific operation. We demonstrate the content of the dataset on several examples. In particular, we study the distribution of solar Langmuir waves in the first year of observations and one Type III burst event. Methods.
The on-board detection algorithm is described in detail in this article and classifies the observed waveform snapshots, identifying plasma waves and dust impacts based on the ratio of their maximum amplitude to their median and on the spectral bandwidth. The algorithm allows TDS to downlink the most scientifically relevant waveforms and to perform an on-board statistical characterization of the processed data. Results. The detection algorithm of TDS is shown to perform very well in its detection of plasma waves and dust impacts with a high accuracy. The initial analysis of statistical data returned by TDS shows that sporadic Langmuir waves that are not associated with Type III events are routinely observed in the inner heliosphere, with a clear increase in occurrence rate closer to the Sun.
Introduction The Radio and Plasma Waves (RPW) instrument  (Maksimovic et al. 2020 ) on Solar Orbiter includes three receivers for the digitization and on-board processing of electromagnetic field data across different frequency bands: The Low Frequency Receiver (LFR), covering the frequency range from well below 1 Hz to 10 kHz with both time series data and spectral data; the Thermal Noise and High Frequency Receiver (TNR/HFR), providing electromagnetic spectra from several kHz to 16 MHz; and the Time Domain Sampler (TDS), a medium frequency receiver dedicated to waveform measurements. Specifically, TDS is designed to capture electromagnetic waveform snapshots in the frequency range from 200 Hz to 200 kHz, resolving, in particular, plasma waves near the electron plasma frequency and in the range between the proton and electron plasma frequencies, as well as transient signals in antenna voltage, associated with interplanetary dust impacts.
In this article, we describe the operation of the TDS instrument and its data products. We also present an overview of the measurements obtained during its first year of operation, covering the commissioning phase and the first part of Solar Orbiter cruise phase  (Müller et al. 2020). All the RPW data, including documentation, are available in the public Solar Orbiter Archive 1. As per the Solar Orbiter data access rules, the data become public after three months from the time they are received on the ground. One of the key science objectives of the RPW instrument is to improve our understanding of plasma instabilities in the solar wind, as well as the generation of high frequency plasma waves and their propagation and occurrence in the inner heliosphere (for a review see  Gurnett 1991). The key measurements for this investigation are provided by the TDS receiver and include observations of Langmuir waves associated with interplanetary shocks, Type III radio bursts, and (at other times) less obvious source processes.
1 ksps. This oversampled digital signal is afterwards decimated by a configurable factor of 4, 8, 16 or 32 after being processed by anti-aliasing FIR filters. The decimating filters are implemented in Field Programmable Gate Array (FPGA) logic, but the filter coefficients are uploaded to the FPGA by the flight software and multiple sets with slightly different characteristics can be chosen by a telecommand. This decimated waveform is then used as the input digital signal for waveform snapshots and TDS statistical products. This configurable decimation allows the TDS sampling rate to be set to one of the four values: 524. 3 ksps, 262. 1 ksps, 131. 1 ksps, or 65. 5 ksps. A large number of input configurations is allowed by the TDS input multiplexers, but only three multiplexer settings listed in Table  1  are used routinely in scientific operations.
Overall, TDS supports all the operational modes of RPW (see  Maksimovic et al. 2020 for discussion). Survey NOR-MAL and BURST modes are implemented in the same way in TDS, but BURST mode has a separate configuration that can be applied when RPW switches to the BURST mode. This typically includes higher rate products, such as the maximum amplitude (MAMP). In the selective burst modes SBM1, which are to be used during interplanetary shock crossings, and SBM2, to be used for in-situ Type III regions, TDS generates all the normal mode products, but can also generate additional periodic RSWF snapshots in SBM1 mode or additional triggered TSWF snapshots in SBM2 mode (see Sect. 3 for more details). TDS also supports the RPW backup mode in its LFM (low frequency mode) configuration, where nominal data is not generated, but TDS replaces LFR in the sampling of low frequency signals from SCM and BIAS.
On-board wave detection The TDS flight software implements on-board processing and detection of the captured snapshots which allows the instrument to classify the observations into three categories: waves, dust impacts and other signals. This algorithm described here is used to select the most relevant waveform snapshots for transmission to the ground by assigning a quality factor and also provides statistical characterization of the processed snapshots. This algorithm efficiently distinguishes between coherent waves, such as Langmuir waves, characterized by a narrow bandwidth and small peak to median ratio, and isolated voltage spikes associated with impacts of interplanetary dust, which result in a very large peak to median ratio and wide bandwidth. In its nominal scientific operation (NORMAL, BURST or SBM1/SBM2 modes), TDS acquires one waveform snapshot every second.
If this is the case, the snapshot with the lowest Q is replaced by this new snapshot. If there are any empty slots in the queue, which typically happens after the queue is emptied and before enough wave snapshots are detected, the algorithm will fill in those slots with any snapshots exceeding the minimum amplitude threshold. If those snapshots are not identified as waves, they are assigned the lowest possible Q = 1. A pre-defined number of slots in the NORMAL queue is allocated for dust snapshots -these are filled with the first dust snapshots acquired and marked as dust (no quality factor is assigned to dust impacts). The content of the snapshot queue is periodically dumped to the RPW data processing unit (and eventually to the ground) in the form of the triggered snapshot (TSWF), which automatically empties the internal queue, making space for new data.
This TSWF dump is performed by a spacecraft telecommand in a configurable interval (typically 1-6 h for survey mode snapshots). This period of snapshot dumps determines the data rate of the TSWF data products. The algorithm ensures that with every data dump TDS returns the best wave snapshots, a pre-defined number of dust events and if free space remains, it is filled with randomly chosen snapshots of above-noise amplitude. The regular snapshots (RSWF) are independent of this triggering mechanism and are transmitted in periodic intervals regardless of their content. To support the operation of TDS in NORMAL and SBM2 modes, TDS implements two separate snapshot queues. One for the NORMAL (Survey) mode TSWF data and one for SBM2 mode triggered snapshots. Each queue can hold up to 6 megabytes of data (up to 48 snapshots of 16 384 samples).
In the SBM2 mode, the algorithm described above writes the data into those two queues in exactly the same manner -the check for the lowest Q snapshot is performed for each queue separately and a sufficiently high-Q snapshot will be stored in both queues. However, the SBM2 snapshot queue is dumped much more frequently, every 5 min, providing a very high rate of TSWF snapshots. This SBM2 TSWF data is identified by a different packet ID recognized by the spacecraft data handling system which routes the SBM2 data to a dedicated data store, available for a selective download. On the other hand, the detection algorithm and the associated data products are not affected by the change to BURST or SBM1 mode. In those modes, only the cadence of periodic snapshot changes and the detection algorithm operates exactly in the same way as in NORMAL mode.
1 ksps mode, with the choice of the steepest anti-aliasing filter, this PCDU interference is already at the very edge of TDS range, attenuated by the filter, and can be relatively easily removed from the data by ground processing. In this mode, the PCDU emission does not strongly interfere with the TDS on-board detection algorithm which then performs nominally even in its presence. The situation is different for the 524. 3 ksps operation mode, where the PCDU interference falls in the center of TDS measurement range (its harmonic is also visible) and the detection algorithm falsely identifies this interference as a plasma wave. The existing implementation does not allow us to reconfigure the algorithm to work in the presence of this interference and we therefore rarely use the 524. 3 ksps mode in the first year of the mission.
The dataset was cleaned by removing intervals when TDS was operating in a non-nominal mode, intervals of 524. 3 ksps sampling and data points acquired close to the active sweeps performed by the BIAS instrument. We also removed the intervals around the flyby of Venus on December 27, 2020, when TDS detected numerous solitary waves, but these were incorrectly counted as dust. For a better graphical representation, the daily counts are presented as five-day averages. Clearly, the daily counts of both waves and dust impacts are modulated by the heliocentric distance with more events being detected close to the perihelion in June 2020. This dependence is of physical origin and as discussed in several studies  (Píša et al. 2021; Zaslavsky et al. 2021). The high wave counts in July and in November correspond to periods of increased solar activity when TDS detected waves associated with Type III burst and other transient phenomena.
When the number of detected events is low and the TDS triggered snapshots are dumped often, most or all of the detected events included in the statistics are also transmitted as triggered snapshots. This allows to verify the correctness of event detection by TDS. Manual validation of the data shows that wave detection is very reliable and false positive wave detections are very rare except for the cases when the spacecraft interference is falsely identified as a wave. On the other hand, the algorithm is not perfect and misses some of the wave snapshots, in particular the broadband and low amplitude ones. The probability of this false negative cannot be easily established because nonselected snapshots are not transmitted to ground. The situation is somehow different in the case of dust impacts where false positives (non-dust waveforms incorrectly identified as dust) are not uncommon.
TDS data products The main data product of TDS are waveform snapshots. These contain up to 65 536 points per channel and up to four TDS channels can be sampled simultaneously. Furthermore, TDS generates multiple other data products, as listed below with the acronyms in parentheses: Regular waveform snapshots (RSWF). These waveform snapshots are taken periodically at configured time intervals and their purpose is to provide un-biased duty cycled coverage of the high frequency waveform, suitable for statistical studies and detection of longer time scale phenomena, such as Type III radio bursts. During science operations, the RSWF are typically generated every 300 or 600 s and their length is set to 4096 or sometimes 8192 samples per channel. They are captured synchronously with the LFR snapshots and synchronized to 5 min boundaries to overlap with the full three-dimensional proton distributions measured by the SWA instrument.
As no detection or thresholding is used for RSWF, the snapshots often contain only noise. Triggered waveform snapshots (TSWF). The triggered waveform snapshots are a subset of waveform snapshots selected by the wave detection algorithm for transmission to the ground. These snapshots thus typically contain signals above the noise floor with a strong presence of waves and dust impacts. The snapshots are collected and stored in an internal queue over a configured time interval (typically 1 to 6 h) and ranked using a quality factor assigned by the algorithm. At the end of this interval, the highest ranked snapshots are transmitted to ground. The triggered snapshots are typically configured to a length of 16 384 samples per channel. Wave, dust, and snapshot statistics (STAT). This short data packet contains several statistical quantities describing the waveform snapshots processed by the TDS detection algorithm, including those which were not selected for download as triggered snapshots.
Statistical properties of detected Langmuir waves In this section, we provide an analysis of the properties of the detected waves extracted from the first year of TDS statistical data in order to demonstrate the capabilities of TDS and its onboard detection algorithm. The dataset was cleaned in the same way as for Fig. 3 , but here we restricted the analysis to wave statistics. Figure  4  shows histograms of the median frequency of the waves reported in the STAT dataset, re-normalized to a number of events per day. Here the median frequency is the median value of the frequencies of all waves detected in the 16 s interval, so it is calculated only over the subset of snapshots (at most 16) identified as waves. The histograms are calculated separately for four intervals of heliocentric distances between 0.
In-situ waves associated with the November 17, 2020 Type III burst Solar Orbiter observed multiple Type III radio bursts in the second half of November 2020 and during some of those events, the associated energetic particles reached the spacecraft and have been detected by the EPD instrument. During those in-situ Type III observations, the RPW instrument sometimes observed the plasma waves generated by plasma instabilities triggered by those solar particles. In this section, we show an overview of the wave observations and an example of the use of TDS histogram data to characterize the wave properties. Figure  6  provides an overview of RPW observations from November 17, 2020. The top two panels show the electric field spectrogram from the TNR receiver (8 kHz to 1 MHz, with the interference lines removed) and from TDS (1 to 110 kHz).
Conclusions The data from the first year of observations by the RPW-TDS instrument show that the instrument performs nominally and returns valuable scientific data even in the presence of the strong interference from the spacecraft PCDU. The TDS wave detection algorithm has been described in detail and shows a good in-flight performance in detecting plasma waves and dust impacts. The PCDU interference prevents the detection algorithm from operating at the highest sampling rate of 524. 3 ksps; therefore, we have been operating TDS at the 262. 1 ksps sampling throughout the cruise phase. At this sampling rate TDS can resolve frequencies up to 100 kHz, which is adequate for the cruise phase, but this issue needs to be corrected by a flight software update before Solar Orbiter ventures closer to the Sun in the nominal mission phase.
In comparison, Solar Orbiter TDS uses a slightly different and arguably more complex algorithm, allowing it to detect a wider variety of events, in particular weaker waves and less intense dust impact spikes. This, combined with its rich statistical products, makes RPW/TDS suitable for both detailed measurements and statistical surveys of high frequency phenomena in the inner heliosphere. An initial analysis of the TDS statistical data products shows that Langmuir waves are sporadically observed outside identified Type III regions with a clear increase in occurrence rate with decreasing distance to the Sun. The amplitude of those waves is typically lower than those associated with Type III events. While the waves associated with Type II and type III events have been studied extensively, including observations from the inner heliosphere  (Gurnett & Anderson 1977) , these more sporadic events have not been specifically addressed in previous studies.
Acknowledgements. Solar Orbiter is a space mission of international collaboration between  ESA  and  NASA , operated by  ESA. RPW-TDS development has been developed with the support from the  Czech PRODEX program  funded by the  Czech Ministry of Education , Youth and Sports. The TDS team thanks the scientific and engineering teams or other RPW subsystems as well as the team from the  French Space Agency CNES  for their support of the TDS development. YK is supported by the  Swedish National Space Agency  grant  20/136. All Solar Orbiter data is available from the public  Solar Orbiter Archive  (http:// soar. esac. esa. int).
Total RNA was extracted from 100 harvested dried seeds in three biological replicates using the NucleoSpin ® RNA Plant and Fungi Kit (Macherey-Nagel, Düren, Germany), according to the manufacturer instructions. RNA quantity and quality were measured using a NanoDrop ND-1000 (NanoDrop Technologies, Wilmington, DE, USA). cDNA library preparation and single-end sequencing (SE50, 20 M) were outsourced to the Beijing Genomics Institute (BGI, https://www. bgi. com, accessed on 4 July 2020) using the DNBseq sequencing technology. After quality control, high-quality reads were mapped on Arabidopsis reference transcriptome version 11 (Araport11) using quasi-mapping alignment and quantification methods of Salmon algorithm v. 1. 2 [49]. For gene expression analysis, raw RNA-Seq data were first normalized as transcripts per kilobase million (TPM).
Transcripts with an average above 0 TPM in at least one developmental stage/tissue and with a coefficient of variation of log2 TPM > 0. 05 among all developmental stages were retained for further analysis, resulting in 27,587 genes. Differentially expressed genes (DEGs) were determined using DESeq2 package (v1. 22. 2)  [50] , in which genes with log2 FC >0 or <0 and p-adjusted value of <0. 05 for multiple testing with the Benjamini-Hochberg procedure, which controls false discovery rate (FDR), were considered as differentially expressed. Gene annotation and GO terms were assigned according to the Araport11 annotation version of the Arabidopsis genome. Over representation analysis (ORA) using GO enrichment terms was performed using the Clusterprofiler  [51]  package in RStudio (version 1. 3.
1073) by applying an adjusted p-value cut-off of <0. 5 obtained from the Bonferroni procedure. Data mining for transcriptomics of Col-0 seeds from fresh seeds to seed imbibition for 48 h was obtained from the Gene Expression Omnibus (GEO, GSE94459) as a publicly available dataset  [52]. Raw data were mapped/quantified using Salmon with the same parameters described above. ImpulseDE2 algorithm [53], available in R, was used to unravel differentially expressed genes along the germination phase using the 'case-only' differential expression analysis and an adjusted p-value cutoff of 1%.
thaliana reference genome index sequence version 10 (TAIR10) using Methylpy software  [54]. Chloroplast genomic sequence from A. thaliana was used as an unmethylated control. After mapping, deduplication of sequences was performed and cytosine methylation sites were determined and quantified using Methylpy. Each context of methylation was considered independently: CG, CHG, or CHH. Putative differentially-methylated regions (DMRs) were identified from merged differentially methylated sites (DMS) using the Methylpy 'DMRfind' method.