dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , high SML coverage). Furthermore, SML coverage was estimated for upper wind speed limits of 8, 10, and 13 m s -1. These values were reported earlier as maximum surface wind speed for SML formation  2, 24, 25. Data on monthly mean surface wind speeds (January 1946-June 2016) were obtained from NCEP/NCAR reanalysis project (http://www. esrl. noaa. gov) and averaged for each month (Supplementary Fig. 10 ). Then, the monthly data were refined from 2. 5°× 2. 5°resolution to 1°× 1°resolution by linear Fig. 5  Seasonal estimates on the organic aerosol mass contribution in the marine boundary layer from oxidation of photochemically produced VOCs for an SML wind speed limit of 13 m s -1. For wind speed limits of 8 and 10 m s -1 , see Supplementary Figs  5  and 6. Here, only OH oxidation of unsaturated volatile organic compounds was taken into account, assuming similar secondary organic aerosol yields as given by Tsimpidi et al.
Values for VOC lab can readily be obtained from photochemical experiments under controlled conditions, using common VOC measurement techniques such as proton transfer reaction mass spectrometry (PTR-MS). In this study we used such data from previous work of George and co-workers  10, 11, 14. For example, to estimate global emissions of isoprene from interfacial photochemistry, we used previously reported values for VOC lab from marine SML and biofilm samples  10, 11, 14  , which are in the range of 3. 71-6. 19 × 10 7 molecules mW -1 s -1. Estimating secondary organic aerosol formation. As depicted in Supplementary Fig. 7 , the VOC module described above is coupled to an SOA module, which we utilized to retrieve estimates on potential SOA formation from photochemically produced and emitted VOCs.
Here we applied established POA emission schemes, which infer POA fluxes from sea surface temperature, chlorophyll-a concentration, and surface wind speed  18, 39. Monthly mean data on sea surface temperature and chlorophyll-a concentrations were obtained from MODIS ocean products (https://modis. gsfc. nasa. gov/). POA emissions were instantaneously diluted into the marine boundary layer (MBL). Monthly mean MBL heights were obtained from the NOAA-CIRES 20th Century Reanalysis project (https://www. esrl. noaa. gov/psd/data/20thC_Rean/) (Supplementary Fig. 12 ). As a simplified example, Supplementary Fig. 13  shows the result of a simulation of VOC formation and oxidation, as well as SOA particle formation for one grid cell. In this case, the following parameters were used: a POA mass concentration of 0.
Acknowledgements This study was supported by the  European Research Council  under the European Union's  Framework Program  ( FP/2007-2013)/ERC Grant Agreement 290852-AIRSEA. This  project has also received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 730997  ( EUROCHAMP2020 ) and 690958 (MARSU). We thank  S. Madronich  and the  NCAR  for providing UV irradiation climatologies from the TUV model. We acknowledge the  Physical Sciences Division of NOAA/ESRL  for providing data on monthly wind speeds and MBL heights.
Based on the publications regarding new or recent measurement systems for the tokamak plasma experiments, it can be found that the monitoring and quality validation of input signals for the computation stage is done in different, often simple, ways. In the paper is described the unique approach to implement the novel evaluation and data quality monitoring (EDQM) model for use in various measurement systems. The adaptation of the model is made for the GEM-based soft X-ray measurement system FPGA-based. The EDQM elements has been connected to the base firmware using PCI-E DMA real-time data streaming with minimal modification. As additional storage, on-board DDR3 memory has been used. Description of implemented elements is provided, along with designed data processing tools and advanced simulation environment based on Questa software.
The methodology is often omitted, focusing mostly on data processing. Many of modern, advanced measurement systems The Author of the paper received funding under the financing doctoral scholarship program from National Science Centre, number 2016/20/T/ST7/00203. A. Wojenski is with Warsaw University of Technology, Institute of Electronics Systems, Nowowiejska 15/19, 00-665 Warsaw, Poland (e-mail: A. Wojenski@elka. pw. edu. pl). works in real-time mode. This means, that any kind of the additional diagnostic needs to be: • Suitable for work in real-time mode, • Optimally designed. The paper focus on the soft X-ray measurement system (GT-SXR), designed for work in real time mode with the feedback loop to control system of the tokamak. Upon results from the GT-SXR unit, the tokamaks' actuators section can undertake necessary actions to keep the plasma.
T Due to the proximity of the tokamak vessel, the analog stage of the system is designed to be rad-hard. The preprocessing stage is done in the FPGAs, where the raw signal is selected and streamed through PCI-E to the high-performance server unit. The system post-processes the data in two modes: offline and online mode. The energy and topology histograms with time resolution < 1ms are produced (10ms for real-time mode). More details regarding system construction can be found in  [8] -  [14]. Since the data will be read by tokamaks' control system, it is especially important to provide high quality output products, based on correct input signals. For that purpose, in the following sections the Authors describes the adaptation of the real-time evaluation and data quality model for the implementation in the FPGAs of the GT-SXR measurement system.
OVERVIEW Since the data quality monitoring discussion regarding the tokamak plasma experiments cannot be found in many publications, Authors of the paper proposed the model approach for input data evaluation and validation from the detector analog signal stage for the high-performance measurement systems based on the FPGAs. The model is divided into two stages: • FPGA raw signal selection and preprocessing stage, • Embedded high-performance real-time data processing algorithms. The model is based on critical analysis of the most common elements used in FPGA and PC data processing firmware. The paper focuses on the FPGA stage of the system. The model of efficient data quality monitoring indicates the need of the proper input signals analysis and dynamic filtering of trigger. The signals can be marked as incorrect based on the Signal Classification (SC) units.
The blocks consist description of the already known malformed signal shapes that can be expected at any time from the detector. In case if the signal is marked by SC, further FPGA stages can either allow to process it or discard it. Since probably many of signal malformation isn't know yet (they aren't many publications regarding GEM signal output related to hard environmental conditions), the model offers the implementation of data recording unittechnical sub-diagnostic, based on DDR3 memory, for further offline analysis. In order to have better knowledge on the topological aspects of signal malformation, there is also proposed a statistic sub-diagnostic unit, providing high time resolution histograms of local occurrences of SC flags or trigger events. Main components of the FPGA evaluation and data quality monitoring (EDQM) sub-systems are: • Signal classification (SC) units, • Technical sub-diagnostic, • Statistical sub-diagnostic.
The external trigger information is based on MLVDS links, connected to FPGAs. In order to provide the best timing-fit between board, special algorithms has been developed for synchronization purposes. There are two synchronization stages: 1. Training of FPGA IDELAYE2 components to detect the center of the data window for MLVDS triggers. Time resolution is several ps. The system is based on pseudo-random patterns, based on the LSFR mechanism. 2. Flip-flops delay compensation in the system structure. Based on signal feedback pulse, the software algorithms detect the delay (in term of clock cycles) of the pulse among different FPGA boards. Based on gathered information, the algorithms then tune all other signals to match the slowest (most delayed) FPGA backplane. The details about the advanced trigger synchronization, which is also an important part of the EDQM, can be found in  [20].
The raw signal is then recorded to the onboard DDR3 memory, for offline data analysis. The memory can store up to 100 000 events. The DDR3 data stream is completely independent from the fast PCI-E DMA main data stream. Another sub-diagnostic implemented is the statistical subdiagnostic. This is a very novel approach regarding the plasma measurements in term of high data quality. The sub-diagnostic is counting local EDQM flags activations, together with counting the local triggers that occurred. The analysis is performed on every channel. Additionality, the sub-diagnostic is designed for further use in a real-time mode, therefore it is possible to produce statistical histograms with 10 ms resolution or even less. Since there are no publications related this kind of analysis, the sub-diagnostic model will also be verified in scope of usefulness in term of the output histogram products.
This is the first implementation of such advanced and novel EDQM diagnostic for the GT-SXR measurement system. The complete integration of the described features is presented in Fig. 5. As stated before, the integration is based on very few elements, preserving the structure of the base firmware. Since the whole concept is based on the reconfigurable signal classificators SC, with additional use of 2 sub-diagnostic, it is possible to design a complete FPGA firmware with 2 main EDQM functions: • Input data quality evaluation and filtering based on reconfigurable signal classification models • Analysis of detector and system behavior in scope of continuous development of newly discovered signal classes based on registered sub-diagnostic data The firmware with adapted EDQM model provides following data output types: • PCI-E stream in DMA mode, working in real-time.
TOOLS As mentioned at the beginning, the EDQM analysis of plasma measurements, especially in term of GEM detector analog signals, are rather poorly explored. The implemented firmware allows to perform standard measurements with additional EDQM discrimination and possibility of offline analysis of new events in form of raw data recordings with high statistics. In order to analyze the EDQM output of the system, several additional tools has been developed. The data processing engine for energy and topology spectra computation is done in Matlab software. The system can output raw data in form of binary filesone is the main real-time PCI-E DMA stream, second is an offline DDR3 readout. The first set of tools, allows to filter the marked PCI-E data by various flags EDQM in offline mode and compute the output histograms.
This allows to check: • The statistics of occurrence of different flags during the event registration (this also depends of EDQM modemark but do not reject) • The influence on spectra by rejecting different EDQM flags from the output products analysis This allows to provide more precise image on how rejecting the certain type of data can influence the output algorithms products. The tools also perform EDQM conversion of measurement files from binary to MAT data format. The second, important mode allows to analyze the output from the 2 sub-diagnostics. The DQMSignalViewer GUI tools has been developed on the base of the base version of SignalViewer for visualization of the measurement data in scope of EDQM. The tool allows to visualize the analog signal registered from GEM, recorded in the DDR3 memory, based on active EDQM flags (with selection in the software).
This mode is very important in term of GEM detector behavior during operation either in laboratory or on the tokamak site. The fast, hard to detect EDQM events can be easily visualized and analyzed in scope of malfunction of the system or discovering a new type of events classesalso including new type of eventstopology events. In addition, the raw data mode (technical sub-diagnostic) allows to observe the specified signals form along with their influence on the other measurement channels. The screenshot from the DQMSignalViewer is presented in Fig. 6. Other useful mode is a comparison of the histograms. Due to availability of the conversion tools BIN/MAT it is possible to produce the spectra from: base dataflow and EDQM mode switched on, with the same dataset. The software outputs 2 separate spectra plots (without and with EDQM), one plot when both spectra overlap and a plot with indicated percent in term of difference in computation of energy and spectra with EDQM enabled and disabled (both output products).
V. ADVANCED EDQM FPGA VERIFICATION STAGE As presented in Fig. 5 , the complete FPGA firmware including EDQM is very expanded. Therefore, in order to verify the correct integrity of the EDQM and base data path, as well as correctness of implemented SC models, it is necessary to perform complex FPGA tests. The verification of implemented firmware was done upon designing an extended and complex testbench module working with Questa simulation software. The testbench include the whole system environment, as implemented in real hardware, especially: • The testbench simulates both the base PCI-E DMA real-time data stream and separate offline DDR3 data from technical and statistical sub-diagnostics. The designed testbench was highly optimized, in order to process as many data as possible in a reasonable time, resulting in simulations based on high statistics.
In current version, around 10 000 events are processed within 1 hour of simulation. The simulation is based on the input data in form of raw signals like from the GEM detector. For that reason, the ADC-simulation block has been designed. It allows to source the BIN files from the real experiments done by the system with base FPGA firmware. Then, in the simulation the component produces the same signals and checks the behavior of the EDQM and base system implementation in FPGA. The components can also work with custom data in term of model signals. The output from the simulation are the binary files, one representing PCI-E stream, second one representing DDR3 memory readout with EDQM technical and statistical data. The format is the same as from the real working system. Data can be directly sourced by Matlab software tools (described in section IV) to produce output energy and topology spectra.
The basic data flow of the simulation engine is presented in Fig. 7. The described functionality is unique for the verification of the measurement behavior when working with real sources of radiation. The significal advantage is that the simulations are based on high events statistics, more then 10 000 events. Since it is possible to run the histogram algorithms based on the output of the simulation using real data, it is especially valuable in scope of testing implementation of the new signal classification models. Also, the influence on the output spectra can be verified, before implementing and updating the firmware in the real measurement system. The model of the EDQM technique in scope of FPGA units has been adapted and implemented for the GT-SXR measurement system. The implementation is designed to work with 2 FPGA boards including 128 measurement channels.
For the beginning, 4 signal classificators has been implemented: overflow, underflow, multievent, long-event. The EDQM model adaptation is complete, including the PCI-E stream monitoring, technical sub-diagnostic and statistical sub-diagnostic. For the sub-diagnostics, the DDR3 available on the FPGA boards has been used, resulting in space to store up to 100 000 raw data events / statistical histograms. Set of Matlab tools has been designed and adapted. One of the most important are converters from binary to Matlab files, for connection with Questa simulation software. Also, the algorithms in Matlab has been adapted to work with EDQM data format. The new DQMSignalViewer has been designed (based on previous version SignalViewer), providing functionality to easily analyze the output of the EDQM algorithms in scope of new type of events and correctness of the behavior of the system.
The complete set of the verification stage has been also designed. The testbench in Questa simulation software replicates the whole system hardware with necessary links (e. g. between FPGAs) and components. The algorithms has been optimized to allow the simulations of large number of events. The simulation engine produces the binary data in the same format as real system with additional data from the DDR3 memory as separate file. Therefore, the simulations are directly linked with spectra computation. This mode is unique and can be used e. g. for verification of the correctness of the implemented signal classification model and their influence on the output products from the system. As for the next steps, it is planned to perform EDQM FPGA firmware verification using the real physical data registered by the base firmware of GT-SXR measurement system.
Plasmid prediction may be of great interest when studying bacteria such as Enterobacteriaceae. Indeed many resistance and virulence genes are located on such replicons and can have major impact in terms of pathogenicity and spreading capacities. Beyond strains outbreak, plasmids outbreaks have been reported especially for some extended-spectrum beta-lactamase or carbapenemase producing Enterobacteriaceae. Several tools are now available to explore the "plasmidome" from whole-genome sequence data, with many interesting and various approaches. However recent benchmarks have highlighted that none of them succeed to combine high sensitivity and specificity. With this in mind we developed PlaScope, a targeted approach to recover plasmidic sequences in Escherichia coli. Based on Centrifuge, a metagenomic classifier, and a custom database containing complete sequences of chromosomes and plasmids from various curated databases, it performs a classification of contigs from an assembly according to their.
predicted location. Compared to other plasmid classifiers, Plasflow and cBar, it achieves better recall (0. 87), specificity (0. 99), precision (0. 96) and accuracy (0. 98) on a dataset of 70 genomes containing plasmids. Finally we tested our method on a dataset of E. coli strains exhibiting an elevated rate of extended-spectrum beta-lactamase coding gene chromosomal integration, and we were able to identify 20/21 of these events. Moreover virulence genes and operons predicted locations were also in agreement with the literature. Similar approaches could also be developed for other well-characterized bacteria such as Klebsiella pneumoniae.
Introduction Recently, several studies have evaluated the effectiveness of in silico plasmid prediction tools  (1, 2). In fact, many bioinformatics methods are now available to detect such mobile elements, with different approaches like read coverage analysis, k-mer based classification, replicon detection; some being fully automatized (3-7), others not  (8). Some of them achieve high sensitivity: for example, PlasmidSPAdes and cBar enable plasmid recall of 0. 82 and 0. 76 on a dataset of 42 genomes, respectively  (1). On the other side some tools display very high precision, as PlasmidFinder which reaches 100%  (1). Unfortunately, neither of them succeeds in finding a good trade-off between sensitivity and specificity, and thus users need to combine different methods to get correct predictions.
Concomitantly more and more sequences are available in public databases, with various level of completeness from large sets of contigs to fully circularized genomes and plasmids. Some people have made an effort to curate these databases and proposed high quality dataset. Carattoli et al. and Orlek et al. , for example, have published interesting and exhaustive plasmid datasets for Enterobacteriaceae  (4, 9). With this in mind, we propose here a method, called PlaScope, to assess the plasmidome of genome assemblies. We took advantage of available genomic data to create a custom database of plasmids and chromosomes, which is used as input of the Centrifuge software, a tool originally developed as a metagenomics classifier  (10). We compared it with others plasmid classifiers, cBar and Plasflow, and showed that with our specific knowledge-based approach we were able to recover nearly all plasmids of various Escherichia coli strains without compromising on specificity.
Workflow description PlaScope workflow is illustrated in Fig. 1. First, users have to provide paired end fastq files. Then assembly is run using SPAdes 3. 10. 1  (11)  with the "careful" option to obtain contigs. Subsequently, Centrifuge  (10)  predicts the location of these contigs thanks to a custom database and sorts sequences into 3 classes: plasmid, chromosome and unclassified. The latter includes sequences shared by both categories (i. e. plasmid and chromosome) and which are therefore indistinguishable, and sequences without any hit. Finally results are sorted based on those three classes and extracted using awk. The complete workflow is available through a unique bash script called PlaScope. sh on github (https://github. com/GuilhemRoyer/PlaScope).
Centrifuge custom database construction We gathered all the complete genome sequences (chromosomes and plasmids) of E. coli from the NCBI on 10/01/2018. We also added the plasmid sequences that were used to create PlasmidFinder database (4) and those proposed by Orlek et al. (9). Finally, we added a very specific dataset containing E. coli plasmids involved in antibiotic resistance (www. agence-nationalerecherche. fr/en/anr-funded-project/) (https://www. ebi. ac. uk/ena/data/view/PRJEB24625)  (  Then, we pooled separately plasmid and chromosome sequences to create a custom database for Centrifuge 1. 0. 3  (10)  with an artificial taxonomy containing only three nodes: "chromosome", "plasmid", and "unclassified" (see README on https://github. com/GuilhemRoyer/PlaScope).
PlaScope classification method PlaScope classifies contigs as "chromosome", "plasmid" or "unclassified" with Centrifuge using our custom database (centrifuge -f --threads 2 -x custom_database -U example. fasta -k 1 --report-file summary. txt -S extendedresult. txt), with the option "k" set to 1 in order to get only one taxonomic assignment. Only contigs longer than 500 bp, with a Centrifuge hit longer than 100 bp and with a SPAdes contig coverage higher than 2 are classified as plasmid or chromosome-related.
Reference dataset for method evaluation To evaluate our tool, we searched for completely finished genomes of E. coli with Illumina reads available on the National Center for Biotechnology Information (NCBI) database. All corresponding chromosome and plasmid sequences and Illumina short reads were downloaded from the NCBI on 10/01/2018, and converted into fastq files with fastq-dump from sra-toolkit (fastq-dump --splitfiles). For evaluation purpose, these genomes were not included in the centrifuge custom database. The short reads were assembled with SPAdes 3. 10. 1  (11)  with standard parameter and "careful" option (spades. py --careful -t 8 -1 read_1. fastq. gz -2 read_2. fastq. gz -o output_directory). After assembly, 16S rapid identification was performed on fasta files using ident-16s  (12).
12 assemblies which did not contained Escherichia 16S or with multiple 16S from various organisms were excluded from the subsequent analyses. Finally, we kept 70 genomes containing 183 plasmids and 7 genomes with no plasmid according to the NCBI database (Supplementary table  2 ). We filtered the assemblies based on contigs length (> 500 bp) and SPAdes coverage (> or = 2). Then, each assembly was mapped against the corresponding complete chromosome and plasmid sequences from the NCBI database using Quast 4. 6 with standard parameters  (13). Contigs that did not aligned on any sequence (chromosome and plasmid) or aligned on less than 50% of their length were not considered, as well as contigs that aligned on both sequences.