dataesr/scientific-paragraphs-categorization huggingface dataset:
In Figure  6 , we distinguish results from hybrid and selfcorrectors, presented with respectively 100x coverage of short reads and 100x coverage of long reads, and only 100x coverage of long reads. Results on more shallow coverage (10x) and impact of simulation parameters on corrected reads sizes are presented in Supplementary Material Sections S7 and S8. Overall, hybrid correctors are less impacted by isoform collapsing than selfcorrectors. LoRDEC shows the best capacity to preserve isoforms in presence of alternatively skipped exons. Thus, regardless of the abundance of inclusion reads in the dataset to be corrected, 99% of reads from inclusion are corrected to inclusion form for an exon size 10, and 100% of reads from inclusion are corrected to inclusion form for exon sizes of 50 and 100.
Error-correction affects splice site detection The identification of splice sites from RNA-seq data is an important but challenging task  Kaisers et al. [2017]. When mapping reads to a (possibly annotated) reference genome, mapping algorithms typically guide spliced alignments using either a custom scoring function that takes into account common splices sites patterns (e. g. GT-AG), and/or a database of known junctions. With long reads, the high error rate makes precise splice site detection even more challenging, as indels (see Section 2. 5) confuse aligners, shifting predicted spliced alignments away from the true splice sites. In this section, we evaluate how well splice sites are detected before and after error-correction. Figure  7  shows the number of correctly and incorrectly mapped splice sites for the raw and corrected reads, as computed by AlignQC.
The same layout applies to the right plot, where self-correctors are presented. measures presented in the previous analysis (made with GMAP). We thus further mapped with Minimap2 (version 2. 14-r883 with parameters -ax splice) the following read datasets: i) raw reads; ii) LoRDEC, the corrector with the least proportion of mapped reads, but which preserves well the transcriptome diversity; iii) LoRMA(s), the tool with the highest ratio of mapped reads and number of reads, but with the lowest mean reads length, number of detected genes, and number of bases; iv) Proovread(t), the method with the lowest error rate, and highest rate of correctly mapped  1 NaS was ran in batches on a different system (TGCC cluster) than other tools; total running time was estimated based on subset of batches. R 2 = 0.
On the other hand, GMAP was considerably better at detecting genes. The error rate of the reads mapped by both tools are very similar, with GMAP reporting slightly more mismatches, and Minimap2 reporting slightly more deletions and insertions. However, these differences are mainly concentrated on the raw reads dataset, which is the less accurate. The data shown and discussed in this section suggest that the main takeaway points from the results presented in the previous sections should not change when GMAP is replaced by Minimap2. The full report of the analysis of both mappers, containing further details, can be found in the support page of our method: https: //leoisl. gitlab. io/LR_EC_analyser_support/. 2. 12 Analysing human Nanopore direct RNA-sequencing data We further analysed a human Nanopore direct RNA-sequencing dataset from the Nanopore-WGS-Consortium (see Section 2.
2 for details). Since there was no corresponding Illumina sequencing for this dataset, we were able to evaluate only the non-hybrid error correction tools. Moreover, although LoRMA could be successfully executed, AlignQC could not process its output so we removed LoRMA from the evaluation. Table  7  presents some main statistics of non-hybrid error correction tools on the aforementioned dataset. In the rest of this section, we highlight the major differences and similarities between cDNA and direct RNA datasets, keeping in mind that they were performed on two different species (human and mouse, respectively). In general, self-correctors discarded more reads on the direct RNA dataset than on the 1D cDNA dataset. Daccord(t) discarded the least number of reads (102k), while Canu and MECAT discarded a considerable amount of reads (361k and 670k, respectively), due to post-correction filtering.
Due to our choice of parameters, the shortest reads in Canu and MECAT outputs were of lengths 101 and 100 bases, respectively. However, the removal of shorter reads explains only a fraction of the read losses, as the raw dataset contains only ∼59k reads smaller than 101 bases. GMAP mapped a smaller percentage of raw reads in the direct RNA dataset (76% vs 83. 5% for the 1D cDNA dataset), but on the other hand, the mapping rate of error-corrected reads was generally higher. Notably, 97. 5% of the daccord reads (resp. 98. 2% for daccord(t)) were mapped, as opposed to 92. 5% (resp. 94%) in the 1D cDNA dataset. The mean length of the corrected reads when compared to the mean length of the raw reads was also in general higher in the direct RNA dataset, which translated into tools having a number of output bases more similar to the number of bases in the raw reads in this dataset, except for MECAT.
The error rate of the raw direct RNA reads was 14. 61%, 0. 89% higher than in the raw 1D cDNA reads. As expected, the error rates in all tools were also higher in the direct RNA dataset, leading to worse results. The largest difference is with pbdagcon(t), where the error rate after correction of direct RNA reads is 2. 55%  higher than on the 1D cDNA dataset. The distribution of errors in the direct RNA dataset was more balanced, with mismatches and deletions having almost the same representation (around 5. 85%), but insertions still being less represented (2. 87%). The correction behaviour of the tools is similar across both datasets: insertion is the best corrected type of error, followed by mismatches, with satisfactory results, and deletions, in which the methods overall did a poor job.
If Illumina reads are unavailable, then the best overall self correctors seem to be daccord(t), pbdagcon and pbdagcon(t). Moreover, trimming and splitting usually increase the mapping rate and the read accuracy, but can lead to loss of information that was present in the raw reads, complicating the correct identification of genes. Our analyses relied on a single mapping software (GMAP  Wu and Watanabe [2005] ) to align raw and error-corrected reads, as in previous benchmarks  Weirather et al. [2017] ,  Križanović et al. [2018]. However, we were also able to verify that Minimap2  Li [2018] , another widely used mapper, produces similar results than GMAP (see Section 2. 11), and thus the main messages of the analyses presented in this paper should not change by replacing GMAP by Minimap2. As a side note, AlignQC reports that raw reads contained 1% of chimeric reads, i.
In the evaluation of tools, we did not record the disk space used by each method, yet we note that it may be a critical factor for some tools (e. g. Canu) on larger datasets. We note also that genes that have low Illumina coverage are unlikely to be well corrected by hybrid correctors. Therefore our comparison does not take into account differences in coverage biases between Illumina and Nanopore data, which may benefit self-correctors. Finally, transcript and gene coverages are derived from the number of long reads aligning to a certain gene/transcript. This method enables to directly relate the results of error-correction to transcript/gene counts, but we note that in current RNA-seq analysis protocols, transcript/gene expression is still generally evaluated using short reads.
FUNDING This work was supported by the  French National Research Agency [ANR ASTER ,  ANR-16-CE23-0001 ]; the  INCEPTION  project  [PIA/ANR-16-CONV-0005 ]; and the  Brazilian Ministry of Science, Technology and Innovation (in portuguese, Ministério da Ciência, Tecnologia e Inovacão -MCTI)  through the  National Counsel of Technological and Scientific Development (in portuguese ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico -CNPq) , under the  Science Without Borders (in portuguese, Ciências Sem Fronteiras)  scholarship grant [process number  203362/2014-4  to LL].
Viable adoption of precision agriculture in large farms, demands sophisticated equipment and sensing systems (Fig. 4 ). The use of hyperspectral imaging and analysis is a key aspect for the use of plants as sensors of chemical change. In particular hyperspectral and non-visible wavelength imagery that can identify sick and healthy vegetation may have value in chemical investigations (this could include satellite imagery). Areas within a field in which "sick vegetation" can be observed might indicate the release of a chemical. Comparing images showing such features with previously-collected data could help narrow down or possibly confirm the timestamp of an incident (assuming images with appropriate temporal and other relevant metadata are available). Many parameters can be monitored to determine plant health, including moisture levels, leaf and organ morphology, and photosensitive pigments.
The most promising methods include thermography, chlorophyll fluorescence, and hyperspectral sensing. The variety of available sensor systems allow high-resolution imagery for crop stands or single plant organs, which can permit early detection and identification of plant Fig. 4 : "If Plants Could Talk". The methods and approaches being adopted in precision agriculture demonstrate how sensor and imaging data can be used to provide real-time indications of (bio)chemical change in vegetation. Data is collected from mobile sources (unmanned aerial and ground vehicles fitted with a range of imaging devices and sensors) as well as remote sources such as satellites (Top). Information collected can be accessed on smart phones and other mobile devices for immediate feedback (Top left). As observable phenotypic changes have an underlying biomolecular basis (Bottom right), it may be possible to correlate spectral and visual signatures with specific types of plant stress (for example: nutrient deficiency, pathogens, diseases, or exposure to specific types of chemicals).
Remote sensing of terrestrial ecosystems Dr. Greg McCarty of the United States Department of Agriculture (USDA) -Agriculture Research Service (ARS) Hydrology and Remote Sensing Laboratory provided workshop participants with an overview of satellite imaging capabilities for environmental monitoring. Advances in space-based sensor technologies are now capable of producing datasets with high spectral, spatial, and temporal resolution, overcoming trade-offs between these domains of resolution that hampered older technologies. The older systems allowed data to be acquired at high spectral resolution with low spatial and temporal resolution; this limited the ability to acquire detailed phenology of an ecosystem and to resolve changes in leaf biochemistry in both space and time. Mature satellite systems such as those provided by the Landsat programme [73] are now being linked by cross-calibration to the Sentinel programme  [74]  to provide validated image datasets with approximately 3-day return frequency.
These advances will better enable data-intensive technologies such as precision agriculture. Detecting phenotypical changes in crops in fields requires availability of data collected at high spatial and temporal frequency, with additional detection of nutrient status requiring high spectral resolution. Advances in data management and processing are required to realize fully the potential of data from advanced Earth monitoring systems. Cloud computing services are vital to this effort: Google Earth Engine (GEE)  [79]  is an excellent example of this rapid advance. Within GEE, various earth monitoring datasets in a consistent geospatial format are available in a multi-petabyte catalogue. Development and implementation of user derived algorithms occur within the GEE environment and algorithm output is computed in real-time using cloud-based processors.
Another application of eNose technology included in the presentation was the diagnosis of medical conditions from breath samples  [103, 104]. Early detection and diagnosis of gastric cancer has been demonstrated using devices comprising a set of nanoparticle gas sensors  [105, 106] , a sampler device and data acquisition system. The system has been used in a clinical study, where patients were evaluated by medical tests (biopsy and endoscopy) before and after using the eNose to acquire a set of breath samples  [107]. Samples were collected from gastric cancer patients, with patients having other gastric problems (e. g. gastritis and ulcers) constituting the control group. The eNose method demonstrated a classification rate of 94 %. To validate the results, another set of breath measurements of gastric cancer patients and controls were acquired by Tenax ® TA tube pre-concentrators and analysed by gas chromatography-mass spectrometry (GC-MS).
The methodology promises to be useful for implementation in clinical settings. Detection limits for eNose sensors appear to be very low under certain conditions (measuring chemical species in exhaled breath). How reliable the tools are for chemical recognition depends on the datasets and algorithms used in the analysis. While an eNose is capable of detecting mixtures of exhaled biomarkers (or a specific gas), it does not recognize molecular structure (instead, it recognizes patterns of signal across the sensors that make up the eNose which correlate to specific chemical species). To use an eNose in this way, requires training of the algorithm that performs the pattern recognition. For disease detection from breath.
Monitoring networks tracking biogeochemical changes in coastal and maritime environments An example of an international monitoring system was provided to the workshop by Dr. Andrés Arias from the Instituto Argentino de Oceanografia (Argentina). Dr. Arias discussed the Global Argentine Basin Array  [158] , which serves as one of the stations of the Ocean Observations Initiative (OOI) [159] (Fig. 7 ). Various scientific networks are currently working in Argentine urban, coastal and maritime environments, performing. The stations comprise an integrated system of platforms and sensors that collect physical, chemical, geological and biological information about the ocean. Data is collected continuously from each station and made available to scientists involved with the initiative. Large-scale monitoring networks such as the OOI offer examples of how data can be collected on an international scale and serve as a means to facilitate scientific collaboration.
In regard to applications Fig. 8 : CFD predictions for chlorine concentration (ppm) from an intentional release from a two ton container in the Jack Rabbit Chlorine Release Experiment  [195]. Initial release (Upper left). Container detail (Upper right). High concentration chlorine cloud (Bottom left). Concentration profiles at selected time points (Bottom right). The simulation was performed using the ANSYS ® 18 CFD software package, Ansys Inc. , Canonsburg, PA, USA  [189]. Brought to you by | University of Waterloo Authenticated Download Date | 10/8/18 11:40 AM relevant to the Convention, an example of dispersion modelling related to events in the Syrian Arab Republic, using open source information (including weather reports and satellite imagery) to inform the model, was recently published  [190].
Acknowledgements: The SAB expresses its gratitude to  Ambassador Ahmet Üzümcü , the Director-General of the OPCW from July 2010 to July 2018, for his support for making the output of the Board available to the States Parties of the Convention to aid their decision making. The SAB also wishes to thank the workshop attendees for their valuable contributions and insightful discussions that helped inform the SAB 's report:  Professor Cristhian Manuel Durán Acevedo  ( Universidad De Pamplona, Colombia ),  Dr. Andrés Arias  ( Instututo Argentino de Oceanografia, Argentina ),  Dr. Jim Birch  ( Monterey Bay Aquarium Research Institute, United States of America ),  Dr. Oscar Björnham  [ Swedish Defence Research Agency (FOI) ,  Umeå, Sweden ],  Dr. Paulo Cabral  ( CBRN Institute, Brazil ),  Professor Luiz Davidovich  ( ABC, Brazil ),  Dr.
The equivalent black carbon (BC e ) mass concentrations, M BCe , were obtained from the MAAP aerosol absorptivity measurements based on ATTO-specific mass absorption coefficients (MAC) as discussed in  Saturno et al. (2017a). The ceilometer has been operated at the ATTO site since December 2014. It has been installed with an inclination angle of 15 • to minimize direct sunlight. Frequent comparisons of the sizing instruments have been conducted at the ATTO site to ensure comparability of the individual data sets. Figure  S3  presents the results of such a comparison for the instruments OPS (0. 3-10 µm), SMPS (0. 01-0. 43 µm), UHSAS (0. 06-1 µm), and WIBS (1. 5-10 µm). The good agreement of the size distributions shows that the results of all instruments are consistent. The ceilometer data was processed according to  Heese et al.
Satellite data analysis The satellite data products used in this study were obtained from the Giovanni web-based application by the Goddard Earth Sciences Data and Innovation web interface (http:// giovanni. gsfc. nasa. gov/, last access: 4 July 2017)  (Acker and Leptoukh, 2007). The following satellite products were used: (i) aerosol optical depth (AOD) at a wavelength of 550 nm from the moderate resolution imaging spectroradiometers (MODIS) on the satellites Terra and Aqua (combined dark target deep blue AOD products MOD08_D3_V6 and MYD08_D3_V6), (ii) cloud top temperature data from the atmospheric infrared sounder (AIRS) instruments on board of Terra and Aqua (AIRX3STD_v006 product), (iii) precipitation data from the tropical rainfall measuring mission (TRMM) mission (TRMM_3B42_Daily_v7 product), and (iv) CO total column data from the AIRS instruments on board of Terra and Aqua (AIRS3STD_v006 product).
Time series of the aerosol coarse mode and related parameters The overview Fig. 2  shows coarse mode aerosol data from almost two and a half years (February 2014 until June 2016) 3. It combines this data with selected meteorological and aerosol time series and, thus, puts the coarse mode trends in the context of the overall atmospheric seasonality at the ATTO site. Figure  2a  displays the daily frequency of occurrence of the individual BT clusters, f BT,cluster (see Fig. S4  for BT cluster overview). This shows the clear prevalence of northeasterly (NE) and east-northeasterly (ENE) trajectories during the wet season (i. e. , February-April), followed by a rather sudden shift to easterly (E) and east-southeasterly (ESE) trajectories towards the end of the wet season (i. e. , May). During the subsequent transition period (i.
The trend was only interrupted by annually reoccurring and well defined peaks, which mostly occurred in the period December to April as detectable in Fig. 2b  and e. The observed peaks in M 1-10 , which Fig. 2  highlights with gray vertical shading, represent the frequent intrusion of African LRT dust and combustion aerosol plumes (called here LRT episodes). The identification of major LRT episodes in the ATTO data was rather straightforward as they are associated with pronounced increases in the aerosol parameters, M 1-10 and M BCe. However, the detection of minor and/or "diluted" LRT episodes turned out to be difficult in certain cases, due to the variable coarse mode background, which is mostly driven by PBAP cycling. The criteria that were used for the definition of LRT episodes are outlined in Sect.
Based on the time series in Fig. 2 , we calculated mean seasonal cycles of selected aerosol, trace gas, and meteorological parameters, which were combined into Fig. 4. Meteorologically, Fig. 4a  shows the seasonal BT patterns. The annual oscillation between wet vs. dry season BTs stands out clearly. More specifically, the BT patterns illustrate the timing of changes in the dominant wind direction, such as its swing from NH to SH and back upon latitudinal passage of the ITCZ. Further details related to Fig. 4a  can be found elsewhere  (Pöhlker et al. , 2018). Figure  4b  shows the seasonality of two different precipitation parameters: P BT and the TRMM-derived precipitation rate, P TRMM , in the ROI ATTO (see Fig. S4 ). The P BT data represents a measure for the aerosol scavenging in the transported air masses and, thus, provides important information about (LRT) aerosol removal en route.
In contrast, the P TRMM data represents a regional characterization of the precipitation in an area around ATTO. The seasonality in P TRMM shows a rather broad maximum spanning most of the wet season (i. e. , February-May), whereas the seasonal trends in P BT show a comparatively narrow and well pronounced maximum in April and May. Figure  4c  shows the seasonality of the pollution tracers M BCe and carbon monoxide mole fraction, c CO , reflecting the pronounced biomass burning seasonality in South America as well as Africa as a major source of LRT pollution. The cleanest episodes in terms of pollution aerosols (see M BCe ) occurred between April and May, which could be explained by the maximum in aerosol scavenging (see highest P BT values in April and May) (see  Pöhlker et al. , 2017). In parallel, a minimum in biomass burning occurrence is typically found during this period (i.
Selected examples of M 1-10 vs. M BCe scatter plots can be found in Fig. S9  with linear bivariate regression fits (with offset), in which the slopes represent the BC fraction relative to the dust aerosol mass. Based on the data shown in Fig. S9 , slopes of 0. 009 ± 0. 001 and 0. 016 ± 0. 015 (mean slope ± 1 SD) were found. Generally, our analysis showed that the relationship between M 1-10 and M BCe is characterized by a comparatively high variability due to the spatiotemporal heterogeneity of the LRT plumes depending on their source regions and transport paths from Africa to South America. Along these lines and in order to analyze the temporal trends of the smoke fraction in the arriving plumes, we conducted a systematic linear regression analysis based on the entire dataset analyzed here (i.
e. , the LRT episode 2015_7 is discussed in detail there). (iv) Intensive aerosol sampling targeting LRT conditions in February and March 2014 was conducted and the corresponding results using microspectroscopic techniques are currently prepared for a follow-up manuscript on the morphology, mixing state, and composition of the LRT aerosol population. For comparison with the conditions in 2014 as shown in Fig. 9 , an analogous overview for the LRT episodes in 2015, including five LRT episodes (2015_1 to 2015_5, see Table  1 ) can be found in Fig. S10. Generally, Figs. 9 and S10 -which display the online aerosol data at hourly resolution in contrast to daily resolution in Fig. 2  -clearly show the characteristic conditions of efficient dust transport towards ATTO as discussed in detail in Sect.
Acknowledgements. This work has been supported by the  Max Planck Society (MPG). For the operation of the ATTO site, we acknowledge the support by the  German Federal Ministry of Education and Research  ( BMBF  contract  01LB1001A ) and the  Brazilian Ministério da Ciência, Tecnologia e Inovação (MCTI/FINEP  contract  01. 11. 01248. 00 )as well as the  Amazon State University (UEA) ,  FAPEAM, LBA/INPA  and  SDS/CEUC/RDS-Uatumã. This paper contains results of research conducted under the Technical/Scientific Cooperation Agreement between the  National Institute for Amazonian Research , the  State University of Amazonas , and the  Max-Planck-Gesellschaft  e. V. ; the opinions expressed are entirely the responsibility of the authors and not of the participating institutions. We highly acknowledge the support by the  Instituto Nacional de Pesquisas da Amazônia (INPA).