dataesr/scientific-paragraphs-categorization huggingface dataset:
SV filtering and output Prior to output, a last filtering step is applied to SV candidates. It can happen that the same SV event is represented by several breakpoint pair candidates whose genomic coordinates are close (within 10 bp from each other) or identical, but with a different annotated type. In such a case, we only report the candidate with the largest cumulative support (barcode and short-read signal supports). LEVIATHAN finally outputs its final list of SVs in VCF format, reporting detailed information regarding the SV, such as its type, its beginning and end positions, its length, the number of barcodes shared between the two involved regions, and the number of discordant paired-reads involved.
Results We evaluated LEVIATHAN on simulated and real datasets. For the real data, we chose a dataset from the butterfly Heliconius numata, which is a non model organism and for which the discovery of structural polymorphism is of special interest. In this mimetic butterfly, it was shown that several large inversions in a 1. 3 Mb locus are associated to its wing color pattern and then play a crucial role in its population biology  [16]. As a non model organism, it does not have a chromosome-level reference genome, instead its 360 Mb draft genome is rather fragmented with a total of 16,950 contigs (N50: 474 kb). This genome, together with 10X genomics whole genome resequencing data of 12 individuals are available under the PRJNA676017 project ID on NCBI. In order to properly evaluate the results quality, we also simulated data with controlled sets of SVs.
To do so, we used LRSim  [17]  to ensure producing data that mimic the actual characteristics of Linked-Reads. We generated two datasets, one from the H. numata genome and one from the H. sapiens GrCh38 chromosome 1 (250 Mb), in order to compare the results between non-model and model organisms with similar genome sizes but different genome complexities. However, since LRSim had troubles simulating data on the highly fragmented assembly of H. numata, we had to filter out contigs shorter than 27,500 bp, resulting in a reference genome composed of 1,054 contigs (total size: 272 Mb and N50: 924 kb). Both datasets were simulated with a 30x coverage, and contained respec-tively 1,348 SVs and 1,048, ranging from 1,000 to 100,000 bp, including, deletions, duplications, and inversions as well as translocations for the H.
numata dataset. No insertions were simulated, since LEVIATHAN is not currently able to process them. Additionally, SNPs were also inserted, in order to further mimic real data. We compared LEVIATHAN against other state-of-the-art Linked-Reads SV calling tools. All tools were run using 8 threads. LEVIATHAN was run both in fast and sensitive mode. For fast mode, the 99-th percentiles of the distributions were chosen, while for sensitive mode, the 95-th percentiles were chosen. Other tools were run with default or recommended parameters. Moreover, both simulated and real data were used in our experiments.
Validation of the method with simulated data To precisely asses the accuracy of the different tools, we first tested them on the simulated datasets, where precise recall and precision could be computed. However, on both datasets, we could not manage to get GROC-SVs, LinkedSV and Valor to run properly. Indeed, GROC-SVs and LinkedSV crashed on both datasets, while Valor also crashed on the H. numata dataset, and ran for more that two days on the H. sapiens dataset. For these experiments, a SV was validated as a true positive if its breakpoints were correctly predicted, within 100 bp from a true SV reported in the simulation file. Since NAIBR only reported the breakpoints of the SVs it detected, we did not take into account the SV types, in order to allow a fair comparison. Statistics of the aforementioned tools on the two simulated datasets, along with their runtime and memory consumption are reported in Table  1.
Results on the H. sapiens dataset show that, in terms of resource consumption, LEVIATHAN was faster than Long Ranger and NAIBR, both in fast and sensitive mode, and also required less memory, especially compared to NAIBR. However, it is worth noting that Long Ranger does not accept BAM files as an input, and that its reported runtime thus also includes reads mapping. In terms of recall, Long Ranger performed slightly better than LEVIATHAN (fast), but reached a much lower precision. Compared to NAIBR, even in fast mode, LEVIATHAN reached both higher recall and higher precision. Moreover, it is also worth nothing that, in sensitive mode, LEVIATHAN reached up to 89% of recall, while still running faster than NAIBR, and consuming the same amount of memory as in fast mode. Precision was however lower in sensitive mode than it was in fast mode, which can be explained by the fact that, in sensitive mode, a larger number of SV candidates are considered, which can increase the false-positives rate.
Nonetheless, LEVIATHAN still reached more than 92% of precision in both modes, and largely outperformed both NAIBR and especially Long Ranger. On the H. numata dataset, Long Ranger could not be run since it does not allow the reference genome to contain more than 1,000 contigs. Once again, the two modes of LEVIATHAN required almost three times less memory than NAIBR, and LEVIATHAN (fast) also ran faster. In terms of recall and precision, both modes of LEVIATHAN outperformed NAIBR, whose recall was particularly low, failing identifying more than half of the SVs (recall of 40. 73%). In comparison, LEVIATHAN (fast) reached a recall of 63. 65%, while running faster than NAIBR, and LEVIATHAN (sensitive) reached a recall of 66. 54%, despite requiring a slightly larger processing time than NAIBR.
In terms of precision, both modes of LEVIATHAN once again outperformed NAIBR, reaching up to 97. 36% in fast mode. While LEVIATHAN still outperformed NAIBR on this dataset, its overall performance was not as good as on the H. sapiens dataset. Although this can be partially explained by the low quality of the reference genome we used, this still leaves us room for improvement, and future works should thus head in the direction of studying why such a proportion of SVs remained undetected.
Application to a real butterfly dataset We then applied the different tools on the real dataset of H. numata. On this dataset, no tool except LEVIATHAN managed to run. Indeed, Long Ranger could not be run since the reference genome was composed of more than 1,000 contigs, GROC-SVs and NAIBR were stopped after 15 days of computation, Valor crashed during processing, while LinkedSV also crashed and required more than 1 TB of RAM. In comparison, LEVIATHAN managed to run in less than two hours, only required 18 GB of RAM, and reported a total of 50,000 SVs. On this dataset, we were especially interested in finding inversions located in the supergene locus, the locus associated to the wing color patterns of H. numata. On the particular individual we studied in this experiment, LEVIATHAN did report the 430 Mb inversion at its expected breakpoints.
Discussion and conclusion We presented LEVIATHAN, a new SV calling tool for Linked-Reads data. LEVIATHAN makes use of a barcode index, which allows us to quickly and efficiently identify the region pairs than share a high number of barcodes, which represent potential SVs. Complementary classical short reads methods are then applied, in order to further analyze such pairs of regions, and determine whether they are actual SVs, as well as their types and breakpoints in such cases. Our experiments show that LEVIATHAN compares well to the state-of-the-art in terms of recall and precision, all the while being faster and consuming less memory. Moreover, LEVIATHAN also allows the analysis of non-model organisms on which other tools do not manage to run or require an unreasonable amount of resources.
As a result, LEVIATHAN thus tackles the main limitations of other Linked-Reads SV calling tools, and allows to better scale to large datasets, as well as to accurately analyze non-model organisms. We thus believe LEVIATHAN could allow the discovery of new sets of SVs on a broad range of such datasets. As future work, we are, first of all, planning to try LEVIATHAN on several other non-model datasets in order to detect new SVs. Moreover, we are also planning to integrate a local assembly feature, that would not only help us detect SV breakpoints more accurately, but that would also allow us to detect novel insertion variants. Other optimizations, such as analysis and comparison of SVs sharing common breakpoints, to better determine their types, and in-depth study of undetected SVs in simulated data are also currently being investigated.
Results reported by the different SV calling tools on the two simulated datasets. 1 Vs crashed after 6 hours on the H. sapiens dataset, and after 24 minutes on the H. numata dataset. 2LinkedSVcrashedafter19minutes on the H. sapiens dataset, and after 23 minutes on the H. numata dataset. 3Valor was killed after 2 days of computing on the H. sapiens dataset, and crashed upon start on the H. numata dataset. 4Long Ranger could not be run on the H. numata dataset, since it does not allow the reference genome to contain more than 1,000 contigs.
Results reported by the different SV calling tools on the two simulated datasets. 1 Vs crashed after 6 hours on the H. sapiens dataset, and after 24 minutes on the H. numata dataset. 2LinkedSVcrashedafter19minutes on the H. sapiens dataset, and after 23 minutes on the H. numata dataset. 3Valor was killed after 2 days of computing on the H. sapiens dataset, and crashed upon start on the H. numata dataset. 4Long Ranger could not be run on the H. numata dataset, since it does not allow the reference genome to contain more than 1,000 contigs. Dataset Tool Recall (%) Precision (%) Time Memory (MB) H. sapiens (chr 1) LEVIATHAN (fast) LEVIATHAN (sensitive) NAIBR Long Ranger GROC-SVs 1 LinkedSV 2 Valor 3 71. 18 89. 03 68. 13 72. 04 --- 97. 14 92. 38 78. 12 50. 23 11 h 29 min 11 min 37 min 44 min -> 6 hours -> 19 min -> 2 days 4,603 4,603 25,764 7,062 11,030 9,311 - LEVIATHAN (fast) 63.
Participants The OCTiMS study (NCT02907281) was a 36-month, prospective, multicenter, noninterventional study, which enrolled 397 participants (333 RRMS and 64 reference participants) across 28 sites in Australia, Europe and North America. The study was approved by respective institutional review boards and all participants provided written informed consent before enrollment. All data were captured and monitored by an independent clinical research organization (CRO) using electronic case record forms (eCRF). As pRNFL thinning may also occur as a result of MS-associated optic neuritis (MS-ON), patients were categorized into those with prior history of MS-ON and those without MS-ON (non-MS-ON) and assessed accordingly. For patients with a history of MS-ON in one eye, the eye with prior MS-ON was studied.
Optical coherence tomography OCT scans in all participants were performed on Spectralis OCT1 machines (Heidelberg Engineering, Heidelberg, Germany), software version 1. 6. 2. 0 or higher, with the eye tracking function enabled for best accuracy and the use of the baseline scan for alignment of subsequent scans on the same area of the retina. Data were collected from a peripapillary ring, a macular volume scan, the optic nerve head volume scan, and the papillomacular bundle volume scan (see supportive information Data S1 for more details on techniques used). All OCT scans underwent quality control (QC) assessment by the central reading center (Vienna Reading Center [VRC]). Figure  S1  gives an overview of the scan patterns and the evaluation fields for each pattern used in the analysis. OCT scans were performed at baseline and at every visit (1, 6 months, and every 6 months thereafter) for both groups.
All operators at participating sites were certified by the VRC for the use of the correct scanning protocol, correct pseudonymization of study-specific image labelling, and image transfer before patient inclusion according to standardized certification processes to obtain images for the above-described acquisitions. Automated segmentation was performed with the manufacturer's software (HEYEX version 1. 9. 10. 0, Viewing Module version 6. 0. 9. 0) and subsequently corrected by certified and trained graders of the VRC, supervised by a retina specialist, according to a predefined reading protocol. All graders were masked to the clinical status of the study participants (MS or healthy control group) and any other diagnostic information. Studies were performed and described in accordance to the QC criteria at the VRC, OSCAR-IB, and the Advised Protocol for OCT Study Terminology and Elements (APOSTEL) criteria.
Magnetic resonance imaging All study participants underwent MRI scanning (1. 5 or 3T) of the brain at screening and at yearly intervals. In the RRMS patient group, pulse-sequences included 3DT1 for brain volume measurement, proton density (PD)/T2 for new lesion count and volume, T1 for new hypointense lesion count and volume, and gadolinium-enhanced T1 sequences for lesion count and volume. Lesions were identified by expert radiology readers and quantified by trained technicians using MIPAV software with local ª 2021 Novartis Pharma AG. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association. thresholding. Brain volume measurements were performed using SIENA[X] (http://fsl. fmrib. ox. ac. uk). In the reference participant group, 3DT1 sequences for brain volume measurements (all MRI scans) and T2 lesions (screening scan only) were performed.
Statistical methodology Statistical analyses were performed using SAS (version 9. 4). All hypothesis tests were evaluated at a 0. 05 level of significance. All confidence intervals (CIs) were two sided at a 95% confidence level. Average pRNFL thickness was summarized by group and ON history at each visit, including change from baseline. A mixed-model repeated measures (MMRM) approach was applied to compare the change from baseline in average pRNFL thickness between groups, considering results from all available time points, and adjusting for age, gender, and baseline pRNFL thickness. The reference subjects were included as the reference group for both the MS-ON and non-MS-ON subgroups. The group difference in least squares (LS) mean of change from baseline in average pRNFL thickness (patients with MS reference subjects), which reflects the change from baseline for the MS group, after adjusting for the change in the reference group (e.
Expanded disability status scale The mean (SD) EDSS increased from baseline by 0. 15 (0. 86) (median = 0. 0 [0. 00-0. 50]) at Month 36 in the MS  N is the total number of participants in the analysis population, n is the number of participants with data available. The average pRNFL thickness was the mean of the circular scan subfields 1 to 8. Eye was nested within subject, and both were included as random effects. For participants with a history of ON, the unaffected eye was excluded. The reference participants were included as the reference group in all models. Participants who experienced an adverse event of ON during the study were re-categorized and included in the ON history subgroup. CI, confidence interval; mGCIPL, combined macular ganglion cell and inner plexiform layers; LS, least squares; MS, multiple sclerosis; MS-ON, MSassociated optic neuritis; pRNFL, peripapillary retinal nerve fiber layer.
It should be noted that since the completion of this work the performance and sensitivity of OCT imaging have increased substantially. At the time of the protocol initiation, the standard recommended macular acquisition on the Heidelberg Spectralis was 25 B scans. The current Heidelberg segmentation software requires at least 61 B scan density, highlighting the technical advances that are rapidly occurring in OCT acquisition and post-acquisition analyses. This will likely improve the accuracy of these measurements, as has happened in the MRI arena over the last three decades, and further supports the potential benefits of incorporating OCT measures in RRMS clinical trials in the future.
Conclusion The OCTiMS study prospectively assessed pRNFL thickness change using OCT in patients with MS in a multicenter setting. The OCT measurements were shown to be highly reproducible, and a significant difference in pRNFL thinning was observed between patients with MS from 18 months onwards compared with healthy controls. This was supported by similar results obtained for mGCIPL thickness, of which changes were found to correlate with brain volume loss. Based on Heidelberg Spectralis data acquired in this study, the effect size for the OCT measures (RNFL, GCIPL) was between small to medium in this overall stable RRMS population, and smaller than that found for MRI brain volume. Of note, the effect size for OCT measures was larger in patients in the early stages of the disease course and without any history of MS-ON.
Acknowledgments The authors wish to thank  Carolyn M. Ervin  for her substantial contribution in the data analyses, as well as  Mark Kirby ,  Aisling Towell , and  Marie-Catherine Mousseau  ( Novartis Ireland Ltd. ) for their writing support, funded by  Novartis Pharma AG, Basel, Switzerland. FB is supported by the  NIHR  biomedical research center at  UCLH. Sincere gratitude also to  Alina Czechner , the senior reader at the  Vienna Reading Center , who has supervised most of the manual OCT analysis, to  Gordon Francis  and  G€ oril Karlsson  ( Novartis Pharma AG ) who contributed to the design and initiation of the study and to  Annik Laflamme  ( Novartis Pharma AG ,  Basel at time of study ) who assisted in the initial development of this publication. The authors also thank patients, investigators, and site staff who participated in these studies, including all OCTiMS Principal Investigators (Table  S2 ).
Sven Schippling received fees for speaking or consulting from  Bayer Healthcare, Biogen ,  Genzyme/Sanofi ,  Merck ,  Novartis ,  Roche  and  Teva ; research grants from  Genzyme/Sanofi ,  Novartis , and  Swiss Multiple Sclerosis Society ; and he is now an employee of  Roche , which was not involved in the study. Patrick Vermersch received honoraria and consulting fees from  Biogen ,  Sanofi-Genzyme ,  Novartis ,  Teva ,  Merck ,  Roche, Imcyse, AB Science, and Celgene. He also received research supports from  Novartis ,  Sanofi-Genzyme , and  Roche. Shiv Saidha  has received consulting fees from  Medical Logix  for the development of CME programs in neurology and has served on scientific advisory boards for  Biogen, Genzyme ,  Genentech Corporation, EMD Serono & Celgene. He is the PI of investigator-initiated studies funded by  Genentech Corporation and Biogen Idec , and received support from the  Race to Erase MS foundation.
StratoClim measurement We will use data from M55-Geophysica flights #5, #6, #7, and #8, which took place from Kathmandu in Nepal (Table  1 ; for the tracks see red lines in Fig. 1  Carbon monoxide was measured by the AMICA instrument  (Kloss et al. , in preparation)  placed on top of the Geophysica aircraft. AMICA employs Integrated Cavity Output Spectroscopy  (ICOS, O'Keefe et al. , 1999)  to measure various trace gases in the mid infrared region. CO mixing ratios were retrieved from observed spectra using a transition at 2050. 90 cm -1 with line parameters taken from the HITRAN 2012 database  (Rothman et al. , 2013)  and no further calibration parameters. Accuracy was tested for a range of mixing ratios (30-5000 ppb) prepared from a 5 ±0. 05 ppm CO standard (AirProducts) and is estimated to be better than 5 % (taking into account the uncertainty of the standard, upper limits for impurities in the dilution gas, and uncertainties in the MFC flows used for dilution.
The initial and lateral boundary conditions for meteorology are provided by the operational European Centre for Medium-Range Weather Forecasts (ECMWF) analyses every 6 h. The initial and boundary conditions for gaseous chemical species including inorganic nitrogen species, e. g. O 3 , CO, SO 2 , NH 3 , NMVOCs (non-methane Volatile Organic Compounds), primary (e. g. black carbon (BC), primary organic aerosols (POA)) and secondary (e. g. inorganic, secondary organic aerosols (SOA)) aerosol species are taken from MOZART-4 (Model for Ozone and Related chemical Tracers, version 4)  (Emmons et al. , 2010)  driven by meteorology from NCEP (National Centres for Environment Prediction). Boundary chemical fields are forced every 6 h. Surface emissions of atmospheric compounds are taken from the ECCAD (Emissions of atmospheric Compounds & Compilation of Ancillary Data) database (https://eccad.
aeris-data. fr) at a 0. 5° horizontal grid spacing. For anthropogenic and biomass burning emissions, we have used the MACCity inventory (MACC/CityZEN EU projects) (Van der  Werf et al. , 2006; Lamarque et al. , 2010; Granier et al. , 2011; Diehl et al. , 2012)  corresponding to August 2017. MACCity provides "off-line" emissions for gases such as alkanes, alkenes, alcohols, aldehydes, ketones and aromatics, lumped into 21 species, and for primary aerosol species. The MEGAN v2 model (Model of Emissions of Gases and Aerosols from Nature)  (Guenther et al. , 2006)  provides "off-line" monthly net biogenic emissions of gases (NO x and VOCs) and aerosols. Finally, the monthly GFEDv3 (Global Fire Emissions Database, version 3) inventory (  Van der Werf et al. , 2010)  was used for biomass burning emissions (e.
Acknowledgement. Authors thank  Céline Marie  ( Laboratoire d'Aérologie ) for her insightful suggestions. This work was supported by  French ANR   TTL-Xing   ANR-17-CE01-0015  project and the  StratoClim  project by the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 603557. We also thank  CEFIPRA  support through grant  5607-1. IAGOS data were created with the support from the  European Commission, national agencies in Germany (BMBF), France (MESR) , the  UK (NERC) , and the IAGOS member institutions (http://www. iagos. org/partners). The participating airlines (Lufthansa,  Air France ,  Austrian ,  China Airlines ,  Iberia ,  Cathay Pacific ,  Air Namibia , and Sabena) supported IAGOS by carrying the measurement equipment free of charge since 1994.
Data availability. Stratoclim data will be freely available at the https://halo-db. pa. op. dlr. de/mission/101 database from the end of June 2020, in the meanwhile they will be available upon request to the authors. Meso-NH output data are available from Eric. L. Flochmoën upon request. The satellite data and the emission data are freely provided by the AERIS data centres (https://en/aeris-data. fr/).
Only a few RTMs simultaneously simulate the canopy spectral radiance and the canopy 3-D radiative budget (RB). To our knowledge, advanced physically-based RTM models have not been fully employed for modelling Tmrt. Therefore, this study seeks to (1) exploit the advanced capabilities of a physically-based 3-D RTM for the estimation of Tmrt of complex urban scenes, and (2) introduce its use for a broader application by planners and urban climate scientists. In this study we propose a new method for detailed 3-D modelling of Tmrt in complex urban scenes by improving the radiative budget module of the Discrete Anisotropic Radiation Transfer model DART  [25]. The characteristics that make DART a powerful tool to model radiation are summarized hereafter. (1) DART considers the full spectral domain from ultraviolet to thermal infrared in solar/atmosphere radiation and optical properties.