dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The authors thank the anonymous referee for useful suggestions that helped to improve this paper, as well as  A. Simionescu  for insightful discussions. This work is supported by the Lendület  LP2016-11  grant awarded by the  Hungarian Academy of Sciences. V. B. acknowledges support by the  DFG  project nr. 415510302. V. B. , S. B. and E. R. acknowledge financial contribution from the contract  ASI-INAF n. 2017-14-H. 0. E. R. and S. B. acknowledge the Ex-aNeSt and  Euro Exa  projects, funded by the  European Union 's  Horizon 2020 research and innovation programme , under grant agreement No  754337. S. B. also acknowledges support from the  INFN INDARK  grant. K. D. acknowledges support through ORIGINS, founded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy -EXC-2094 -390783311.
Challenge trial data Data were obtained from a Phase 2a RTS,S/AS01 challenge study, detailed trial descriptions can be found in in Regules et al. [23]. Briefly, 46 malaria naïve adults, defined as those with no previous history of malaria or receipt of an investigational malaria vaccine (mean age of 33. 6 years), received full vaccination schedules, 30 on the Fx017M schedule and 16 on the 012M schedule. Volunteers underwent mosquito challenge with the bites of five P. falciparum (3D7, a clone of the NF54 strain) infected Anopheles stephensi mosquitoes three weeks after the third vaccine dose. Parasitemia levels were monitored through blood slide reading and PCR with monitoring performed daily from days 5-20 after challenge and then every two days thereafter up to 28 days postchallenge. Volunteers who tested positive for malaria during the follow-up period were treated with chloroquine phosphate.
Statistical methods Immunological data was re-evaluated to assess for any statistically significant difference in immune measurements. Nonparametric Mann-Whitney U tests were used to compare antibody titres and avidity indices between vaccination schedule arms. A Spearman's rho was calculated to test for any correlation between immune measurements and a log rank test was performed to test for significant differences in time to onset of parasitaemia. Fishers exact test was used to compare the observed efficacy in the delayed-fractional vs the standard arms.
Model fitting To assess the contribution of anti-NANP antibody titre and avidity to overall vaccine efficacy, we compared the fit of models incorporating either IgG titre data, avidity data, or both titre and avidity from the challenge study using Bayesian methods. The model that provided the best fit to the data included both anti-NANP antibody titre and antibody avidity, with the DIC indicating this to provide a better fit than models with either antibody titre or avidity alone (Table  2  and Table S1 ). Furthermore, based on the DIC, avidity Threshold number of parasites for detection of infection (occurrence of parasitaemia, defined by positive blood slide) 50,000,000 parasites  [41]  m Mean number of merozoites released per sporozoite 30,000  [40]  alone provided a better fit than antibody titre alone.
Discussion Understanding the nature of the protective immune response elicited by second-generation RTS,S vaccines and the identification of immunological markers that reliably predict protection against malaria infection is critical for vaccine development and evaluation. Here, by fitting a biologically-motivated mathematical model of sporozoite infection to data from a recent challenge study, we sought to estimate dose-response relationships between antibody immune response markers and protection from malaria infection. We have demonstrated the added predictive value of including IgG avidity indices, measured using inhibition ELISAs, into the model. And model fits demonstrate how the combination of both IgG titre and avidity help to explain the observed efficacy improvements of the delayed-fractional regime better than if titre alone was used to predict efficacy.
5) reflect those recorded from RTS,S/AS01 vaccinated children in malaria-endemic countries (median 41. 2  [36] , mean 45. 5  [34]  and geometric mean 39  [37] ). While the approaches and methodologies of the studies vary making comparisons difficult, if the improvements in avidity observed in the delayed-fractional arm translate to target populations in malaria endemic countries we could see further increases in efficacy. To further optimise the protective efficacy of RTS,S it will be important to disentangle the influence of the fractional dose versus the delay on both immunological markers and vaccine efficacy. Such evaluations are currently ongoing in naturally exposed populations (clinicaltrials. gov identifiers: NCT03276962). Our results show that it will be important to measure both antibody markers in these studies to further assess their utility as predictors of protection in naturally exposed populations.
An important consideration for field trial immunogenicity evaluations will also be the baseline levels of anti-NANP antibodies in pre-exposed populations and the potential for parasite exposure between vaccine doses which could impact the adaptive immune response  [56]. As challenge trial volunteers have been shown previously to have no or low detectable levels of anti-CSP antibodies pre-vaccination  [57] [58] [59] [60]  and parasite exposure is timed we cannot take these factors into account in this work. There are several other limitations to this study. Firstly, the dataset and the number of individuals who became infected following challenge (ten volunteers, six standard and four delayedfractional) was small, which limits the precision of our estimated biological parameters. Given the small study size informative priors were therefore needed to ensure convergence during model fitting.
Their results suggest that, with an additional fourth booster dose, this regimen could potentially avert 21-25% more clinical cases than the current RTS,S/AS01 dosing regimen across four representative transmission settings  [64]. It will be important to further refine these estimates when antibody decay dynamics have been characterised following longer-term field trials with the updated delayed-fractional regime, which are now underway (clinicaltrials. gov identifier: NCT03276962).
Funding sources This work was funded by a  UK Medical Research Council  studentship to HAT [grant number: G98669 https://mrc. ukri. org/]. ABH and ACG acknowledge additional related support from  PATH  [grant number:  GAT. 0888-07-00444524 -CRT https://www. path. org]. HAT, ABH,  PGTW  and  ACG additionally acknowledge joint Centre  funding from the  UK Medical Research Council  and the  Department for International Development  [grant number:  MR/ R015600/1 ]. ABH additionally acknowledges support from her  Imperial College Research Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Results We present VG-flow, a new approach to haplotype aware genome assembly from mixed samples. This algorithm takes as input a data set of next-generation sequencing reads and a collection of strain-specific contigs; note that de novo assembly into strain-specific contigs can be performed using various tools (e. g. [16, 19, 26, 27] , depending on the application). The output of VG-flow consists of maximal length haplotypes along with relative abundance estimates for each of these sequences. In addition, the algorithm yields abundance estimates for each of the input contigs. Our approach consists of five steps, as depicted in Figure  1 : (1) We construct a contig variation graph V G C by performing Multiple Sequence Alignment (MSA) on the input sequences. Node abundances are obtained by mapping sequencing reads (2) We build a flow network F G using V G C.
VG-flow outperforms existing tools We evaluate performance of VG-flow on three simulated viral quasispecies data sets from  [17]  and one real HIV benchmark presented in  [34] , also referred to as the labmix. The simulated data sets are based on true genomic sequences from the NCBI nucleotide database; the characteristics of all data sets (virus type, genome size, number of strains, relative strain abundances, and pairwise divergence) are described in Table  1. All data sets consist of Illumina Miseq reads with an average sequencing depth of 20. 000x. For each data set, including the labmix, the true haplotypes and their relative abundances are known. Table  2  presents assembly statistics for all methods on the three simulated data sets (HCV, ZIKV, and Poliovirus) and Table  3    methods produce full-length haplotypes for all simulated data sets, with much higher assembly N50 and NG50 values than SAVAGE.
The improved assembly contiguity comes with only slightly higher error rates compared to the SAVAGE contigs. Table  2  shows that VG-flow builds contigs with even lower error rates than Virus-VG (0. 108% versus 0. 115% on ZIKV data and 0. 036% versus 0. 064% on Poliovirus data for VG-flow and Virus-VG, respectively). On the Poliovirus data set we do not only observe a lower error rate for VG-flow compared to Virus-VG, but also a higher target coverage (90. 2% for VG-flow versus 80. 7% for Virus-VG). The frequency estimation errors (AFE and RFE) in Table  2  show that the increase in assembly accuracy also leads to lower frequency estimation errors. On real data (Table  3 ) we observe that VG-flow produces the same number of contigs as Virus-VG, leading to identical target coverage and N50 values; the only difference between the assemblies is a slightly lower NG50 for VG-flow (4608 versus 4642) and a slightly higher error rate (0.
In other words, they assemble each of the five HIV strains at full-length. However, PredictHaplo does so at almost twice the error rate of VG-flow (1. 066% for PredictHaplo versus 0. 535% for VG-flow) and the ShoRAH assembly has an even higher error rate of 3. 910%. Moreover, ShoRAH greatly overestimates the number of strains in all data sets considered. Figure  3  shows the relative frequency estimation errors per method as a function of true abundance per strain, for each of the simulated data sets. Results are divided into bins (binsize=0. 05) and average errors are shown. Figure  3  highlights the advantage of de novo methods VG-flow and Virus-VG, which have much smaller relative errors than PredictHaplo and ShoRAH. On the HCV and ZIKV data sets, VG-flow and Virus-VG show nearly identical performance; on the Poliovirus data, we observe a small advantage for VG-flow.
We consider various thresholds for the relative edit distance and plot precision, recall, and F-measure as a function of the threshold α. Each of these measures takes values between 0 and 1, with 0 the worst possible score and 1 the best possible score. We observe that, in general, SAVAGE achieves high values for all three measures already at low relative edit distance. However, SAVAGE only assembles short contigs. VG-flow and Virus-VG show very similar performance, with slightly better values for VG-flow on the ZIKV and Polio data sets, and slightly better performance of Virus-VG on the labmix. All other methods are outperformed by these de novo approaches: PredictHaplo and ShoRAH do not achieve comparable F-measure scores on the simulated data. On the labmix these methods obtain similar scores only at an allowed relative edit distance of 4%, which is nearly as high as the maximal pairwise divergence between strains in this data set.
6-276 CPU hours). In comparison, PredictHaplo is faster (2. 0-7. 4 CPU hours) and ShoRAH is slower (209-814 CPU hours, unable to process the poliovirus data set). However, it is important to realize that all methods except PredictHaplo are able to profit from multithreading, leading to competitive wall clock times on sufficiently large computing clusters. Moreover, constructing a consensus reference genome to be used for reference-guided methods ShoRAH and PredictHaplo also incurs some additional costs (0. 07-0. 44 CPU hours). More details are presented in the Supplementary Material.
Discussion Many genomic data sets contain mixtures of closely related sequences, such as viral quasispecies or bacterial populations, where the number of haplotypes is generally unknown and relative abundances may differ per haplotype. VG-flow addresses these challenges: we successfully reconstructed haplotypes from several mixed samples, both simulated and real, and obtained highly accurate frequency estimates for each haplotype. VG-flow performs full-length haplotype aware genome assembly, without using existing linear reference genomes, but by constructing variation graphs from pre-assembled contigs. This approach establishes a reference system that allows for analyses that do not suffer from any kind of haplotypespecific mutation-induced biases. We compute abundance estimates for the input contigs, which are of value in its own right.
We also enable haplotype reconstruction in polynomial time, with runtimes depending linearly on genome size in practice. We have shown that VG-flow scales well to bacterial sized genomes, hence proving its to contribute also to metagenomic assembly. In benchmarking experiments on simulated viral samples, our method outperformed the state-ofthe-art in full-length viral quasispecies reconstruction in terms of assembly completeness, assembly accuracy, and abundance estimation quality. Finally, we also demonstrated the value of our method on real HIV and HCV data sets. In view of general benefits of Virus-VG  [17]  and the fact that its brute-force solution experiences severe limitations with respect to genome size and the number of contigs, our main goal was to find a polynomial time solution producing high quality assemblies like Virus-VG.
Availability of data and material Software and analysis scripts are publicly available at https://bitbucket. org/jbaaijens/vg-flow. The synthetic benchmarking datasets analysed during the current study are available at https: //bitbucket. org/jbaaijens/savage-benchmarks. The real HIV data (labmix) is available at https://github. com/cbg-ethz/5-virus-mix. The real HCV data are available in the Sequencing Read Archive under accession number SRR3951347.
Fisheries that meet the requirements of the standard are approved by the certifiers and deemed sustainable, and products from these fisheries have the option of using the MSC label (see Table  1  for details on the certification process). Based on the MSC's online database, as of 31 December 2017, 210 fisheries were certified (S1 Table ), 76 suspended (i. e. the fishery was certified but then lost the Table  1. Summary of the certification process a. Source:  [4]. Step Description 1 The candidate fishery chooses a third-party certifier (e. g. Lloyds Register) and establishes a contract with it once the 'unit of assessment' is determined. The candidate fishery remunerates the certifier. 2 The certifier conducts a pre-assessment (which is optional) at the request of the candidate fishery, to assess whether certification is achievable.
msc. org/database/streamlining). b According to the MSC, the cost of a full assessment ranges from USD15,000 to USD120,000  [4]. Due to its leading position on the labeled seafood market, the MSC has been widely studied by scholars. Some studies note the MSC's positive impact, especially by i) the empowerment of small island developing states to gain greater sovereign control over fisheries and increase generation of wealth  [10] [11] [12] [13] ; ii) their rigorous standards/processes and strong market intake as a means to achieve sustainability and increase public awareness  [5, [14] [15] [16] [17] [18] [19] [20] ; and iii) the creation of economic value and other benefits [e. g. improved governance, reputational benefits etc. ;  12, 19]. Another body of work is critical of the MSC. These papers have focused on i) outcomes favoring large-scale, independent and internationally-oriented fisheries  [1, [21] [22] [23] [24] [25] [26] [27] , ii) certification of overexploitation of overfished stocks  [28, 29] , iii) certification of invasive species  [30, 31] , iv) certification of key species for marine ecosystems [e.
Materials and methods To compare promotional materials with on-water certifications, we created two datasets that included 1) all photographs showcasing fisheries that were used in public reports published by the MSC and on Facebook since 2009, and 2) all fisheries (by catch, gear type, and scale) that have been MSC-certified until 31 December 2017. The vessels involved in the MSC-certified fisheries were classified as either 'small scale' or 'large scale'. We used the European definition of 'small-scale, coastal fisheries': "fishing vessels of an overall length of less than 12 m and not using towed fishing gear" [i. e. bottom trawls & dredges;  41, 42]. Vessels longer that 12 m or using towed fishing gears were deemed 'large scale'. We categorized gears in the photographs as either 'active' or 'passive' (Table  2 ), a distinction that is used by a wide range of fisheries actors around the world, such as the United Nations, the European Commission, national administrations (e.
g. the National Oceanic and Atmospheric Administration in the USA), environmental NGOs, and fishers' representatives. 'Passive' (or 'static' gears) are often seen as a proxy for small-scale vessels  [43] , although this is not always true (e. g. many longline or pot-and-trap vessels are 'large scale' according to the definition used here, i. e. larger than 12 m). 'Passive' gears and small-scale vessels are often considered to have less of an environmental footprint (e. g. little physical impact on marine habitats, low fuel consumption) and are often promoted as the most sustainable practices by fisheries scientists [e. g. 44, 45] , environmental NGOs [e. g. 46], and small-scale fishers' representatives [e. g. 47]. Table  3  describes the process used to produce and complement these two datasets.
Raw data as well as processing scripts are available at: https://doi. org/10. 17632/gpynbmn7f9. 1. Using these two datasets, we were able to compare the gear types (i. e. 'active' vs. 'passive') and vessel lengths (i. e. 'large scale' vs. 'small scale') of MSC-certified fisheries (in terms of both catch and number) with the fisheries represented in the MSC's promotional materials.
Gears included Passive (or 'static') Deployed in a given space and subsequently left for a certain amount of time. Caught through their own interaction with the gear. Entangling nets, hooks & lines, pots & traps, longlines, handoperated gears, other set gears (e. g. ropes for mussel cultivation). Active (or 'mobile' or 'towed') Engine-propelled and dragged, towed or moved along the seabed or across the water column. Caught through the motion of the gear. In terms of number of MSC-certified fisheries (i. e. all fisheries having the same weight regardless of their catch), active gears accounted for 60% of the MSC-certified fisheries since Table  3. Creation of the two datasets used to compare the fisheries the MSC features in its promotional materials and MSC-certified fisheries.
Dataset 1: promotional materials a Dataset 2: MSC-certified fisheries b Step 1: data aggregation All reports that contained photographs of fisheries-related activities (i. e. in which either at least part of a vessel and/or a fisher was visible) were downloaded from the MSC's main website (www. msc. org) between May and October 2017, and an update was performed in February 2020 during the review process. This amounted to 27 reports published since 2009 (documents specific to particular fisheries-e. g. 'Community catch' newsletters-were excluded so as not to bias the analysis towards particular fisheries). c In addition, photographs of fisheries-related activities were also compiled from Facebook (page of the London office). In total, 399 fisheries-related photographs were analyzed. d Due to the fact that many photographs (especially from Facebook) could not be associated to a specific MSC-certified fishery-either because it showcased an undetermined or a non-certified fishery-we did not attribute a 'reference catch' (see opposite) to them.
As a result, our analysis of promotional materials was only conducted in terms of number of photographs, but not in terms of catch (which is the case for Dataset 2). The list of MSC-certified fisheries was downloaded on the MSC's fisheries portal (https://fisheries. msc. org) in January 2018. Data provided on this website did not always include a 'reference catch' for the fisheries. Therefore, we collated this important information from other sources, as described in S1 Text. This step allowed us to obtain a time-series of the MSCcertified fisheries not only in 'number', but also in 'catch'. As a result, the timeseries in catch gives more weight to fisheries catching large quantities of seafood, whereas the time-series in number gives the same weight (i. e. one unit) to all fisheries. Step 2: gear category When possible, photographs were associated to a specific fishery and/or gear (e.
g. 'Australian Western rock lobster' using 'Pots & traps'). In a few cases, photographs could thus be associated to either a specific fishery or to a gear, but not to both (e. g. 'Unidentified fishery' using 'Pots & traps', or 'Alaska salmon fishery' using 'Unidentified gear'). For fisheries that used multiple fishing gears, the proportion of the catch by gear type, when available, was sourced from the most recent 'public comment reports' and 'annual surveillance reports'. These proportions were assumed to be constant over time and were applied throughout. Step 3: scale Each photograph was categorized as either 'small scale' or 'large scale' in two ways: when the picture did not suffice to visually determine the scale of the vessel, the second dataset allowed us to do so. Note that, due to the EU definition of 'small scale' vessels used here, all photographs of bottoms trawls & dredges were automatically considered as 'large scale'.
These data indicate that IRF5 does not directly regulate monocyte class switch nor morphological changes; however, a dependent relationship exists between IRF5 and HLA-DR. To evaluate whether the inflammatory response mediated by IRF5 could infer protection or severity in COVID-19, we stratified our patients based on their admission to the ICU. In the monocyte pool, we observed a 2. 1-fold increase in expression in patients admitted to the ICU, significant only amongst ND COVID-19 patients  (Fig 3G). Interestingly, in monocyte subpopulations we observed a specific signature associated with ICU admission taking into account patient diabetic status. Classical monocyte expression of IRF5 is specifically increased by 2. 1-fold in patients admitted to the ICU, whereas intermediate monocyte expression of IRF5 is only increased in T2D patients admitted to the ICU (respectively, 3.
Human populations All consecutive patients hospitalised in April 2019 for COVID-19 infection in the dedicated University Centre of Diabetes and its Complications, Lariboisiere Hospital, Paris, France, were prospectively included in the study. Clinical and anthropometric data are summarised in Table  1. COVID-19 diagnosis was based on positive oropharyngeal swab RT-PCR. Exclusion criteria were type 1 diabetes, immunotherapy for previous transplantation and treatment of COVID-19 by corticotherapy or immunotherapy before inclusion. For the analysis, patients were divided into two groups: with or without T2D. Patients with T2D are patients already known to have it before hospitalisation or patients for which diabetes has been discovered during hospitalisation (HbA1c > 6. 5% or two fasted glycaemia above 7 mmol/l).