dataesr/scientific-paragraphs-categorization huggingface dataset:
Predicting mortality from peak concentrations in COVID-19 treatment regimens We used two pharmacokinetic models to predict peak chloroquine concentrations for six chloroquine regimens. Five of the simulated regimens are potential COVID treatment regimens, and one is the standard malaria treatment regimen. A very wide range of chloroquine and hydroxychloroquine regimens are currently in use for the treatment of COVID-19 (Appendix 5). The COVID-19 regimens we simulated correspond to the highest chloroquine treatment regimen (in terms of total dose) registered at ClinicalTrials. gov (Borba et al. , 2020) ; the regimen given in the World Health Organisation SOLIDARITY trial (NCT04330690); a lower 7 day adaptation of the SOLIDARITY regimen; and weight-optimised equivalent regimens. The pharmacokinetic model estimated from whole blood concentrations  (Höglund et al.
Implications for COVID treatment regimens Chloroquine and hydroxychloroquine are already being used extensively, and often in high doses, to prevent and treat COVID-19 despite the current lack of convincing evidence of benefit. Use of these drugs has become extremely politicised. The first large randomised trial in the treatment of hospitalised patients has just reported (RECOVERY, NCT04381936: press release only at the time of writing, results are not yet peer reviewed). The prevention trials will take longer to report. Confusion over the risks associated with chloroquine and hydroxychloroquine has been compounded by a recent high profile claim of increased mortality and high rates of ventricular arrhythmia in a very large observational data set that was almost certainly fabricated (and the paper subsequently retracted), and by misunderstanding of their clinical pharmacology.
Mainly because of vomiting, admission blood concentrations only weakly correlate with the self-administered chloroquine doses  (Clemessy et al. , 1995). Our model was based on a majority of admission whole blood concentrations. The data from patients whose peak drug concentrations occurred after hospital admission were used to approximate the difference between unobserved peak concentrations and admission concentrations in the remaining patients. Overall, the admission concentrations were estimated to be approximately 5% lower on average than the peak concentrations. This corresponds to prior expectations: the mean interval to hospitalisation for this large self-poisoning cohort was 4. 5 hours compared with an average time to peak concentration after oral administration of 3 hours (range 1-6 hours)  (Pukrittayakamee et al.
Although hydroxychloroquine has a wider therapeutic margin in experimental animals  (McChesney, 1983) , it has generally similar pharmacokinetic properties and, although there are limited data, the pharmacodynamic properties are also generally similar to chloroquine. The clinical features of hydroxychloroquine and chloroquine in overdose are also similar. But unlike chloroquine, there are no large cohorts of hydroxychloroquine self-poisoning on which to define toxicity thresholds. Thus, given available toxicity data, it is very unlikely that equivalent hydroxychloroquine concentrations to chloroquine are more dangerous, but rather that the safety margins are wider. Our systematic review of regimens currently being used for the treatment of COVID identifies one trial currently recruiting patients (PATCH, NCT04329923) which is administering very high doses of hydroxychloroquine (Appendix 5).
, 2020; Bourré-Tessier et al. , 2015). Reviews and authoritative websites all state that hydroxychloroquine causes TdP. Yet despite this enormous use in a systematic review of the literature we can find only a single reported case of TdP outside of COVID-19 treatment. This was in a 67 year old Taiwanese female with systemic lupus erythematosus, cirrhosis and a previous myocardial infarct who was receiving hydroxychloroquine  (Chen et al. , 2006). The patient survived. The WHO pharmacovigilance database (VigiBase) contains reports of 83 episodes of ventricular tachycardia associated with hydroxychloroquine over a 52 year period of which 7 were fatal (this does not distinguish acute toxicity from the well described chronic toxic cardiomyopathy associated with use over many years). Overall, in systemic lupus erythematosus, patients receiving chloroquine have fewer arrhythmias than those on other medicines  (Alkmim Teixeira et al.
Whole blood chloroquine concentrations were measured on admission and at varying times subsequently. The whole blood chloroquine concentrations following self-poisoning were determined using ultraviolet spectrophotometry at a wavelength of 343 nm. This analytical 10 of 26 method does not differentiate between chloroquine and the biologically active desethyl metabolite. Original drug concentration measurements, electrocardiograph QRS durations and outcome data were available for all prospectively studied patients from 1987 to 1994  (Clemessy et al. , 1995 (Clemessy et al. , , 1996) )  and a later cohort studied from 2003 to 2007  (Mégarbane et al. , 2010). Patients were included in the prospective studies if there was a history of attempted suicide and chloroquine was present in the admission whole blood sample.
Literature review We conducted a literature review of all published case reports and hospital cohorts on chloroquine poisoning. We extracted data from 12 case reports (13 patients) in which whole blood or plasma chloroquine concentrations were reported. We analysed only case reports in which blood or plasma concentrations were obtained ante-mortem as the post mortem redistribution of chloroquine from tissues to the blood is unknown. However, the data from the case reports exhibited significant bias towards patients with high concentrations who survived (i. e. unusual cases) so these were excluded from the final model. To review reports of Torsade de Pointes associated with chloroquine or hydroxychloroquine PubMed and EmBase were searched using the terms 'HY-DROXYCHLOROQUINE' or 'CHLOROQUINE' AND either 'TORSADE', 'ARRHYTHMIA', 'SUDDEN DEATH' or 'CARDIAC ARREST'.
We searched ClinicalTrials. gov on the 11th of June 2020 (https://clinicaltrials. gov/ct2/home) for clinical trials using hydroxychloroquine and chloroquine for the treatment of COVID-19 (search terms: condition=COVID; other terms=hospital; intervention=hydroxychloroquine OR Chloroquine; status=recruiting). This gave 77 results. After filtering out prevention studies, we could determine the total dose in base equivalent for 55 treatment studies (these are presented in a supplementary file). see Appendix 1). We therefore excluded the 91 retrospectively studied patients reported in Riou et al. but retained the 11 prospectively studied patients from that publication. This gave a total of 302 unique patient observations. We modelled the probability of death as a function of the log whole blood peak chloroquine concentration ( mol/L) using Bayesian logistic regression.
Concentration-dependent model of QRS widening We estimated the relationship between chloroquine concentrations and QRS interval duration by fitting a Bayesian sigmoid Emax model. The likelihood function of the regression model was specified as: QRS ∼ Normal E max -E max -E min 1 + (log 10 -E 50 ) , ( ) , = {1, 2, 3} where QRS is the observed QRS duration and is the chloroquine concentration for individual. This concentration is simultaneously measured (plasma converted to whole blood) for the healthy volunteers, and is the admission chloroquine concentration (70% of measured whole blood concentration to remove the metabolite contribution) for the self-poisoning patients. The standard error is modelled separately for the self-poisoning patients and the healthy volunteers. This helps to partially adjust for the concentration-dependent heteroskedasticity in QRS values and the differences in measurement error between pooled datasets (manually read ECGs in the Clemessy cohort and much harder to correctly read at the high concentrations seen in self-poisoning (cohorts & 3) versus automated reading in the healthy volunteer data).
The measurement error model, (3) , was then given a normal prior with mean 5 and standard deviation 3). The QRS interval data from the healthy volunteers taken in absence of drug were used to estimate the mean increase in QRS at detectable drug levels (the difference between E min and the mean QRS when no drug is detectable). From a visual check of the raw QRS data (Appendix 6), the QRS values from the Clemessy series appear to slightly biased downwards, and so we model this with an additional bias term (prior is a normal distribution with mean -20 and standard deviation 5). E max is the maximum mean QRS duration (prior is a normal distribution with mean 180 and standard deviation 10); E min is the minimum mean QRS duration (prior is a normal distribution with mean 90 and standard deviation 4); E 50 is log 10 chloroquine concentration that results in a mean QRS duration of half the maximal increase (prior is a normal distribution with mean 1.
In the genome of Meloidogyne incognita, one of the most devastating plant parasites, we found ca. 10,000 proteins with orthologs restricted only to phytoparasitic species and no further homology in protein databases. Among these phytoparasites-specific proteins, ca. 1,000 shared the same properties as known secreted effectors involved in essential parasitic functions. Of those, 68 were novel and showed strong expression during the endophytic phase of the nematode life cycle, based on both RNA-seq and RT-qPCR analyses. Besides effector candidates, transcription-related and neuro-perception functions were enriched in phytoparasites-specific proteins, revealing interesting targets for nematode control methods. This phylogenomics analysis, constitutes an unprecedented resource for the further understanding of the genetic basis of nematode adaptation to phytoparasitism and for the development of more efficient control methods.
We selected the genome of M. incognita, arguably one of the most damaging PPN, to further identify proteins having the same characteristics to known effectors, and thus possibly involved in plant parasitism. We employed a bioinformatics functional annotation and enrichment analysis to assess whether certain functional categories were significantly enriched in the set of PPN-specific genes and in candidate effectors. Cross-referencing the list of candidate M. incognita effectors with PPN-specific proteins, we aimed to identify novel RKN proteins amenable for the development of specific and sustainable control methods. Because in-planta RNAi-based strategies constitute a promising approach, we then further analyzed life-stage specific RNA-seq data in M. incognita to identify candidate effector genes expressed in the endophytic phases of the life cycle, and used qRT-PCR to confirm the expression patterns and validate them as promising targets.
Orthogroups inference A total of 2,228,060 proteins were downloaded from 79 nematode genomes and two tardigrade outgroup species. We assessed the proteomes completeness using BUSCOv3, according to the Eukaryotic odb9 dataset  [40] , setting up a minimal threshold of 70% complete BUSCO genes for inclusion in further analysis. For each species, when multiple proteome versions were available, only those with the highest BUSCO completeness scores were retained. This resulted in a selection of 63 protein sets covering 1,607,105 proteins, from nematodes with free-living, plant, vertebrate and insect parasite lifestyles, as well as two outgroup free-living tardigrade species. We employed OrthoFinder (version 2. 3. 3) with default inflation parameters  [41, 42]  to define orthogroups between the 63 different species, based on an all-against-all comparison of the proteomes with Diamond  [43]  run in sensitive mode.
The list of analyzed species, with BUSCO completeness scores, sequences per species, nematode lifestyles, as well as source links for data download and references, is available at https://doi. org/10. 15454/IIAQOW. We used the OrthoFinder option -m to generate multiple sequence alignments for each orthogroup using MAFFT  [44] , with phylogenetic trees generated with FastTree  [45]. A species tree was inferred from gene trees using STAG, with the tree rooted using STRIDE  [42]. The rooted species tree is available at https://doi. org/10. 15454/MBMOQC. Global statistics of the OrthoFinder analysis and full results are available at https://doi. org/10. 15454/ZGUP7N and https://doi. org/10. 15454/ZAYJBC, respectively.
Identification of M. incognita putative secreted proteins and candidate effectors Effector proteins are secreted by the nematode into the plant. In order to identify putative secreted proteins (PSP) in the M. incognita protein set, we searched for signal peptides as evidence for secretion using SignalP v4  [46]. This program improves the differentiation between transmembrane regions present in the N-terminal part of a protein and the presence of a signal peptide. To further circumvent this potential problem, predicted signal peptides were cropped, with the remaining part of the proteins subjected to TMHMM  [47]  for prediction of transmembrane regions. All proteins bearing a predicted signal peptide and no transmembrane region were considered as putative secreted proteins (PSP). Accession numbers of the proteins satisfying these two criteria are available at https://doi.
As expected, 68% of the hits were assigned nematode species taxids while metazoa as a whole represented 90% of taxid assignments. It should be noted that only 1,403 hits (<5%) were assigned to the Meloidogyne genus or more broadly to the Tylenchina clade, reflecting the poor representation of plant-parasitic nematodes in the NCBI's nr databank. Complementary approaches such as the nematode-centered orthology search we carried out are thus necessary. Overall, eukaryota represented 94% of the hits taxonomic annotations, while unassigned cellular organisms and bacteria represented only 4% and 0. 7%, respectively, and might correspond to horizontal gene transfers or contaminants in the genome of M. incognita. Conversely, 11,907 proteins (>27% of the predicted proteins) did not return any hit against nr and might represent orphan proteins.
The taxonomic distribution of M. incognita hits against nr and the corresponding Krona graphs available at https://doi. org/10. 15454/FROF42. By comparing the 12,683 M. incognita PPN-specific proteins identified by OrthoFinder and the Diamond homology search, we found that only 22% (2,770) returned hits against the NCBI's nr databank. In total, only 222 taxids could be assigned and, in contrast to the whole proteome analysis, the phylum Nematoda was now minority and represented only 41% of the taxonomic assignments (of which 27% were Tylenchida). Overall, more than 78% (9,913/12,683) of the PPN-specific proteins identified by OrthoFinder returned no significant hit at all against the NCBI's nr database. These proteins lack homology in other nematodes and more broadly in other species. The accession numbers of M.
incognita proteins (Figure  2 ). We also explored the RNA-seq based transcriptional profiles of genes belonging to each category through the construction and analysis of heatmaps (all data are available at https://doi. org/10. 15454/OTKID3). Terms related to post-transcriptional modification were strongly enriched in PPN-specific proteins. Surprisingly, 92 out of 99 M. incognita proteins annotated under RNA polyadenylation process (GO:0043631 activity (GO:0004652) were PPN specific. These terms were the two most significantly overrepresented terms. Furthermore, a whole set of GO terms related to transcriptional processes was also enriched in the PPN-specific proteins, such as DNA-templated transcription, initiation (GO:0006352); Regulation of transcription; DNA-templated (GO:0006355); and terms related to DNA binding such as DNA binding function (GO:0003677); Sequence-specific DNA binding (GO:0043565), DNA binding transcription factor activity (GO:0003700); and Zinc ion binding (GO:0008270).
Consequently, all the proteins present in more than three copies were considered as possibly coming from a multigene family and were therefore eliminated. The application of all the above criteria led to the selection of 89 candidate effector proteins in M. incognita that were specific to PPN species, according to OrthoFinder (list available at https://doi. org/10. 15454/WBZZ5M. Of these 89 M. incognita candidate effector proteins, only 21 returned hits against the NCBI's nr, of which 13 corresponded to previously published M. incognita effectors and one to a putative M. javanica effector (Diamond hit accession numbers in bold in https://doi. org/10. 15454/WBZZ5M). Because we aimed at identifying novel candidate effectors, lacking homology in other species, the 21 M. incognita proteins returning hits were eliminated.
Although we focused on candidate effectors, representing the most obvious targets, functional analysis of the ensemble of PPN-specific proteins revealed other novel and interesting candidates. As expected, because the majority of these PPN-specific proteins lack recognizable homology, only a few possess a predicted conserved domain that allows functional annotation. Indeed, only 9. 6% of the PPN-specific proteins in M. incognita had at least one GO term assigned, in comparison to more than 47% for the whole proteome. This illustrates the high proportion of potential pioneer proteins without evident functional annotation in the PPN-specific datasets. Despite that, among the few PPN-specific proteins with functional annotation, some terms were significantly enriched, with functions deserving further attention in the future.
Candidate HGT also revealed new proteins not previously described in PPN, such as 'adenovirus packaging protein 1' and 'alginate lyase'. The origin and possible function of these proteins in Meloidogyne and other PPN deserve further investigation. Although HGT are significantly enriched in the PPN-specific dataset, this only explains the origin for a minority of the genes (maximum 14%). This is consistent with the observation that 88% of the PPN-specific M. incognita proteins return no significant hit at all against the NCBI's nr library. Thus, these proteins more likely represent lineage-specific orphan genes, with two main hypotheses to explain their evolutionary origin  [76]. The first postulates that duplications followed by rearrangements and extremely rapid sequence divergence can lead to gene copies lacking recognizable homology to other sequences.
1,000 PPN-specific proteins in M. incognita sharing the same characteristics as known effectors. Targeting specific genes of an endoparasite can be conducted through plant transformation and RNA-mediated gene silencing. Towards this, we further filtered the dataset to retain a number of novel PPN-specific candidate effectors expressed while the nematode is within the plant. Employing RT-qPCR, we confirmed their higher expression during endophytic phases of parasitism on a second host species, suggesting a conserved and important role in plant parasitism. Collectively these datasets of proteins constitute promising resources towards a better understanding of the genetic basis of nematode adaptation to phytoparasitism and development in the future of more efficient and specific nematode control strategies.
(www. preprints. org) | NOT PEER-REVIEWED | Posted: 22 October 2020 doi:10. 20944/preprints202010. 0449. v1 molecular function (MF), 654 in the biological process (BP) and 205 in the cellular component (CC) ontologies. For PPN-specific PSP and candidate effectors, only 7. 7% (103) and 7. 9% (83) had at least one GO term assigned. This illustrates the high proportion of potentially pioneer proteins in these PPN-specific datasets.
Acknowledgments: EGJD would like to sincerely thank  Ana Paula Zotta Mota  for her constant help and support during the whole duration of this bilateral collaborative project between Brazil and France. The authors would like to thank  Sebastian Eves-van den Akker ,  John T. Jones ,  Catherine Wram , and  Philipp H. Schiffer  for providing access to the predicted proteins in Heterodera schachtii, Globodera pallida newton, Radopholus similis, Rotylenchulus reniformis, Panagrolaimus sp. JU765 and Panagrolaimus sp. PS1159. We would like to thank  Regina Maria Dechechi Gomes Carneiro  for providing M. incognita populations for the bioassays.
Funding: This research was funded by the  CNPq PVE Science Without Borders Program  (project  401541/2014-2 ),  CAPES PrINT  (project  88887. 310583/2018-00 ),and  INCT PlantStress Biotech  ( CNPq  project  465480/2014-4 ;  FAPDF  project  193. 001. 265/2017 ). LDdF and JDA were supported by scholarships from  CAPES  (project finance code 001). MFGS, RM and PMG were supported by fellowships from the  CNPq.
Preprints (www. preprints. org) | NOT PEER-REVIEWED | Posted: 22 October 2020 doi:10. 20944/preprints202010. 0449. v1 molecular function (MF), 654 in the biological process (BP) and 205 in the cellular component (CC) ontologies. For PPN-specific PSP and candidate effectors, only 7. 7% (103) and 7. 9% (83) had at least one GO term assigned. This illustrates the high proportion of potentially pioneer proteins in these PPN-specific datasets.
85 4 Def. Manipulation GH32 invertase Glycoside hydrolase, family 32 (IPR001362) 306. 79 6 Nutrient processing VB5 PanC Pantoate-beta-alanine ligase (IPR003721) 137. 24 2 Nutrient processing Candidate GSI Glutamine Synthase Candidate GSI Glutamine Synthase (IPR008146) 248. 7 5 Nutrient processing NodL-like Hexapeptide transferase, conserved site (IPR018357) 136. 85 2 Feed. site induction Aromatic amino acid Candidate L-threonine Aldolase beta-eliminating lyase/threonine aldolase 271. 61 3 Unknown (IPR001597) Candidate Phosphoribosyltransferase Phosphoribosyltransferase domain (IPR000836) 154. 59 2 Unknown Candidate Cyanate Lyase Cyanate lyase, C-terminal (IPR003712) 3. 49 0 Detoxification.
Structure and design Rivet is structured in a layered fashion, with a C++ shared library at its core, supplemented by C++ "plugin" libraries containing collider analysis routines, a Python programming interface built via the Cython system, and finally a set of Python and shell scripts to provide a command-line interface. The principle deployment targets are Unix-like systems, primarily Linux and Mac OS. Rivet's design is motivated by ease of use, in particular aiming to provide a natural & expressive analysis-writing interface with minimal technical "boilerplate", as far as possible while also being computationally efficient and supporting a wide range of use-cases.
Dependencies The core library provides machinery for structuring "runs" of the code, i. e. the feeding of simulated collider events into it for analysis, and for output of histogram data. These input and output roles are not played entirely by Rivet itself: it uses the HepMC  [48, 49]  and Yoda libraries for I/O and in-memory representation of events and histograms/analysis summary data. HepMC events read into Rivet are wrapped into a more convenient Rivet::Event object, with a potential event-graph tidying step before storage as the event currently being analysed. The Yoda library was developed primarily for use with Rivet, and has a similar layered structure with C++ and Python interfaces and user scripts, but is a general-purpose tool for statistics without particle-physics specialisations.