dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of putative orthologs and protein analysis To identify putative orthologs of the chorismate binding domain of the ADCS folate pathway genes, full-length protein ADCS sequence from Arabidopsis thaliana (Open Reading Frame, ORF, Name: AT2G28880) were used as a query to run a BLASTp on the NCBI non-redundant sequence database  (Pruitt et al. 2005) , the JGI genome portal  (Nordberg et al. 2014)  or specific databases such as, plasmoDB  (Aurrecoechea et al. 2009 ) and TAIR  (Berardini et al. 2015)  (table  1 ). Phylogenetic studies (section 2. 2) of the bifunctional enzyme ADCS were performed with the largest domain in each case, i. e. the Chorismate Binding Domain which have been identified using Conserved Domain Search Service v3. 15 61 Parameters: Expect Value threshold=0. 01, with an E-value cutoff less or equal to 1e-15  (Altschul et al.
Phylogenetic analysis The alignment of the Full-length cDNA sequence Chorismate Binding Domains was carried out at the amino acid level using MAFFT  (Katoh and Standley 2013). This alignment was then used to generate the corresponding nucleotide sequence alignment using TranslatorX  (Abascal et al. 2010). During the process, alignments was curated using both the multiple alignment editing softwares ClustalX 2. 0  (Larkin et al. 2007 ) and Jalview 2  (Waterhouse and al. 2009 ). The phylogenetic relationship was inferred using the maximum likelihood method PhyML  (Guindon et al. 2010 ). The evolutionary model selection was done with MEGA 7  (Kumar et al. 2016)  under the Bayesian information criterion  (Neath and Cavanaugh, 2012). According to the model selection process, the best evolutionary was GTR+G+I  (Nei and Kumar 2000).
Sequences as points in a multidimensional space Each biological sequence can be represented in a so-called protein sequence space, for example the Configuration Space of Homologous Proteins (CSHP)  (Bastien et al. 2005; Bornberg-Bauer and Chan 1999; Dryden et al. 2008). Many classical distances between sequences in this space have been proposed, considering it as a metric space including "edit", "hamming" and "phylogenetic" distances  (Setubal and Meidanis 1997) , some of them being evolutionary distances. The choice of a specific distance depends on what is upon scrutiny. Here, we focused on evolutionary process and distances between sequences were computed using the classical PAM250 matrix with the Phylip prodist program  (Felsenstein 1981). Such set of items may be represented as points in a Euclidean space showing same distances between them as demonstrated in  (Young and Householder, 1938).
However, in their work, trees code for data proximity and show clusters, conversely to our article where trees inform on distances between biological sequences and consequently are expected to show the phylogenetical relationships. Moreover, a phylogenetic tree-based color-code is also provided using ColorPhylo  (Lespinats and Fertil 2011). A unique color is associated to each species according to its position in the phylogenetic tree. In that purpose, distances between species in the unrooted tree are mapped onto a 2D space which are related to the base of the HSV color-space (i. e. H and S components). Proximity between two species in terms of color informs on their proximity from phylogenetic point of view. Consequently, colors carry the same information as phylogenetic tree.
Acknowledgments This work was supported by the French National Research Agency (ANR-10-LABEX-04 GRAL Labex, Grenoble Alliance for Integrated Structural Cell Biology; ANR-11-BTBR-0008 Océanomics; ANR-15-IDEX-02 GlycoAlps and "Origin Of Life" Cross Disciplinary Projects of the Univ. Grenoble-Alpes).
1, 2 ] [37] [38]  We used non-HDL cholesterol rather than LDL cholesterol because most studies in our analysis had measured TC and HDL cholesterol, from which non-HDL cholesterol can be calculated by subtraction. In contrast, LDL cholesterol was directly measured in only 13% of studies. When LDL cholesterol is not directly measured, its estimation requires data on triglycerides, which were available in only 61% of studies. Further, the most commonly used estimation method, i. e. the Friedewald equation, can be inaccurate. 39 We found that non-HDL and LDL cholesterol were correlated in studies with data on both variables (r ¼ 0. 93) (Supplementary Figure  1 , available as Supplementary data at IJE online). Non-HDL cholesterol predicts CHD risk at least as well as LDL cholesterol  40, 41  because it includes cholesterol in LDL, lipoprotein(a), intermediate-density lipoprotein, very-low-density lipoprotein and lipoprotein remnants, and is thus a simple measure of cholesterol content within all atherogenic lipoproteins.
Data sources We used studies that had measured cholesterol in representative samples of the national population or of one or more subnational regions and communities. We used a database on cardiometabolic risk factors collated by NCD-RisC. ] [44] [45]  The database was collated through multiple routes for identifying and accessing data. We accessed publicly available population-based measurement surveys [e. g. Demographic and Health Surveys (DHS), Global School-based Student Health Surveys (GSHS), the European Health Interview and Health Examination Surveys (EHIS and EHES) and those available via the Inter-university Consortium for Political and Social Research (ICPSR)]. We requested, via the World Health Organization (WHO) and its regional and country offices, help with identification and access to populationbased surveys from ministries of health and other national health and statistical agencies.
Requests were also sent via the World Heart Federation to its national partners. We made similar requests to the co-authors of an earlier pooled analysis of cardiometabolic risk factors  5, [46] [47] [48]  and invited them to reanalyse data from their studies and join NCD-RisC. Finally, to identify major sources not accessed through the above routes, we searched and reviewed published studies as detailed previously  [42] [43] [44]  and invited all eligible studies to join NCD-RisC. Anonymized individual record data from sources included in NCD-RisC were reanalysed by the Pooled Analysis and Writing Group or by data holders according to a common protocol. Within each survey, we included participants aged 18 years and older who were not pregnant. We dropped participants with implausible cholesterol levels (defined as TC <1.
75 or >20 mmol/L; HDL cholesterol <0. 4 or >5 mmol/L; TC values < HDL values) (<0. 1% of all subjects). To ensure summaries were prepared according to the study protocol, the Pooled Analysis and Writing Group provided computer code to NCD-RisC members who requested assistance. All submitted data were checked by at least two independent members of the Pooled Analysis and Writing Group. Questions and clarifications were discussed with NCD-RisC members and resolved before data were incorporated into the database. Finally, we incorporated all nationally representative data from sources that were identified but not accessed via the above routes, by extracting summary statistics from published reports. Data were extracted from published reports only when reported by sex and in age groups no wider than 20 years.
We also used data from a previous global data pooling study 5 when such data had not been accessed through the routes described. All NCD-RisC members are asked periodically to review the list of sources from their country, to suggest additional sources not in the database and to verify that the included data meet the inclusion criteria listed below and are not duplicates. The NCD-RisC database is continuously updated through this contact with NCD-RisC members and all the above routes. For this paper, we used data from the NCD-RisC database for the 23 countries included in the analysis, for years 1970-2018 and ages 40-79 years.
Data inclusion and exclusion Data sources were included in the NCD-RisC lipids database if: • measured data on total, LDL, HDL cholesterol or triglycerides were available; • study participants were 10 years of age or older; • data were collected using a probabilistic sampling method with a defined sampling frame; • data were from population samples at the national, subnational (i. e. covering one or more subnational regions, more than three urban communities or more than five rural communities) or community level. We excluded all data sources that included only hypercholesterolemia or dyslipidaemia diagnosis history or medication status without measurement of at least one of the above biomarkers. We also excluded data sources on population subgroups whose lipid profile may differ systematically from the general population, including: • studies that had included or excluded people based on their health status or cardiovascular risk; • studies whose participants were only ethnic minorities; • specific educational, occupational or socio-economic subgroups, with the exception noted below; and • those recruited through health facilities, with the exception noted below.
We used data whose sampling frame was health insurance schemes in countries where at least 80% of the population were insured. Finally, we used data collected through general practice and primary care systems in highincome and central European countries with universal insurance because contact with the primary care systems tends to be as good as, or better than, response rates for population-based surveys. We used data sources regardless of fasting status because the differences between fasting and non-fasting measurements are negligible for our primary outcomes. 49 rom the CDC-NHLBI Lipid Standardization Program in the 1950s, there has been an understanding of the need for, and systematic efforts to achieve, standardization of lipid measurements. The difference between any standardized method and the CDC Reference method should be less than 3% for TC and less than 5% for HDL cholesterol (less than 10% before the mid-1990s).
50 More than threequarters of the studies in our analysis participated in a lipid standardization programme (Supplementary Table  1  and Supplementary Figure  2 , available as Supplementary data at IJE online). A summary of data available by country is shown in Supplementary Table  2 , available as Supplementary data at IJE online, and characteristics of each study are shown in Supplementary Table  1 , available as Supplementary data at IJE online. We extracted data for ages 40-79 years because people aged below 40 years have a lower cardiovascular risk and because data in older ages were available in fewer surveys. CHD mortality increases with age whereas hazard ratios for the effects of cholesterol on CHD decrease with age. 1, 2 s a result, a larger share of CHD deaths are attributable to elevated cholesterol in middle-older ages, but the number of cholesterol-attributable deaths continues to increase with age.
Statistical methods For each study, we calculated mean total, HDL and non-HDL cholesterol and mean total-to-HDL cholesterol ratio by sex and 10-year age groups. The total-to-HDL cholesterol ratio was calculated using individual records before averaging for each sex and age group. All analyses incorporated appropriate complex survey design and survey sample weights in calculating age-and sex-specific means. For each primary outcome and for each country, sex and age group, we calculated average annual change over the entire period of data availability by fitting a linear regression with the study-specific means as the dependent variable and year as the independent variable. Each data point was weighted by the inverse of the square of its standard error, so that larger studies had more influence on the estimated change.
We multiplied the slope of the fitted line by 10 to calculate average change per decade. We also used the fitted line to estimate total, HDL and non-HDL cholesterol and the total-to-HDL cholesterol ratio values for a consistent period of 1980-2015 for all countries. For countries with data starting before 1980 and/or ending after 2015, this is equivalent to using the fitted line to interpolate for 1980 and/or 2015; for those with data starting after 1980 and/or ending before 2015, values for 1980 and/or 2015 were extrapolated using the fitted line. In a sensitivity analysis, we fitted a non-linear (LOESS) regression to examine by how much our results are influenced by use of linear trend. For each primary outcome and for each country, results were calculated by 10-year age groups, separately for men and women, and then age-standardized into two age bands (40-59 and 60-79 years) by taking a weighted average of age-specific results using weights from the WHO standard population.
Data availability We used 438 population-based studies, collected from 1970 to 2018 in 21 countries that met our inclusion criteria for TC as well as lipid fractions. An additional 20 studies were used for analysis of TC in two additional countries (Thailand and Sweden). Together, these studies included blood lipid measurements in 82. 1 million participants, 79 million of whom were aged 40-79 years. The number of data sources ranged from 5 in Slovakia to 56 in Japan. The average time between the first and last studies in a country was around three decades. For the primary analysis, we used 425 studies with data for ages 40-59 years. All these 425 studies had data on TC. In the 21 countries included in the analysis of lipid fractions, 368 of 405 studies (90. 9%) had data on HDL cholesterol and 367 (90.
Preselection of relevant patient outcome domains. Preceding the video conference on the selection of outcome domains, 2 separate systematic literature reviews were performed in December 2016 using the PubMed database, in order to identify outcomes relevant to patients for the included conditions that were modifiable in principle and feasible to implement. In the first search, we used the "Cochrane Highly Sensitive Search Strategy for identifying randomized trials"  (28)  to identify 25 recent randomized trials in each of the conditions included in the Standard Set. For each randomized trial, we checked the relevant online repositories and conducted a second PubMed search using the name of each trial and trial registration number, in order to find additional publications on the same study. All outcomes assessed in each trial were extracted.
Reports on randomized trials in languages other than English were excluded. In the second search, we identified reports on qualitative studies in which patients with 1 of the relevant conditions were asked about the most important outcomes of their disease. We included only papers in which an open-ended question format was used, in order to prevent investigator-imposed biases on the list of patient-generated outcome domains. All outcome domains considered important by patients were extracted from each paper by 2 reviewers independently. Disagreements were resolved during a consensus meeting with a third reviewer present. Previously published core set recommendations for outcome measurement in randomized trials were also consulted, as was the European League Against Rheumatism standardized data set for observational research  (29) (30) (31) (32) (33).
Data collection time points. To allow meaningful outcomes comparisons between health care providers, we recommend that all risk-adjustment variables to be collected at the first assessment. All PROMs and clinical measures should also be collected at the first assessment and annually thereafter. In instances of active disease, we recommend that the patient's disease activity status be recorded at least every 6 months, but likely more frequently, at the discretion of the patient and their health care provider. Adverse events should be collected at each assessment point after baseline. A reference guide with detailed instructions for implementation and exact definitions for all of the data elements can be downloaded (www. ICHOM. org). Finally, we stress that these recommendations are intended only for quality improvement purposes and should not be understood as more than minimally acceptable clinical guidelines in patients with established disease.
DISCUSSION We present a standard set of outcomes for inflammatory arthritis that health care providers worldwide can use in routine clinical care to help quantify the value provided for patients in different centers, countries, and health care systems. This Standard Set was developed through consensus of an international working group with expertise across a range of disciplines relevant to outcome assessment and care for patients with inflammatory arthritis. We used a multiple methods approach, in which extensive patient input as well as published qualitative and quantitative data were used to develop a minimally sufficient set of outcomes that we believe represents outcomes that matter to patients with inflammatory arthritis. We also proposed time points for data collection and relevant risk-adjustment variables to enable comparisons between providers with different patient populations.
This way, users of these sets use 1 PROM for each domain, but results can be scored on the PROMIS metric. In the work on the ICHOM inflammatory arthritis Standard Set presented here, this is taken one step further, by linking multiple PROMs to an IRT-based common reporting metric  (39). This makes it easier for new or ongoing data collection initiatives to contribute their data, since it allows users of the ICHOM inflammatory arthritis set to choose 1 instrument from a number of alternatives for each domain. Provided that 1 of the endorsed instruments (Table  2 ) is collected, outcomes can be compared with those from other health care providers who use the ICHOM Standard Set. For example, outcomes assessed using the VAS scale for fatigue can be directly compared with outcomes of a different group of patients assessed using the FACIT-Fatigue subscale.
This will include, but will not be limited to, reviewing PROMs that are endorsed in the Inflammatory Arthritis Standard Set, the ease in accessing and monitoring these PROMs, and the outcomes related to personal goals that individual patients identify. In summary, we propose a standard set of outcomes for patients with inflammatory arthritis that providers of care for patients with inflammatory arthritis can track to facilitate the global reporting of outcome data and shared learning. A detailed reference guide is available (www. ichom. org).
Acknowledgements. The authors thank the anonymous referee for constructive comments that improved the manuscript. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreements No. 679030/WHIPLASH  and  771620/EXOKLEIN ), and has been carried out within the framework of the  NCCR PlanetS  supported by the  Swiss National Science Foundation. S. Mathis acknowledges funding by the  European Research Council  through the  ERC  grant No. 647383/SPIRE  and by the  PLATO CNES  grant at  CEA Saclay. E. Bolmont  and  P. Auclair-Desrotour  are grateful to  O. Grasset  and  G. Tobie , whose models were used in this work to calculate the internal structure of the studied rocky planets and the tidal visco-elastic response of their solid part, respectively.
Statistical analysis All statistical analyses were performed using the R software, version 3. 5  (39). We analysed the feeding success (coded as 1 for fed mosquitoes and 0 for unfed ones) with a binomial logistic mixed-effect model using function 'glmmTMB' in the 'glmmTMB' package  (40). We analysed the number of probing events with a zero-truncated negative binomial mixed-effect model using function 'glmmTMB'. We analysed durations (probing, feeding and prediuresis) with a mixed effect Cox proportional hazard model using function 'coxme' of the 'coxme' package  (41). We analysed weighted blood meal size with a linear mixed effect model using function 'lmer' in the 'lme4' package  (42). All models included the kdr genotypes (SS, RS or RR), type of pre-exposure (UTN, Permethrin or Deltamethrin) and their interactions as fixed terms explanatory variables and the date of the experiment as a random intercept.
In order to discriminate between the effect of KD phenotype and other pleiotropic effects of the kdr mutation on feeding success and behavioural parameters, we fitted all previously described models on the dataset but for KD mosquitoes. To complement the later analysis, we tested the effect of the KD phenotype on feeding success. For this task, we added KD phenotype and its interactions as explanatory variables in the binomial model of feeding success fitted only on mosquitoes exposed to the insecticide treatments (Permethrin and Deltamethrin). This allowed us to compare feeding success between KD and non-KD mosquitoes of each genotype and treatment. In order to assess the relationship between KD phenotype and the genotypes for the kdr mutation, KD (coded as 1 for KD mosquitoes and 0 for others) was analysed using a binomial model with the kdr genotypes (SS, RS or RR), type of exposure (Permethrin or Deltamethrin) and their interactions as fixed terms.
Because the dataset showed data separation, we fitted the model using the bias-reduction method developed by Firth  (43). We used the 'brglmFit' function of the 'brglm2' package  (44)  for this task. We used Tukey's post-hoc test to perform multiple comparisons among genotypes and treatments using 'emmeans' function  (45). We computed Odds Ratios (OR) for the binomial models, Hazard Ratios (HR) for Cox models, Rate Ratios (RR) for the negative binomial models, Mean Differences (MD) for the linear models and their 95% confidence intervals. We calculated the binomial confidence interval of feeding rates and knock-down rates with the Wilson's score method using the 'binconf' command from the 'Hmisc' package  (46). Effect of feeding duration on blood meal size and prediuresis duration was analysed using a linear mixed effect model and a mixed effect cox proportional hazard model, respectively.
Acknowledgements MD was supported by  IRD 's PhD student fellowship. The authors would like to acknowledge the technical/research platform dedicated on vectors at the IRD centre and all members of the platform for providing mosquitoes and technical support. This platform is a member of the  Vectopole Sud network and of the LabEX CEMEB  ( Centre Méditerranéen de l'Environnement et de la Biodiversité ) in Montpellier. We thank  Dr Anna Cohuet  and  Dr Thierry Lefèvre  for their valuable comments on the manuscript. We also thank  Dr Etienne Bilgo ,  Dr Niels Verhulst ,  Dr Thomas Muillemaud  and one anonymous reviewer for their relevant comments which helped improve this manuscript. Version 5 of this preprint has been peer-reviewed and recommended by  Peer Community In Zool  (https://doi. org/10.
15 GeV/c are used in the jet reconstruction of different R values, thereby allowing us to test perturbative and non-perturbative aspects of jet production and fragmentation as implemented in MC event generators  [25, 26]. ALICE reported similar measurements of charged jet production in pp  [27, 28] , p-Pb  [29, 30] , and Pb-Pb collisions  [31]  using data from the first LHC run. A brief description of the ALICE detector and the selected data sample are introduced in Sec. 2. MC simulations and theoretical calculations used for comparison to data are presented in Sec. 3. The cross section definition is given in section 4 and the unfolding procedure is described in Sec. 5. Systematic uncertainties on the cross section measurements are addressed in Sec. 6. Finally, the results without and with underlying event (UE) subtraction are presented and discussed in Sec.