dataesr/scientific-paragraphs-categorization huggingface dataset:
We also test if there is a statistically significant difference between independent and dependent similarity measures. Answering these questions will help us determine the usefulness of developing these two variations of the multivariate similarity measures. It will also help us to construct ensembles of similarity measures with more diversity, that is expected to perform well in terms of accuracy over a wide variety of datasets. Figure  3  shows the difference in accuracy between independent and dependent versions of the measures -deeper reddish colours indicate cases where independent is more accurate (positive on the scale), and deeper bluish colours indicate cases where dependent is more accurate (negative on the scale). The datasets are sorted based on average colour values to show contrasting colours on the two ends (dimensions D / length L / number of classes c are given in the bracket after the dataset name).
From Figure  3  we observe that there are datasets for which either independent or dependent is always more accurate. For example, the independent versions of all measures are consistently more accurate for datasets "PEMS-SF" and Basic Motions (indicated by red colour rows in the heatmap). On the other hand, we see that "Handwriting" always wins for the dependent versions (indicated by the blue colour row). When we observe the columns, we see that there is no measure where either independent or dependent versions always win across all the datasets. Next we present the results of a Wilcoxon signed-rank test on each dataset across the 10 pairs of measures (without L 2 ), to test if the difference between accuracy of independent and dependent versions are statistically significant or not. We conduct this test with the null hypothesis that the mean of the difference between accuracy of independent and dependent versions will be zero with statistical significance value (α = 0.
05). We reject the null hypothesis, and accept that there is a significant statistical difference in accuracy, if the obtained significance value (p -value) obtained after the Wilcoxon signed-rank test is less than 0. 05. Table  1  shows the p-value for each dataset and an asterisk marks the p-values obtained datasets for which there is a significant difference (p-value less than 0. 05). We also report the p-values after Holm-Bonferroni correction. Before Holm-Bonferroni correction, out of the 23 datasets, we observe that for 7 datasets there is a statistically significant difference in accuracy between independent and dependent measures. After Holm-Bonferroni correction, we conclude there is indeed a statistical difference between independent and dependent measures.
rank 3. 087) performs better than both  Tables  5  and 6  (on page 29 and 30) show the accuracy of these ensembles as well as other multivariate algorithms obtained from the recent paper  (Ruiz et al, 2020). These algorithms were briefly introduced in Section 2. 3. From Table  5  we observe that ROCKET has the most number of wins (7) followed by InceptionTime (6 wins). Next is, MUSE and ResNet with 5 wins, and EE I and CIF with 4 wins. We also observe that EE D and EE I D win only on one and two datasets, respectively. Figure  5  shows accuracy ranking of our ensembles vs top 10 other multivariate algorithms. The most accurate algorithm, ROCKET, obtained a rank of 5. 087. Although Table  5  reports 2 wins for EE ID , compared to HIVE-COTE 1. 0 (4 wins) and CIF (2 wins), Figure  5  shows that average error rate of EE ID (ranked 6.
Conclusion In this paper, we present multivariate versions of commonly used elastic similarity measures. Our approach is inspired by independent and dependent DTW measures, which have proven very successful as strategies for extending univariate DTW to the multivariate case. Our experiments show that each of the univariate similarity measures excel at nearest neighbour classification on different datasets, highlighting the importance of having a range of such measures in our analytic toolkits. It has been shown that there are some datasets for which the independent version of DTW is more accurate than the dependent version and vice versa. Until now there was no way to determine whether this is a result of a fundamental difference between treating each dimension independently or not, or whether it arises from other properties of the algorithms.
Our results showing that there are some datasets for which dependent or independent treatments are consistently superior across all distance measures provides strong support for the conclusion that it is a fundamental property of the datasets, that either the variables do need to be considered in lock step or do not. Inspired by the Elastic Ensemble of nearest neighbour classifiers using different univariate distance measures, we then further experiment with ensembles of multivariate similarity measures and show that ensembling results in accuracy competitive with the state of the art. Our three ensembles establish a baseline in our future plans to create a multivariate TS-CHIEF which would combine similarity-based techniques with dictionary-based, interval-based for multivariate TSC.
Human biomonitoring (HBM) data measured in specific contexts or populations provide information for comparing population exposures. There are numerous health-based biomonitoring guidance values, but to locate these values, interested parties need to seek them out individually from publications, governmental reports, websites and other sources. Until now, there has been no central, international repository for this information. Thus, a tool is needed to help researchers, public health professionals, risk assessors, and regulatory decision makers to quickly locate relevant values on numerous environmental chemicals. A free, on-line repository for international health-based guidance values to facilitate the interpretation of HBM data is now available. The repository is referred to as the "Human Biomonitoring Health-Based Guidance Value (HB2GV) Dashboard".
The Dashboard represents the efforts of the International Human Biomonitoring Working Group (i-HBM), affiliated with the International Society of Exposure Science. The i-HBM's mission is to promote the use of population-level HBM data to inform public health decision-making by developing harmonized resources to facilitate the interpretation of HBM data in a health-based context. This paper describes the methods used to compile the human biomonitoring health-based guidance values, how the values can be accessed and used, and caveats with using the Dashboard for interpreting HBM data. To our knowledge, the HB2GV Dashboard is the first open-access, curated database of HBM guidance values developed for use in interpreting HBM data. This new resource can assist global HBM data users such as risk assessors, risk managers and biomonitoring programs with a readily available compilation of guidance values.
, 2019). The RVs are based on the upper end of the exposure distribution, such as 95th percentile concentrations and may be used for comparisons  (Vogel et al. , 2019). Risk assessors can also compare biomarker concentrations measured in specific contexts or populations to health-based guidance values derived from human or animal data using uncertainty factors or   (Hays et al. , 2008; Pletz et al. , 2020). These efforts have resulted in a large number of human biomonitoring health-based guidance values (HB2GVs), but to locate these values, interested parties need to seek them out individually from publications, governmental reports, websites and other sources. Until now, there has been no central, international repository for this information. Thus, a tool is needed to help researchers, public health professionals, risk assessors, and regulatory decision makers quickly locate relevant data on numerous environmental chemicals.
ADI An effort to develop a repository for health-based guidance values to facilitate the interpretation of HBM data started with the formation of the International Biomonitoring Network (IBN) in 2018. The IBN objectives were to enable knowledge exchange, collaboration, and harmonization across international biomonitoring programs (St-Amand, 2021; Nassif and St-Amand 2021). In 2020, the International Human Biomonitoring Working Group (i-HBM), proposed by the IBN, became formally affiliated with the International Society of Exposure Science (ISES). The i-HBM's mission is to promote the use of population-level HBM data to inform public health decision-making by developing harmonized resources to facilitate the interpretation of HBM data in a health-based context (https://intlexposurescience. org/i-hbm/).
A first step was the development of a free, on-line repository (the "Human Biomonitoring Health-Based Guidance Value (HB2GV) Dashboard") for health-based biomonitoring guidance values. The objectives of the Dashboard are: This paper provides a description of the HB2GV Dashboard including the methods used to compile the values contained in the database, and how the Dashboard can be accessed and used. Finally, we offer thoughts on caveats with using the Dashboard for interpreting human biomonitoring data.
Methods To develop the database, we conducted literature searches during two periods: during March 2020 to October 2020, and February 2022 to March 2022 using PubMed and Google Scholar. Although these literature searches were non-systematic, we targeted several search terms that sought to capture as many health-based biomonitoring guidance values as possible, including "biomonitoring", "biological monitoring", "blood guidance values", "guidance values", "biomonitoring equivalent(s)", "human biomonitoring values", "HBM", "HBM-I", "HBM-II", and "HBM-GV". Of note, we did not target search terms for occupational exposures or for nutritional or medicinal chemicals. In addition to literature searches, we also consulted websites of certain organizations, including Health Canada and the HBM Commission of the German Federal Environment Agency (https://www.
umweltbundesamt. de/en/topics/h ealth/commissions-working-groups/human-biomonitoring-commiss ion-hbm-commission), the Human Biomonitoring for Europe (HBM4EU) initiative (https://www. hbm4eu. eu/), and i-HBM Working Group member affiliations. We extracted information from the identified literature to populate the Dashboard. While the preponderance of the Dashboard data (e. g. , biomarker name, biological matrix, biomonitoring guidance value) were taken directly from the associated primary publications, some information required coding or re-coding for the purposes of consistency (e. g. , risk level associated with guidance values developed from cancer endpoints). The Dashboard includes numerous commonly used terms for HBM guidance values; the terminology and acronyms are described in (Table  1 ).
Results Five hundred eighty eight (588) biomonitoring guidance values were identified from 58 sources. The Dashboard was designed with a landing page (https://intlexpo surescience. org/i-hbm/) that features the database of guidance values shown in a table, as well as a sidebar with multiple reactive search filter options that can be selected by the end user. The Dashboard has two main functionalities. The first is a Guidance Value search function. The user can search for chemicals by chemical group, chemical name or CAS RN, biomarker name, matrix (e. g. , urine, serum), or the units of measure. As the user selects search filters, the table automatically updates with only the relevant guidance values. The following information is shown in the table: Chemical, Chemical CAS RN, Biomarker, Matrix, Group, Exposure Guidance Value Info (Type, Value, Risk Type, Source) and Biomonitoring Guidance Value Info (Type, Value, Units, Reference).
Either the complete or the filtered database can be downloaded in a spreadsheet (XLSX) format. When the user selects one chemical, biomarker, matrix, and units from the filters, a figure will be automatically generated below the table that depicts the applicable guidance values as horizontal lines at the appropriate concentrations. The second main functionality is a comparison feature. The user can input one or more biomonitoring-based chemical concentration(s) of their choosing and compare it with the available guidance values. The user can provide custom labels that describe those concentrations, such as the collection period, name of the biomonitoring program, or citation for the biomonitoring study. These results and labels will be added as bars to the figure in the Dashboard, which allows the user to visually compare them against the applicable guidance values.
Discussion The HB2GV Dashboard aggregates existing guidance values and facilitates understanding of how they may be used as screening tools in the interpretation of HBM data. The Dashboard is for informational purposes only and is not intended for drawing conclusions regarding health risk for individuals. For example, it would be appropriate to employ population data in the Dashboard to inform chemical prioritization for further follow up. It should be noted that the guidance values have been developed by researchers from different sectors (private, government) and areas of expertise (toxicologists, PBPK modelers), and with different funding sources (private and public) and different levels of confidence underpinning the values. These specific details have not been incorporated into the Dashboard database.
, 2011). This can make use of the HBM data for interpreting human exposure difficult. For example when a chemical concentration is non-detectable, it could imply no exposure or alternatively it could mean that the sample was collected after the chemical was already excreted. • Many studies include a single (spot) blood or urine sample, generally due to cost and participant burden considerations. A single sample often will not represent long-term exposures and can result in exposure misclassification, especially for chemicals with short physiological half-lives and infrequent exposure patterns  (LaKind et al. , 2019; Pleil et al. , 2013; Verner et al. , 2020). • Placing HBM data into context by comparing results with datasets such as those from national surveys provides information on relative exposures and if the sample size of a study is large enough, the HBM data can represent the population's exposure status but does not provide information related to health.
Conclusions To our knowledge, the HB2GV Dashboard is the first open-access, curated database of human biomonitoring guidance values developed for use in interpreting human biomonitoring data. It allows user-friendly searches for specific chemicals and/or biomarkers of exposure. It also allows users to compare biomonitoring data to the Dashboard values and provides standardized outputs in the form of figures and descriptive text. This new resource can assist global HBM data users such as risk assessors, risk managers and biomonitoring programs with a readily available compilation of guidance values. An additional value of the Dashboard is to facilitate future HBM efforts by helping to identify knowledge gaps and areas where more research could be conducted to improve reliance of GV estimates.
Acknowledgments The authors of this paper are members of the  ISES i-HBM Working Group. The i-HBM is chaired by the  National Institute of Environmental Health, Japan  and co-chaired by  Health Canada , and the group is comprised of an international assemblage with expertise in various aspects of biomonitoring (i-HBM members are co-authors of this paper or included in the Acknowledgments section). We would like to thank other Working Group members for their insights:  Jillian Ashley-Martin ,  Ana Canas ,  André Conrad ,  Scott Hancock ,  Carin Huset ,  Tomohiko Isobe ,  Jung-Taek Kwon ,  Jueun Lee ,  Marc A. Nascarella ,  Andy Nong ,  Susana Pedraza-Diaz ,  Eva Sugeng ,  Natalie von Götz ,  Jiyoung Yoo.
Virus strains Five YFV strains (Bolivia, Ghana, Nigeria, Sudan and Uganda) were obtained via the EVAg consortium (https://www. european-virus-archive. com/). The viral stocks were produced on Ae. albopictus C6/36 cells (ATCC®, Virginia, USA) and stored at -80°C until use. Viral titers are expressed as FFU/mL (focus-forming units/mL). The Bolivia strain was isolated in 1999 and belonged to the American II genotype (GenBank accession number: MF004382), the Nigeria strain in 1970 (West African I genotype, AF369681), the Ghana strain in 1927 (West African II genotype, MF405338), the Uganda strain in 1948 (East African genotype; sequence from https://www. european-virus-archive. com/virus/yellow-fever-virus-strain-uveyfv1948ugmr896-tvp3236), and the Sudan strain in 1940 (East/Central African genotype, MF004383) (Fig  S1 ).
Dissemination and transmission were furthermore allowed to vary according to the log10-transformed viral load (v). The full model was therefore: P (Y tra = 1) = P (Y inf = 1) * P (Y dis = 1|Y inf = 1) * P (Y tra = 1|Y inf = 1, Y dis = 1) l (θ inf , θ dis , θ tra |Y tra ) = l (θ inf |Y inf ) + l (θ dis |Y dis ) + l (θ tra |Y tra ) ⎧ ⎪ ⎪ ⎪ ⎨ ⎪ ⎪ ⎪ ⎩ θ inf = (μ inf , β dpi 1 , β p 1 , β g 1 , β gp 1 ) θ dis = (μ dis , β dpi 2 , β p 2 , β g 2 , β gp 2 , β v 2 ) θ tra = (μ tra , β dpi 3 , β p 3 , β g 3 , β gp 3 , β v 3 ) The model was developed in Stan using the rstan package (v. 2. 21. 8) in R (v. 4. 3. 0). Four independent chains of 5,000 iterations were run, the second half serving for sampling. Convergence was assessed using the Gelman-Rubin R-hat statistic  21. Weakly informative priors were used for the logistic regression coe cients when building the joint Bayesian model, as recommended by Gelman et al.
Acknowledgments Authors thank  Drs. Heather Coatsworth  and  Caroline J. Stephenson  for collecting mosquitoes in Gainesville, Florida, as well as  Malika Hocine  for her help in rearing mosquitoes and  Patricia Smiley  for proofreading. We also thank  Prof. André Cabié  ( CHU of Martinique ) for his support. This work was supported by the  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases"  (grant  n°ANR-10-LABX-62-IBEID ), and the  "Agence Régionale de Santé" (ARS) of Martinique. GG received a doctoral fellowship from the  Territorial Collectivity of Martinique. The funders had no role in study design, data collection and interpretation, or decision to submit the work for publication. Authors'contribution GG, LM, CB, MVI, and MVA did the investigation. TO and CD performed data analysis and interpretation.
1c ). Consequently, after cryo-EM data collection and initial image processing, particle picking was performed using both template and neural-network based picker  19, 20  , thereby facilitating the selection and separation of monomers from both symmetric dimers and hexamers (Supplementary Fig. 2  and 3 ). From this dataset, multiple structures of apo HTNV-L were obtained in different oligomeric states: (i) The structure of monomeric apo HTNV-L WT, determined at an overall resolution of 2. 6 Å, exhibits a conserved architecture compared to the previously obtained apo HTNV-LD97A 8 (Fig. 1b , Supplementary Fig. 2 , Supplementary Table  1 ). The polymerase core is clearly visible whereas the ENDO and the C-terminal region are too flexible to be resolved. (ii) The structure of an isolated apo HTNV-L WT symmetric dimer, that is determined at an overall resolution of 3.
Cloning, expression and purification The full-length HTNV-L gene (strain 76-118/Korean haemorrhagic fever, GenBank: X55901. 1 UniProt: P23456) flanked in 5′ by a sequence coding an N-terminal hexa-histidine tag was cloned in a pFastBac vector between NdeI and NotI restriction sites. The expressing baculovirus was prepared using the Bac-to-Bac method (Invitrogen)  31. Trichoplusia ni High 5 cells were infected at 0. 7x10 6 cells/mL with 0. 1 % v/v of baculovirus and harvested 120 h after infection. Culture medium was centrifuged at 1 000 g for 15 min. The cell pellets were resuspended in lysis buffer (30 mM HEPES pH8, 300 mM NaCl, 10 mM Imidazole, 2 mM TCEP and 5 % glycerol) supplemented with cOmplete EDTA-free protease inhibitor complex (Roche) and ribonuclease A (Roche). The lysate was sonicated during 3 min 30 s (10 s ON, 20 s OFF and 40 % intensity) and centrifuged during 45 min at 20 000 g and 4 °C.
5 L of GF buffer was deposited on a well of the silicone gasket and used to find the focus. 0. 5 L of HTNV-L WT was added and gently mixed to reach a final concentration of 30 nM. Data acquisition was carried out as for mass/contrast calibration. All measurements were performed in triplicate. Data analysis was performed using DiscoverMP software (Refeyn Ltd, version 2. 3. 0).
Grid preparation was identical to the one described for HTNV-L apo. To obtain 2D class averages of HNTV-L bound to 5′mut and 3′ vRNA, 2003 micrographs were collected on a 200kV Glacios cryo-TEM microscope (Thermo Fisher Scientific) equipped with a K2 summit direct electron detector (Gatan). Coma and astigmatism correction were performed on a carbon grid. Automated multi-holes (3x3) data collection was performed with SerialEM  32. Movies containing 50 frames were acquired with a defocus ranging from -0. 8 m to -2. 2 m at a nominal magnification of 36. 000x with a pixel size of 1. 145 Å. The total exposure dose was 50 e -/Å 2. To obtain 3D structures of apo HTNV-L and 5′vRNA-bound HTNV-L, 14. 650 and 26. 745 micrographs were respectively collected on a 300kV Titan Krios cryo-TEM microscope (Thermo Fisher Scientific) equipped with a K3 direct electron detector (Gatan) and a Gatan Quantum LS energy filter  33.
Coma and astigmatism correction were performed on a carbon grid. Automated multi-holes (4x4) data collection was performed with EPU. Movies containing 40 frames were acquired with a defocus ranging from -0. 8 m to -2. 0 m at a nominal magnification of 105. 000x with a pixel size of 0. 839 Å. The total exposure dose was 40 e -/Å 2.
Model building in the cryo-EM maps For the refinement of HTNV-L apo and 5'vRNA-bound monomers, HTNV-L apo core (PDB: 8C4S) and HTNV-L 5'vRNA-bound cores (PDB: 8C4T) were rigidly fitted in Chimera  36. In Coot 0. 9. 8. 1  37  , the restraint module was used to generate restraints at 4. 3 Å that were used for flexible refinement to fit the main chain into density. The missing regions were manually built into COOT. Careful validation of every side chain position was performed in COOT before refinement in real-space using Phenix  38. For the refinement of HTNV-L dimer, HTNV-L ENDO crystal structure (PDB: 5IZE), HTNV-L WT apo core (described in this article) and an AlphaFold model  39  of the mid-link, the CBD and the CTER domains of HTNV-L were rigidly fitted in Chimera  36. The missing regions, in particular the lariat domain, the C-terminal region of the ENDO and missing loops of the core region were manually built into COOT.
PDB ID 8QE5 EMD-18343 HTNV-L  apo monomer  PDB ID 8QGT EMD-18390 5′vRNA -bound HTNV-L monomer in the intermediate state PDB ID  8QH3 EMD-18397 5′vRNA -bound HTNV-L monomer in the activated state PDB ID  8QGU EMD-18391  chimeric map of HTNV-L dimer associated with the complete model of HTNV-L dimer  EMD-18392  cryo-EM map of HTNV-L dimer  EMD-18393  cryo-EM map focused on the ENDO of a protomer from HTNV-L dimer  EMD-18394  cryo-EM map focused on the CBD of a protomer from HTNV-L dimer PDB ID  8QHD EMD-18408  chimeric map of HTNV-L hexamer associated with the complete model of HTNV-L hexamer  EMD-18406  cryo-EM map of HTNV-L hexamer  EMD-18405  cryo-EM map of HTNV-L hexamer focused on one central protomer and two external protomers. ACKNOWLEDGEMENTS We thank  Guy Schoehn  for setting up and maintaining the IBS/ISBG EM platform and for discussion;  Eleftherios Zarkadas  for support and technical advices on cryo-EM data collection on the IBS/ISBG Thermofisher Glacios;  Lindsay Mcgregor  and  Romain Linares  for data collection on the ERSF CM01 Krios;  Martin Pelosse  for technical advices on expression;  Caroline Mas  for training on the mass photometer;  Aymeric Peuch  for setting up and maintaining the EM computing cluster;  Ambroise Desfosses  for advices on cryo-EM image processing ;  Daphna Fenel  and  Madalen Le Gorrec  for technical support.