dataesr/scientific-paragraphs-categorization huggingface dataset:
November 2021). The (b) nine subsequent ML phylogenies compared the translation of each individual protein annotated for AngV (N, P, C, V, W, M, F, G, L) against the top 50 BLASTx sequence hits for each protein collapsed on 98% similarity. Distinct outgroups were applied: (a) Sunshine Coast Virus (GenBank Accession #: YP_009094051. 1) for the first L protein phylogeny, (b) Human orthopneumovirus (HRSV, GenBank Accession #: NC_001781) for N, P, M, F, G, L proteins, and (c) Sendai virus (GenBank Accession #: NP_056872) for gene C.
Acknowledgements The authors thank  Anecia Gentles  and  Kimberly Rivera  for help in the field and the lab and acknowledge the  Virology Unit at the Institut Pasteur de Madagascar and Maira Phelps of the Chan Zuckerberg Biohub (CZB)  for logistical support. They additionally thank  Angela Detweiler ,  Michelle Tan , and  Norma Neff  of the CZB genomics platform for mNGS support. Molecular graphics and analyses were performed with  UCSF ChimeraX , developed by the  Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco , with support from the  National Institutes of Health   R01-GM129325  and the  Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases.
Only in recent years, regular observations of precipitation appear, which are included in the GNIP database  (Vasil'chuk et al. , 2022). Nevertheless, for rivers that form the runoff of the largest water arteries of the EPR, measurements of stable water isotopes are completely absent. The Hydrometeorological Service of Russia provides regular observations of river runoff at 2092 gauging stations, these data are suitable for approximate into general runoff models; obtaining observations of isotope characteristics opens up additional opportunities for studying the precipitation-groundwaterrunoff relationship. For this purpose, 3 river basins were selected: the basin of the Sosna River in the south part of central region, the basin of the Dubna River in the north of the Moscow region and the Zakza river in the southwest of the Moscow region.
cugms. ru, accessed 20 March 2022). These data Dubna (Verbilki) 56. 53 N, 2100 179 02. 10. 2019-109 107 64 cannot be provided with our dataset due to legal restrictions; however, they can be requested free of charge from hydrology 37. 6 E. 31. 10. 2021 department (URL: https www. cugms. ru; Dubna river at Verbilki -#75079; Zakza river at Bol. Sareevo #75438; Sosna river at Sosna (Elets) 52. 62 N. 16300 156 01. 09. 2019-108 54 27 Elets #78054). Alternatively, data on the water level of Dubna and Sosna rivers at corresponding gouges are available via open 38. 47 E. 01. 10. 2021 access database https://allrivers. info (https://allrivers. info/gauge/dubna-verbilki?; https://allrivers. info/gauge/byistraya-sosna-Zakza (Bolshoe 55. 71 N, 17 191 03. 10. 2019-115 114 103 elec ). Sareevo) 37. 18 E.
49). Antibody kinetics vary with disease severity  5, 16, 17  , and whole blood donors represent predominantly asymptomatic or mild SARS-CoV-2 infections due to donation eligibility criteria  5. As such, we sought to estimate a time-to-seroreversion distribution that accurately re ected the blood donor convenience sample used in this study. We identi ed and tested 7675 repeat whole blood donors in Manaus who had made multiple donations throughout for 2020-21 and used these data to estimate the timeto-seroreversion probability distribution (see Methods). The results are shown in Figs. 1B, 1C , and 1D, which compare the halflife, peak signal-to-cutoff values and time-to-seroreversion of repeat whole blood donors to the cohort of symptomatic convalescent plasma donors used to determine sensitivity.
As blood donation programs are an existing component of medical infrastructure globally and in which blood samples are readily available in many locations, this approach can be rapidly implemented and carried out in large populations. We estimated larger attack rates in individuals aged 16-54 years. This is consistent with previous work examining age patterns of transmission from mobility data in the United States  23  , but we have measured infection directly rather than making inferences indirectly on the basis of COVID-19 deaths and movement. Possible reasons for the higher attack rate in people aged 16-54 years include, but are not limited to, different risk perception and shielding practises, and disease biology with more frequent asymptomatic infections in younger people, which increases infection risk in this age group due to greater mixing among working age adults.
In Manaus, however, donor postcodes were not reliably collected, so that the number of missing and wrong values make this strategy unfeasible. So, samples were selected consecutively with no postal code restrictions. We also developed a study management system to operationalize this sampling strategy, whereby blood donor postcodes and epidemiological data were automatically extracted and selected. After that, the selected donation sample IDs were released for the research assistant to be separated for testing. In Brazil, blood donation samples are usually saved for six months, so when serological test kits were made available in July 2020, we could retrospectively select and test frozen samples from February to July. After this period samples were selected and tested in real time. Antibody tests results were not made available to the blood donors themselves.
Acknowledgments This work was supported by the  Itaú Unibanco "Todos pela Saude" program  and by  CADDE/FAPESP  ( MR/S0195/1  and  FAPESP   18/14389-0 ) (http://caddecentre. org/);  Wellcome Trust  and  Royal Society Sir Henry   Dale Fellowship   204311/Z/16/Z  (N. R. F. ); the  Gates Foundation  ( INV-034540  and  INV-034652 ) the  National Heart, Lung, and Blood Institute Recipient Epidemiology and Donor Evaluation Study  (REDS, now in its fourth phase,  REDS-IV-P)  for providing the blood donor demographic and zip code data for analysis (grant  HHSN268201100007I ); and the  UK Medical Research Council  under a concordat with the  UK Department for International Development and Community Jameel  and the  NIHR Health Protection Research Unit  in Modelling Methodology. CAPJ was supported by  FAPESP  ( 2019/21858-0 ).
8% (95% CrI 36. 4-186. 4) in individuals aged 55-64 to 273. 5% (95% CrI 167. 8% - 423. 4%) in individuals aged 45-54 when compared to 22. 8-41. 1)to46. 0%(95%CrI32. 8-60. 6) in individuals aged 45-54 and 55-69 (Supplementary Fig. 12), showing small variation of the attack rate among age groups. The estimated upper bound for the age-standardised cumulative attack rate in the second period dominated by the Gamma variant was 37. 5% (95% CrI 35. 3-42. 6),signicantlysmaller than the cumulative attack rate of 75. 0%(Fig. 2) estimated for the rst period dominated by non-Gamma variants. Comparing to the COVID-19 attributable hospitalisations and deaths reported to the SIVEP database, we next used the estimated upper bounds of the age-speci c attack rates in the Gamma period in Manaus to calculate lower bounds of the agespeci c Infection-Hospitalisation Rate (IHR) and Infection Fatality Rate (IFR) for the Gamma period.
Characterizing surface chemistry. The chemical composition of the topmost sample surfaces was determined using X-ray photoelectron spectroscopy (XPS) on a VG SCIENTA SES-2002 spectrometer equipped with an Al Kα monochromatic x-ray source (1486 eV) at a power of 420 W. XPS analysis was carried out under pressure of 10 -9 mbar on areas of about 4 mm × 6 mm and at a pass energy of 500 eV for survey spectra and 100 eV for high-resolution spectra of carbon (C1s), oxygen (O1s) and silicon (Si2p). The peaks were fitted by Gaussian-Lorentzian functions using XPS-CASA software (casaXPS software 2. 3. 18 Ltd. , Teignmouth, UK) after subtracting a Shirley background  [19]. The atomic concentration analysis was based on the integrated peak area of each component and took into account the sensitivity factor, mean free pathway of an electron, and the transmission function of the analyzer.
AFM images were treated and the mean surface roughness (Sa) was calculated using Gwyddion® software  [22]. Characterizing surface wettability. Surface wettability was determined by measuring water contact angles in static mode. Measurements were made using an Attension Theta tensiometer (Biolin Scientific, Sweden). The sessile drop method was used to evaluate the contact angle at equilibrium (Ɵ) of a 5 µL distilled water drop on the sample surface. Briefly, the drop was deposited on the material surface via a 0. 7 mm inner diameter needle, and 1 min long videos were recorded by a CCD camera (1 image / 0. 07 s) from the moment the water droplet hit the material until it reached a stable shape, i. e. , when equilibrium was reached. The contact angle was evaluated at this point. Values of Ɵ were extracted from the images using numerical fits of the droplet shape based on the Young-Laplace model  [23].
The ConA powder was diluted in a 0. 1 M sodium bicarbonate (NaHCO3) solution to obtain a 2 mg/mL concentration, which was added to the final washing medium. The final ConA concentration was 125 µg/L. After 30 min of incubation at 30 °C in the dark, at least 5 3D-images (Z-stacks) were taken in situ in the final washing medium. The 561 nm excitation wavelength laser was used to excite the ConA. The fluorescence emitted by ConA was collected between 582 nm and 650 nm. The locations analyzed were determined randomly for all analyses. Each experiment was repeated at least 3 times. The number of adhered and retained bacterial cells was determined from the microscopy images with automatic counting of the individual cells using ImageJ 2. 0 software  [30]  and the Analyze Particles plugin. Cell tracking of sessile bacteria in the one-minute videos was done with the NIS-Elements Advanced Research Imaging software® (Nikon Group) using the automated object tracking module.
A list of the consecutive coordinates for the position of each bacterium during the one-minute video was generated. Further analysis of bacterial mobility was done using these data. The biovolume per surface unit corresponding to cell and matrix of the biofilm, that is GFP-and ConA-related biovolumes per surface unit, respectively, were extracted with the Comstat2® software and expressed in µm 3 /µm 2  [31]. Data and statistical analysis. Statistical significance of the differences in adhered or retained bacteria numbers, as well as biofilm features (biomass), was determined with t-tests. The H0 hypothesis (µ1 = µ2) was rejected for p-values < 0. 05, p-values < 0. 01 or p-values < 0. 001 depending on the experiment (see figure captions). The raw data used for mobility analysis were the E. coli (x,y) positions at successive times.
Preprocessing of the experimental data and simulated annealing were performed with home-made computer codes written in Fortran. From the triplet (Di, rC,i, wi) a confinement index (i  [0,1]) could be defined with Equation  9. 𝜌 i = <𝑑 net 2 > i 2 𝑟 C,i 2 (9) for each of the m subpopulations. Analysis of the whole proteome. Protein extraction. Proteomic analyses were carried out on cells adhered to the HA-44Pa and HA-2kPa, and PDMS-9kPa and PDMS-574kPa materials. HA and PDMS substrates were inoculated for 3 h and rinsed as described above. Adhered cells were delicately detached with a cell scraper. The scraped cells were removed from the cell scraper with fresh M63 rinses. Solution containing the cells was transferred into 1. 5 mL Eppendorf tubes, which were centrifuged at 10000 rpm for 30 min.
One microliter of sample (1 µL) was injected onto an enrichment column (C18 PepMap100, Thermo Scientific). The separation was performed with an analytical column needle (NTCC-360/internal diameter: 100 µm; particle size: 5 µm; length: 153 mm, NikkyoTechnos, Tokyo, Japan). The mobile phase consisted of H2O/0. Protein quantification. A label-free experiment was performed as previously described by Obry et al. [35]. Briefly, after MS analysis, raw data were imported into the Progenesis LC-MS software (Nonlinear Dynamics, version 4. 0. 4441. 29989, Newcastle, UK). For comparison, one sample was set as a reference and the retention times of all other samples within the experiment were aligned. After alignment and normalization, statistical analysis was performed for one-way analysis of variance (ANOVA) calculations.
For quantitation, peptide features presenting a p-value and a q-value of less than 0. 05, and a power of more than 0. 8, were retained. MS/MS spectra from selected peptides were exported for peptide identification with Mascot (Matrix Science, version 2. 2. 04) against the database restricted to E. coli K12 MG1655 from NBCI. Database searches were performed with the following parameters: 1 missed trypsin cleavage site allowed; variable modifications: carbamidomethylation of cysteine and oxidation of methionine. Mass tolerances for precursor and fragment ions were set at 5 ppm and 0. 35 Da, respectively. False discovery rates (FDR) were calculated using a decoyfusion approach in Mascot (version 2. 2. 04). Identified peptide-spectrum-matches with a -10logP value of 20 or higher were kept at an FDR threshold of 5 %.
Acknowledgments: The authors would like to thank the  Region Grand Est  ( ERMES  project ) , the  ANR  ( BABE  project) as well as the " Groupement de Recherche "  GDR B2I  ( GDR3751 ) for their supports. They would also like to thank  Prof. Corinne Nardin  and  Victor Igbokwe  for their considerable help in reviewing the manuscript.
Finally, how these WTs and their corresponding main ACAs relate to daily extremes of precipitation and air temperature across ANZ remains unclear to date. The present paper aims to address these questions. Hence, this work aims at establishing the relationships between surface climate variables in ANZ (namely, daily minimum and maximum air temperature and daily precipitation amounts) and the diversity of synoptic configurations, considering both inter-type and within-type variations. The latter is estimated by changes in the strength and location of the main ACAs associated with the WTs. Finally, we focus on daily (temperature and rainfall) extremes, in order to assess the extent to which WTs conducive to climate extremes differ synoptically from their other occurrences. Section 2 presents the datasets used, the WT classification and the descriptors associated with their main ACAs.
Daily rainfall and temperature data for ANZ come from the National Institute of Water and Atmospheric Research (NIWA) Virtual Climate Station Network (VCSN). Data coverage for the NIWA VCSN  (Tait and Turner, 2005; Tait et al. , 2006; 2012)  is available from 1972 to the present for 13 daily climate variables on a 5 × 5 km grid covering the country. The approach to generating the NIWA VCSN uses a thin-plate smoothing spline model for spatial interpolation between in situ station data, incorporating two location variables (latitude and longitude) and a third "pattern" variable. For daily minimum (Tn) and maximum (Tx) temperature, elevation (Figure  1 ) was included in the model. For precipitation, a digital version of an expert-guided 1951-1980 mean annual rainfall isohyet map was used as the pattern variable as a way to represent orographic influences that arise from prevailing circulation interacting with mountainous terrain.
Given the respective climatology of these regions, the 95th percentile of daily precipitation anomalies correspond to regionally-varying daily amounts spanning from 6 to 182 mm. For comparative purposes, the largest daily amounts received over the 1979-2019 period range between 51 and 1,193 mm, from one region of ANZ to another. While it offers a continuous picture of daily rainfall fields at high resolution, the VCSN database has also been shown to produce larger errors for high precipitation amounts, especially in regions with steep topography  (Tait et al. , 2012; Mason et al. , 2017). Thus, the values cited above should be taken with care, the general coherence of the precipitation fields and the general order of magnitude of the anomalies being more meaningful than the individual values themselves.
ACKNOWLEDGEMENTS This study is a contribution to the International Research Programme VinAdapt funded by France and New Zealand, and sea4seas project funded by the Université de Bourgogne. Andrew Lorrey and Nicolas Fauchereau were supported by the NIWA Strategic Science Investment Fund project "Climate Present and Past. " All analyses were made with Python (numpy, pandas, scipy, sklearn, math, matplotlib, cartopy, seaborn, and netCDF4), the developers of which are thanked. Two anonymous reviewers are thanked for their constructive comments that helped improve the text and the figures of this article. Calculations were performed using HPC resources from DNUM CCUB (Centre de Calcul de l'Université de Bourgogne). ORCID Benjamin Pohl https://orcid. org/0000-0002-9339-797X.
I G U R E 1 5 (a) 95th percentile of daily precipitation anomalies (with respect to the mean annual cycle) based on the VCSN dataset. (b) Weather type associated with the largest number of precipitation extremes (daily anomalies above the 95th percentile) for each grid point of the VCSN dataset. (c) Fraction (%) of all precipitation extremes of each grid-point associated with the dominant weather type shown in (b). All analyses are performed on the period 1979-2019 [Colour figure can be viewed at wileyonlinelibrary. com].
I G U R E 1 6 Coherent climate regions for extreme precipitation analysis, and their associations with different weather types. (a) Three coherent regions of rainfall extremes (see text for details). (b-d) The relative contribution of all 12 WTs to precipitation extremes for each region shown on the map. The inner circle corresponds to NCEP/NCAR weather type distribution, and the outer circle to the ERA5 redefinition. All analyses are based on the VCSN dataset over the period 1979-2019 [Colour figure can be viewed at wileyonlinelibrary. com].
F I G U R E 1 5 (a) 95th percentile of daily precipitation anomalies (with respect to the mean annual cycle) based on the VCSN dataset. (b) Weather type associated with the largest number of precipitation extremes (daily anomalies above the 95th percentile) for each grid point of the VCSN dataset. (c) Fraction (%) of all precipitation extremes of each grid-point associated with the dominant weather type shown in (b). All analyses are performed on the period 1979-2019 [Colour figure can be viewed at wileyonlinelibrary. com].
F I G U R E 1 6 Coherent climate regions for extreme precipitation analysis, and their associations with different weather types. (a) Three coherent regions of rainfall extremes (see text for details). (b-d) The relative contribution of all 12 WTs to precipitation extremes for each region shown on the map. The inner circle corresponds to NCEP/NCAR weather type distribution, and the outer circle to the ERA5 redefinition. All analyses are based on the VCSN dataset over the period 1979-2019 [Colour figure can be viewed at wileyonlinelibrary. com].
Introduction The COVID-19 pandemic has highlighted persistent challenges in global preparedness and response. ] [3]  The rapid availability of pathogen genetic resources and genetic sequence data (known as digital sequence information [or DSI] under the Convention on Biological Diversity) are crucial to responding to animal, human, and plant infectious disease outbreaks. Beginning with the identification and characterisation of the causative agent, to the development of risk assessments and evidencebased interventions, through to the development of countermeasures (eg, diagnostics, vaccines, and therapeutics),  1, 4  every step requires access to pathogen genetic resources and to genetic sequence data. Despite this global reliance, countries are not obligated to share pathogen genetic resources or genetic sequence data.
The COVID-19 pandemic case study-access and benefit-sharing practices within EVA To explore access and benefit-sharing practices by EVA during the COVID-19 pandemic, we examined SARS-CoV-2 resource distribution and how access and benefit-sharing worked in light of the current legal regime. Notably, none of the SARS-CoV-2 pathogen genetic resources shared by EVA had access and benefitsharing obligations at the time of access. All SARS-CoV-2 resources shared through the EVA platform were accessed and isolated from patients in 12 countries that do not have applicable access and benefit-sharing regulations (table; appendix p 1). This finding also includes material based on the original SARS-CoV-2 genetic sequence data uploaded publicly at the beginning of the pandemic in January, 2020. 9 If SARS-CoV-2 materials could only have been obtained through bilateral negotiations (ie, came exclusively from countries with access and benefit-sharing restrictions), the politicisation of the epidemic response could have had wide-reaching consequences.
4, 18 owever, although this advocacy rightfully calls for multilateral sharing of benefits, including vaccines, this can only be realised through a multilateral system that also covers open access to pathogen genetic resources and its associated genetic sequence data, both of which are essential to develop countermeasures in the first place. In the current COVID-19 pandemic, the pathogen genetic resources and genetic sequence data used for vaccine development was accessed from countries without access and benefit-sharing regulations (table). Viral resources from LMICs, shared perhaps under Article 8b of the Nagoya Protocol, are conspicuously absent. Thus, the calls for multilateral sharing of vaccines are based on ethical imperatives rather than the letter of the law of the Convention on Biological Diversity and its Nagoya Protocol, given that pathogen genetic resource access (and thus required benefit-sharing) cannot be demonstrated.
EVA-a biobanking infrastructure committed to pathogen access and benefit-sharing The EVA model exemplifies how rapid access to pathogen genetic resources, capacity building, and services can be provided to the global community during outbreaks, and, in parallel, serve as a multilateral system for benefitsharing from pathogen genetic resources. Based on the data we have presented in this Personal View, we recommend two future actions to WHO and Convention on Biological Diversity policy makers. First, WHO's BioHub initiative should not waste time in trying to create new models, but rather make use of successful models as a foundation. In May, 2021, WHO and the Swiss Confederation launched the BioHub initiative, which has an objective to set up a global system for the sharing of pathogen and clinical material to facilitate the rapid development of medical countermeasures as global public goods.
Because of EVA's hands-on experience with access and benefit-sharing and indepth understanding of the needs of the broader community of users of pathogen genetic resources, EVA is a key stakeholder that should be included in discussions around the development of multilateral mechanisms for pathogen genetic resources. EVA can bring empirical data based on global distribution of pathogen genetic resources and access and benefitsharing experience to these discussions as well as provide a scientific perspective and input while the global health community looks to find a benefit-sharing mechanism that properly addresses the cosmopolitan nature of pathogens.