dataesr/scientific-paragraphs-categorization huggingface dataset:
The following list contains the storm surge forecasting systems in the Adriatic Sea, divided according to the Institution in which they operate: The Institute of Marine Sciences (ISMAR) of Venice (Italy) is part of the National Research Council (CNR) and has been 395 developing a hydrodynamic model called Shallow water HYdrodynamic Finite Element Model (SHYFEM) for over thirty years. SHYFEM is an open source finite element model available on Github (https://github. com/SHYFEM-model/shyfem).
Acknowledgments This work was partially supported by the  STREAM  project  (Strategic development of flood management , project  ID   10249186 ) funded by the  European Union  under the  V A Interreg Italy-Croatia CBC  programme. Moreover, this scientific activity was performed with the contribution of the Provveditorato for the Public Works of Veneto,  Trentino Alto Adige  and 785  Friuli Venezia Giulia , provided through the concessionary of  State Consorzio Venezia Nuova  and coordinated by  CORILA  under the project  Venezia 2021. The work of  M. Orlić  has been supported by  Croatian Science Foundation  under the project  IP-2018-01-9849  (MAUD).
Data collection Maternal characteristics, laboratory findings, CT scans, management, and maternal and fetal outcomes were obtained from patients' medical records. All laboratory tests were performed at the Strasbourg University Hospital, including the laboratory confirmation of COVID-19 using qRT-PCR to tests for SARS-CoV-2 nucleic acid on nasopharyngeal swabs. 29 Primer and probe sequences target 2 regions on the RdRp gene and are specific to SARS-CoV-2. Assay sensitivity is around 10 copies per reaction. For women with multiple RT-PCR assays, the diagnosis of COVID-19 was confirmed when any 1 nucleic acid test result was positive. Typical radiographic features of COVID-19 on chest CT were consolidation, ground-glass opacity, or bilateral pulmonary infiltration. 27 eonatal throat and rectal swab samples were also collected to test for SARS-CoV-2 using RT-PCR assays at birth or on day 1 of life, on day 3 of life in term newborns, and on days 7 and 14 of life in preterm neonates.
Results of the study in the context of what is known Most of the data about the maternal, fetal, and neonatal outcomes of pregnant women with COVID-19 have been globally reassuring. 7e21 Despite the unavailability of numerous details about maternal characteristics, including body mass index (BMI) and preexisting disease, or about the third stage of labor,  7, 11, 14  the authors considered that the women's outcomes were "good"  13  or "satisfactory"  11  or that there is "no evidence to suggest that COVID-19 pneumonia causes severe maternal and neonatal complications. "  14  Zaigham and Andersson 20 summarized these reports in a systematic review up to April 1, 2020, which included 108 pregnant women with COVID-19 from 18 case reports or series, mostly from China. They underlined that 3 of 108 women (3%) were admitted to ICUs and noted that severe maternal morbidity could therefore not be ruled out in COVID-19 during pregnancy,  20  whereas Della Gatta et al  21  pointed out in a systematic review of 6 Chinese studies totaling 51 pregnant women 7,9,10 that preterm-mainly indicated-deliveries (31%) were frequent in women with COVID-19.
While  Blowes, Belmaker & Chase (2017)  found the latitudinal change in reef fish richness to scale with abundance, no one has so far analysed how species richness of marine fish found on soft or sandy bottoms is related to density or abundance on a basin-wide scale. To understand how fish species richness in different fish communities is related to density or abundance, species length, and environmental conditions, we analyse an extensive dataset, generated by collating results from 31 standardised bottom trawl surveys from the continental shelves of the northern Atlantic and adjacent areas (Figure  1 ). Our analysis is based on 123 million individual demersal or benthopelagic fish caught in 53 thousand hauls taken within a total survey area of 3. 1 million km 2. Bottom trawl surveys are often stratified to account for spatial or depth related differences in fish assemblage composition and density.
Survey data Average catch in number of individuals per species and haul was provided from 31 scientific bottom trawl surveys. The time period from which data was obtained from each survey was selected to provide temporal overlap between the surveys and as long a time period from each survey as feasible to minimise the influence of random fluctuations in recruitment and population abundance. Surveys with less than eight years of data were hence excluded. Although the earliest trawl hauls were taken in 1977 and the most recent in 2013, the period from 2001 to 2006 was covered by all surveys. Slightly more than half of the surveys took place in the period from October to March, a third in the period from April to September, and the remaining surveys included hauls obtained throughout the year (See Appendix S2 Table  S2.
1 in Supporting Information). Different bottom trawls were used in the surveys. Cod-end mesh sizes ranged from 13 to 40 mm, horizontal trawl openings (wing spread) from 13 to 28 m, vertical openings from 1. 9 to 7 m, and towing speeds from 3 to 4. 5 knots. Many of the surveys used a stratified random sampling design to account for spatial and depth related differences in species composition. We retained the major strata used in the surveys, providing us with richness and density data from 50 different strata. The average depth in these strata ranged from 28 to 950 m.
Fish species data Among the fish taxa recorded some individuals had not been identified to species. If possible, we allocated these individuals to species, assuming that their relative species composition would be identical to that of the individuals identified within the same survey stratum, and family or genus. Where no species from the family or genus had been identified in a stratum, the family or genus name was retained. Information about the maximum length of each species was downloaded from FishBase  (Froese & Pauly, 2016)  and used to bin the observations into 11 log maximum length intervals of equal width (from now on denoted log maximum length groups). In 1% of the species records no maximum species length was available. These records were excluded from further calculations. To estimate absolute fish density and abundance in a given stratum or area we first calculated swept area density for each species.
Average absolute density and abundance were finally cumulated across species within each log maximum length group and survey stratum producing 550 data points as input to the models. To confirm that the richness of the species in group four had been reasonably well sampled by the surveys we furthermore used the vegan package  (Oksanen et al. , 2019)  to estimate the number of unobserved species in each survey stratum and found that on average a minimum of 7-8% of the species in a particular stratum may not have been recorded. However, considering all of the species found across the surveys few species appear to have been missed (see Appendix S4, Table  S4. 1 in Supporting Information).
Identifying functional relationships To find the 'best' descriptive model we used a Generalised Additive Model (GAM;  Wood, 2006)  to identify the functional form and error structure of the relationship between the number of species caught per log maximum length group and the independent variables using the R-package mgcv ver. 1. 8. 22. In the GAM the log of the expected mean number of species caught,  , , in survey stratum , maximum length group , was described using: log( , ) =  +  1 (_  ) +  2 (  ) +  3 ( , ) +  4 (ℎ  ) +  5 (  ) +  6 (  ) +  7 (  ) +  8 (ℎ , ) +  9 (  ) +  10 (  ) +  11, (ℎ  ) where  is a proportionality constant; suffix  and  signify survey stratum and maximum length group, respectively, _  is the intra-annual temperature range in the stratum (Kelvin);   is temperature (Kelvin);  , is the average absolute abundance of fish of maximum length  in stratum ; ℎ  is depth (m);   is annual net primary production (gC m -2 y -1 );   is the total stratum area (km 2 );   is midpoint of log maximum length group (cm); ℎ , is the total number of fish caught in stratum , maximum length group  over the time period of the survey;   is area swept by the survey trawl (km 2 ); ℎ  is mesh-size (mm); and   is the vertical opening of the trawl (m).
Estimating model parameters We used the non-linear model fitting R-package TMB  (Kristensen, Nielsen, Berg, Skaug & Bell, 2015)  to estimate the parameters of the four non-linear models. Fitting each model to the number of species observed we removed any insignificant variables, except if they were important for the theoretical underpinning of a model. We visually inspected the Pearson residuals of each model for normality and plotted them against each co-variate to reveal potential heteroscedasticity. To compare the models we calculated AIC-values  (Burnham & Anderson, 2002) , R 2 from observed and predicted number of species, and proportion of deviance explained. The latter was estimated by fixing the estimated scale parameter, , of the negative binomial distribution used in each of the models, comparing the difference in deviance between a saturated model (with one parameter for each of the 550 observations) and the actual model, to the difference in deviance between a saturated model and a model with only one parameter  (Cameron & Windmeijer, 1996).
Acknowledgements Thanks to all the scientists and crew members engaged in the Scientific Trawl Surveys that provided the data for this paper. Thanks also to  E. Collie  and four anonymous referees for suggesting improvements to the manuscript. Initial data compilations from EU waters were conducted as part of the  EU Network of Excellence MarBEF  ( Marine Biodiversity  and Ecosystem Functioning; contract number  GOCE-CT-2003-505446 ), and supported by the  Center for Macroecology, Evolution and Climate  ( Danish National Research Foundation ). The first author received support from the  EU   FP7-KBBE  project  VECTORS  (project  ID :  266445 ).
A multitude of articles regarding PAMD@ have been published that reinforce the utility of adding this testing method to the care of the allergic patient. 1  Thus, it appears useful to provide an update to the WAO -ARIA -GA  2  LEN consensus document on PAMD@ which was published in 2013. 2  Nomenclature Today, many of the most common allergenic molecules have been cloned or purified, have had their 3-dimensional structures elucidated, and can be consistently produced. 7 Because of the growing number of allergens identified, a systematic allergen nomenclature, approved by the World Health Organization and International Union of Immunological Species (WHO/IUIS) Allergen Nomenclature Subcommittee, has been established. 8 The subcommittee has been charged with developing and maintaining not only the systematic nomenclature developed for allergenic molecules but also a comprehensive database of known allergenic proteins, which can be accessed at http://www.
Multiplex diagnosis based on allergen microarrays consists of the evaluation of more than 100 different components belonging to inhalant, food, latex, and Hymenoptera allergen sources. The number of interactions between the different components can be extremely high, and for this reason, the interpretation of allergen microarray results can sometimes be complex. In the 112component version, ImmunoCAP ISAC producers improved the interpretation of component results by the use of Xplain, specific software that arranges the components into different families and adds relevant information for the interpretation of the results. The other commercially available allergen array, ALEX by MADx, offers a link to a dedicated expert system. 76 The support of artificial intelligence tools has allowed new opportunities for interpretation and introduced new concepts to the diagnostic approach.
Molecular allergen nomenclature: A code to describe molecular allergens. As an example, take rBet v 1. 0101. The first letter (r or n) is for recombinant or natural. The first 3 letters are the beginning of the genus name; here Bet for Betula. The single letter is for the species name (v for verucosa). The first number is a sequential number initially given in order of discovery, but the numbers were subsequently associated with protein families. The first 2 digits after the dot designate isoallergens. The third and fourth digits distinguish different variants of an isoallergen. (from Update of the WHO/IUIS Allergen Nomenclature Database based on analysis of allergen sequences, 2014) Molecular allergen: see allergen. Pan-allergens (see also cross-binding allergens): Groups of proteins that are involved in the general life processes of plants and animals and are therefore widely distributed in nature.
Acknowledgments I. V. A and G. P. G. are grateful for the support of the  National Science Foundation  (Grant  CHE-1800329 ). Computational resources were provided by  NSF   XSEDE (TG-CHE160006 ), and  FSU Research Computing Center. G. P. G. thanks  Professor Alán Aspuru-Guzik  ( University of Toronto, Vector Institute for Artificial Intelligence ) for the support and  Compute Canada  for some computational resources. NEDA computations were performed on the niagara supercomputer at the  SciNet HPC Consortium. SciNet is funded by: the  Canada Foundation for Innovation ; the  Government of Ontario; Ontario Research Fund -Research Excellence ; and the  University of Toronto. X. L. and G. Z. thank the  CSC  for PhD grant. P. B. A, S. R. , M. S. thank the  LabEx MiChem part of French  state funds managed by the  ANR  within the  Investissements d'Avenir programme  under reference  ANR-11-IDEX-0004-02 , and  Cyclolab (Hungary)  for a generous supply of acyclodextrin.
Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a crucial tool for fighting the COVID-19 pandemic. This dataset brief presents the exploration of a shotgun proteomics dataset acquired on SARS-CoV-2 infected Vero cells. Proteins from inactivated virus samples were extracted, digested with trypsin, and the resulting peptides were identified by data-dependent acquisition tandem mass spectrometry. The 101 peptides reporting for six viral proteins were specifically analyzed in terms of their analytical characteristics, species specificity and conservation, and their proneness to structural modifications. Based on these results, a shortlist of 14 peptides from the N, S, and M main structural proteins that could be used for targeted mass-spectrometry method development and diagnostic of the new SARS-CoV-2 is proposed and the best candidates are commented.
Each gel band was then sliced along their molecular weights in five fractions which were subjected to trypsin proteolysis  [8]  and analysed separately by liquid chromatography (LC) HR-MS/MS with a Q-Exactive HF mass spectrometer. The 90 min LC gradient comprised two slopes of solvent B (80% acetonitrile, 0. 1% formic acid in water): 4% to 25% during 75 minutes, and 25% to 40% during 15 minutes. The MS/MS spectra were assigned to peptide sequences using the Mascot Server 2. 5. 1 (Matrix Science) against an "in-house" contaminants database (cRAP + additional contaminants + 23 Bos Taurus sequences; 384 sequences; 187,250 residues), followed by a search against a database comprising the Uniprot Chlorocebus sequences (20,576 sequences; downloaded March 2020) and the Italy-INMI1 SARS-CoV-2 protein sequences (10 sequences; downloaded March 2020).
The following parameters were used for peptide assignation: trypsin as enzyme, two missed cleavages allowed, precursor charges of +2 and +3, mass tolerances of 5 ppm for the precursor ions and 0. 02 Da for the fragment ions, carbamidomethylated cysteines as static modification, and methionine oxidation, asparagine and glutamine deamidation as dynamic modifications. Mascot DAT files were processed for filtering peptide-to-spectrum matches (PSMs) with false discovery rates (FDR) inferior to 1%. Proteins identified by one or more specific peptides were retained and a labelfree quantification based on PSM counts for each peptide and protein was performed following the principle of parsimony. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [9]  partner repository with the dataset identifier PXD018804 and 10.
6019/ PXD018804. S1  contains the list of proteins and peptides identified. The ten nanoLC-MS/MS runs originated a total of 485,396 MS/MS spectra, from which 167,048 were attributed to 16,929 non-redundant peptide sequences. This data allowed identifying and quantifying 2,900 protein sequences from the whole-cell content of the sample. The viral proteome represents a small but valuable fraction of the dataset: 3,363 PSMs (2%), 101 peptide sequences (0. 6%), and 6 proteins (0. 2%). The six viral proteins identified in these samples were proteins N (Nucleoprotein -39 Proteomics This article is protected by copyright. All rights reserved. peptides), S (Spike protein -31 peptides), M (membrane glycoprotein -8 peptides), ORF1ab (12 peptides), ORF3a (3 peptides), and ORF8 (1 peptide). To propose the most suitable viral peptides for the development of targeted approaches, we analyzed the dataset within the Skyline software (v20.
1. 0. 76, University of Washington)  [10]. We created a spectral library based on the two DAT files from the Mascot search (cut-of 0. 95) and uploaded the MS1 full scan information contained in the ten RAW files. The protein database previously used for the Mascot search was used as background proteome. Only the viral proteins were added to the target panel. The peptide settings were then changed as follows: trypsin as enzyme; no missed cleavages; enforce peptide uniqueness by species; use measured retention times; peptide length from 7 to 25 amino acids; no structural modifications; and pick peptides matching the library. For transitions: precursor charges of 2 and 3; ion charges of 1; ion types p, b and y; up to 5 product ions picked; ion match tolerance = 0. 05 m/z; method match tolerance = 0.
Thus, 7 peptides could be interesting ultra-conserved candidates. Among the 5 peptides from the 7-25 min window, 4 peptides are well-conserved and only one possible variant would have to be monitored if necessary (one HTPINLVR variant from a non-human virus). Table  1  also shows some additional parameters for guiding further the selection of peptides for targeted assays. Notably, we provide the potential susceptibility of each peptide to be modified, by looking into our DDA data, and through searching the peptide sequences in the Global Proteome Machine Database (GPMDB, https://www. thegpm. org/index. html) and checking the number of occurrences of modifications in other datasets containing these peptides. As detailed in the table, four peptides are more prone to modifications, peptides IGMEVTPSGTWLTYTGAIK, WYFYYLGTGPEAGLPYGANK, and NPANNAAIVLQLPQGTTLPK from protein N, and peptide GWIFGTTLDSK from protein S, respectively.
In terms of occurrence of missed cleavages in the DDA data, 6 peptide sequences are present as one single sequence: IAGHHLGR, FQTLLALHR, GWIFGTTLDSK, HTPINLVR, LQSLQTYVTQQLIR, and IGMEVTPSGTWLTYTGAIK. Peptide ADETQALPQR appears to be the most susceptible to the occurrence of missed cleavages, with four different sequences observed in the data: ADETQALPQRQK, ADETQALPQRQKK, KADETQALPQRQR, KADETQALPQRQRQK, KKADETQALPQR. Furthermore, as observed in Figure  2 , these peptides are belonging to potential mutation hotspot zones being also more prone to residue changes. In summary, this dataset provides new data on SARS-CoV-2 protein and peptide sequences that could be of use for future developments of targeted assays for the detection of the virus in medical samples. Based on our experimental data and decision criteria, we consider that the best theoretical candidates for this purpose would be peptides LQSLQTYVTQQLIR, FQTLLALHR, HTPINLVR from protein S, along with peptide VAGDSGFAAYSR from protein M.
We believe this study reinforces and complements some currently on-going projects that aim at providing MS-based tools for use in diagnostic of SARS-CoV-2, but until now unpublished  [13] [14] [15]. To our knowledge, only one proteomic dataset related to COVID-19 has been published  [16] , presenting an interactomics study with viral proteins individually produced in host cells. The present dataset is the first published work on proteomics carried out with SARS-CoV-2 virus. Compared to other on-going studies, the information provided here covers important analytical characteristics of peptides from the different structural viral proteins for the development of MS-based diagnostic. We present the conservation of peptides among the different types of SARS-CoV-2 sequenced up until recently because mutations naturally occurring in the viral genome upon its spread may challenge molecular biology diagnostic tests.
Furthermore, the search of naturally attenuated variants that has been recently proposed as an interesting perspective  [17]  could be carried out with a systematic search of these peptides. This experimental dataset allows researchers to use this information in order to select and develop the most appropriate MS methods for the detection of the virus. All the MS raw files and peptide search results are made available for researchers to mine the data and search for other relevant peptides that could be of use for their studies. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
Acknowledgements The authors are indebted to  Dr Silvia Meschi  ( National Institute for Infectious Diseases "Lazzaro Spallanzani" IRCCS, via Portuense 292, 00149 Rome, Italia ) for making the Human 2019-nCoV strain 2019-nCoV/Italy-INMI1 ( 008N-03893 ) available. This publication was supported by the  European Virus Archive goes Global (EVAg)  project that has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  N°653316. The authors are also grateful to the  French Alternative Energies and Atomic Energy Commission (CEA) , the  French joint ministerial program  of R&D against CBRNE threats, and the  ANR program  " Phylopeptidomics " ( ANR-17-CE18-0023-01 ) that supported part of this study. Proteomics This article is protected by copyright.
Data Analysis Complete freedom was left to the participants regarding deconvolution of both non-isotopically and isotopically resolved TD/MD MS spectra, as well as calculation of masses based on known elemental composition and product ion assignment (Table  S10 , Supplementary Information). The list of deconvolution algorithms specified by participating laboratories included: Xtract, THRASH, ReSpect (found in both a commercial package from Thermo Scientific and in an open resource as implemented, for example in the MASH Suite), SNAP and MaxEnt (Bruker Daltonics),  66  Intact Mass (Protein Metrics), and UniDec (Oxford University, UK). 67 The list of software used for spectral processing, product ion assignment and validation included the commercially available BioPharma Compass (Bruker Daltonics), ProSight PC and BioPharma Finder (Thermo Scientific), TDValidator (Proteinaceous),  68  Peak-by-Peak and AutoVectis (both from Spectroswiss); and the freeware packages MASH Suite Pro,  45  Informed Proteomics suite,  47  ProSight Lite,  44  and ISDetect.