dataesr/scientific-paragraphs-categorization huggingface dataset:
(4) The LSTM eliminates the need for integrating a DA process, typically required by traditional hydrological models, while still providing high-quality forecasts. The findings of this study have highlighted the advantages, limitations, and specific evaluation of the LSTM performance in streamflow forecasting for all seasons. Overall, this study shows that LSTM is a promising model for forecasting short-term streamflow, and confirms previous findings in other regions and catchments. It is recommended that this LSTM forecasting model be implemented for all seasons and lead-times up to 9 days, except for spring, for which the model should not be used for forecasts beyond 6-7 days of lead-time. It is likely that more advanced deep learning networks and data integration strategies will lead to even more significant improvements, such as training LSTM models on a large set of catchments to increase the size of the dataset (as in  Kratzert et al.
Acknowledgements This study was partially funded by the  Natural Sciences and Engineering Research Council of Canada (NSERC)  under the  Collaborative Research and Development  grant  CRDPJ-522126-17. The authors would like to thank the anonymous reviewers who contributed to improving this manuscript through their insightful and constructive comments. The authors would also like to thank  Rio Tinto  for sharing their hydrometeorological data on the Lac-Saint-Jean catchment and the  European Center for Medium-Range Weather Forecasts (ECMWF)  for providing access to the historical forecast data from their MARS computing and archiving facilities. In this study, the ERA5 reanalysis dataset produced by Hersbach et al. , (2018) was used. It has been downloaded from the Copernicus Climate Change Service (C3S) Climate Data Store: https:// cds.
climate. copernicus. eu/cdsapp#!/dataset/reanalysis-era5-singlelevels?tab=overview. The base map in Fig. 1 was created using ArcGIS® software by Esri. ArcGIS® and ArcMap™ are the intellectual property of Esri and are used herein under license. Copyright © Esri. All rights reserved. For more information about Esri® software, please visit www. esri. com.
Vol. mean Dev. mean Vol. 1σ Dev. 1σ Vol. mean Dev. mean Vol. 1σ Dev. 1σ Vol. mean Dev. mean Vol. 1σ Dev. 1σ ACKNOWLEDGEMENTS The financial support from the  Swedish Research Council (Vetenskapsrådet)  under grant no. 491401 ,  FORMAS  under grant no. 2016-01070  and Trafikverket in the framework  Branschsamverkan i Grunden  are gratefully acknowledged. Author G. B. acknowledges the financial support towards the experimental campaign from  Chalmersska forskningsfonden.
, 2022). Dependent on scientific and practical goals, the available databases can be tapped with various, goal-dependent data extraction and curation criteria  (Fantke et al. , 2020a; Müller et al. , 2017; Spilsbury et al. , 2020). For example, the global preference for environmental chemical safety assessments is to use chronic NOEC data for a minimum number of species and trophic groups, which differs across jurisdictions. Over time, the global available test data grows, and furthermore that automated protocols can be employed for updates (see e. g. ,  Scharmüller et al. , 2020). Some data portals present data from a variety of data owners, such that new data can be added and other data removed (see e. g. https: //echa. europa. eu/legal-notice). Such undocumented changes in the content of primary data sources hamper transparency and reproducibility of ecotoxicity characterization factors.
CRF = 0. 2 10 log HC20 EC10 eq = 0. 2 HC20 EC10 eq (8) Recommendations presented in Table  1  do not prescribe which database to use for extracting effect data or which methods should be used to estimate chronic EC10 eq from other effect data such as acute EC50 values, if empirical EC10 data from chronic tests are missing. Also, the minimum number of data points, species and taxonomic groups required to construct a robust SSD are not prescribed. In the present study, for statistical reasons, chronic EC10 eq were derived from SSDs using at least 5 different species, from at least 3 distinct trophic levels (primary producers, primary consumers, secondary consumers or  decomposers). With a minimum of 5 species, at least 1 data point on the SSD falls into the range below the 20th percentile of an SSD, so that the final HC20-estimate is not based on an extrapolation beyond the actual dataset.
e. , acute NOEC, acute EC10 and acute EC50) effect data. Raw data on effects from lab-based ecotoxicity studies for deriving HC20 EC10 eq were extracted from the SOLUTIONS database  (Posthuma et al. , 2019). This database was used, because (1) it comprises curated data points for >12,000 chemicals compiled from different sources; and (2) it includes a subset of data traceable in the peer-reviewed literature, next to data entries obtained for research from the data portal from the European Chemicals Agency (ECHA) that presents regulatory dossier data for chemical safety assessments. To make the choice of data consistent with recommendation no. 7 (Table  1 ), for the present paper, the subset of data traceable in the peer-reviewed literature only was used. We note that with increasing numbers of observations, the SSDs of compounds based on peer reviewed data and those exclusively based on ECHA-portal data resembled each other more than with lower data numbers (unpublished observation of RIVM).
Other ecotoxicity data collection and curation efforts (e. g. , JRC's ecotox explorer, Envirotox database, US-EPA's ToxValDB) could be considered as potential alternative or complementary data sources for deriving ecotoxicity effect factors. To illustrate the workflow and make use of the formulated recommendations, a case study on chemicals used in rice production and consumption is implemented. Characterization factors derived for the rice case study chemicals, however, are only calculated for demonstration purposes and are not official USEtox characterization factors, which are only formally included in official USEtox release versions. Case study SSDs were characterized based on the number of available data points and coverage of taxonomic groups, and SSD-quality earmarks were assigned following the recommendation of  Posthuma et al.
Freshwater ecotoxicity characterization factors For ecotoxicity characterization, we considered all 115 organic chemicals reported in rice case study inventories, for which we could determine non-zero emissions along the rice production and preparation life cycle for at least one of the three considered scenarios  (Fantke et al. , 2021). CAS registry numbers were used to extract effect data (EC10s, EC50s, and NOECs) from the SOLUTIONS database. Confidential data and data points for genera with unspecified species names were excluded. For 7 chemicals, concentration-response slope factors could be computed based solely on empirical chronic effect data (i. e. without acute-to-chronic extrapolations). Additionally, a set of 24 chemicals was covered by combining chronic and acute effect data in order to reach the minimum requirement of at least 5 species from at least 3 trophic levels.
In turn, these matters influence the robustness of across-chemicals impact scores. First, we tested the implications of excluding a significant portion of the available effect data because full species names were not provided. This was done by assigning genus and/or species names to unspecified algae, daphnids and fish based on the statistical distribution of species used in ecotoxicological experiments in the SOLUTIONS database for 31 chemicals from the case study. For example, 90% and 7% of tests on daphnids were performed using Daphnia magna and Daphnia pulex, respectively, with contributions <1% for 8 other daphnia species, and this statistic was used to assign all those possible names to unspecified daphnids. Results showed that assigning names to unspecified algae, daphnids and fish increased the number of characterized substances from 31 to 33 (Table  2 ).
Acknowledgement The authors would like to thank all members of the  UNEP GLAM Ecotoxicity taskforce  for their input into the consensus-building process. The present work was supported by the " Global Best Practices on Emerging Chemical Policy Issues of Concern under UN Environment's Strategic Approach to International Chemicals Management (SAICM) " ( GEF  project  ID 9771 , grant no. S1-32GFL-000632 ), by the  "Safe and Efficient Chemistry by Design (SafeChem) " project funded by the  Swedish Foundation for Strategic Environmental Research, MISTRA  (grant no. DIA 2018/11 ), and by the " European Partnership for the Assessment of Risks from Chemicals (PARC) " funded by the  European Commission  (grant no. 101057014 ).
The Ssp structure reveals a distinct AT three-dimensional architecture To determine the three-dimensional structure of Ssp by X-ray crystallography, isomorphous crystals were obtained for both unlabelled and selenomethionione (SeMet) labelled Ssp passenger. A preliminary partial model of Ssp was built based on the IcsA autotransporter (PDB: 5KE1)  35  using the homology model server SWISS-MODEL  36. A number of SeMet datasets were merged, which were used along with the Ssp model to solve the structure using single isomorphous replacement with anomalous scattering (SIRAS) and molecular replacement with single wavelength anomalous diffraction (MRSAD). One molecule of Ssp is present in the asymmetric unit, where the resulting structure was refined against a higher resolution native (2. 0 Å) dataset with a final and R factor /R free of 16.
Ssp's subtilase domain has a unique substrate binding cleft The core architecture of the protease domain of Ssp is typical of the subtilisin-like folds observed in other subtilase enzymes  42. It is comprised of a globular α/β fold centrally featuring a twisted sevenstranded parallel β-sheet adjacent to two central α-helices and encompassing the active site residues D76, H112 and S341 (Figs. 1c, d  and 2c ). A disulfide bond (Cys288 to Cys295) connects the last β-strand of the β-sheet to its preceding loop (Fig. 1d ). DALI analysis  43  revealed that the most structurally similar protein to Ssp is subtilisin BPN' from Bacillus amyloliquefaciens (31% identity, PDB: 1LW6, Z-score: 32. 3) with a r. m. s. d of 2. 0 Å over 255 Cα atoms. Superimposition of the two structures confirms a similar central α/β core with the active site residues of Ssp overlaying well with those of subtilisin BPN' (Fig.
The potential individual substrate-binding pockets of Ssp, were identified by overlaying the structure of subtilisin BPN' in complex with the inhibitor CI2 (PDB: 1LW6)  44  with the subtilase domain of Ssp. The bound reactive site loop of CI2 provided a guide to the subsite positions in Ssp (Fig. 2e, g ). From this analysis it appears that the Ssp substrate binding site is capable of binding at least five residues P1-P5 (Fig. 2g ) on the N-terminal, non-prime side of the scissile bond compared to four residues (P1-P4) for subtilisin BPN' (Fig. 2e ). Additionally, the electrostatic properties of the Ssp substrate binding region revealed that it is more positively charged than that of subtilisin BPN' (Fig. 2e, g ). Indeed, the afore identified putative S1 and S5 subsites of Ssp appear to be highly positively charged suggesting that it is likely to favour binding of negatively charged amino acids in these positions.
Ssp subtilase has a short β-helix The protease domain of Ssp sits on top of the β-helical stalk domain (Fig. 1b ). This β-helix is a common scaffold found in the majority of AT structures determined to date  39, [45] [46] [47] [48] [49] [50] [51]. DALI analysis  43  of solely the βhelical stalk of Ssp indicated that this domain shares low structural similarity to known structures with the closest match being the C-terminal fragment of the AT IcsA from Shigella flexneri (15% identity, PDB: 5KE1, Z-score: 15. 7) with an r. m. s. d. of 3. 0 Å over 197 Cα atoms. Interestingly, the Ssp β-helix is the shortest seen to date for an AT, comprising of seven right-handed triangular turns (~40 Å in length), with the last three β-strand rungs resembling an autochaperone domain (Supplementary Fig. 3  and Supplementary Table  3 ), which previous studies have shown are involved in AT passenger folding  52, 53.
Ssp is stabilised by calcium The structural characterisation of Ssp revealed the presence of three calcium binding sites, two in the subtilase domain and one in the β-helical stalk domain (Fig. 1b ). The first calcium ion maps to a common high affinity calcium binding site (Ca-I, Dohnalek et al. nomenclature  57  ), found in many subtilases such as subtilisin BPN'. In Ssp, Ca-I is coordinated by Glu45, Glu84 Asp125, and has backbone contacts to Lys123, Ala127 and Met129 (Supplementary Fig. 4a ). By comparison, the second calcium binding site (Ca-IV, Dohnalek et al. nomenclature  57  ) appears to be less common with the site first characterised in sedolisin (formally known as PSCP), a subtilisin-like protease from the structurally related S53 protease family  58. Structural alignment of Ssp and sedolisin (PDB: 1GA1) shows that Ca-IV is coordinated by similar residues, Asp328, Asp348, and has backbone contacts to Val329, Gly344 and Gly346 in sedolisin, compared to Asp376, Asp383, and has backbone contacts to Val376, Leu377 and Gly381 in Ssp (Supplementary Fig.
1 ). This finding is in contrast to previous predictions of a β-helix being absent in subtilase ATs  33. The Ssp subtilase domain reveals unique features not observed in any of the over 60 subtilases with structures determined to date. In addition to being attached to a 248 residue β-helix, the most notable features of the Ssp subtilase domain includes three finger-like protrusions, two of which form β-hairpins, that surround the active site (Fig. 1b ). Extensions around the active site of subtilases are not common occurring in ~13% of subtilases with known structures. Even more unusual is that two of these protrusions, the E2 loop and E3 β-hairpin are unique to Ssp. We found that Ssp's E2 loop and E3 β-hairpin were involved in substrate recognition/binding. This is akin to the disulfidetethered protrusion near the active site of AprV2 from Dichelobacter nodosus (PDB: 3LPC), which has been shown through mutagenesis studies to facilitate substrate-enzyme interactions  3.
These protrusions are in stark contrast to other features associated with subtilase active sites such as the much larger protease-associated subdomains and fibronectin subdomains found in Streptococcus pyogenes ScpA (PDB: 3EIF)  64  and tomato subtilase 3 (PDB: 3I6S)  65. Interestingly, an alignment with other subtilase ATs (Supplementary Fig. 6 ) has shown that insertions, like the unique finger-like extensions seen in Ssp, are prevalent suggesting that these additions may be a feature adopted by subtilase ATs to increase substrate specificity and/or recognition. Another notable feature of the Ssp subtilase domain is a wide substrate binding cleft, with a preference for binding substrates with negatively charged substrates at the P1 and P5 positions. These characteristics would conceivably increase Ssp's substate specificity.
Thermal melts Thermal denaturation of Ssp was monitored in an Agilent Cary 3500 Multicell UV-Vis spectrophotometer. The thermal melts were conducted from 20-90 °C monitoring at 280 nm using 10 mm pathlength ultra-micro quartz cuvettes (Hellman), with a ramp rate of 0. 5 °C/min. Data was collected at a 0. 5 mg/mL Ssp in 25 mM HEPES, 150 mM NaCl, pH 7. 0 (150 μL) with the addition of either 10 mM EDTA, 10 mM CaCl 2 or no additive. Data was fitted to the following equation to estimate the apparent melting temperature (T m app ): y = k 1 + k u + u 1 x À Á À l + l 1 x À Á Â Ã + l + l 1 x ð1Þ Where k = e ½ , y is absorbance, x is temperature, h is enthalpy, t is T m app , u is folded absorbance, l is unfolded absorbance, and u 1 and l 1 are linear corrections for folded and unfolded as function of temperature, respectively.
Crystallisation and diffraction data measurement Ssp, native and SeMet, were crystallised using the hanging drop vapour diffusion method with drops containing 1 µL of protein solution The data were indexed and integrated using XDS  71. The native data were scaled using AIMLESS  72  to 2. 0 Å resolution, belonging to space group P1 with cell dimensions of a = 47. 48 Å, b = 55. 36 Å, c = 61. 88 Å and α = 91. 52°, β = 93. 04°and γ = 102. 76°. This was consistent with one molecule in the asymmetric unit. The anomalous data from SeMet crystals were analysed using BLEND  73  for isomorphous unit cells, whereby two datasets were merged using POINTLESS and AIMLESS  72. See Table  1  for all data-collection and processing statistics.
Structure determination and refinement The structure of Ssp was solved using a combination of SIRAS method and MRSAD phasing protocol in the Auto-Rickshaw automated crystal structure determination platform  74. FA values were calculated using the programme SHELXC  75  with the native and the merged SeMet dataset by combining isomorphous and anomalous signal. A total of nine selenium atoms were found per asymmetric unit using SHELXD  76. After atom refinement and density modification, an initial model was generated using the programme BUCCANEER  77  with SAD refinement, and then further improved using the MRSAD phasing protocol of Auto-Rickshaw  74, 78. The resulting model was refined against the native dataset using REFMAC5  79  and further built using COOT  80. The quality of the Ssp model was assessed by MolProbity  81.
Acknowledgements This research was undertaken in part using the MX2 beamline at the  Australian Synchrotron , part of  ANSTO , and made use of the  Australian Cancer Research Foundation  (ACRF) detector. The authors would like to acknowledge  Peter Lock  and  Chad Johnson  from the  La Trobe University Bioimaging Platform. This work was supported by the  Australian Research Council (ARC)  project grants ( DP150102287 ,  DP180102987 ,  DP210100673 ), Fellowship (FT130100580) and a  National Health and Medical Research Council (NHMRC)  project grant ( GNT1143638 ).
Data availability The crystallography, atomic coordinates, and structure factors reported in this paper have been deposited in the Protein Data Bank, under PDB ID 8E7F. The following structural models from the PDB were also used in this study: 1LW6 (subtilisin BPN'), 5KE1 (IcsA autotransporter), 1GA1 (sedolisin), 3LPC (AprV2 from Dichelobacter.
Virfam analysis and a BLAST homologies search, conducted in public protein databases and the Pfam protein family database, predicted Orf38 to Orf47, Orf50, and Orf52 and Orf53 as putative structural proteins involved in the assembly of a complete phage particle (see Table  S2  in the supplemental material). In addition, and as already observed, a predicted lysis module was found, composed of orf53 and orf54 coding for predicted holin and amidase proteins, respectively (Table  S2 )  (12). Quantification of U1207. 3 in enriched phage preparations. Quantification of U1207. 3 was performed by qPCR and with a semiquantitative approach based on the TEM imaging in phage preparations from both untreated and mitomycin C-treated FR1 cultures. Phage preparations, obtained after sequential centrifugation steps, were concentrated 308.
3 and assess the family of the head-tail-neck module. A BLAST search was conducted in public protein databases and the Pfam protein family database using default parameters, and only alignments with E values below 0. 001 were considered. Quantitative PCR. Quantitative real-time PCR assays were performed with the Kapa SYBR Fast qPCR master mix (2Â) kit (Kapa Biosystems) on a LightCycler 1. 5 (Roche) instrument, following the described procedure  (37, 38). The reaction mixture contained, in a final volume of 20 mL, 1Â Kapa SYBR Fast qPCR reactive, each primer at a 200-mM final concentration. The following templates were used: (i) the FR1 liquid culture, (ii) the phage preparations from the FR1 untreated culture, and (iii) the phage preparations from the FR1 mitomycin C-treated culture. Templates were incubated for 5 min at 90°C to allow bacterial cell and phage particle lysis, and then 1 mL was added to the PCR mix.
ACKNOWLEDGMENTS We thank  Eugenio Paccagnini  and  Pietro Lupetti  ( Department of Life Sciences, University of Siena ) for their help with transmission electron microscopy. This work was supported in part from the  Italian Ministry of Education, University and Research (MIUR-Italy)  under grant number  20177J5Y3P  ( Progetti di Ricerca di Rilevante Interesse Nazionale-Bando 2017 ) and in part from the  European Union   Seventh Framework Program  ( FP7/2007-2013)  under grant agreement  241446  (project  ANTIRESDEV ).
The latter provides a forum where scientists from industry, government, and academia can exchange information and initiate activities to advance science related to DART and develop a consensus on the appropriate use of experimental data for human health risk assessment. One of the key challenges in replacing animal reproductive toxicity studies is the highly complex nature of the reproductive cycle, such that not all mechanisms involved can be reflected by the NAMs  [71]. Therefore, it is recommended to use a panel of NAMs which cover different stages of the reproductive cycle as part of a tiered approach to address DART  [33, 36, 4, 59]. In this work, we present the performance of three NAMs, alone and in combination (i. e. , Integrated Testing Strategies), for their ability to assess the teratogenic potential (from the developing embryo to birth) of a panel of chemicals.
The NAMs were the DART decision tree model, the devTox assay and the Zebrafish Embryotoxicity Test (ZET). The starting training set of 85 test chemicals comprised 46 which were classified as non-teratogenic and 39 which were classified as teratogenic, based on information provided in the open literature and public databases (e. g. , ECHA, LOLI and the EMA Summary of Product Characteristics  [SmPC] ). All 85 (including 3 non-conclusive) chemicals were tested in the in silico DART decision tree model, whereas 82 (including 1 non-conclusive) and 61 (including 4 non-conclusive) were tested in the devTox assay and the zebrafish assays, respectively. The DART decision tree helps evaluate chemicals for their potential for developmental and reproductive toxicity. It is an open access model which has been included in OECD QSAR Toolbox for some years [OECD QSAR Toolbox] and is recognized by US EPA and selected for the Tox-Cast program.
It is based on in vivo experimental DART data on 716 chemicals (664 positive, 16 negative and 36 with insufficient data) which are grouped into defined receptor binding and chemical domains  [78]. An alert is given if a chemical has receptor binding properties and structural features consistent with chemical structures known to have toxicity for DART endpoints. It has been proposed as a screening method to identify chemicals of potential concern and as a component of a weight-of-evidence strategy to fill data gaps for biological coverage without generating additional test data  [78]. The devTox assay is a human induced pluripotent stem (iPS) cellbased assay in which the readout is based on changes in the metabolome of the cells in response to a chemical. More specifically, the assay compares the ratio of cystine and ornithine to predict the concentration at which a chemical exhibits developmental toxicity potential  [55, 80].