dataesr/scientific-paragraphs-categorization huggingface dataset:
Approaches and tools Some commercial software tools use matrix-based computation (e. g. Simapro, Umberto) and could thus work with the process differentiation framework for the calculation of temporally differentiated LCI. To our knowledge, this option has not been implemented comprehensively in DLCA studies because LCA databases do not offer temporal details. The ESPA method has also not been developed into a computational tool and its implementation has been limited to one simplified case study  (Beloin-Saint-Pierre et al. , 2017). Nevertheless, two options currently exist for full DLCI computations and are introduced in the following sub-sections. 3. 3. 2. 1. DyPLCA. DyPLCA has been implemented as a web tool (available at http://dyplca. univ-lehavre. fr/), originally presented by  Tiruta-Barna et al.
(2016) , which uses the depth-first graph search strategy. The main parameters that balance accuracy vs. computation time in this tool are the temporal resolution of function integrals and the back time span. Common values for both are respectively 1 day and -50 years (i. e. 50 years before the period of occurrence for the FU). The computational intensity of the DLCI calculation has thus been resolved by a trade-off between accuracy and cut-offs. The processrelative temporal distributions can have different levels of detail to describe the flows in the system models. For instance, they can be detailed for foreground processes, as presented in  Shimako et al. (2018) , and can be rather generic for the background datasets. DyPLCA currently works with a temporal differentiated ecoinvent v3. 2  (Pigné et al.
, 2020) , providing generic temporal descriptions to most background inventory processes. The DLCI results can be further used with static or DLCIA methods, as shown in studies on bioenergy production from microalgae  (Shimako et al. , 2016)  and on grape production  (Shimako et al. , 2017). 3. 3. 2. 2. Temporalis. Temporalis  (Cardellini et al. , 2018 ) is a free and open source package of the Brightway2 LCA tool  (Mutel, 2017) , using the best-first search strategy. The tool is fully compatible with many existing commercial LCA databases, but temporal descriptions of datasets are currently not provided. Temporalis does not require a fixed and continuous temporal resolution over any system models to provide DLCI or results for the impact assessment. Nevertheless, a DLCIA method for GWP based on the IPCC methodology (2013), is included.
, 2016) , which builds on three cultural perspectives, proposed by  Hofstetter et al. (2000). These perspectives are associated with different sets of calculation assumptions, including CFs with different THs for each impact category. For example, the "hierachist" perspective retains a 100-year TH for GWP and other categories, while "individualist" and "egalitarian" perspectives respectively use THs of 20 and 1000 years. Furthermore, very long THs are suggested for some impact categories such as for climate change (i. e. 1000 years) and ionising radiation (i. e. 100,000 years). The ILCD handbook (2011) and the SimaPro Database Manual (PRé, 2016) provide additional insights into the use of THs in different LCIA methods, but there is not yet any standard on how to deal with long-term impacts and related uncertainties within all categories.
Such a correspondence is desirable if CFs vary significantly over time, but it is currently difficult to implement in the available databases and software tools. 3. 4. 2. 2. Short-vs long-term analysis. Much like it has been said in the definition of the goal & scope (Section 3. 1. 3), the adjectives of short-and long-term have been used to describe the temporal scope of LCIA methods  (Arodudu et al. , 2017; Chowdhury et al. , 2017; Reap et al. , 2008b). This lack of a precise temporal definition when stating short-, medium-and long-term can be partly explained by the differences in time scales of life cycles and environmental impacts for different systems. Furthermore, a commonly accepted standard does not yet exist to deal with long-term impacts and related uncertainties within all categories.
, 2002) , water scarcity  (Boulay et al. , 2015) , human toxicity  (Manneh et al. , 2012)  and photochemical oxidant formation  (Shah and Ries, 2009). Such frequencies therefore highlight relevant temporal resolutions for the temporal differentiation of elementary flows in databases and DLCIs. Temporal inherent features may vary with hourly, daily, monthly or yearly constraints depending on temporal patterns or modelling time steps of the characterisation models  (Collet, 2012; Owens, 1997b). The temporal scope of impact assessment itself may be aligned with the dynamics of governing biogeochemical processes to more accurately represent certain fate dynamics. For instance,  Liao et al. (2015)  found that common seeding-to-harvest assessment periods in agricultural LCAs do not correspond to the actual dynamics of fertilising substances, some of which contribute to eutrophication during the next crop rotation.
Temporal considerations in the goal and scope definition Temporal considerations, presented in Section 3. 1, mostly offer partial, implicit and qualitative information about when LCA studies are temporally representative or for when potential impacts are occurring. Temporal scopes of results in LCA studies are sometimes more explicitly defined, but they are not commonly provided, which hinders transparent and fair comparisons among results of different studies  (Caffrey and Veal, 2013; Dandres et al. , 2012; Huang et al. , 2012; Woo et al. , 2015). Lack of consistency in the vocabulary that describes the models' components and the linked LCIs or LCIA methods also brings some issues to simplify the exchange of temporal information. These obstacles should be addressed to access the wealth of information and metadata that is currently provided in LCA databases and studies.
Two propositions are thus made for potential development pathways: 1. Recognise and use a common structure and vocabulary to discuss and exchange on the subject of temporal considerations in the DLCA framework, databases and studies (see Section 2 for propositions). 2. Employ common metadata formats to automate the exchange of temporal information and thus provide access to the wealth of temporal information that is currently provided in LCA databases and studies, as well as to manage the expected significant increase in data requirements for this subject. A specific example for automation is the development of guidelines to define the different temporal scopes consistently and periods of validity that should be provided in LCA databases for all datasets and studies for all processes. The authors are well aware of the challenge in asking a community to accept a common framework for such a broad subject, but data providers would benefit from the identification of common patterns and of "translation" options between data format.
No with process differentiation, which turns out to be challenging due to the large amount of temporal information needed whenever a comprehensive and detailed description of the life cycle is expected. The temporal differentiation of flows with process-relative temporal distributions has also been shown to be feasible, but has not yet been implemented in commercial databases. Given the current challenges and options, the next steps of development for time-dependent system modelling are suggested, as follows: 1. Carry out a comprehensive review of methodologies and approaches where dynamic modelling is considered in other fields of research to identify strategies that might not yet be proposed for the DLCA framework. For instance, DLCA is intrinsically rooted on modelling the dynamics of systems.
Many models' components describe a large system, featuring several thousands of processes in the technology matrix and many hundreds of elementary flows in the intervention matrix. The introduction of timed variables in the matrixes and vectors of calculation can induce non-linear trends in the causal relationships. Delays might appear in the datasets (e. g. storage processes) or in the interventions (buffer zones at technosphere/ecosphere interface). The discontinuities form due to temporal switch between technical flows (e. g. seasonal supply) or abrupt release could also arise. All these aspects cause a real issue for solving, simulating and providing DLCA results under a reasonable computation time. Nonetheless, system dynamics is a well-studied topic in applied mathematics and control theory.
e. data management and gathering). 3. Consider that some technosphere flows or processes might have fixed historical settings when human activities are represented. For example, all elementary flows that are linked to the construction phase of hydro power plants in a country will not have different periods of occurrence if they are linked to past or future products. 4. Identify and define the temporal correlation of flows in current databases. From a mechanistic point of view, these relationships exist (e. g. carbon content in CO 2 from tailpipe emission depends on fuel consumption) and LCA practitioners can use them when creating datasets. By making these relationships explicit, one could simplify the introduction of temporal considerations in datasets, as some are intrinsically linked over time (e.
g. nitrate emissions at the crop level are strongly related to the crop production cycle). 5. Find solutions for temporal considerations with co-product management and allocation. Indeed, the avoided product approach raises the question of how avoided product(s) can be modelled in time. Should it be simultaneous to the co-product or following the co-product creation, assuming that the replacement will take place afterwards? A non-physical allocation raises other questions about temporal considerations. For instance, to ensure carbon balance, corrections are made when multi-output processes are split into several singleoutput processes. Artificial positive and negative CO 2 emissions are added up to match the carbon fixations to the carbon content of a product  (Weidema, 2018). This approach is, for example, used in the ecoinvent database under "At Point of Substitution (APOS)" and "Cut-off" system models  (Wernet et al.
Inventory calculation: keeping time in the LCI The recently developed conceptual frameworks and tools (see Sections 3. 3. 1 and 3. 3. 2) employ a common computational structure based on graph search algorithms to calculate DLCIs. This structure uses process-relative temporal distributions to describe the flows within system models. Such a consensus suggests that the computational structure for DLCA and the corresponding tools could become a standard, but implementation challenges are still limiting their use. It thus seems relevant to: 1. Carry out more DLCA studies with these tools to increase the understanding of the LCA community and to develop the use of processrelative descriptions in LCA databases. 2. Check the importance of temporal resolution for flows in DLCI. A LCA system can represent many dynamics, because of the size of the system and the inherent temporal variations of the production processes, emissions and resource consumption, as well as of the environmental mechanisms.
For instance, by determining the use of different THs or setting different end-years in the dynamic results when using period specific CFs. 2. Propose a clear list of the relevant time scales for each LCIA category to fix database requirements in the definition of elementary flows for any datasets. As explained before, environmental mechanisms for different impacts of substances will occur within diverse temporal scopes. These specific periods for each impact category can therefore provide guidance on the required resolution of temporal distributions to describe the elementary flows of LCIs, while minimising the temporal uncertainty. 3. Update the considered background concentrations in ecosphere models (i. e. impact assessment methods) when they substantially change the obtained CFs for an impact category.
Conclusions Considerable efforts have been made in the last 20 years to include temporal considerations within the LCA framework and to show that accounting for such aspects significantly affects the results of, at least, some case studies. For instance, LCA studies on systems with long lifespan (e. g. buildings) can benefit from models and tools where the dynamics of energy flows are considered with more details (i. e. variations and evolution). Periods of validity for datasets, which represent rapidly progressing technologies (e. g. photovoltaic cells), are important temporal information, provided in some LCA databases. Furthermore, dynamic LCIA methods have now been developed to account for impacts that vary significantly when the timing of emission change. Overall, the suggested approaches, tools and strategies increase the temporal representativeness of LCA studies and decrease the temporal uncertainty of models the technosphere and its impacts.
Nevertheless, their uses in current LCA studies are still uncommon, which can be explained mainly by a lack of consistent descriptions and the challenges of gathering temporal information. With that in mind, we offer some propositions for the next steps of developments in the DLCA framework. A glossary is proposed to build a common and consistent understanding on the key concepts that often come up in discussions on the subject. This common understanding should then help in the use of the already available information that can be found in LCA databases and studies under different names. The consistent description of this metadata should also simplify the automated exchange of information between different software options and practitioners. The temporal boundaries of DLCIs (i. e. temporal scope) should be defined within a calendarbased description (e.
g. between 1990 and 2020) to improve the potential for representativeness of the impact assessments and the fairness of comparison between systems. In addition, our overview on temporal considerations in the LCI phase suggests that a preferred pathway seems to emerge in the computational approach (i. e. graph search algorithms) for DLCA, but it will require the use of process-relative temporal distributions to describe flows in datasets (i. e. input format). This information should then provide temporal distributions for all elementary flows. A balance between necessary data collection efforts and reduction of uncertainties should define the temporal resolution of such distributions. It will also be important to consider the chosen DLCIA methods when selecting the temporal resolutions of flows.
Structural characterization of the hantavirus glycoproteins We produced the soluble ectodomains of the hantavirus Gn H /Gc heterodimer using a single chain construct in which a soluble linker bypassed the Gn TM and intraviral/cytosolic segments to connect directly to the Gc N-terminus (Figures  1A  and B ). Of the various hantavirus orthologs we tested, only constructs from the related New World hantaviruses ANDV and MAPV (accession numbers NP_604472. 1 and YP_009362281, respectively) yielded enough protein for structural studies (an aa sequence alignment of Gn and Gc from selected hantaviruses is provided in Figure  S1 ). We obtained monoclinic crystals for MAPV Gn H /Gc diffracting to 2. 2 Å resolution and rhombohedral crystals for ANDV Gn H /Gc diffracting to 2. 6 Å (Table  1 ) and determined the structures by molecular replacement.
We also increased the resolution of the map by using a larger dataset of imaged particles combined with strategies for removing noise and accounting for conformational flexibility of the spike itself. In addition, because purified hantavirus particles display a broken surface lattice, we restricted the dataset to include only regular patches on the virion surface for 3D classification. This strategy therefore allowed us to use for averaging a sub-volume of the lattice containing a central spike and the contacts with its four immediate neighbors. At 11. 4 Å resolution, the resulting cET map of Tula virus is representative of the hantavirus prefusion spike due to high sequence identity (see Figure  S1 ) of the glycoproteins across hantaviruses (e. g. Tula virus Gn and Gc display 63% and 73% amino acid sequence identity with their ANDV counterparts, respectively).
SUPPLEMENTAL FIGURE TITLES AND LEGENDS Figure Top view of the quasi-atomic model for the PUUV spike (PDB: 5FYN) obtained by docking the X-ray structure of PUUV Gn H (PDB: 5FXU) into the 16 Å resolution cET map of Tula virus (EMDB: EMD-3364) reported previously  (Li et al. , 2016) , which had the inverted hand. The Gn molecules are in surface representation (top panel) with the two discontinuous peptides identified as part of the epitope of the PUUV neutralizing mAb 5A2  (Heiskanen et al. , 1999)   3G1, 1C9, and Y5 colored as indicated  (Heiskanen et al. , 1999; Koch et al. , 2003; Xu et al. , 2002). Note the orange and pale blue epitopes spanning across spikes. to compare the different flexion about the domain II hinge. The conformation of ANDV-GnGc-H953F, which fits best the cET map (Table  2 ), is also very similar to that observed in the previously reported structure of HNTV-Gc/scFv-A5 (PDB: 5LJY), which formed a crystallographic Gc dimer recapitulating the contacts between spikes on the viral particle  (Bignon et al.
, 2016; Rissanen et al. , 2017)  to the complete Gc ectodomain. Drosophila Schneider 2 cells stably transfected with the Gn H /Gc construct secreted large amounts of protein, which were purified using affinity and size exclusion chromatography (SEC). The proteins behaved mostly as monomers in solution as assessed by multi-angle light scattering (MALS) (data not shown). Using the same approach, we produced Gn H /Gc heterodimers from two hantavirus orthologs, Maporal virus (MAPV, strain HV-97021050, NCBI code YP_009362281. 1) and from Longquan virus (LQNV, strain Longquan-Ra-90, NCBI code AGI62346. 1). We also introduced the H953F mutation in Gc of the Andes virus construct to destabilize its transition to the post-fusion form (as explained in the text). The aa sequence identity of the polyprotein precursor of Andes viruses with MAPV and LQNV is 85% and 40%, respectively.
1 M Tris-HCl pH 8. 5, and those of MAPV-Gn H /Gc in 0. 1 M MgCl2, 0. 1 M Tris pH 8. 5 and 17% (w/v) PEG 20000. Crystals were flash-frozen by immersion into a cryo-protectant containing the crystallization solution supplemented with 20% (v/v) glycerol, followed by rapid transfer into liquid nitrogen. The structure of ANDV-Gn H /Gc was solved by molecular replacement (MR) with Phaser from the suite PHENIX  (Liebschner et al. , 2019)  using PUUV Gn H (PDB code 5FXU) and HNTV-Gc (5lJY) as search models. The final model was built by combining real space model building in Coot  (Emsley et al. , 2010)  with reciprocal space refinement with phenix. refine. The final model, which was validated with Molprobity  (Williams et al. , 2018) , contained aa 23-374 of Gn and 659-1069 of Gc with two disordered regions: aa 124-126 (loop i2DA) and 697-702 (loop C0D0).
Crystals grown in the neutral pH condition belonged to the tetragonal space group I4 with two molecules in the asymmetric unit and diffracted to 2. 4 Å resolution. We used experimental phasing with sodium iodide to determine their structure, as we found no good search model for MR. We soaked crystals for 20 minutes in mother liquor supplemented with 500 mM NaI before plunging them directly into liquid nitrogen. In order to optimize the anomalous signal, we collected a highly redundant dataset merging three independent datasets collected from two different crystals using a kappa goniometer. The final SAD dataset showed anomalous signal up to 3. 6 Å. Substructure determination and phasing were carried out using the SHELX suite  (Sheldrick, 2010 ) and an initial model was built using Phenix AutoBuild  (Terwilliger et al.
We obtained crystals diffracting to 1. 9 Å growing at pH 7. 5 in a condition containing 25% (w/v) PEG 6000, 0. 1M LiCl, 0. 1M Hepes pH 7. 5, and 20% (v/v) glycerol. We determined the structure using PUUV Gn H (5FXU) as search model. The resulting HTNV Gn H structure resolved aa 20-373 with a single internal break in the η1 loop (aa 281-290). The X-ray diffraction data reported in this manuscript were recorded at beamlines Proxima I and II at the Synchrotron Soleil (St. Aubin, France). The statistics of all the crystals and refinement are provided in Table  1.
Conservation analysis The conservation scores per residue shown in Figures  S1  and S3  were obtained by using the program Espript  (Robert and Gouet, 2014)  and a multiple sequence alignment generated by Clustal omega  (Sievers et al. , 2011)  using a set of 69 hantavirus M segment sequences extracted from  (Zhang et al. , 2013). The GenBank accession numbers for the sequences used in the analysis are: JX465398, JX465397, JX465402, JX465399, JX465400, JX465401, JF784178, NC_010708, EF641799, EF641806, HQ834696, FJ539167, EU929073, EU929074, HM015219, EF543526, JX465390, JX465391, JX465392, JX465393, JX465394, JX465395, JX465403, Y00386, AF143675, AB620029, AY675353, AB027115, GF796031, AY168577, AJ410616, GQ205412, AY961616, JQ082301, AF288298, GU592827, EF990916, L08756, GQ274938, HM756287, AB297666, AB433850, EF198413, AJ011648, EU072489, AJ011647, EU072488, Z69993, X55129, L36930, DQ177347, L39950, DQ284451, DQ285047, AB620104, AB620107, U26828, AB620101, U36801, U36802, AF030551, L33474, L37903, AF291703, AF028024, AF005728, FJ608550, AY363179, AF307323.
, 2017). Contrast transfer function (CTF) parameters were estimated using CTFFIND4  (Rohou and Grigorieff, 2015)  and a dose-weighting filter was applied to the images according to their accumulated electron dose as described previously  (Grant and Grigorieff, 2015). These pre-processing steps were carried out using a custom set of scripts named "tomo_preprocess" (https://github. com/OPIC-Oxford/TomoPreprocess). Tilt images were then aligned using gold fiducial markers in IMOD  (Kremer et al. , 1996) , corrected for the effect of CTF by phase flipping and used to reconstruct the tomograms by weighted back projection in IMOD  (Mastronarde and Held, 2017). Subtomogram averaging was performed in Dynamo  (Castano-Diez et al. , 2012). Initial particles for this refinement were created by modelling the surface of the membrane using the Dynamo's tomoview function and defining their initial orientation normal to this surface.
These initial 'seeds' were more densely populated than the true spikes and overlapping spikes were removed based on cross-correlation after each iteration in the refinement. Refinements were carried out with C4 symmetry using the existing Tula virus spike map as a reference (EMD-3364). The map was low-pass filtered to 50 Å resolution to avoid model bias. A custom script (PatchFinder; available at https://github. com/OPIC-Oxford/PatchFinder) was used to restrict the dataset to only those particles that having regular glycoprotein surface patches. The script locates the eight closest spikes for each particle, and these are classified as interacting neighbors only if their lattice constraints are within set tolerances (maximum 49-Å and 25-degree deviation from expected location). Spikes were defined as being part of a lattice if they had at least three interacting neighbors.
The final dataset was subjected to 3D classification in RELION  (Bharat and Scheres, 2016)  before further refinement in Dynamo. The amplitudes of the final maps were weighted to correct for the effect of dose-weighting of the original images using a custom script (available on request). The final reconstruction, comprised of ~18,064 subvolumes, was filtered to 11. 4 Å as determined by FSC (0. 143 threshold). The threshold value for the rendered isosurface was determined according to the molecular weight of the viral ectodomain proteins assuming an average protein density of 0. 81 Da/Å³.