dataesr/scientific-paragraphs-categorization huggingface dataset:
Electron microscopy sample preparation and data acquisition Purified NTCP EM -Nb or -Mb complexes were applied to glow-discharged Au 300 mesh Quantifoil R1. 2/ 1. 3. Typically, 4 µl of sample at 3-4 mg ml -1 was applied to the grids, and the Vitrobot chamber was maintained at 100% humidity and 4 °C. Grids were screened in 200 kV Talos Arctica microscope (ThermoFisher) at the IECB cryo-EM imaging facility. Final data collection was performed in 300 kV Titan Krios microscope (ThermoFisher) at EMBL-Heidelberg Cryo-Electron Microscopy Service Platform, equipped with K3 direct electron detector (Gatan). Final images were recorded with SerialEM 58 at a pixel size of 0. 504 Å. Dose rate was 15-20 e -pixel s -1. For NTCP EM -Nb87 complexes, 21,792 images were recorded with -0. 5 to -1. 5 µm defocus range.
Cryo-EM data processing, model building and structure analysis All datasets were processed with cryoSPARC v2 and v3  59. Movies were gain corrected, and aligned using in-built patch-motion correction routine. Contrast transfer function (CTF) parameters were estimated using the in-built patch-CTF routine in cryoSPARC. Low-quality images were discarded manually upon visual inspection. For the NTCP EM -Mb91 complex, 5,796,802 particles were templatepicked from 21,390 micrographs, and selected through several rounds of 2D, as well as 3D ab initio classifications. Particles from 3D ab initio classes displaying interpretable density for transmembrane helices were pooled, and used for homogenous refinement (Extended Data Fig. 2 ). Cryo-EM density corresponding to both detergent micelle and megabody scaffold were masked out, and particles were further subjected to local refinement using a fulcrum that localized to center of NTCP EM transmembrane region.
Secondary structure predictions using Psipred  65  and bacterial homologue structure (Protein Data Bank ID 3ZUY) were used to help initial sequence assignment. Initial Nb models were created with I-TASSER  66  , and then fit as rigid bodies into the density, followed by manual building and modification in Coot  63, 64. The inward-facing conformation in the NTCP EM -Nb87 complex was built by fitting core and panel domains from NTCP EM -Mb91 structure as separate rigid bodies into the density, followed by manual modification in Coot. All atomic models were refined using PHENIX  67. Structural analyses were carried out as follows: protein cavity calculations with CASTp 3. 0 68 , pore calculations MOLEonline 2. 5  69  , proteinprotein interfaces with PISA  70.
Acknowledgements We acknowledge the  European Institute of Chemistry and Biology cryo-EM imaging facility  for support with cryo-EM sample screening and initial data acquisition, the  European Molecular Biology Heidelberg Cryo-Electron Microscopy Service Platform  (for NTCP  EM -Mb91  and  NTCP EM -Nb87 ), and the  Institut Pasteur cryo-EM Nanoimaging Facility (for NTCP EM -Nb91 ) for support in image acquisition. Access to  Institut Pasteur cryo-EM Nanoimaging Facility  was partly supported by  CACSICE infrastructure grant  ( ANR-11-EQPX-008 ). We thank  K. Willebal  and Nanobody4instruct center for technical support in nanobody generation and  T. Uchanski  for the Mb construction. This research was funded by the  European Research Council (ERC)  under  European Union   Horizon 2020 Program  (grant  771965 ) with additional contributions from  ANRS  (grant  17125/18141 ),  IDEX Senior Chair Universite de Bordeaux , and  Region Nouvelle-Aquitaine  (grant  8166910 ) (all to N.
Data availability Structural models of NTCP EM -Nb87 and NTCP EM -Mb91 complexes have been deposited in the Protein Data Bank (PDB) with accession codes 7PQG and 7PQQ, respectively, and the corresponding cryo-EM maps were deposited in the Electron Microscopy Data Bank (EMDB) under accession numbers EMD-13593 and EMD-13596. Materials are available upon reasonable request and signing of a Material Transfer Agreement.
Reactive transport modelling allows modelling and estimating the distributions in space and in time of chemical reactions occurring in a specific environment. The resulting tailings releases are compared with those calculated through the approach, based on a first order law of kinetics, used in the ecoinvent database. The results from the ecoinvent approach are calculated both with generic and sitespecific parameters. These different approaches are applied on the case-study of a polymetallic sulphidic ore from a mine in Northern Europe. The tailings releases obtained through the different approaches differ. Overall, the reactive transport model leads to quantities released at both short-term and long-term timeframes being smaller than those calculated with the ecoinvent approach. As a result, the impacts on ecotoxicological and toxicological categories show differences between two to eight orders of magnitude.
, 2014). Edraki et al. ,  underline that data availability regarding tailings management and the associated emissions is a reason of this noninclusion (  2014 ). An assessment of the Web of science bibliography database made in June 2020 shows that about 400 scientific publications address the specific topics of "LCA" and "mining" while ten times less publications address "LCA" and "tailings". Yet, when taken into account, tailings disposal generally contributes to the environmental impacts of mining operations assessed in a life cycle perspective. For example, a LCA of a lead-zinc ore mining and concentration system showed that tailings disposal is the highest contributor for 4 environmental impact categories out of 18 using the ReCiPe life cycle impact assessment (LCIA) method  (Tao et al.
, 2019). A similar conclusions can be drawn when assessing the mining datasets of the ecoinvent 3. 6 database, a life cycle inventory (LCI) database widely used in the LCA community  (Wernet et al. , 2016). As an example, for the dataset 'copper concentrate, sulphide ore, copper mine', tailings management contributes the most to 7 impact categories out of 18 using ReCiPe as well. Such important contributions to the environmental impacts emphasize the need to accurately accounting for the releases induced by tailings when carrying out the LCA of mining operations. However, the lack of consideration for tailings releases may, in part, pertain to the difficulty for mining companies to obtain precise on-site measurements of direct leakages from tailings to groundwater. Only two studies were found to rely on on-site measurements from mining companies for quantifying these releases to water.
(2009)  estimated them based on both data drawn from literature and data obtained from experiments on samples of the mine tailings at a laboratory scale, further complemented by numerical modelling for longer-term horizons;  Broadhurst et al. (2015)  primarily derived their groundwater releases from literature information, their own knowledge and calculations. The ecoinvent database, also relies on literature data and calculations to account for releases to groundwater from tailings, considering worldwide averages in terms of tailings composition or substances leakages to water. One may identify different limits with respect to these different quantification approaches: • Considering worldwide averages for the tailings composition may show significant uncertainties and a certain lack of representativeness, given that ores and tailings compositions are significantly different from one deposit to another, which accordingly may not reflect the characteristics relative to a specific mine site  (Doka, 2008; Rader et al.
In fact, the goal of reactive transport modelling is, to better understand and represent the distribution in space and time of the chemical reactions occurring in a specific environment  (Muniruzzaman et al. , 2020). In this study, reactive transport modelling is applied on releases associated with tailings generated in an European copper mine. The results are then compared with the approach used in the ecoinvent database, using both generic and site-specific data. The different compared approaches as well as the considered case study are described in the next chapter. Chapter 3 describes and discusses the results of the comparison between the approaches used to quantify tailings releases to the environment. Chapter 4 concludes on the assessment and provides some perspectives on the coupling between RTM and LCA.
Models to consider releases from tailings to groundwater Releases to groundwater resulting from tailing ponds after mine closure can be modelled through different methods. This section presents the three approaches applied in this study to obtain these releases for each substance. These three approaches consider transfer coefficients expressing the fraction of the element presents in the tailings that can be released during a specific time period. From these transfer coefficients the releases are derived using Eq. (1) where the initial mass of the substance in the tailings is obtained from on-site data collection (see Section 2. 2). In this study, the tailings releases are considered over two time frames representing respectively short-term and long-term behaviors (see Section 2. 3). m i (t) = TK i (t)*m initial,i (1) where m i (t): mass of substance i released to groundwater over a time period t TK i (t): transfer coefficient of substance i that can be released during a time period t m initial,i: : initial mass of substance i in the tailings.
Generic and semi-specific approach: Use of Doka's model as applied in the ecoinvent database The primary ecoinvent dataset "treatment of sulfidic tailings"  (Althaus and Doka, 2018)  was developed by Gabor  Doka (2017 Doka ( , 2009 Doka ( , 2008)) , originally to consider releases resulting from landfills and then adapted for tailings ponds. For each chemical element present in the tailings, the model defines a TK i (t) that expresses the cumulative mass fraction released at time t. Generic TKs were determined for each element based on average tailings composition, leaching concentration and climatic conditions derived from literature data. In the ecoinvent mining datasets, these TKs are applied to quantify releases to groundwater following Eq. (1) regardless of the composition of the sulfidic ore mined and the geographic context, thus leading to significant uncertainty factors associated with these TKs.
Indeed, uncertainty values on parameters used to derive TKs and expressed as GSD vary from 147% to 11500%  (Doka, 2017 (Doka, , 2009 (Doka, , 2008)). This dataset "treatment of sulfidic tailings", is commonly used to model tailings management in diverse sulfidic ores mining datasets in the ecoinvent database regardless the type of ore mined or the mine's geographic location. If generic TKs were used until the release of ecoinvent 3. 5, the model developed by Doka could also be applied to obtain more specific TK associated with a given mine site with a given tailings composition. This was done for ecoinvent 3. 6 in which specific literature data on tailings composition and climatic conditions of various mine sites were gathered allowing defining TKs for these specific cases turning the number of datasets concerning the treatment of tailings from 2 in ecoinvent 3.
e. at mine closure) and the volume of water that flows through the tailings depending on the site climatic conditions such as rainfall and evapotranspiration. All the parameters included in Eq. (  2 ) and Eq. (  3 ) are more specifically described in Table  1  and can be quantified from onsite data collection (see Section 2. 2) or using generic values. In this study, Doka's model will be applied to derive semi-specific TKs (referred to as semi-specific scenario) taking into account the specific composition and climatic conditions of the studied mine site (see Section 2. 2). These calculated semi-specific TKs and the associated releases will be compared to the ones from the generic dataset "treatment of sulfidic tailing, off-site -GLO" (referred to as generic scenario) and from the most representative dataset, regarding both the main exploited metal and the.
To facilitate the solving of the thermodynamics and kinetics equations that drive the transport phenomena, specific databases on properties of the chemical elements and specific software were developed  (Jacques et al. , 2008; Lichtner, 2007; Mayer et al. , 2002; Steefel, 2009; Thiéry, 2015). In this study, a representative cross-section of the tailings dam under assessment is considered in a 2D model (see Fig. 1 ). In this cross-section, several hydrogeochemical units are considered, each with their specific properties: porosity, permeability, dispersivity, mineralogical composition and pore water chemical composition. The numerical model applied is the coupled reactive transport simulator MARTHE-PHREEQC  (Thiéry, 2015)  that couples a geochemical code, PHREEQC  (Parkhurst and Appelo, 2013; Parkhurst and Wissmeier, 2015) , to a flow and mass transport code MARTHE  (Thiéry, 2013; Thiéry and Picot-Colbeaux, 2020).
In the absence of information as to potential future post-mining operations, it is decided in this study to not consider any tailings management operations that could potentially limit releases to groundwater from tailings in the long-term. Different datasets are needed to apply Eq. (  1 ) in the different approaches. Table  3  provides a summary of the data and their sources needed to perform the TKs calculation for each scenario. The first dataset is the initial tailings composition (i. e. m initial,i ) given in Table  2. This composition is quantified based on the assessment of the tailings mineralogical composition using QXRD (quantitative X-Ray Diffraction) on a specific tailing sample and is used for the semi-specific and the site-specific scenarios. When available for the mine site, data needed to derive Eq.
Resulting TKs and impacts Fig. 2  presents the TKs obtained for the four scenarios and for the short-term and the long-term timeframes. The graphically represented TKs are the ones that correspond to the substance initially present in the studied tailings (cf. Table  2 ). When looking at the scenario 'Generic' (linked to the ecoinvent dataset "treatment of sulfidic tailing, off-site -GLO") or 'Generic, Cu' (linked to the ecoinvent dataset "treatment of sulfidic tailings, from copper mine operation, tailings impoundment -RU") for which the model as applied in ecoinvent 3. 6 are used, TKs are available for a larger amount of substances. In fact, they are available for substances present in the average tailings composition considered in the Doka model  (Turner et al. , 2019). These generic TKs values are reported in the Supplementary Information.
These substances that are not initially present in the tailings under study but assumed to be present in the average tailings composition are used to derive TKs for the "Generic" scenario. Fig. 3  shows the specific contribution of such substances not present in the tailings generated by the mine under study for the "Generic" and "Generic, Cu" scenarios. Results presented in both Table  4  and Fig. 3  show large differences between the different scenarios, with a maximizing trend when looking at the 'Generic' and the 'Generic, Cu' scenarios based on a first order law of kinetics equations and a minimizing trend when looking at the 'Sitespecific' scenario based on reactive transport modelling. More specifically and as summarized in Table  4 , comparing results from the 'Site specific' scenario and the 'Semi-specific' scenario underlines the differences implied by the model used to quantify the releases associated with the tailings from the same initial composition dataset.
Representativeness of tailings releases modelling approaches Table  5  summarizes the different elements that are effectively considered in each scenario. Every scenario differs in terms of data needed to run the model and in terms of sophistication of the considered model. If for the 'Generic' and the 'Generic, Cu' scenarios, resulting releases are directly available in the ecoinvent database, for the 'semispecific' scenario, a data collection considering the site tailings characteristics and climatic conditions is necessary to perform the calculation manually or using  Doka's dedicated Excel's file (2020). More initial data as well as competencies in modelling hydrogeochemical systems are needed in the case of the reactive transport model ('Sitespecific' scenario). Using a more sophisticated model helps reduce the uncertainty associated with the model in the obtained results.
In the 'Generic' scenario based on the ecoinvent generic dataset, the uncertainty, expressed in terms of a geometric standard deviation (GSD), associated with the resulting long-term releases varies between 100% and 3,158%. This parameter uncertainty takes into account uncertainty associated with the considered waste composition and the TKs calculations  (Doka, 2020). Expressing uncertainty in terms of GSD implies expressing the uncertainty as following: there is a 95% chance that the studied value belongs to the interval [µ g /GSD; µ g *GSD] where µ g expresses the geometric mean. Thus, a GSD value of 3,158% implies quite a large interval where maximal values might not even be physically reached (for explanation on uncertainty as defined and expressed in the ecoinvent database see  (Muller et al.
, 2016). Parameter uncertainty can be reduced using on-site data, as it has been done here in the 'Semispecific' scenario. However, if the parameter uncertainty is expressed for the datasets available in the ecoinvent database  ('Generic' and 'Generic, Cu' scenarios) , it was not quantified for on-site parameters leading to no quantification of the uncertainty reduction potential associated with the use of more reliable data. Even if parameter uncertainty remains in on-site data (due to the data acquisition method for example, see next paragraph), one may consider such data as less uncertain compared to data coming from average mining sites drawn from literature. Despite reactive transport modelling provides sound bases for understanding coupled processes and thus improve predictions  (Glassley et al.
, 2003; Steefel and Maher, 2009; Steefel et al. , 2015 Steefel et al. , , 2005)) , uncertainties must also be reported for more mechanistic and site specific models for the following reasons. i) The whole set of site-specific data required for a fully comprehensive reactive transport model may never be available at the scale of the mining site and over sufficient time frames to calibrate unequivocally the model. However, one can expect that the site-specific information about the mineralogical data will be more systematically acquired, and thus made available for numerical simulations, thanks to the development of on-site characterization tools aimed at optimizing the exploitation of the mines  (Dold, 2017). ii) Measured data are inherently error-prone. In particular, chemical data are measured at the lab, on samples taken on site.
More specifically, aqueous solutions properties may have evolved in the meantime because of their extraction from the field. iii) Simplifying assumptions are made in order to help the numerical convergence, speed-up the calculations, or because not all of the numerous ongoing processes are fully understood or identified. iv) Property values reported in thermodynamic and kinetic databases are also characterized by uncertainty or could be missing. For all these reasons, even if reactive transport modelling improves the estimation of the release of chemical elements from tailings ponds compared to more generic approaches, its results must also be considered with caution. The timeframe considered for estimating long term releases, namely 10,000 years, is also a matter of discussion since there is no such model capable of predicting the evolution of climate and its consequences on surficial facilities over such long time durations.
Results differ by two to eight orders of magnitude depending on the considered characterization model and impacts categories. Each of the approaches have pros and cons. While more reliable results may be brought by reactive transport models, their accuracy depend on the access to specific data from the mine site and on the required time and competencies to run the model. While generic data and models used in LCI database are less representative of a certain reality, they give nonetheless an idea of potential impacts induced by mining. In fact, no matter the value chain assessed in a LCA study, there is a good chance that production of metals and so the mining and the beneficiation of the associated ores are part of this value chain somewhere in the background system. In this case, the practitioner or the study sponsor have no access to the specific mine site and to the specific value chain of the metals present in the system.
In this absence of specific knowledge of the background, using generic data allows quantifying these impacts rather than ignoring the potential significant impacts in the life cycle of the system under study. Finally, this coupling allows to generate more robust and reliable data regarding releases from tailings and thus to generate more reliable and complete mining LCI datasets. One common assumption to the different approaches is the time duration used for the long-term perspective. In each approach, the model was applied in a 10,000 year time perspective without considering changes such as climatic variations that can possibly occur in this time frame. Results expressed in this long term time perspective must therefore be seen as a tendency rather than a reliable quantification of long-term releases associated with tailings.
This study added 27 new BINs for the BOLD database, 120 new for Madagascar, but only 159 were assigned a precise species name. By referring to the updated checklist of Madagascar fishes, 10 new species were detected for Madagascar. This number will probably increase when all the barcoded specimens become assigned to precise species names. These preliminary findings stress our poor knowledge of marine fish biodiversity in Madagascar and demonstrate the relevance of DNA barcoding in improving this knowledge.
Identification Process In the laboratory, living individuals from light traps, and dead fishes from mosquito nets, were sorted by morphospecies, i. e. , individuals presenting a similar morphology. Each morphospecies was identified at the lowest possible taxonomical level by using published keys  [28] [29] [30] , the identification of some specimens remaining to family or order level. One specimen per morphospecies and per sample was randomly selected and photographed with a camera Nikon model D90 equipped with a Sigma 105 mm macro lens. Post-larvae were euthanized with an overdose of clove oil. The camera was connected directly to a computer equipped with the Adobe Lightroom 5. 7 ® software ⎯ created by Adobe Systems Inc. in San Jose California USA-used for managing the photos and all the information related to each specimen.
Identification Process In the laboratory, living individuals from light traps, and dead fishes from mosquito nets, were sorted by morphospecies, i. e. , individuals presenting a similar morphology. Each morphospecies was identified at the lowest possible taxonomical level by using published keys  [28] [29] [30] , the identification of some specimens remaining to family or order level. One specimen per morphospecies and per sample was randomly selected and photographed with a camera Nikon model D90 equipped with a Sigma 105 mm macro lens. Post-larvae were euthanized with an overdose of clove oil. The camera was connected directly to a computer equipped with the Adobe Lightroom 5. 7 ® software-created by Adobe Systems Inc. in San Jose California USA-used for managing the photos and all the information related to each specimen.