dataesr/scientific-paragraphs-categorization huggingface dataset:
Borehole and spring databases A database of the 58 boreholes (Appendix 1) located on the volcano flanks has been assembled thanks to the BRGM archives collected since the 1960s. The database contains the main hydrogeological and geological available data: location, elevation, borehole depth, water level, aquifer geology, transmissivity, and hydraulic conductivity). Geological drilling descriptions were harmonized in order to take into account the most recent geological knowledge  (Boudon and Balcone-Boissard 2021). Within this database, transmissivity data are available for 21 boreholes. The springs database (Appendix 2 and Appendix 3) contains 119 springs, compiled from field reconnaissance carried out between 2005 and 2021. The database contains location and spring elevations. Freshwater springs (96, Appendix 2) and thermal springs (23, Appendix 3) are also discerned.
The survey was mainly carried out along the N-S direction with a 400-m spacing and along the W-E direction with a 4,000-m line spacing; the spacing was locally refined to 200 m over areas of interest such as the Montagne Pelée. Along each flight line, AEM data were acquired every ~30 m, with an average ground clearance of 64 m due to the sharp topography of the island. Figure  3  (and Figure  SI_3  in the ESM) shows the locations of the data over the studied area. The AEM method allows one to image the conductivity/ resistivity contrasts of the subsurface  (Ward and Hohmann 1988). Its depth of investigation (DOI), around 200 m for this research, depends on the emitted magnetic moment, the bandwidth used, the conductivity of the subsurface and the signal/noise ratio  (Spies 1989). AEM data were processed by following the procedure described in  Reninger et al.
(2020) , which is based on the use of the singular value decomposition  (Reninger et al. 2011). The aim of the applied processing was to keep as much resolution as possible  (Reninger et al. 2020). Finally, a manual editing procedure was performed, mainly to remove remaining inductive/galvanic coupling noises. In order to improve the coverage of the dataset, good quality portions of ferry lines (helicopter routes between each flight line departure and between the airport and each day first flight line departure) were also considered during the processing  (Reninger et al. 2020). Data were then inverted in "smoothed" one-dimensional (1D) models using a quasi-3D spatially constrained inversion (SCI) algorithm  (Viezzoli et al. 2008). Each 1D model displays the resistivity variations with depth.
Discussion The hydrogeological conceptual model of the Montagne Pelée volcano has been defined thanks to the correlation of rarely available datasets on the same edifice-helicopter-borne electromagnetic data with a high resolution, a relatively high borehole density database, with water level and hydraulic conductivity data, and a spring database. The results, highlighting thick permeable pyroclastic deposits along the volcano flanks, should help researchers to better understand the hydrogeological functioning on the proximal (2-10 km) part of an andesitic stratovolcano, following the description of  Vessell and Davies (1981) ,  Bogie and Mackenzie (1998)  and  Selles et al. (2015). This model, nevertheless, could not be extrapolated to the rest of Martinique Island, as pyroclastic deposits only concern the Montagne Pelée.
As demonstrated with the hydraulic conductivity dataset for Montagne Pelée, the younger the pyroclastic deposits are, the higher their hydraulic conductivity. The decrease of hydraulic conductivity as a function of time should be the result of weathering processes. If there is enough rainfall, significant aquifers are expected within flank collapses, as supported by self-potential measurements  (Zlotnicki et al. 1998)  showing the floor of the horseshoe-shaped structure. Conversely, the opposite flank should present a superposition of increasingly old deposits with decreasing hydraulic conductivity and lower storage coefficient. Paleo-soil, erosion and weathering processes  (Rad et al. 2013)  between each eruptive sequence also lead to horizontal discontinuities with lower permeability, allowing perched aquifers and associated springs.
Conclusion The hydrogeological conceptual model of the Montagne Pelée Volcano has been defined thanks to the correlation of rare data on the same volcanic massif-helicopterborne electromagnetic data with a high resolution, a relatively high density of boreholes with water level and hydraulic conductivity data, and a spring database. The study has also demonstrated that the older the pyroclastic deposits, the lower their hydraulic conductivity. The on-land and underwater asymmetric topography of the volcano has induced flank collapses leading to a structural difference between the northeastern and southwestern flanks, as reflected in its hydrogeological functioning. Finally, this paper discusses relationships between past phreatic eruptions and the hydrothermal system. The way forward now is to describe the geochemical characteristics of these multiple aquifers, to more deeply characterize the structure of the volcano below the DOI of the geophysical survey (≈200 m), in order to build a 3D geological and hydrogeological model at the volcano scale and to improve understanding of groundwater flows at the scale of each flank.
Considerable progress has been made in the availability of internationally comparable data for SDG monitoring: the number of indicators included in the global SDG database increased from 115 in 2016 to 217 in 2022  [40]. However, significant data gaps still exist making it difficult to understand the progress towards the SDG of the 2030 Agenda. For eight of the 17 SDGs, fewer than half of the 193 countries or areas have internationally comparable data from 2015 or later. Governments in the Western countries have included a range of legislation to realise changes to improve resource efficiency, reduce waste and pollution and shape a new circular economy. Some of those key sustainability-related regulations include the following: In the United Kingdom, the Climate Change Act 2008  [41] , in the United States, the Clean Water Act  [42]  and Energy Policy Act of  2005 [43]  and in Australia the Environmental Sustainability Policy  [44].
The IFU targets the global fruit juice industry, and AIJN targets the European producers. On the other hand, there is a difference in the methodology to obtain the answers. While this study gathered information through the survey, AIJN used targeted interviews of selected main European juice producers and bottlers. In addition, the questionnaire form used enables us to collect data that can be compared by future follow-on works while maintaining respondent's confidentiality and obtaining an information analogous to what could be obtained during written interviews. This study focuses not only on European companies but also on businesses located out of Europe to understand whether or not the main understandings and concerns on sustainability are comparable. The questionnaire was launched using the Survey Monkey platform and it was open from 26 April 2022 until 4 May All the surveys were carried out anonymously.
The Data Analysis Method The answers corresponding to the close-ended questions (yes/no format) were transformed to 1/0 type to measure the frequency of each answer and develop further analysis. With the multiple-choice answers, the number of responses for each given option was counted, and Pareto and comparative bar diagrams were elaborated for analysis. The answers corresponding to the open-ended questions were analysed following the open coding analytic process, as mentioned in the grounded theory method  [50, 51]. The received expressions were transformed in key words with Texalyzer to obtain relevant annotations and concepts from the participants  [52]. Texalyzer is a free software that helps to understand a text by mining the text for data on readability, word count, frequency and density.
The data resulting from the questionnaire were firstly separated by different types of stakeholders, which are: industrial companies classified (including the corporate, SMEs, multinational and micro-SMEs), national juice producer associations and research centres and universities. Most of the answers were provided by participants from industrial companies (11 multinational, 15 corporate, 4 micro SMEs), but also answers from national associational and research centres working directly with the fruit juice industry were gathered: four and two questionnaires were completed, respectively. This distribution is shown in Figure  1. and concepts from the participants  [52]. Texalyzer is a free software that helps to understand a text by mining the text for data on readability, word count, frequency and density.
The data resulting from the questionnaire were firstly separated by different types of stakeholders, which are: industrial companies classified (including the corporate, SMEs, multinational and micro-SMEs), national juice producer associations and research centres and universities. Most of the answers were provided by participants from industrial companies (11 multinational, 15 corporate, 4 micro SMEs), but also answers from national associational and research centres working directly with the fruit juice industry were gathered: four and two questionnaires were completed, respectively. This distribution is shown in Figure  1. The mentioned 11% corresponds to micro-SMEs, companies with fewer than 10 employees and an annual turnover below EUR 2 million. This category in the questionnaire was identified as the category "Friends".
However, its expression by lung macrophages has so far been controversial  (Buhling et al, 2004; van den Brule et al, 2005; Rapa et al, 2006). To further investigate the potential specific role of CtsK in lungresident macrophages, we measured its enzymatic activity in lung-and peritoneum-resident macrophages and compared it with ubiquitously present CtsD. As a result, CtsD activity was similar in the cell lysate of both RTMs (Fig 3C ), whereas CtsK response was specific to lung-resident macrophages  (Fig 3D). These data could potentially explain the enhanced phagosomal proteolytic efficiency of lung-resident macrophages compared with peritoneal macrophages  (Fig 1E). To address this question, we inhibited CtsK enzymatic activity by a specific CtsK inhibitor L 006235 and measured phagosomal proteolysis in peritoneum-and lung-resident macrophages.
Flow cytometry RTMs were collected at 5 × 10 5 per FACS tube, washed with FACS buffer (1% BSA/5 mM EDTA/PBS, pH 7. 2), and incubated in blocking buffer (1:100 anti-CD16/CD32 in FACS buffer) for 15 min at 4°C. After blocking, RTMs were washed with FACS buffer and stained by anti-CD45-BV711 (BD), F4/80-PE (BioLegend), CD11b-FITC (BD), Siglec-F-PE (BD), CD11c-APC-Cy7 (BioLegend), or CD80-APC (BD) at a dilution of 1:200 by FACS buffer for 30 min at 4°C. After washing with FACS buffer, cells were analysed by LSRFortessa X-20 (BD) and FlowJo software, v 10. 8. 1.
Proteomics data search Proteomics datasets were processed by MaxQuant, ver. 1. 6. 10. 43  (Cox & Mann, 2008). Data were searched against reference proteome of Mus musculus (UP000000589; 7 August 2020) downloaded from UniProt. The MaxQuant-implemented database was used for the identification of contaminants. Protein identification was done using these MaxQuant parameters as follows: mass tolerance for the first search 20 ppm, for the second search from recalibrated spectra 4. 5 ppm; maximum of two missed cleavages; maximal charge per peptide z = 7; minimal length of peptide 7, maximal mass of peptide 4,600 D; and carbamidomethylation (C) as fixed and acetylation (protein N-term) and oxidation (M) as variable modifications with the maximum number of variable modifications per peptide set to 5.
Data interpretation Annotation of proteins by gene ontology was done in Perseus, ver. 1. 6. 2. 1  (Tyanova et al, 2016) , and the enrichment was determined by the Fisher exact test. For protein clustering, LFQ intensities of the given protein in each sample were divided by the median protein intensity and ratios were log-transformed. Values were clustered by K-means in R, and the data were visualized by the "pheatmap" package employing hierarchical clustering of samples using the Euclidean distance. Only proteins identified in all three replicates in each tissue were considered for the clustering. The triwise plot and rose plots were constructed using the "triwise" package (van de  Laar et al, 2016)  in R by taking log-transformed protein LFQ intensities averaged from at least two replicates per tissue.
Acknowledgements This work was funded by the  Knut and Alice Wallenberg Foundation  and the  Wallenberg Centre for Molecular and Translational Medicine  (dedicated to A H ärtlova),  University of Gothenburg , Sweden, Cancerfonden 19 0352 (dedicated to A H ärtlova), Wilhelm och Martina Lundgrens Stiftelser 2020, 2021 (dedicated to A H ärtlova), and MH CZ -DRO (UHHK,  00179906 , dedicated to I Fabrik). We would like to thank the  Proteomics Core Facility at Sahlgrenska Academy at the University of Gothenburg, Sweden.
MUSE observations For this work we followed up on 56 dwarf galaxies from the MATLAS survey. We obtained the data from MUSE  (Bacon et al. 2010 (Bacon et al. , 2012) )  at the Very Large Telescope (VLT) of the European Southern Observatory (ESO) from four different proposals and observational periods: P103 (PI: Marleau, proposal ID: 0103. B-0635), P106 (PI: Marleau, proposal ID: 106. 21A1), P108 (PI:  Marleau, proposal ID: 108. 2214) , and P109 (PI: Marleau, proposal ID: 109. 22ZV). The goal of these proposals was to obtain a reference sample of dwarf galaxies identified in the low-to moderate-density MATLAS fields and the galaxies were observed under relaxed seeing conditions (filler conditions), with an average seeing of 1. 0 arcsec. We selected our targets to have an average surface brightness µ e,g < 25.
Sky subtraction In order to enhance the sky subtraction performed in the reduced MUSE data cubes by the ESO pipeline, we first used the MUSE Python Data Analysis Framework (MPDAF) to collapse the data cube along the wavelength axis. We then detected all sources in the produced 2D image and created a binary mask, with 1 corresponding to a detection and 0 to the background. For this task we used a combination of Source Extractor  (Bertin & Arnouts 1996)  and MTObjects  (Teeninga et al. 2015) , employing their respective strengths of detecting faint point sources (Source Extractor) and the low surface brightness galaxy itself (MTObjects). This mask was used as an input for the Zurich Atmosphere Purge (ZAP;  Soto et al. 2016)  developed for MUSE, which uses principal component analysis in order to isolate and subtract sky features from the data cube.
Spectral extraction To extract the galaxy spectra, we created masks for all of the dwarf galaxies on an individual basis, in order to isolate the dwarf spectra from those coming from other sources in the field of view. We used a combination of Source Extractor and manual masking in order to eliminate bright sources. Depending on the shape of the dwarf we drew circular or elliptical apertures centered on the galaxy. The resulting mask contained values 1 for the dwarf flux and 0 in all other regions (i. e. , for radii beyond the defined aperture), where the collapsed image contains bright sources and where the median flux has values ≤0. 2 × 10 -20 erg/(Å cm 2 s). The latter constraint aids the optimization of the signal-to-noise ratio (S/N) per spaxel. We extracted the noise from the second extension of the MUSE data cube using the same mask.
Stellar populations Dwarf galaxies in the Local Universe follow the universal stellar mass-metallicity relation  (Kirby et al. 2013). In order to relate our data to this observation, we estimate the stellar mass M * of our dwarf galaxies by first transforming the apparent g-band magnitude m g from  Poulain et al. (2021)  to the V-band by using the transformation equation from  Lupton (2005) , V = g -0. 5784 * (g -r) -0. 0038, (1) and use the g-r colors from  Poulain et al. (2021). For the galaxies without g-r values from Galfit we use the g-r estimates from Source Extractor on the MATLAS images by using an aperture of 3r eff. We then transform the apparent V-band magnitude to the absolute magnitude M V. For this we estimate the distance to the dwarf galaxy as described in Sect. 3. 2. Finally, we convert the absolute magnitude M V to L V and use the stellar mass-tolight estimate from pPXF to estimate the stellar mass M *.
Acknowledgements. We thank the referee for the constructive report, which helped to clarify and improve the manuscript. N. H. and O. M. are grateful to the  Swiss National Science Foundation  for financial support under the grant number  PZ00P2_202104. M. P. is supported by the  Academy of Finland  grant no:  347089. S. L. acknowledges the support from the  Sejong Science Fellowship  Program by the  National Research Foundation of Korea (NRF)  grand funded by the  Korea goverment (MSIT)  (No. NRF-2021R1C1C2006790 ). N. H. and O. M. thank  Guisseppina Battaglia  for pointing out the work by Boecker et al. (2020). We thank the  International Space Science Institute (ISSI) in Bern (Switzerland)  for hosting our international team for a workshop on " Space Observations of Dwarf Galaxies from Deep Large Scale Surveys: The MATLAS Experience ".
| Environmental datasets Environmental data encompassed spatiotemporal fields of Sea Surface Temperature (SST) and salinity data from the AHOI dataset  (Núñez-Riboni & Akimova, 2015)  and eastward (u) and northward (v) velocity of surface current data from ORAS5  (Zuo et al. , 2019)  for the North Sea. These three environmental datasets were chosen due to their good spatial and temporal coverage to serve as proxies for changes in lower trophic levels (e. g. through match-mismatch dynamics;  Asch et al. , 2019; Beaugrand et al. , 2003; Kristiansen et al. , 2011) , which are more limited in availability and spatiotemporal coverage.
| Pre-processing of environmental covariates and model fitting Time series from seasonally averaged (DJF -December-January-February, MAM -March-April-May, JJA -June-July-August, SON -September-October-November) and annual (YR) spatiotemporal environmental fields were extracted using EOF analysis (i. e. temporal principal components, PCs), and significant PC scores were selected by the broken stick criterion (Table  S2 ). The pool of potential covariates for each stock was passed to a multi-objective genetic search algorithm (NSGA-II;  Deb et al. , 2002)  that allowed for identification of significant covariates by repeated (n = 30) fivefold crossvalidation. The search was guided by an adapted fitness function (2*RMSE test -RMSE train ) to identify models that performed equally well on training and test data sets, to act as an additional form of regularisation, similar to the idea a learning curve  (Perlich, 2010).
13652400, 2023, 4, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/fme. 12629 by IFREMER Centre Bretagne BLP, Wiley Online Library on [24/07/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License TA B L E 1 Stocks included in the North Sea mixed-fishery model: Scientific name -binominal nomenclature showing genus and species, stock code -ICES stock code, FAO -species code used by the FAO, ICES data category -determines the type of data and assessment available for the stock (1 -data rich with quantitative assessment, 2 -qualitative assessment, 3 -stocks, for which survey-based indices and assessment are.
13652400, 2023, 4, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/fme. 12629 by IFREMER Centre Bretagne BLP, Wiley Online Library on [24/07/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 365 KÜHN et al. Preparation of climate projections for FLBEIAProjections were bias corrected via the delta method (Maraun & Widmann, 2018) , removing the monthly mean per grid-point in the mutual overlapping period(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) between historic observations and forecasts, to match the historical trend and minimise the offset to AHOI and ORAS5 datasets that have been used to identify significant environmental covariates for EMSRRs.
mental projections and the people of the ICES WGMIXFISH group, who collated a large portion of input data relevant for the FLBEIA model. Additionally, we thank two anonymous reviewers and the editor for refining and scrutinising the article. Open Access funding enabled and organized by Projekt DEAL. FU N D I N G I N FO R M ATI O NThis research has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement numbers 773713 (PANDORA) and 101000318 (SEAwise).
In comparison, the in vivo adventitious virus test, while more sensitive at detecting VSV and influenza in their suitable animal model (eggs for influenza, and mice/eggs for VSV), was unable to detect five of the viruses. Aside from this study, there is little publicly available information to guide biopharmaceutical manufacturers in assessing when, and in what situations, it may be appropriate to replace the expected existing broad spectrum in vivo adventitious virus test with an alternative method. To partially address this gap, the CAACB collected historical data from 20 biopharmaceutical industry members in Spring of 2021, through a 36-question survey, on their experience with the in vivo adventitious virus test, the in vitro virus test, and the use of next generation sequencing for viral safety.
The goal of this work was to provide a retrospective historical review of the traditional virus detection assays to guide biopharmaceutical manufacturers in conducting viral risk assessments and assessing the value of such assays. The CAACB also collected information on its members' current status in the use or exploration of NGS as a viral safety tool. Survey participants include both biopharmaceutical manufacturers and contract testing laboratories that perform virus testing. In this paper, the findings from the survey data are presented. Considerations for replacing a biological assay with a molecular method, current risks to replacing the in vivo adventitious virus test with NGS, and some potential challenges to implementing NGS in the industry are also discussed.
All responding CAACB member companies indicated that, in their experience, this had not happened. Of the three virus positives obtained by the in vivo adventitious virus tests reported to the CAACB, all were also detected by the in vitro virus test. While it was often difficult for members to collect all historical data related to the in vivo adventitious virus assay, the information reported to the CAACB, together with published reports indicative of the in vivo adventitious virus test missing a viral contaminant  [2, 19]  raises questions as to the continued use of the in vivo adventitious virus test as a tool for viral safety in biologics manufacturing. This is particularly true when compared to other broad spectrum virus testing  [2, 4, 14]. In addition to a test's ability to detect potential contaminants, test reliability is a key component of evaluating the suitability of a test both from a safety and a business perspective.