dataesr/scientific-paragraphs-categorization huggingface dataset:
The wood chips supply chain was described by CNPF (National Center of Forest Property) illustrating a common French scenario for forest cleaning operations  [38]. Technical data related to the different processes involved were recovered from  [39]. At present, trees are manually cut down with a chainsaw with a productivity of 0. 94 ton•h -1 and a fuel consumption about 1. 42 kg•ton -1. Then, wood is chipped onsite with a chipping facility (9 ton•h -1 ), consuming 0. 31 kg of fuel per ton of chipped wood. Finally, the wood chips are transported to the drying/storage platform, where a final mass loss of 36% after drying is assumed  [38]. A low heating value of 3. 4 kWh per kg of wood fuel was assumed with an average process efficiency estimated at 90%  [40]. Data related to emissions and electricity consumption of the biomass boiler were recovered from the Ecoinvent database.
In this regard, a consumption of 0. 0036 kWh of electricity is estimated per MJ of heat produced, considering the French electrical mix as for all other electricity consuming processes (reference year 2012 in the Ecoinvent v3. 3 database). At the end of the combustion process, a wet collection of WA is modeled, as it is the main method of WA collection for BPPs with an installed capacity higher than 1 MW, with up to 25% moisture content in WA  [4]. In consequence, the actual amount of dried WA contained in 1 ton of wet WA produced is about 750 kg; hence, 250 kg of water is used to moisturize it. In order to determine the main inputs and outputs of the combustion process (wet/dried WA, water, wet/dried wood chips, electricity, heat, etc. ), calculations were realized in the reverse order of the process, from ash production to wood chip production.
Thus, 76. 5 tons of dried wood chips are necessary to produce 1 ton of wet WA. Regarding transportation of wood chips, road transport was assumed to be performed by 7. 5-16 tons lorries complying with EURO5 standards  [41]. The supply transport includes two specific components: transport from logging sites to drying/storage platforms, and final transport to the BPP. Due to lack of data on transport from logging sites, only the final transport of dried wood chips was taken into account. Smaller BPPs stock up within a radius smaller than 50 km. Nonetheless, larger BPPs often have a multitude of suppliers for an average transport distance within a radius of less than 100 km  [42]. Given the size of the considered BPP (more than 1 MW), an inter-regional supply of 50 km is considered  [43]. Note that infrastructure construction, combustion of additional energy resources (e.
Life Cycle Impact Assessment Data were recovered from the Ecoinvent v. 3. database  [36] , for impact assessment of the different processes considered. In case of WA production, air quality, energy, and natural resources consumption are crucial issues. To address these challenges, 11 environmental indicators were calculated with SimaPro 9. 0. 0. 49 software, Amersfoort, The Netherlands  [44] , using the two internationally accepted methods CML-IA Baseline v. 3. 04/EU25 (2013)  [45] : abiotic depletion, depletion of abiotic resources (fossil fuel), climate change (GWP100a), ozone layer depletion, photochemical oxidation, human toxicity, freshwater and marine aquatic ecotoxicity, terrestrial ecotoxicity, acidification and eutrophication. Only midpoint indicators focusing on single environmental impacts were considered in this study as a result of the higher level of uncertainty of endpoint models compared to midpoint models  [46].
Environmental Burden of Wood Ash Due to their very different natures (heat and ashes) the energy (C en ) and mass allocation (C m ) methods are not directly suitable for our study, as ash has no energy content and heat, no weight. However, the energy and the mass are crucial to determine the economic allocation (C ec ), based on recent values. For the mass allocation, as we know the amount of wood burned (119. 5 tons of wet wood chips) to produce 1 ton of WA, we could estimate that the burned part of the biomass was transformed into heat (main product) and other gaseous emissions; the remaining part of the wood is composed of the ashes. Using this data, we could indirectly estimate the C m as 1. 3% with this method (1t of WA is generated from an input of 76. 5t of wood). Using data from Table  1 , for an average over Brittany region considering the different biomass mixes and their own ash content, and the fact that the BPP process requires 25% of moisture to collect them, the mass allocation (C m ) would vary between 1.
Estimating the impacts of multifunctional processes (with more than one product per process) is a burning issue, especially to assess the potential for gains in the circular economy through valorizing waste streams. Other methodologies can be used such as the Consequential LCA using substitution, as well as the recent version of Ecoinvent database (consequential or allocation at point of substitution) that allows broadening of system boundaries to avoid IAP  [65, 66]. However, the purpose of this study was to estimate the amounts of WA available in a specific region and decide if WA can be considered as a co-product or as a waste product of energy production. Besides, consequential LCA (CLCA) requires a complete picture of the valorization scenarios (i. e. , precise percentage of cement substitution by WA) and it would provide results corresponding to the specific set of parameters used in this CLCA (and its sensitivity analysis), whereas the purpose of the current work was to assess whether WA is a co-product of heat generation or its waste, using a screening ALCA, to be used within different valorization development work.
This raises the issue of the allocation of environmental impact between the heat produced in the BPP and the co-product, namely the WA. Quantitative estimates relating to environmental burden are crucial to provide a basis for further work, which might consider the use of WA as a resource in another industrial process. The present study describes an approach to assessing resource implications of production of wood ash within a particular French region: Brittany. Cooperation between local wood energy experts (AILE and Fibois experts) in the studied area and academia (environmental experts) helped to strengthen the methodology implemented, by using verified data and standardized methods (Life Cycle Assessment, Impact Allocation Procedure). Moreover, the analysis of wood ash deposits permitted an increase in the stakeholders' knowledge, which can be shared not only with the technical public but also the nontechnical public.
The growth and evolution of crustal-scale magmatic systems play a key role in the generation of the continental crust, the largest eruptions on Earth, and the formation of metal resources vital to our society. However, such systems are rarely exposed on the Earth's surface, limiting our knowledge about the magmatic processes occurring throughout the crust to indirect geochemical and petrographic data obtained from the shallowest part of the system. The Hf isotopic composition of accessory zircon is widely used to quantify crust-mantle evolution and mass transfers to and within the crust. Here we combine singlegrain zircon Hf isotopic analysis by LA-MC-ICP-MS with thermal modelling to one of the best-studied crustal-scale igneous systems (Sesia Magmatic System, northern Italy), to quantify the relative contribution of crustal-and mantle-derived magmas in the entire system.
On average, the crustal contribution to the entire system determined by mixing calculations based on Hf isotopic data range between 10 and 40%, even with conservative assumptions, whereas the thermal model suggests that this space-and time-averaged contribution does not exceed 20%. However, spatial and temporal variations in the crustal melt proportion (from 0 up to 80% as observed in the thermal model) may impart significant isotopic variability to different batches of magma observed on the outcrop scale, emphasizing the need to consider a magmatic system as a whole, i. e. , by integrating all spatial and temporal scales, to more precisely quantify crustal growth vs. reworking.
The question of crustal contamination in the Sesia Magmatic System (SMS) has dominantly been addressed from a whole-rock geochemical perspective (e. g. ,  Voshage et al. 1990; Sinigoi et al. 1991 Sinigoi et al. , 2011 Sinigoi et al. , 2016;; Pinarelli et al. 2002) , thermal modelling (e. g. ,  Barboza and Bergantz 1996 , 1997 , 1998)  and recently by garnet xenocrysts  (Devoir et al. 2021 ) so far. However, there was no attempt to reconcile observations from both methods or obtain a self-consistent dataset at the scale of the entire magmatic system, i. e. , from both the lower and upper crustal levels. In addition, the lifetime of the entire magmatic system and the composition of the crust at the time of mafic magma emplacement, which are key parameters constraining crustal fertility (e. g.
,  Annen and Sparks 2002; Annen et al. 2006; Karakas and Dufek 2015) , are still debated. In this study, we combine (1) a new, self-consistent dataset of zircon Hf isotope measurements in representative samples of both lower mafic and upper silicic igneous rocks that were previously dated by high precision CA-ID-TIMS zircon U-Pb single grain analysis  (Karakas et al. 2019) ; with (2) a two-dimensional thermal model of polybaric mantle-derived magma differentiation and associated crustal melting (modified after  Karakas et al. 2017 and Karakas et al. 2019). We use our results, in combination with previously published data, to constrain the evolution of the SMS and clarify the relative contribution of mantleand crustal sources, and how these vary in space and time throughout the formation of such crustal-scale magmatic systems.
The data were processed offline with the Igor Pro-based Iolite v2. 5 software  (Hellstrom et al. 2008) , using an inhouse data reduction scheme. Instrumental mass bias for Yb and Hf isotopes were corrected to the natural abundance ratios 173 Yb/ 171 Yb and 179 Hf/ 177 Hf and the isobaric interferences of 176 Yb and 176 Lu on 176 Hf were corrected using the natural abundance ratios of 176 Yb/ 173 Yb and 176 Lu/ 175 Lu, with all natural abundance ratios taken from  Chu et al. (2002). The mass bias correction factor obtained for Yb isotopes was applied to Lu isotopes. Accuracy and external reproducibility of the method were controlled by repeated analyses of reference zircon standards GJ-1  (Morel et al. 2008) , Plešovice  (Slama et al. 2008) , 91500  (Wiedenbeck et al. 1995) , and Temora  (Woodhead and Hergt 2005).
Concentrations were set as to vary between 1 and 6 ppm Hf, obtained from the representative range of Zr contents in samples of the Mafic Complex excluding outliers  (30-200 ppm; Rivalenti et al. 1975; Sinigoi et al. 1994; 2011)  and a canonical Zr/Hf ratio of 35  (Rudnick and Gao 2014). In crustal rocks, Hf is mainly hosted in detrital zircons, whose isotopic variability is huge in this context. Indeed, the inherited zircon grains measured in this study show a range of εHf calculated at 283 Ma of 0 to -38 (Fig. 3 , lower panel) and down to -55. Ewing et al. (2014)  obtained Hf isotopic compositions of detrital zircon from metapelites of the Kinzigite Formation and reported similar ranges of εHf (0 to -70). However, the data stem from individual detrital/inherited cores, which would have been dissolved in melt in the high-temperature conditions of the lower crustal mush (> 900 °C maintained for several Myr; see modelling here and in  Karakas et al.
Monte Carlo mixing simulations based on Sr and Nd isotopic data We have implemented the Sr, Nd and bulk rock isotope geochemical dataset of  Voshage et al. (1990)  from lower crustal rocks into a Monte Carlo mixing model similar to that conducted based on our Hf isotopic record (Fig. 5C ; Supplementary Tables  S3, S4 ). Kernel density functions of resulting mixing calculations follow an overall trend similar to the findings based on the Hf isotope data. Pyroxenites show a bimodal distribution (ca. 8-45% for Sr; ca. 10-50% for Nd) with a main peak representing the lowermost crustal contribution, followed by gabbros and "diorites", with overlapping crustal melt contribution (ca. 15-50% for Sr; ca. 20-50% for Nd). The concentration ratios for Sr and Nd by  Voshage et al. (1990)  show a large spread of a potential primary mantle-derived melt mixing with a crustal contaminant (Fig.
Hafnium When compared to the extreme εHf variability observed between the local crust (εHf of inherited zircons between 0 and -55 and average values between -6 and -20; Fig. 3  and  Ewing et al. 2014 ) and putative mantle components (εHf from ca. +4 to +2; see "Methods" section), zircon Hf isotopic compositions across different igneous bodies of the SMS show a relatively tight range (Fig. 3 ). Specifically, 90% of the dataset shows εHf between -0. 5 and -6. 7 and 50% between -2. 0 and -4. 0 (with typical 2σ uncertainties of ± 1. 1 εHf-unit). Ranges of εHf observed in all individual samples overlap with each other, suggesting that the crustal scale magmatic system is not only temporally  (Karakas et al. 2019 ), but also genetically linked. Our results based on Hf isotopes therefore confirm the vertical stratigraphic link between all igneous units in the system, in agreement with the observations and interpretations of  Voshage et al.
The range of Hf isotopic compositions in zircon grains of a given sample indeed record the complex history of magma formation and evolution over time and space. Depending on their location in the evolving magmatic body, different samples are therefore expected to show deviating ranges in zircon Hf isotopic composition, which can in turn allow to place constraints on the history of the system as a whole. In the lower crust, the only outlier in our Hf isotopic dataset is the pyroxenite body located in the southeastern portion of the Mafic Complex (near Castello di Gavala). This kind of pyroxenites have been studied by  Garuti et al. (2001)  and interpreted as ultramafic pipes (εNd: +1. 6 to +3. 69). Voshage et al. (1990)  measured highest Nd isotopic values in cumulus peridotites (εNd: +7) and pyroxenites whereas all other units showed negative values.
Strontium and neodymium vs. hafnium Monte Carlo mixing calculations of Sr and Nd isotopic data on lower crustal lithologies in the Ivrea system show moderate crustal contribution (20-50%) when the lowest and highest concentrations measured in the  Voshage et al. (1990)  dataset were used in the mixing model. Although the absolute shift in crust/mantle ratios of different isotope systems (e. g. , Hf vs. Sr and Nd) depends on the endmembers chosen for the mixing model, the overall trend (increasing crustal contribution from pyroxenites to gabbros and diorites) remains similar for all of them. A possible explanation to the wide range in modelled crustal contributions is that the Hf, Sr and Nd concentrations and isotopic signatures are controlled by different mineral phases (zircon for Hf; micas, feldspars and apatite for Sr; apatite, monazite and titanite for Nd).
In contrast, as discussed previously, isotopic data may sample spatially or temporally discrete variations of the crust/mantle ratio, can be already contaminated in mantlederived material (crustal contribution from the lithospheric mantle is not considered in the thermal model) and depend on the behaviour of the mineral phase hosting the isotope of interest (i. e. , zircon in the case of Hf). We also show that most of the assimilation occurs in the lower crust (with only negligible amounts occurring in the upper crust). Finally, we stress out that the Kinzigite Formation was at least partly melt-depleted by the time the Mafic Complex has intruded  (Barboza et al. 1999; Barboza and Bergantz 2000). Therefore, the crustal melting proportion estimated from our model may be regarded as a maximum, as modelling of phase relations in the crust was performed in our model using a fertile metapelite composition in the mid-upper crust  (Vielzeuf and Holloway 1988)  and more restitic composition (from  Sinigoi et al.
Constraints and limitations of thermal and geochemical models Overall, the crustal contribution estimated by the bulk rock Sr and Nd isotopic data in the lower crust agree well between each other. This dataset also shows a similar range with the estimations from the zircon Hf isotope measurements, the thermal model, and the Hf mixing model using our range of Hf concentration ratios. As discussed above, we conclude that this discrepancy between crustal contribution estimations are not dramatic considering the limitations of both geochemical and thermal models, complexity of the system, and resulting assumptions and simplifications. Following the detailed discussions in the preceding sections, we summarize these limitations and assumptions below. The limitations in the geochemical models include (1) selection of the end-members in concentration ratios and isotopic compositions, which depend on crust and mantle source compositions, melting reactions and their spatial-temporal evolution, (2) bulk rock analysis of Sr and Nd isotopic measurements versus single-grain analysis of Hf measurements, (3) the use of the same samples for Sr and Nd analyses while Hf analyses were done on a different set of samples, and (4) possible sampling biases in and between different studies.
Also, the thermal model does not consider the possible efficiency of reactive bulk rock assimilation  (Beard et al. 2005) , which possibly acted in the Mafic Complex  (Sinigoi et al. 2016). Nevertheless, the combination of thermal modelling with (isotope-)geochemical datasets as done in this study, (1) delivers new insights on the magmatic evolution of the SMS as a whole, (2) places constraints (outcrop scale) spatial heterogeneities that form and vanish through space as a function of time.
Acknowledgements We thank  J. Dufek  for thermal modelling discussions, and  P. Brack  for support during fieldwork. In depth discussions with  P. Ulmer ,  P. Brack ,  L. Tavazzani , and  R. Economos  on thermal modelling, crustal melting, and Ivrea magmatism are greatly appreciated. We acknowledge  O. Müntener  for editorial handling and constructive reviews by two anonymous reviewers that helped to sharpen the presentation of the main points of this manuscript. Funding of this project was provided to J. -C. S. , O. K. and C. C. -M. by  ETH Zürich. O. B. was supported by  SNF  Grant # 200021_178928. Funding Open Access funding provided by  Universität Bern.
Patient Samples Patients included in this study belonged to three previous projects approved by the Sud Mediterranean  III Ethical Committee [clinical trials no. NCT01212120 (period 2011 -2012 ), NCT01551667 (period 2012 -2016 ), and NCT02565940 (period 2015 -2017) ] and carried out in accordance with the Helsinki Declaration as revised in 2000. All patients gave written informed consent for participation in these studies. From 1st January 2011 to 31st December 2017, we prospectively enrolled all diabetic patients managed in the Diabetic Foot Clinic Gard Occitanie at the Nîmes University Hospital (France) with a suspected newly presenting episode of DFI, without systemic antibiotic treatment within the previous 15 days and with at least one sample taken for bacterial culture. Every wound was assessed for presence and severity of infection by a trained diabetologist using the PEDIS classification of the IWGDF consensus conference  (Lipsky et al.
, 2020). Some clinical data (antibiotic and antiseptics used) and the wound evolution (healing/non healing) were collected from the previous studies. After wound debridement, samples for bacterial culture were obtained by scraping and collecting debris by swabbing at the wound base, or by a tissue biopsy using the procedure previously described  (Lipsky et al. , 2020). All the samples were immediately sent to the Department of Microbiology.
Whole-Genome The filtered trimmed reads were included in the downstream analysis. Obtained reads were mapped against S. aureus NCTC 8325 genome (GenBank accession number: GCA_000013425. 1), employing the CLC genomics workbench 7 (Qiagen Inc. , Valencia, CA), using default parameters; length fraction: 0. 5, similarity fraction: 0. 8. The assembled contigs were processed by Prokka software for microbial genome annotation  (Seemann, 2014). Virulence factor database (VFDB) 2  was used to infer virulence factor-encoding genes from genome sequences  (Chen et al. , 2005). Antimicrobial resistance genes were obtained from ABRIcate with the ResFinder database on assembled genomes  (Zankari et al. , 2012; Bortolaia et al. , 2020). The two WGS were aligned using the MAFFT software  (Katoh and Standley, 2013).
Single nucleotide polymorphism calls were made from the PE library raw reads. For SNP analysis, we employed the following software: SNP-sites for variants calling  (Page et al. , 2016)  and SnpEff (v. 1. 3T) for SNP annotation in coding regions  (Cingolani et al. , 2012). SNP annotations of affected genes were searched within wild-type genome and their effects were classified according to mutation impact. Genes affected by stop gain mutations were searched in Uniprot database for virulence classification. Additional analyses were performed on WGS such as circular genome representation [BLAST Ring Image Generator software (BRIG);  Alikhan et al. , 2011].
1% of the patients in our study), a low implantation rate compared with other chronic conditions like lung colonization in CF patients  (Kahl et al. , 2003). We assume that the dynamics of S. aureus implantation in DFU differ from FIGURE 3 | BLAST Ring Image Generator software (BRIG) analysis of Staphylococcus aureus genomes isolated from a same diabetic foot ulcer at a 48-week interval. Genome of C01P19 isolate was compared against the genome of C1BP152 isolate and virulence factors obtained from the virulence factor database (VFDB) analysis were annotated on the circular genome of each strain. Circular genome representation was done using BRIG  (Alikhan et al. , 2011). In green, the main virulence factor-encoding genes known in Staphylococcus aureus. Frontiers the dynamics of bacterial colonization and adaptation in the lower respiratory tract during CF due to distinct environmental conditions and infection management  (Kahl et al.
Objectives and evaluation criteria The first objective of the project was to evaluate if the strategy led to an increase of a reported appropriate behavior in case of malaria symptoms, defined as using a certified ACT after a positive malaria test by using a kit or turning to the health system. It was assessed by the pre/post intervention surveys. • The last malaria episodes, before or after the Malakit implementation, were classified according to Suppl. Mat. 1. • Data on the behavior during the last malaria episode was derived from the question "what did you do during your last episode of malaria symptoms?". Given the mixed care pathways, we took into consideration the first action taken. Healthseeking behavior was thus classified into four groups,: i) use of self-medication with UTC drugs; ii) care sought in a health center/hospital (in any country); iii) use of a malakit; iv) other: traditional plant-based remedies, treatment of symptoms only, or no memory about the behavior.