dataesr/scientific-paragraphs-categorization huggingface dataset:
Pyrolysis gas chromatography mass spectrometry analysis (pyGC-MS) of AF4 fractions PyGC-MS is a mass-based quantification method used to identify the chemical composition of organic samples. Plastics can be distinguished by their pyrolysis fragments using, e. g. , a database of commercial polymers to identify them. Furthermore, pyGC-MS allows for mass-concentration analysis by calibration, although this needs to be performed for each material individually due to different pyrolytic efficiencies and the technique is unable to directly provide any indication of particle number  [9, 14]. Analysis of the PS NPLs and PS NPL fractions from AF4 analysis with pyGC-MS was conducted by two independent laboratories to allow comparison of the method and a degree of validation of its reproducibility. Both laboratories employed Agilent GC-MS instruments fitted with identical Frontier Multi-shot EGA/PY-3030D microfurnace pyrolyzers that were operated in single shot mode.
CF3-MALS: coupled analysis of fractionation and particle size measurements CF3-MALS was selected to measure the particle size distribution after fractionation of polydisperse samples into narrow size distributions by separating the particles according to their size and density. Broad size ranges and high polydispersity values were observed for the PE1 and PS3 samples (Table  2 ). The size ranges for PS3 determined across the two laboratories differ only at the upper end, which might be explained by the different separation profiles used. Furthermore, different R g values were determined for PE1, which is mainly attributed to a degree of agglomeration of the PE1 sample received by laboratory 1. For this material, a better fit of the laboratory 1 MALS data was observed using a random-coil fit, while for laboratory 2, a spherical fit was better.
In general, spherical and random-coil fits result in similar R g values. However, larger particles show stronger scattering contributions in a forward direction that can be better described with the random-coil fit. For the monodispersed PS1 and PS2 samples, a narrow size distribution was observed, with a small degree of agglomeration for PS2. The laboratory 2 data for these two materials showed visible agglomeration, which explains the relatively high full width at half maximum (FWHM) and the comparatively large size range. The spherical morphology of the PS samples was also confirmed by MALS evaluation due to the accordance of the scattering data with a spherical fit model. Furthermore, high repeatability between replicates was indicated by small variations (< 2. 8%) in retention times. This is supported by low standard deviations for additional criteria, such as FWHM, R g at peak maxima, and recoveries (Tables  S10  and S11 ).
For the FeOx100 sample, the spherical shape of particles was clearly confirmed by the agreement of the scattering data with the spherical fit model, in line with morphology detected by the TEM images. The R g size distribution ranged from around 66 nm up to 156 nm, with 77. 4 nm ± 1. 1 nm at the peak maximum. MALS and UV-vis data further suggested a broader distribution, as shown by the fractograms in Fig. 2. The broader size distribution of FeOx100 was also observed with CLS, which offers even higher resolution. Additionally, the rodlike particles of the FeOx2000 sample also yielded a broad size distribution, ranging from around 76 nm up to 216 nm, with the R g at the peak maximum being 92. 8 nm ± 0. 6 nm. Furthermore, high recoveries from around 84% up to 101% for all PS samples, FeOx samples, and the PE1 sample were obtained (Table  S10 ), suggesting the CF3 method was able to characterize the complete size distribution of all samples.
Offline-coupled AF4-pyGC-MS for particle characterization and quantification FFF followed by offline analysis of polymeric particles is an alternative to CF3-RM for chemical identification, also offering the possibility to semi-quantify the mass concentration of the particles belonging to different populations in the sample. For this reason, AF4-pyGC-MS was applied to the different PS samples and to their mixtures. In addition to the PS samples produced by SINTEF Industry, a well-known monodisperse NIST traceable standard, the PLS60, was included in the analysis. The AF4-MALS data from the fractionation shows mean R g values of 25 nm for PSL60, 65 nm for PS2, and 85-145 nm for PS3, respectively (Fig. 4 ). This is in accordance with the data acquired from CF3 separation and shows good comparability between the two separation methods.
Mono-modal samples For PS2 and PS3, there was good agreement between the two laboratories for the absolute mass of PS determined by pyGC-MS for each of the fractionated mono-modal samples produced by AF4 (Table  5 ). However, a ca. × 10 difference was observed for PS2, possibly reflecting the difficulties in re-suspending this material. The overall recoveries in relation Fig. 3  Online CF3-RM analysis of different particle mixtures. Separation of mixture RM1 (top) and mixture RM2 (bottom). The time of all coupled detectors was corrected to match the CF3 retention time. The radius of gyration (R g ) determined by MALS data using a spherical fit is shown as red dots. A and C show the UV-vis data for the two mixtures including the UV-vis data for the single components, while B and D show the Raman data for the two mixtures.
Lignin compositional analyses are generally carried out using standardized protocols provided by NREL  [17] , Van Soest  [18] , or TAPPI  [19]  method. Spectroscopic methods such as Fouriertransform infrared spectroscopy (FT-IR)/attenuated total reflectance (ATR), Raman, and nuclear magnetic resonance spectroscopy (NMR) are used to determine variation occurring in functional groups present. To analyze the morphological changes related to enzyme accessibility, scanning electron microscope (SEM), transmission electron microscope (TEM) and atomic force microscope (AFM) are used. X-ray diffraction (XRD) and solid-state NMR techniques provide data on the physical properties of biomass such as crystallinity, crystallite size, and fiber diameter. Size exclusion chromatography (SEC) is used to examine the molecular distribution and polydispersity ratio.
Realization of BMP Tests The BMP tests were performed using an automatic methane potential test system (AMPTS II, Automatic Methane Potential Test System, Bioprocess Control AB, Lund, Sweden). The AMPTS II is a standardized analytical device designed for the online measurement of biomethane obtained from the anaerobic digestion of biodegradable substrates. It consists of 15 parallel reactors of 500 mL each that are connected to separate gas flow meters through an 80 mL trap bottle of 3 M sodium hydroxide solution used for absorbing CO2 from the biogas. The remaining gas after scrubbing is measured using the gas flow counters which are connected to the data analysis and acquisition system. The experiments were conducted with 400 g of the total reaction medium, in which substrates and inoculum were mixed at a weight ratio of 1:3 in terms of volatile solids (VS) at mesophilic conditions (37 °C) with continuous mixing.
Statistical Analysis and Kinetics of Biomethane Production Statistical significance in the difference between the net biomethane production between all the samples was studied using ANOVA (single factor test) at 0. 05 level in XLSTAT (Addinsoft, Paris, France), an add-in software on Microsoft Excel. To better understand the influence of the pretreatment methods used on the anaerobic digestion efficiency, a first-order kinetic model (Equation (  1 )) was used as the degradation of biomass is assumed to follow a first-order decay. V (t) = V max (1 -exp (-kt) ) (1) where t [days] is the incubation time, V [NLCH4/kg VS] is the cumulative volume of biomethane at time t (d), Vmax [NLCH4/kg VS] is the maximum cumulative volume at the end of the BMP test, and k (d -1 ) is the specific rate constant  [29].
Based on such differences, spectra can be separated into two groups, depending on whether they were autoclaved or not. Namely, the bands at 1235 cm -1 and 1200 cm -1 , both related to the OH out-of-plane vibration mode of cellulose  [65] , are broadened and shifted at lower frequencies for samples not subjected to autoclave. Moreover, the band at 1160 cm -1 , related to the antisymmetric bridge stretching of C-O-C of cellulose and hemicellulose  [60] , is sharper after the autoclaved samples. Lastly, the intensity of the band at 1100 cm -1 , related to the amorphous cellulose  [60, 66] , was reduced after the autoclave. These data suggest that slight, also significant, differences in the cellulose and/or hemicellulose can be evidenced by second derivative ATR-FTIR spectra after autoclave.
Acknowledgments: We would like to thank  Thierry Ribeiro  and  Laura André  from  Institut Polytechnique UniLaSalle (Beauvais)  for providing us with AMPTS devices to conduct our studies. We are also grateful to  Adama Konate ,  UTC  for performing the SEM analysis. The authors thank  Stéphane Mottelet  for his valuable contribution to the realization of non-linear regression in Scilab. The authors also thank  Josefine Schnee  ( LRS, Sorbonne University ) for her kindness and technical help with WAXD analysis.
Measurement steps Mass yields are obtained from experimental position 1 (see Fig. 1 ) after an integration over the kinetic energy and the ionic charge distributions of the count rates measured with the ionisation chamber. Due to the hostile environment at the fissile target position close to the reactor core (T∼800 K), a significant part of the target is continuously lost through self-sputtering in addition to the nuclear fission induced consumption of the target. Repeated scans of masses 137 and 90 were carried out in order to monitor this effect. The Burn-up (BU) evolution shown in Fig. 6  represents the time evolution of the target along with a linear fit of the measured count rates used to compute the corresponding correction factor. Another important aspect of the data analysis is the estimation of the correlation between the fission product kinetic energy and its ionic charge.
Predicting and exploring protein structures and conformation switching Protein sequences were aligned with NBCI's blastp  (Johnson et al. , 2008) , using standard parameters. For Pir1-Pir5, tertiary structures and the corresponding structural confidence scores were obtained from EMBL-EBI AlphaFold Protein Structure Database  (Jumper et al. , 2021; Varadi et al. , 2022). The structures were visualised in PyMOL 2. 5  (Schrodinger, 2022)  and structurally superposed with PyMOL's align command. The structural similarity of the β-trefoil domain was investigated with Dali  (Holm, 2020) , using a heuristic PDB search. The structures of the identified structurally similar β-trefoil domains were superposed with PyMOL's cealign command. The structures of rationally engineered Hsp150-β-lactamase fusion proteins were predicted with Colabfold  (Mirdita et al.
Data were analysed in the R computing environment (R Core Team, 2022) using openCyto  (Finak et al. , 2014)  and ggcyto packages  (Van et al. , 2018) , with the fluorescence assessed on the flowJo biexponential scale. For confocal microscopy, following staining, the cells were centrifuged (13,000 g/30 sec), resuspended in 10 μl of PBS and imaged on a ZEISS LSM 980 confocal microscope (Carl Zeiss, Jena, Germany) under a 40x oil objective, using laser wavelengths of 488 and 561 nm, as in  Žunar et al. (2022b). Images were processed using Zeiss Zen Blue software. To measure supernatant fluorescence, cells were grown in 10 ml of standard -ura medium to stationary phase, at 30 °C/180 rpm/overnight, and centrifuged (3,000 rpm/5 min). The red fluorescence was measured with a Perkin Elmer LS55 fluorescence spectrometer (Perkin Elmer, Waltham, United States), by exciting 3 ml of supernatant in quartz cuvettes at 569 nm and measuring emission at 594 nm.
cerevisiae Pir proteins followed the same basic structure, forming β-trefoil domains with highly conserved β-stranded cores yet divergent β-trefoil turns (Figure  1D ). To deduce the function of the Pir proteins, we compared the Hsp150-predicted β-strain domain with experimentally solved protein structures available in Uniprot, using Dali  (Holm, 2020). This methodology uncovered significant similarities to several lectin β-trefoil domains (Fig  1E ), e. g. , domains contained in nontoxic non-hemagglutinin subcomponent (NTNHA) from Clostridium botulinum (PDB: 3VUO), agglutinin from Amaranthus caudatus (PDB: 1JLX), and β-trefoil lectin from Entamoeba histolytica (PDB: 6IFB). Thus, Pir proteins are likely a family of topologically knotted cell wall-bound lectin-like proteins.
We analysed 406 Pir proteins from 76 species that diverged from S. cerevisiae up to 125 million years ago  (Lozančić et al. , 2021)  (Figure  2B ). While the repeat number varied appreciably across the dataset, only Cis3 homologues retained a single Pir repeat. Other Pir homologues consistently maintained more than one Pir repeat, once again suggesting a positive selection for multiple repeats. Although Alphafold2 modelled all but one internal repeat as an intrinsically disordered loop (Figure  1C ), we wondered whether such a disordered structure could nevertheless switch conformation, i. e. , fold. Emulating a recent Alphafold2-based study of protein conformation switching  (Wayment-Steele et al. , 2022) , we focused on the S. cerevisiae Hsp150, which carries 11 internal repeats. We clustered the Colabfold-generated multiple sequence alignment of Hsp150 and performed the Alphafold2 modelling using each alignment branch as a separate input.
S. cerevisiae encodes a Pir6 remnant Next, we focused on Ykl162c-a, a seemingly devolved remnant of S. cerevisiae Pir6. Ykl162c-a is not specific to the S288c background as it is also present in other commonly used laboratory S. cerevisiae strains (Saccharomyces Genome Database, data not shown) and probiotic S. cerevisiae var. boulardii (Figure  2A ). As full-length Pir6 persists in the remaining seven Saccharomyces species, including closely related S. paradoxus, S. cerevisiae must have lost it recently, within the last 5 million years  (Shen et al. , 2020) , probably through frameshift, as the Pir6 loci of S. cerevisiae and S. paradoxus differ in several indels. Out of the 50 amino acid residues of Ykl162c, only the final 35 are homologous to the C-terminus of its paralogue Cis3. Unexpectedly, a bioinformatic analysis suggested Ykl162c-a evolved a new signal sequence at its N-terminus (Figure  3A , DeepSig reliability: 1.
Furthermore, we were unable to detect secreted Ykl162c-a, despite immunoblotting acetoneprecipitated proteins secreted to the medium (data not shown). However, the Saccharomyces Genomics Viewer  (Lardenois et al. , 2011)  indicated that the YKL162C-A locus is transcribed to a potentially significant level in respiring and sporulating but not in fermenting diploid cells (Figure  3C ) and only poorly transcribed in cell-cycle synchronised haploid cells (data not shown). Moreover, the viewer indicated Ykl162c-a transcription footprint spans only YKL162C-A and not the entire ancestral PIR6 locus. Finally, ribosome profiling  (Brar et al. , 2012)  suggested such transcripts might also be translated (data not shown). Together, the newly-evolved signal sequence and the finely-tuned transcription response suggest that Ykl162c-a might be positively selected for, despite currently having no assigned function.
Over recent decades, the potential of remote sensing techniques to identify and map mangrove forests has been extensively researched  (Satyanarayana et al. , 2011; Diniz et al. , 2019; Simard et al. , 2019; Pham et al. , 2019; Valderrama-Landeros et al. , 2021). A number of studies have used the global mangrove maps to locate mangrove range limits and model how mangrove distributions may be impacted by climate change  (Quisthoudt et al. , 2012; Osland et al. , 2017b). However, small differences in the location of mangrove range limits in the datasets could influence the climatic thresholds associated with mangrove presence and absence  (Cavanaugh et al. , 2015; Ximenes et al. , 2021). Despite these issues, global maps are widely used by the scientific community, but the advantages and limitations of available global products have never been compared and discussed.
A new initiative in this matter allows users to compare global maps of mangrove extent, biomass and carbon (see further information in Section 4. 3). To date, four global maps of mangroves have been produced and released publicly  (Spalding et al. , 1997 (Spalding et al. , , 2010;; Giri et al. , 2011; Bunting et al. , 2018). These global maps cover different time periods and were derived using different datasets and methods. As a result, there are differences that may be due to changes in actual mangrove extent, mapping error and differences due to methods and datasets used  (Bunting et al. , 2018). While these maps have been validated using published records of mangrove presence, challenges associated with conducting comprehensive global accuracy assessments make it difficult to quantitatively compare the performance across maps.
Also, the accuracy of each map suffers from spatial heterogeneity where regional mapping quality varies and is represented by a global statistic of accuracy alone. Hence, we need a better understanding of the differences in these datasets in order to understand mangrove response to future climatic perturbations and modelling. In particular, the correct location of each poleward mangrove range limit is crucial to understand the climatic drivers or range limitation and project the impacts of climate change. There are at least 21 poleward mangrove range limits  Quisthoudt et al. (2012)  and the correct mapping of these mangroves at their range limits is challenging. Due to extreme climate conditions at these locations, the mangrove trees are usually smaller in structure, lower density, and smaller in extent as compared with their counterparts closer to the equator.
For this reason, identifying errors in the geographical location of the mangrove range limits is fundamental for future mapping efforts. Here, we present the first comparative study for global mangrove datasets with a clear focus on range limits worldwide and identify potential discrepancies between these products. We investigate differences in latitudinal range limits between the four global datasets and how these discrepancies translate climatologically considering factors known to control mangrove distributions. Based on our observations and climate data analyses, we formulate recommendations to inform the future production of global mangrove maps.
Material and methods The global mangrove maps considered in this study: (1) the World Atlas of Mangroves (WAM-1)  Spalding et al. (1997) , (2) the World Atlas of Mangroves (WAM-2)  Spalding et al. (2010) , (3) the Global Distribution of Mangroves (GDM)  Giri et al. (2011)  and (4) the Global Mangrove Watch (GMW)  Bunting et al. (2018). These maps are made available at: https://data. unep-wcmc. org/. The main characteristics of these different products are summarised in Table  1  and detailed in the following subsections. It is worth noting that the Global Database of Continuous Mangrove Forest Cover for the 21st Century (CGMFC-21)  (Hamilton and Casey, 2016)  was not included in this paper because it used the GDM map as a reference of mangrove mapping. Therefore, although the CGMFC-21 map is more restrictive in its definition of mangroves (the total mangrove area in CGMC-21 is 39 % smaller than in the GDM map), the CGMFC-21 map and the GDM map are spatially correlated.
Global mangrove maps 2. 1. 1. World Atlas of Mangroves-1 (WAM-1) The first World Atlas of Mangroves (WAM-1) was launched by the World Conservation Monitoring Centre (WCMC) and released in 1997 (Table  1 ). The starting point to map the mangrove coverage for the WAM-1 was originally taken from The Conservation Atlas of Tropical Forests which involved several organisations, governments, agencies, and scientists  (Spalding et al. , 1997). Identifying gaps, updating obsolete data, improving low resolution data, and adding new datasets were done through correspondence and discussion with many authorities on this topic. In addition, the WAM-1 used satellite images acquired at different dates and spatial resolutions such as: the National Oceanic and Atmospheric Administration/ Advanced Very High Resolution Radiometer (NOAA-AVHRR), the Land Remote Sensing Satellite Program (LANDSAT) -Mutispectral Scanner (MSS) and Thematic Mapper (TM) sensors, the Satellite Pour l'Observation de la Terre -High Resolution Visible sensor (SPOT-HRV), and the European Remote Sensing Satellite (ERS-1 and 2)  (Spalding et al.
World Atlas of Mangroves-2 (WAM-2) The second World Atlas of Mangroves (WAM-2) was reformulated from the WAM-1 and was published in 2009, twelve years later  (Spalding et al. , 2010). The WAM-2 was led by the Food and Agriculture Organization of the United Nations (FAO) and the United Nations Environment Programme -World Conservation Monitoring Centre (UNEP-WCMC). The WAM-2 map improvements over the WAM-1 were mainly: (i) gather higher spatial resolution images for nearly all mangrove areas globally; (ii) mapping improvements with 98. 6 % of the total global mangrove area coverage mapped by the WAM-1. To assist the production of the WAM-2 map, four datasets in particular were considered: (i) Topography and Bathymetryextracted from SRTM and ETOPO1 global relief model; (ii) Populated places, rivers and lakes derived from Global Rivers database; (iii) Coastal geographical featuresextract from World Vector Shoreline; (iv) Protected areasprovided by UNEP-WCMC and produced by World Bank Database on Protected Areas.
The WAM-2 was built using various techniques, including the selection of classes from unsupervised classifications of remote sensing images, the use of a digital elevation model to exclude unsuitable sites for mangroves, secondary sources of mappings, visual interpretation by local field experts and geographic context data layers to assist producing the final mapi. e. , populated places, rivers and lakes, coastal geographical features, and protected areas  (Spalding et al. , 2010). UNEP-WCM began to map mangroves in several countries for which data were available. FAO prioritised countries where the 1997 World Mangrove Atlas data was outdated. UNEP-WCMC built a geodatabase mainly using the Landsat 5 TM and Landsat 7 ETM+ images, dating predominantly from 1999 to 2001. The satellite images used in the mapping were composed by band 5 (Short-Wave Infrared -SWIR), band 4 (Near Infrared -NIR), and band 3 (Red) to supply semi-automatic classifications.
, 2010). Between the years 1999 and 2003, FAO worked with Landsat ETM+ images and secondary mapping sources. Landsat images were used for visual interpretation and compositions of spectral image bands were used to enhance mangrove stands at 1:250. 000 scale. To review the visual interpretation from WAM-1, other partnersthe International Society for Mangrove Ecosystems (ISME) and the International Tropical Timber Organization (ITTO) and UNEP-WCMCassisted by local experts to improve digitisation of particular sites. This methodology covered 57 % of the global mangrove area (86,000 km 2 ). The second methodology was mapped at 1:250. 000 scale, which came from several institutions: FAO for African and Red Sea coastline, The Nature Conservancy (TNC) mainly responsible for the Caribbean, the Central America and the Pacific region, and National data from approx.