dataesr/scientific-paragraphs-categorization huggingface dataset:
| Modelling We analysed the whole community and the high RR group for each of the eight (one all-region, three regional, four individual) datasets, that is, 16 models (Figure  S1 ). Following  Zuur et al. (2010) , we used variance inflation factors (VIF) to identify collinearity among candidate predictor variables representing river drying and, in all-region models, climate (Table  S3 ). We retained three predictor variables with VIF <2: aridity (as 1-AI), drying frequency and the proportion of human impacts (Table  S3 ), with their non-collinearity evidencing the limited influence of hydrological alteration on the human impact gradient. We calculated skewness values to assess the distribution of each response variable, then used square-root or log transformations to reduce values >0. 5. We ran linear mixed-effects models to characterize metric responses to the three predictor variables and their pairwise interactions.
To account for the non-independence of samples from the same site and dataset, we included site nested within dataset as random factors in all-region and regional models and site as a random factor in individual models. We used a multi-model inference approach to quantify the size and significance of metric responses  (Anderson & Burnham, 2002). We assessed model performance using Akaike information criteria (AIC), considered models with a ΔAIC <2 as equally good, and averaged these models  (Anderson & Burnham, 2002). We partitioned the variance explained by predictors and their interactions in the top all-region models. We used marginal and conditional goodness-of-fit statistics (R 2 m and R 2 c , respectively) to evaluate model performance  (Mac Nally et al. , 2018). Independent (single, additive) and interactive (antagonistic, opposing, synergistic) response types were classified using the sign and significance of responses to predictors and their interactions  (Feld et al.
Ensuring representation of sufficient taxa with sufficiently variable impact sensitivities  (Hering et al. , 2006)  within high RR groups defined for specific regional river types (van de Bund, 2009) could thus strengthen metric responsiveness, using the approaches outlined below. First, to improve representation of impact-sensitive high RR taxa within region-specific groups, temporary river specialists could be better represented. For example, the traits conferring resistance to drying on specialist insect species associated with small, lowland, temporary rivers  (Armitage & Bass, 2013)  in our Atlantic dataset were obscured by the traits of dominant generalists in our familylevel assignment. As also indicated by our genus-level ES_S analyses, such examples highlight that finer-resolution taxonomic identification can enhance characterization of biological responses to natural stressors  (Chadd et al.
, 2014) , due to their prevalent resistance traits  (Tachet et al. , 2010). We also identified beetles and true bugs as common high RR families, reflecting resilience traits including strong aerial dispersal  (Sarremejane et al. , 2020). In particular, the single, strong responses to human impacts identified for the beetle and bug-rich ES_S dataset from semi-arid Spain evidences the potential use of these taxa in temporary river biomonitoring  (Bilton et al. , 2006). However, metric performance is constrained by representation of sufficient taxa with sufficiently variable sensitivities to impacts, restricting the potential of these groups to regions with adequate taxonomic richness  (Gutiérrez-Cánovas et al. , 2019). Third, the performance of high RR metrics could be improved by recognizing that traits conferring resistance and/or resilience to drying vary among river types.
, our results highlight the need to develop region-specific indices for use in ecological status assessments. By identifying priority sites for further investigation, such assessments can inform management actions that support biodiversity within dynamic river ecosystems. TA B L E 1 Linear mixed-effects model results: (a) goodness-of-fit (marginal R 2 b-g) significance (p) and effect size (ES) of (b) human impacts, (c) river drying, (d) aridityand (e-g) their interactions for response metrics summarizing the all-region community dataset (n = 1,763); and (h-j) the independent (additive) and interactive(antagonistic, synergistic).
represent Great Britain within the Atlantic biogeographical region (hereafter, Atlantic). Five West Mediterranean datasets (ES_E, ES_NE1, ES_NE2, ES_S and PT) represent the Ibero-Macronesian ecoregion of the Mediterranean biogeographical region. Three East Mediterranean datasets (CY, GR_NW and GR_SE) are from western Balkan ecoregions within the Mediterranean biogeographical region and Cyprus, which had com- parable communities (Figure.
Statistical Analysis A statistical analysis of the experimental data was carried out using MATLAB and SPSS version 24. 0. (SPSS Inc. , Chicago, IL, USA). Two analytical methods were used:  (1)  for the HR and SCL data before and after the LDT, related paired t-tests were carried out to verify whether the LDT played a role in increasing stress levels, and (2) for the same indicators (HR and SCL), the differences between the pre-and post-test for RE and DE were used to determine whether these environments played a role in relieving stress. Additionally, a one-way analysis of variance (ANOVA) with Bonferroni correction was used to evaluate the effects of the two tested environments (RE and DE) on relieving psychological stress levels. The effect size was calculated using Cohen's d and partial eta squared (ηp2).
Statistical Analysis A statistical analysis of the experimental data was carried out using MATLAB and SPSS version 24. 0. (SPSS Inc. , Chicago, IL, USA). Two analytical methods were used: (1) for the HR and SCL data before and after the LDT, related paired t-tests were carried out to verify whether the LDT played a role in increasing stress levels, and (2) for the same indicators (HR and SCL), the differences between the pre-and post-test for RE and DE were used to determine whether these environments played a role in relieving stress. Additionally, a one-way analysis of variance (ANOVA) with Bonferroni correction was used to evaluate the effects of the two tested environments (RE and DE) on relieving psychological stress levels. The effect size was calculated using Cohen's d and partial eta squared (η p 2 ).
Bacteria counts and statistical analysis. For the image analysis, a 4 µm size threshold was set based on the fact that S. aureus cells typically form singlets, duplets, triplets and quadruplets (made of 4 one-micron cells in a clusters) whose sizes are below 4 µm. Bacteria were counted manually for three independent replicate experiments. Approximately 2000 bacterial cells were counted for each replicate experiment and for each pH value, leading to a total of more than 6000 bacterial cells for each data points. The number of bacteria aggregated in all three replicate experiments were averaged and the standard deviation of the average was used for the error bars (error = Stdv/ √ 3 ). The size measurements of aggregates were obtained with ImageJ using the greatest cross- area length of individual aggregates from top views.
The software used for the data analysis was MICROSOFT EXCEL. Zeta potential and pH measurements. The Zeta potential of bacteria was measured on a Zetasizer Nano Serie 200. All bacterial samples were centrifuged and re-suspended in either 0. 015% or 0. 5% NaCl aque-ous solutions (S. aureus and P. aeruginosa were centrifuged twice at 10,000 rpm for 10 min; E. hirae was centrifuged once at 10,000 rpm for 30 min). For S. aureus, the surface charge for each pH value was measured in three independent replicates. For each replicate, three sequences of 10 measurements were performed at each pH value. The charge for one replicate was obtained by averaging the measurements from all three sequences. The final charge was obtained by averaging the charge obtained for the three replicates. For P. aeruginosa and E.
Acknowledgements The authors acknowledge the support of the  French National Agency of Research (ANR)  to the project  REACT  through the grant  ANR15-PIRE-0001-06. SEM imaging was performed in facilities supported by the  NSF   MRSEC program  under award No  DMR-1720530. The authors also thank  Dr. Denis Bendejacq ,  Dr. Kamel Ramdani  and  Dr. Matthew Taylor  for fruitful discussions.
Statistical analysis All analyses were carried out in triplicate and the results were reported as mean ± standard deviation. Statistica 5. 5 software (StatSoft ® Inc. USA) was used to perform the analysis of ANOVA followed by the post-hoc Tukey HSD test to analyze the significance of the differences between homogeneous groups at a significance level of 0. 05. Principal Components Analysis (PCA) was carried out using the XLSTAT software (Version 2009. 1. 01. Addinsoft ® ). This factor analysis also allows the conduction of Person (n) correlation tests to visualize possible correlations between phenolic compounds and their antioxidant activities.
e. , mCherry-Atg8a). A microscope setup (spinning-disk or confocal) with compatible lasers -488 nm and 532 nm -are needed to detect the fluorescent signal of the GFP and mCherry, respectively. CO 2 delivery to a blowgun and pad for anesthesia are required for fly sorting. An image-processing software to analyze the degree of colocalization between GFP/mCherry is required. This protocol was developed for use with Image Pro Premier 3D.
LIMITATIONS Using a commercially-available software vs an open-source solution This protocol was originally performed using the Image-Pro Premier 3D software, which consists of a comprehensive framework for the semi-automatic detection of autophagosomes positive for mCherry-Atg8a and their colocalization with mitochondria positive for mito-GFP. Using a predefined, commercially-available software for data analysis could potentially reduce the diffusion of the present protocol to study mitophagy. However, similar analyses can be performed using an open-source, community-driven software as Fiji (https://imagej. net/Fiji) with plugins that perform colocalization analyses on individual objects, such as JaCoP  (Bolte and Cordelieres, 2006; Cordelieres and Bolte, 2008). When using this solution, the user must pre-determine the optimal parameters allowing the detection and segmentation of autophagosomes.
TROUBLESHOOTING Problem 1 Generating Drosophila stock, steps 2a, c, d, e, f, g, 4a, c: the desired progeny do not eclose after 10 days. Top row images show raw data projections for a representative sample of (A) park +/+ and (C) park -/-PPL1 neurons expressing mCherry-Atg8a and TH-antibody staining (blue). Red puncta indicate lipidated mCherry-Atg8a. In the second row, red, blue and grey isosurfaces highlight mCherry-Atg8a puncta selected by the imaging software. We counted and compared the number of puncta located within TH-labeled region (bottom row) and did not detect an effect of the park-null mutation on autophagosome formation (B). Scale bar represents 10 mm. Data are represented as mean G SEM. The effect of parkin loss of function was determined using a Mann-Whitney test. This figure has been modified from  (Cackovic et al.
ACKNOWLEDGMENTS We would like to thank  Leo Pallanck  at the  University of Washington  for his gift of the park 25 fly stock. We acknowledge  Gerald Call at Midwestern University  for generating the genetic crossing scheme for flies used in these experiments. This work was funded by the  Biomedical Sciences program at Midwestern University  to L. M. B. and from the  Centre National de la Recherche Scientifique (CNRS)  and an intramural grant of the  University of Rennes 1 ''Collaborations internationales " to G. B.
Filters were scanned using a high-resolution scanner (Epson Perfection 4990 PHOTO) at 12,800 dpi for numerical particle quantification with ImageJ software v. 1. 52. Scanner surface was carefully inspected and cleaned with filtered (1. 2 µm) 70% ethanol. All of the particles (organic and inorganic), identified on the high-resolution scan of the filter, were pointed out, one by one, and quantified by dissociating fragments from fibres.
Size distribution Particle size analysis was performed with ImageJ software v1. 52, using high-resolution scans of the filters. We focused on the cleaner filters (n = 4 per study site) for the samples for which the digestion step of organic matter had been the most efficient (i. e. no visible organic traces). Particle size was measured by setting a scale based on a known distance in millimetres, converted into pixels. Contrast and threshold were adjusted in order to get as close as possible to the original picture. Particles were then fully analysed to obtain the width and the height of each in millimetres (mm). Width and height data were merged and classified according five size classes: 0. 020-0. 200, 0. 200-0. 335, 0. 335-1, 1-5 and > 5 mm. Filters were analysed individually, then collected data were pooled from all study sites and treated separately according to sample type (i.
In this method, colour of selected particles was monitored and single particles were then placed in a diamond micro-compression cell, one by one, and flattened between the two diamond anvils of a SPE-CAC® micro compression cell (Eurolabo, Paris, France). To obtain reliable results, comparison with a reference spectra database is absolutely necessary to unambiguously identify polymer type. Polymer identification was, therefore, made using the Thermo IR polymer database, with a 75% accuracy. Identified polymer types were categorized in three main groups: plastic, non-plastic and non-identifiable. The rayon polymer, which is often included in the classification of MP found in the marine environment, was excluded from this study. Indeed, FTIR-based studies showed that 30% of suspected rayon fibres turned out to be cellulose, which is a natural product.
Multivariate analysis To confirm the nature of the processed particles and analyse all spectra, we used chemometrics. The first step of this approach is to apply a spectral data pre-processing (Whittaker baseline correction) followed by a normalization method so that all the spectra would be at the same intensity, which facilitates the comparison with the database. The second step is to extract the meaningful information to identify the sample. Spectra were analysed using independent component analysis (ICA). ICA is a statistical and computational technique for extracting source signals from mixtures. The observed signals are considered as weighted sums of pure source signals, the weights being proportional to the contribution of the corresponding pure signals to each mixture  (Hyvärinen and Oja, 2000; Wang et al.
, 2008). The objective of ICA is, therefore, to search for the least Gaussian possible sources, i. e. the most independent  (Rutledge and Jouan-Rimbaud Bouveresse, 2013). Data treatment was done using MATLAB R2017a (The Math Works, Natick, USA). Once polymer types were confirmed, microplastic concentrations were calculated by transposing the percentage of particles certified as plastic, in the sub-sampling of particles analysed by FTIR, to the total number of quantified particles for each sample  (Kedzierski et al. , 2019).
It is common to use a 95% degree of confidence (i. e. α = 0. 05; u 1-α 2 = 1. 96)  (Kedzierski et al. , 2019). All data were expressed as percentages (mean ± standard deviation, SD). Maps were produced based on mapping data from  Andréfouët et al. (2006)  and using QGIS software (v2. 18. 24).
Based on the statistical sub-sampling procedure (Kedzierski et al. , 2019), 1571 particles were randomly selected for being characterized by FTIR spectroscopy. Synthetic matter was detected in all samples, composing 65%, 53% and 45% of analysed particles (classified as fragments or fibres) in Ahe, Manihi and Takaroa, respectively (Fig. 2B ). Natural matter (e. g. cellulose, cotton or protein) was also detected in every sample, composing 25%, 37% and 40% of analysed particles in Ahe, Manihi and Takaroa, respectively (Fig. 2B ). The remaining portion, i. e. 10%, 10% and 15% in Ahe, Manihi and Takaroa, respectively, was assigned to non-identifiable particles (i. e. particles whose spectra did not match any of those present in the databases, so that no signal was obtained). Corresponding raw data of particles analysed by FTIR and their relative abundance in surface water samples are given in Table  S5.
Acknowledgements This study was financially supported by the  MICROLAG  project funded by  Direction des Ressources Marines (DRM) of French Polynesia. T. Gardon was funded by a  Doctoral Research Grant  No. 09793  from  Institut français de recherche pour l'exploitation de la mer (Ifremer). We really thank  M. Kedzierski  for helpful assistance defining the subsampling procedure. Our thanks extend to boat drivers  G. Haumani  (DRM) and  M. Matarere  (Ifremer) as well as pearl-farmers who supplied us with pearl oysters.
Analysis VOC concentrations were monitored by FTIR. The furans generally have a number of major absorption bands in the infrared. The main bands used for analysis are shown in Table 1 (bold) along with other characteristic bands for each compound. Reference spectra of the major bands for each compound taken in the chamber at a resolution of 0. 25 cm -1 are provided in the Supplement (Figs. S8-S14 ). The ANIR curve-fitting software (Ródenas, 2018), which implements a least squares fitting algorithm, was used to generate time profiles for each compound based on their reference spectra. Profiles were checked by doing a number of manual subtractions. Example time profiles from an experiment with α-angelicalactone and furan, with cyclohexene as the reference compound, are shown in Fig. 1. Further example plots are provided in the Supplement (Figs.
S1-S7). All of the concentration-time profiles are provided in. txt format at https://doi. org/10. 5281/zenodo. 5721518, and all of the raw FTIR output is provided in. csv format at https://doi. org/10. 5281/zenodo. 5721518. Relative rate plots for all of the experiments are shown in Fig. 2. Relative rate experiments were performed, whereby a compound (or two) with an unknown reaction rate coefficient (k VOC ) with NO 3 was added to the chamber with a reference compound with a known NO 3 reaction rate coefficient (k ref ). A plot of the relative loss of the compound against the reference compound following addition of NO 3 (via N 2 O 5 decomposition), accounting for both chamber dilution and wall losses (k d ), gives a gradient of k VOC /k ref (Eq. 1).
ln ([VOC ] 0 ) ([VOC] t ) -k d t = k VOC k ref ln [ref] 0 [ref] t -k d t (1) A number of reference compounds were used for each VOC; they were chosen so that the reference rate coefficient was roughly within a factor of 5 of the expected unknown rate coefficient, and an attempt was made to use different references that had both larger and smaller NO 3 reaction rate coefficients than the VOC. Rate coefficients of the reference compounds (Table  2 ) are taken from the Database for the Kinetics of the Gas-Phase Atmospheric Reactions of  Organic Compounds v2. 1. 0 (McGillen et al. , 2020) , available at https://data. eurochamp. org/data-access/kin/#/home (last access: 27 January 2022). N 2 O 5 was not present at detectable levels (by FTIR) during most of the experiments. The only experiments in which N 2 O 5 concentrations built up in the chamber were those with the slowest reacting VOCs (i.
Conclusions Rate coefficients are recommended for reaction of seven furan-type VOCs with NO 3 at 298 K and 760 Torr, based on a series of relative rate experiments. These new recommendations highlight the importance of NO 3 chemistry to the removal of furans and other similar VOCs under atmospheric conditions. The measured rate coefficients suggest that for the three furans reported here, as well as for pyrrole and α-angelicalactone, reaction with NO 3 is likely to be their dominant night-time sink. For the alkyl furans and pyrrole, reaction with NO 3 may also be a significant sink during the daytime. This work also extends the existing database of VOC + NO 3 reactions, providing valuable reference values for future work. Data availability. Further example plots and experiment information are provided in the Supplement.
All of the response-time profiles from the FTIR are provided in. txt format at https://doi. org/10. 5281/zenodo. 5724967 (Newland, 2021a) , and all of the raw FTIR output is provided in. csv format at https://doi. org/10. 5281/zenodo. 5721518  (Newland, 2021b). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-22-1761-2022-supplement. Author contributions. MJN performed the experiments with technical support from YR and MRM; MJN was also responsible for performing the data treatment, data interpretation, and writing the paper. All co-authors revised the content of the original manuscript and approved the final version of the paper.
Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Special issue statement. This article is part of the special issue "Simulation chambers as tools in atmospheric research (AMT/ACP/GMD inter-journal SI)". It is not associated with a conference. Acknowledgements. This work has been supported by the European Union's Horizon 2020 Research and Innovation programme through the EUROCHAMP-2020 Infrastructure Activity (grant no. 730997), Labex Voltaire (grant no. ANR-10-LABX-100-01), and ANR (SEA_M project, grant no. ANR-16-CE01-0013, programme ANR-RGC 2016). Financial support.
Study Design CUMPNS is a prospective, randomized controlled open-label monocentric study with a 12-month follow-up period, which was designed to assess the added value of implanting PNfS in PSPS-T2 patients previously implanted with SCS, who did not achieve adequate back pain relief after attempting to achieve optimal pain coverage and relief using all possible SCS programming modalities and optimized medical management. Patients were enrolled from the University Hospital of Poitiers between February 2013 and April 2017. Informed consent was obtained from all patients before any data collection. The study was conducted in accordance with the Declaration of Helsinki and complied with CONSORT guidelines for RCT, Good Clinical Practice guidelines and was approved by an ethics committee (CPP-Ouest-III and ANSM: 2012-A00532-41).
Procedures and Additional PNfS Implantation Eligible patients were randomly assigned to one of two parallel groups in a 1:1 ratio. Randomization was conducted through a clinical data collection website (https://www. dirc-hugo-online. org/csonline, accessed on 31 August 2021) designed for the purpose of this trial, accessible to the investigators through a personal identifier and password. Neither the study medical staff, nor the patients were blinded to the randomization. In addition to SCS device implantation, patients in the intervention group ("SCS + PNfS") were implanted with PNfS at baseline, whereas patients in the control group ("SCS only") were implanted with SCS alone for 4 months and then implanted with a PNfS device at the 4-month visit (Figure  1 ).