dataesr/scientific-paragraphs-categorization huggingface dataset:
This is the case, for example, of species with complex mating strategies (such as some labrids and scorpenids) and species showing sex-reversal, in particular protogynous ones such as the dusky grouper E. marginatus, for which the size selection by SSF may be magnified  (Lloret et al. , 2012). Concerning the economic descriptors, differences in the species mostly contributing to RPUEs seem to reflect the variability in CPUE between areas, as higher catches produce, generally, higher revenues. However, RPUE is also driven by catch composition, as certain species are more valuable than others. For example, as shown also in other global ex-vessel price databases and local studies carried out in the Mediterranean Sea, lobsters are the species with the highest ex-vessel price  (Gómez et al. , 2006; Sumaila et al.
In that respect, a more detailed investigation of fishers' revenues should also take into account the variation in species ex-vessel price due to the total amount of fish caught in a day (the price of a species generally decreases with increasing amounts caught)  (Sumaila et al. , 2007). Although we did not take these elements into account in our analyses, we highlight that the ex-vessel database compiled in this study is, to our knowledge, the most detailed and complete available for coastal species of the Mediterranean Sea, and complements other global databases being built for large-scale data-rich fisheries that are scarcely applicable for assessing economic aspects of SSF at regional or local scales  (Swartz et al. , 2013; Tai et al. , 2017). Overall, our integrated approach for data collection enabled us to comprehensively gather an unprecedented amount of information, reconciling data quality (ensuring accurate information for each fish individual caught and fishing operation monitored) with feasibility (minimizing the time required for fishers and the manipulation of the catch).
Our study highlights that regional patterns of SSF associated with MPAs derive from a multitude of facets at local scale, that can profoundly differ even at relatively small spatial scales. This being so, governance and management actions cannot neglect the particular characteristics of SSF when specific regulations target fleet characteristics, gears and species. Regional management measures (e. g. in terms of fishing gears or species restrictions) could unevenly affect communities with different socio-ecological features, in some cases potentially disadvantaging SSF communities dependent on few/single species or gears. Overall, the approach used in this study enabled us to gather an unprecedentedly detailed dataset on SSF socio-ecological features associated with Mediterranean MPAs, both in terms of data reliability and spatial coverage, and to draw conclusions at large scale (covering multiple locations) highlighting at the same time local specificities.
Acknowledgements This research was carried out in the framework of the  FishMPABlue2 and FishMPABlue2 Plus  projects (https:// fishmpablue-2. interreg-med. eu/) funded by the  European Territorial Cooperation Programme  MED and co-financed by the  European Regional Development Fund (ERDF) , and of the  MAPAFISH-MED study  ( CINEA/ EMFF/2021/3. 1. 2/SI2. 868140-SC02 ) funded by the  European Maritime, Fisheries and Aquaculture Fund (EMFAF). Antonio Calò  was also funded by the  Italian Ministry of Education, University and Research (MIUR)  in the framework of the  PON 'Research and Innovation 2014-2020 ' (D. D. 407/2018  and D. M. 1062/2021 ), co-financed by the  European Social Fund. Authors are grateful to  Frédéric Bachet ,  Olivier Bretton ,  Milena Ramov ,  Leila Seddiki  and  Valentina Cappanera  for their invaluable help in data collection.
The CAZyme database splits carbohydrate-active enzymes into classes of glycoside hydrolases (GH), glycosyl transferases (GT), polysaccharide lyases (PL), carbohydrate esterases (CE), auxiliary activities (AA), and carbohydrate binding molecules (CBM), defined by sequence similarity  [32]. Enzyme commission numbers are based on the reactions catalyzed instead of sequence homology  [33]. Using these different tools allows for a broad analysis of the potential organic matter degrading functions of proteins based on their sequence homology. Hierarchical clustering and principal coordinate analysis were used to find correlations between the sites and the enzyme families found within them. By combining the maximum potential enzymatic activity, measured by low molecular mass fluorogenic substrate proxies, with the metagenomic annotations, we can see a larger scope of the potential heterotrophic activity within these sites.
0)  [40]. The clean reads were mapped back to the assemblies for read coverage using bowtie2 (v 2. 3. 5. 1)  [41]. Hierarchical clustering based on spearman correlation, was performed using hclust from the base R package (R version 4. 2. 1). Total microbial community analysis of these sites is the focus of previous work, therefore, taxonomic identification was only performed for contigs for the high temperature sites HV21, HV22, and KR22 using gottcha2 on Kbase (Figure  S1 )  [42]. CAZy family abundances were annotated using dbcan2. Annotations were selected if they were annotated with at least two tools: HMMER and DIAMOND. The annotated gene IDs were then combined with the prokka gene ID and contigs to gain the abundance of each contig. Then the read abundance of each annotation normalized to the total assembly size was used to generate a heatmap with hierarchical clustering of the CAZyme families and site locations.
Enzymatic Assays Enzymatic assays were performed following the methods of Bell (2013)  [43]  with slight modifications. Briefly, sediments were weighed out at 2. 75 grams wet weight. The sediments were then combined with 91 mL of 0. 5 M Tris-HCl buffer with a matching pH to the original site locations. The sediment slurries were blended for one minute to homogenize them. Then 800 μL Differences in enzyme activities among provinces were tested using a Kruskal-Wallis test, implemented in R, due to the strong non-normal distribution of activities in the data set.
Sites Three different geographical areas were analyzed: the Central American and Andean convergent margins, and Iceland (mantle plume-influenced spreading center). Large variations in fluid sources (i. e. mantle, slab, crust, or surficial) are expected due to the contrasting geologic and environmental settings of the studied areas. We include data from 22 sites in Costa Rica that are influenced by the convergent margin, with analyses of the geochemistry, respirations and taxonomic identities published previously  [3, 4, 7, 44]. We include 47 additional sites spanning Costa Rica and Panama that are also influenced by the convergent margin, 13 sites from the Andean convergent margin and 3 sites from Iceland. Sites were grouped by geographic-tectonic setting: Costa Rica outer forearc, Costa Rica active volcanic arc, Costa Rica backarc, Cordillera Talamanca, Panama, Argentina backarc, and Iceland (Figure  1 , Table  1 ).
Multivariate analysis We performed multivariate analysis on the datasets using a principal component analysis (PCA) to perform an unconstrained coordination analysis. For cell-degrading enzymes, sites do not cluster based on province (Figure  7A ). Instead, we see that high temperature sites (HV21, HV22, KR22, GF1, GF2, and BQ) cluster together while all other sites are indistinguishable based on cell-degrading enzyme abundance. Photosynthate-degrading enzyme abundances, however, do cluster by provinces, with the Costa Rica active volcanic arc and Argentina active volcanic backarc tending to group together. Sites with volcanic activity correlate with CAZyme families GH1, GH5, GH9, GH51, and GH116, which are specifically related to cellulose degradation  [48]. Here, high temperature sites continue to cluster together, but do not correlate with any specific enzymes.
Standard protocol approvals, registration, and patient consents Patients enrolled in OFSEP (registered to clinicaltrials. gov [NCT04028232]) provide written informed consent for participation. In accordance with the French legislation, the present study was approved by both the national data protection agency (Commission Nationale Informatique et Libertés [CNIL]; approval DR-2019-132) and the French expert committee for research, studies and evaluations on health (Comité d'expertise pour les recherches, les études et les évaluations dans le domaine de la santé [CEREES]; approval TPS 216966).
Study population The data was provided from a retrospective cohort from 18 of the 36 French MS expert centers participating in the French MS database "Observatoire Français de la Sclérose En Plaques (OFSEP www. ofsep. org)". 5, 21 The 18 centers were selected for their inclusion of patients for serveal decades, allowing sufficient follow-up to study mortality. For each patient, a neurologist from each center entered clinical and imaging data during routine follow-up visits using dedicated software (European Database on Multiple Sclerosis [EDMUS]). 22 The same cohort has been used for a previous study on excess mortality in MS, using the same data and the same inclusion criteria. 5 These data were retrospectively collected at the time of the first visit after the center's systematic registration date, and prospectively thereafter, in other words ambispectively.
Socio-economic indicator Socio-economic deprivation was based on the patient's address as available in the OFSEP database, geolocated by the Geographic Information System (GIS ARCGIS 10. 2®), which was then assigned to an "Ilot Regroupé pour l'Information Statistique (IRIS)", the smallest French geographic unit for which census data are available. Each patient's IRIS was then attributed to an EDI (European Deprivation Index) score, an ecological deprivation index based on 10 weighted variables (low level of education, no access to a car, overcrowding, no access to a system of central or electric heating, non-owner, unemployment, foreign nationality, unskilled workerfarm worker, household with more than six persons, single-parent household). 23 This score was kept as a continuous variable ranging from -11 to +32 (median value: -0.
Role of the funding sources This study was funded by the ARSEP foundation "Fondation pour l'aide à la recherche sur la Sclérose en Plaques" (Grant Reference Number 1122). Data collection has been supported by a grant provided by the French State and handled by the "Agence Nationale de la Recherche," within the framework of the "Investments for the Future" programme, under the reference ANR-10-COHO-002, Observatoire Français de la Sclérose en Plaques (OFSEP). The funding sources had no role in the writing of the manuscript or the decision to submit it for publication.
38  The second option is to use ecological data, based on neighbourhood level census data. Krieger studied the use of census level data to approximate individual data to compensate for lack of individual socio-economic data in medical records in the United States, and found satisfying results. 39 A study in Belgium found that both individual SES and neighbourhood deprivation were linked to cause-specific mortality in cancer, and neighbourhood SES measure remained associated with this mortality after controlling for individual socio-economic position. 40 Using a neighbourhood level proxy could thus provide additional information compared to individual level measures of SES. However ecological level SES measures are necessarily subject to ecological bias. A further potential bias resides in the fact that linkage to the IRIS was performed according to the address available for each patient in the OFSEP database and that this address could have been recorded at the first clinical  visit following inclusion, with first inclusions dating back to 1976 in the OFSEP database, or have been updated throughout follow-up.
No date corresponding to the address in the database was available. This exposes to the risk of reverse causation, whereby patients may live in more socio-economically deprived areas as a consequence of disease progression due to lower income. 41 Updated addresses for each patient with the date of each address could have prevented the afore mentioned issue or at least allowed to measure the extent of the potential reverse causation occurring. In addition, the socio-economic environment of an IRIS could have evolved between 1976 and 2011, year for which the EDI score was developed. To restrict the implications of this evolution, follow-up was censored at 30 years to limit the timescale between address collection and attribution of the EDI score whilst also allowing sufficient follow-up for the survival study (data for 55 years of follow-up shown in etable 3 online-only supplements).
It is conceivable in our study that the more deprived patients of deprived areas died first, leaving the least deprived patients of this area alive, resulting in an accentuation of ecological fallacy, in which living in a deprived area does not necessarily mean every individual within this area is deprived. A prospective cohort with recording of dates corresponding to each change of address for each participant would allow correct attribution of neighbourhood socio-economic status specific de each time period of each address. So far, few studies have examined the effect of neighbourhood-level socioeconomic status on neurological disease outcomes; this data has therefore rarely been recorded in past years. Scandinavian population-based registries demonstrate the benefit of having such data, however such registries do not currently exist in France or in many other European countries.
44 A study by Marrie et al. using individual-level data found that lower health literacy in MS patients was linked to an increase in the risk of smoking, obesity, as well as increased risk of visits to the emergency room and overnight hospitalizations,  45  suggesting that preventive behaviours were less adopted by patients with lower health literacy. Further studies such as this study by Marrie et al. , examining individual-level data to study the effect of education level, income, and occupation are necessary to adapt public health policies and preventive practices to try to reduce the deprivation gap. The analysis of missing data on EDI in this study suggests that data is not missing completely at random, however imputation techniques are not yet available in the survPen package used. 28 One center was responsible for over 50% of missing data on EDI, however when the analysis was repeated excluding all patients from this center, no difference in selection of models was found.
The OFSEP database provides a large cohort of MS patients with a long enough follow-up to enable decent statistical power for an excess mortality study. 21 However, the database is not provided from a national registry but from an observatory, based on MS expert centers. Patients are recruited in the OFSEP database by neurologists in these expert centers. Recruitment bias is possible with an overrepresentation of more active MS forms than in the general MS population since patients with more active MS tend to be treated in expert centers. In order to measure the magnitude of this bias, a previous study on the same cohort provided a sensitivity analysis with censorship of follow-up in 2000, before widespread access to DMTs. 5  The results found negligible differences compared to the main analysis, suggesting that this recruitment bias of more active MS patients was limited.
Emmanuelle Le Page: received payment or honoraria for consulting or lectures from  Biogen ,  Merck ,  Teva ,  Sanofi-Genzyme ,  Novartis Alexion ; received research support from  Teva and Biogen , and received academic research grants from  PHRC  and  LFSEP , and a travel grant from the  ARSEP Foundation ; received payment for consulting from  Biogen ,  Merck ,  Sanofi-Genzyme , and Novartis; received invitations for national and international congresses from  Biogen ,  Merck ,  Sanofi-Genzyme ,  Novartis Alexion , all of the above unrelated to this work. Jonathan Ciron: participated on a  Data Safety Monitory Board of Advisory Board  with  Biogen ,  Novartis ,  Merck ,  Sanofi ,  Roche ,  Alexion  and  BMS-Celgene  (all unrelated to this work). Aurélie Ruet: Consultancy fees from  Roche  and  Biogen , payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from  Merck ,  Roche ,  Biogen , research grants (paid to the institution) from  Roche ,  Biogen  and  Sanofi-Genzyme , and support for attending meetings and/or travel from  Biogen ,  Novartis  and  Alexion , all of the above unrelated to this work.
Acknowledgements The authors thank the patients and neurologists for their participation in the  OFSEP  project by contributing and facilitating access to clinical data; the research assistants in all participating centers who contributed to data collection; and the geographical platform MAPinMED and " Cancers and Preventions" group for performing geolocation of our study population of MS  patients, with a particular mention to  Ludivine Launay  for her large contribution in this task.
Funding This study was funded by the  ARSEP foundation "Fondation pour l'aide à la recherche sur la Sclérose en Plaques " (Grant Reference Number 1122). Data collection has been supported by a grant provided by the  French State  and handled by the  "Agence Nationale de la Recherche ," within the framework of the "Investments for the Future" programme, under the reference  ANR-10-COHO-002 ,  Observatoire Français de la Sclérose en Plaques (OFSEP).
However, recent kidney care innovations may enable health systems to establish acute and maintenance in-house kidney care services by providing analytics, specialized care management, and simplified dialysis equipment, reducing infrastructure and the number of skilled staff hours needed to treat patients. Starting with insourcing acute dialysis, one recent example of a health system that made this shift was a 316bed multispecialty acute care hospital in Salt Lake City, Utah. This hospital leveraged new technology and insourced all inpatient dialysis treatments, including staff, equipment, and dialysis device maintenance. Data regarding training time and treatment costs were recorded from April through November 2020 across the 577 treatments conducted and 15 providers. Comparing outsourced dialysis provider fees to the hospital in the prior year (annual cost of $650,000) to the new in-sourced model (annual cost of $200,000), an annual savings of $450,000 was projected for the first year alone because of the improved efficiency and productivity of providing dialysis internally.
Dialysis Initiation During Inpatient Stay Currently, a patient who initiates maintenance dialysis while admitted to the hospital often experiences a lengthy inpatient stay before being discharged to a community-based dialysis clinic. An analysis performed by Health Management Associates of the 2016-2018 Medicare Limited Data Set showed the average hospital loses $8,500 per patient dialysis initiation, with more significant losses tied to lengths of stay beyond 10 days (Table  1 ). In this analysis, Medicare patients with International Classification of Diseases, Tenth Revision diagnosis of end-stage renal failure who received dialysis as an inpatient as the first event in 2017 but did not receive dialysis in 2016 were included, and patients who did not receive dialysis for at least 90 days after initiation were removed.
As of this publication, only one technology has ever been approved for CRA TPNIES based on meeting CMS substantial clinical improvement criteria for home hemodialysis treatment, effective for the 2022-2023 calendar years. Assuming use of a new home dialysis machine that that qualifies for CRA TPNIES and using the expected expenditures for TPNIES that CMS provides in the calendar year 2022 End-Stage Renal Disease Payment System final rule, analyzing Medicare 2018 dialysis facility cost report data as a baseline and adjusting for assumptions such as the reduced space required for home therapies and investment in training clinicians to support patients, we estimate $9. 5 million in total health system treatment costs for this same population over 5 years, leading to a potential net operating income of $4.
Growth monitoring Cell viability in preculture and during fermentation was determined by flow cytometry. Dissolved propidium iodide (PI; Invitrogen, Molecular Probes, ThermoFisher Scientific, Illkrich, France) in water at a concentration of 0. 1 μg. ml -1 was used as fluorochrome. We added 1 μl of PI to 100 μl of diluted suspension in PBS buffer (137 mM NaCl, 2. 7 mM KCl, and 11. 9 mM Phosphate, pH 7. 2; Fisher Scientific, Illkirch, France). Samples were incubated in the dark for 15 min before analysis. Flow cytometry was performed with a BD Accuri C6 flow cytometer (Becton, Dickinson and Company Franklin Lakes, United States), and the data were processed using the BD Accuri C6 software. For each sample, 20 μl were analyzed at 34 μl. min -1. The FSH threshold used was 80,000. A 488-nm wavelength argon laser was used to excite the cells (autofluorescence).
Oenological analysis Samples corresponding to three biological replicates were centrifuged at 8,000 ×g for 5 min at 4°C. Sugar concentration and ethanol degree were monitored daily by FTIR (Fouriertransformed infrared) spectroscopy (OenoFOSS™, FOSS, Hilleroed, Denmark). Fermentation kinetics could be established as sugar concentration against time. The maximal rate of sugar degradation and time to degrade 50% of the initial fermentable sugars were determined graphically. A t-test was performed to compare these two parameters between pure culture and coculture conditions in each modality and an ANOVA (value of p ≤ 0. 05) was also performed followed by a Tukey HSD test on the data by modality (S2S3, S4S3, and S8S3) using R software (R-4. 0. 4).
This calibration was carried out in "enhanced quadratic" mode with an error of less than 0. 5 ppm. An internal calibration allowing a post-acquisition recalibration was also performed. The mass range was between 100 and 15,000 m/z. Inter-batch (standard peptide and polyphenol mixture) and intra-batch (experimental QC, sample mixture) quality controls were performed to ensure the repeatability and stability of the system during the analysis (Supplementary Figure  S1 ). All the samples were randomly injected into the same batch to avoid batch-to-batch variability. The raw mass spectra of each sample were recalibrated using Compass DataAnalysis v4. 3 software (Bruker, Bremen, Germany), to achieve a mass deviation after calibration of 0. 5 ppm. The m/z associations and retention times (RT) also called features were extracted (S/N >30 and intensity thresholds >1,000).
Features of each sample were aligned according to their RT and mass with a tolerance of less than 30 s and 10 ppm, respectively, using homemade R script: MXSAlign. 1 Features were kept if they are present in more than 20% of the samples. The data resulting to positive and negative ionization mode were merged in one dataset, with tolerance, 5 ppm for m/z and 10 s for retention time. 5,191 features were obtained. Elementary formula was determined using isotopic profile (tolerance: 5 ppm and mSigma <20) and in house database. Only annotated features were selected for statistical analysis (2054 features). Once analyzed, Matlab (R202115a) was used for statistical analysis (PCA, t-test) and data visualization. Isolated significant features were annotated using the online tools of Metlin, KEGG, MassTrix, YMDB database and Oligonet.