dataesr/scientific-paragraphs-categorization huggingface dataset:
| Outcome metrics and statistical analysis The data were collected via the DBLG1 system and were extracted from YourLoops databases. No additional data were collected from any clinical report form. In this retrospective analysis, we looked at the glucose metrics among the FAS population, as defined by the International Consensus on Use of Continuous Glucose Monitoring. 9, 10 The primary endpoint The data were collected at the requested time, and for each day, the percentage of time in the closed-loop was computed. Only patients with >14 days of data (a day of closed-loop defined by 75% of glucose measurements under the closed-loop) were selected to compute the endpoints. All glucose-related endpoints were averaged per patient. Among the CAS population, we computed an expected TIR based on a previously published formula using the HbA1c of the patient before the use of the DBLG1 system.
However, as two-thirds of the patients had entered their baseline HbA1c in the handset, a pre-initiation TIR could be extrapolated from these data. Although the results must be interpreted very cautiously, because of this extrapolation and because HbA1c was self-reported, the improvement in TIR following closedloop initiation exceeded 18 percentage points in this subpopulation characterized by a poor baseline TIR of $54%. All HbA1c subgroups show satisfactory results, with TBR between 1. 0% and 1. 7%, well F I G U R E 2 DBLG1 system performance pre-and post-initiation. Data were extracted from the comparative analysis set (n = 2460; see text for Methods). Data are median (IQR). HbA1c, glycated haemoglobin below the international target of 4%, and with TIR between 65. 8% and 81. 3%, strongly influenced by baseline HbA1c.
We confirmed that patients with the lowest baseline A1c achieved the highest TIR, as previously reported by others. 19 Of note, our observation of a GMI of 7. 0% is consistent with the findings recently reported from a crosssectional analysis of the T1D Exchange database. 20   7  They observed a median TIR of 76. 7% and a GMI of 6. 8%. The difference in TIR pre-and post-initiation could be studied among 812 subjects, exhibiting a 12. 1 percentage point improvement from 63. 4% to 75. 5%. The TBR was higher in their study than in our cohort (2. 5% for the time <70 mg/dl and 0. 5% for the time <54 mg/dl, vs. 0. 9% and 0. 1%, respectively), whereas the percentage of users achieving the recommended goal for TBR (both TBR <70, recommended <4%, and TBR <54 mg/dl, recommended <1%) was 78. 9%, versus 98.
Regarding this latter point, a recent report about the MiniMed™ 780G system among 12 870 real-world users showed very similar outcomes between patients that were younger or older than 15 years. 21 r report has several limitations. It was a retrospective design and there was no control group. We did not have access to preinitiation information, except age, sex in all patients and patient-reported HbA1c in a subset of patients. Clinical data are lacking, such as reasons for initiation or discontinuation of closed-loop, duration of disease, status of complications and patient-reported outcomes. The side-effects of treatment were not systematically recorded. However, the rate of drop-outs from treatment did not exceed 1. 5%, which is low. Its strengths are the very large dataset of several thousands of patients and its nationwide and real-world setting without any selection of patients.
In conclusion, this study shows real-world data of a large dataset of 3706 adult patients with T1D who started using the DBLG1 system and whose data could be analysed for up to 1 year of retrospective follow-up. Our results strongly suggest that this treatment helped to comply with standards of care for T1D: the study population reached a TIR (70-180 mg/dl) of 72. 1%, the time in hypoglycaemia (<70 mg/dl) did not exceed 1%, the median GMI reached 7. 0%, and the median closed-loop usage was 95%.
Acknowledgements The authors thank  Andrew Ross  ( National Museum of Scotland, Edinburgh ) and  Peta Hayes  ( Natural History Museum, London ) for access to the specimens. They acknowledge  Keith A Seifert ,  Mary Berbee ,  Sébastien Duplessis  for helpful discussion about the fossil structures. CS-D thanks the  Fondation ARS Cuttoli-Paul Appell/ Fondation de France  for supporting her work on fossil fungi (grant no. 00103178 ) and a grant to EG ( RBG Kew ) by the  Evolution Education Trust.
Samples were visualized with a 30 µm confocal pinhole, and 100-400 optical slices per z-stack were acquired for each detector. The fluorescence signal from each z-stack was then projected onto a false-color maximum-intensity projection image and used to generate depth-coded visualizations of the sample (Nikon NIS-Elements software, http://www. nis-elements. com) (see  16, 19  for additional information). The resultant z-stack datasets (. nd2) and animation movies (. mp4) have been uploaded to a Zenodo repository  76.
The CaFC (coffee agroforestry business-driven clusters) concept was developed to meet this objective  (Bertrand et al. , 2019) , initially within the framework of a project in 2017 (MATRICE project led by CIRAD), then within the framework of the EU/BREEDCAFS H2020 project initiated in 2019. The aim is to establish agroforestry clusters according to specifications jointly defined by all partners and local actors. In this article, we first describe the general concept of the CaFC model, its vision and objectives. Based on the pilot project in Nicaragua, initiated in 2016, we illustrate how the CaFC model can be implemented in real conditions and show evidence of its potential. Using data shared by key stakeholders of the Nicaragua project -ECOM (coffee-buyer), the Moringa partnerships fund, NicaFrance (foundation), CIRAD (research institution) and Nespresso (roaster) -as well as data from a similar project in Peru, we assess the profitability of the CaFC model by comparing realistic scenarios and applying sensitivity analysis.
As they are also considered to be the most impactful standards  (Dietz et al. , 2018)  and to complement each other regarding economic, social and environmental sustainability issues  (Parvathi et al. , 2017) , the choice of FT-organic as a baseline to compare with the CaFC model appears the obvious one. We also add a baseline scenario with no certifications (conventional). Data sources. To assess the profitability of a hypothetical CaFC model, we use available primary data from the Nicaragua pilot project combined with secondary sources for various scenario, including production costs data available from cooperative members in Peru. Data was obtained from ECOM and Nicafrance regarding the costs related to the processing of coffee within the Nicaragua pilot project, and the final purchase price from Nespresso.
Data used include renovation costs, costs of production and yields under organic production for two varieties: F1 hybrid -Starmaya and pure line Typica. Using production costs from another country is a limitation to our study. However, our objective is to compare the profitability of different replanting scenarios, with an emphasis on the variety and overall organisational model used. Therefore the comparisons remains valid as the cost differentials affect the different scenarios in the same way. Morevoer, typica is widely used in many producing countries  (Mexico, Peru, Jamaica, etc. )  Processing and handling costs amount to 0. 33 US$/lbs of green coffee, which sets the CaFC farm gate price for producers at: 0. 7 $ / CaFC FGP Cprice US lbs = + Moreover, as we are under the assumption of organic production, we add an extra organic premium to the CaFC farm gate price, set at 0.
Profitability indicators. Assessing the impact of the CaFC model involves projecting streams of costs and benefits that vary throughout the year as coffee productivity varies worldwide  All scenarios were modelled based on available data, with parameters set for each scenario and profitability indicators computed using RStudio -R version 4. 1. 0 (RRID:SCR_000432). The source code is available publicly  (Meter et al. , 2022b). = = - = + ∑ 0 0 ( ) (1 ) / ( ) (1 ) t n t t t t n t t t B r B C C r = = = = + = + ∑ ∑ Where B t = benefit.
Data availability Underlying data CIRAD Dataverse: Underlying data for 'Local value-chains dedicated to sustainable production (coffee agroforestry business-driven clusters or CaFC): a new organizational model to foster social and environmental innovations through farm renovation', 'Profitability assessment of the coffee agroforestry business-driven clusters (CaFC)'. https://doi. org/10. 18167/DVN1/ 8RKHFX  (Meter et al. , 2022a)  The profitability assessment is based on the following underlying data:.
c. Are the certification costs included in the analysis (>2year)? They do not appear in the renovation cost files. R : No the costs of certification are not included, as they would be the same for all three options. d. Does the data from Peru come from an experiment or plots in different commercial coffee plantations? R : The data on production costs from Peru were collected from three cooperatives of more than 1000 farmers each. This information was added in the text. e. The production data (yields) available in the files show that the production values from year 3 to year 6 are the same for the period from year 8 to year 11 (except year 10). Please comment on this. Did the reported data come from actual yields from coffee plantations for the 12 years? If so, the production data are an average of different plantations or came from a single plantation? R : The production of coffee trees is cyclical and the years 6 and 10 are years of pruning therefore without production.
This study assessed zooplankton and ichthyoplankton density estimates in a near-surface SSL using four different methods: mesozooplankton net (MultiNet), macrozooplankton trawl (Tucker trawl), and the forward and inverse methods applied to broadband acoustic data collected with an autonomous surface vehicle. The survey was conducted as a case study in the Tromsøflaket area, a bank north of the northern Norwegian Sea (70 • N). We deployed nets and trawls from a research vessel while an autonomous surface vehicle equipped with a broadband echosounder surveyed the same region  (Camus et al. 2019). We also tested the applicability of using theoretical scattering models  (Chu and Ye 1999; Khodabandeloo et al. 2021 ) to reduce the dependence on relative density estimates from net and trawl sampling when conducting autonomous hydroacoustic surveys.
Biological sampling Mesozooplankton were sampled by vertical hauls (towing speed 0. 5 m s -1 ) using a multiple opening/closing net (MultiNet, Hydro-Bios, Kiel, Germany, www. hydrobios. de; mouth opening 0. 25 m 2 , mesh size 180 μm). Five depth strata (bottom-100, 100-30, 30-10, 10-5, and 5-0 m) were sampled at each station, but data below 100 m were not used in this study because it was outside the range of the echosounder mounted on the Sailbuoy. At Station 13, samples were taken by a ring net (WP2 net, Hydro-Bios), with the same mouth opening, mesh size and depth strata as the MultiNet, but did not include the 0-5 m depth stratum. All samples were preserved in 4% formaldehyde-in-seawater solution buffered with hexamine. Taxonomic analyses were completed in the laboratory. Large organisms (total length > 5 mm) were picked out using forceps, identified, and counted from the whole sample.
Acoustic data processing The autonomous hydroacoustic survey was completed using a Sailbuoy equipped with a WBT Mini (Kongsberg Maritime AS) with a 333 kHz transducer (ES333-7CDK splitbeam) operating in broadband mode (283-383 kHz, 1. 024 ms pulse duration, 0. 5 Hz pulse repetition, fast ramping) for 5 min every half hour. The transducer was mounted on the bottom of the Sailbuoy keel at 0. 5 m depth. The Sailbuoy keel was always in the water and the transducer was al-ways submerged. Echosounder calibration was performed before the deployment and after the retrieval with a 22. 0 mm tungsten carbide (6% cobalt binding) calibration sphere  (Demer et al. 2015). Broadband calibration parameters were calculated with the EK80 calibration wizard (version 2. 0. 1, EK80 software, Kongsberg Maritime AS), and the parameter values were linearly interpolated over the inhibition bands that covered the nulls.
Sound scattering layer backscatter spectra SSLs forming discrete horizontal bands of backscatter above the background noise  (Proud et al. 2015)  were identified using k-means clustering, an unsupervised machine learning algorithm  (Lloyd 1982). Each raw data file output from the echosounder was converted into a netCDF4 file with the open-source software echopype (version 0. 5. 3;  Lee et al. 2021; Fig. 2a). Data analysis was restricted to the region between the near-field (3 m range) and the signal-to-noise ratio limit (50 m range). In all echograms, a maximum of one SSL was detected by the clustering algorithm in the upper 50. 5 m of the water column. The SSL varied in strength, thickness, and depth. The pulse-compressed volume backscattering strength (S v in dB re 1 m -1 ) averaged over the frequency spectrum was pre-processed with a mean filter to smooth the backscatter in time (35 pings; or 70 s) and depth (15 bins; or 0.
2019). We identified the most abundant species of each taxonomic group to determine the model parameters. Copepods were modeled as Calanus finmarchicus copepodite stage V (CV) (61% of copepods in the MultiNet samples, Table  S1 ), euphausiid larvae were modelled as Thyssanoessa inermis (100% of euphausiid larvae in the Tucker Trawl samples, Table  S2 ) and amphipods were modeled as Themisto abyssorum (100% of amphipods in the MultiNet samples, Table  S1 ). We ran 1000 model simulations for each taxonomic group using the ZooScatR package (version 0. 5; Gastauer et al. 2019) with varying shape, size, and material properties parameters. These parameters were selected based on literature or net and trawl samples (Table  2 ). The length distribution for euphausiid larvae was calculated using the measurements of Thyssanoessa inermis in the Tucker trawl subsamples from stations 7, 8, and 9 (Fig.
Inverse method Alternatively, the inversion of the broadband backscattering data can be used to solve eq. 1 with a least-squares data fitting solver, as in  Lavery et al. 2007 and Lavery et al. 2010 (Greenlaw 1979). From the scattering model simulations for each taxonomic group, we calculated the median cross-sectional backscatter, σ i bs (f) (eq. 2) and 90% bootstrap interval of the median across the frequency spectrum. To calculate the density of each taxonomic group for the autonomous hydroacoustic survey with the inverse method, we solved eq. 2 for density ρ i as a linear least-squares problem by using a Trust Region Reflective algorithm as described in  Branch et al. (1999). The optimiser (Python ver. 3. 7, scipy. optimise. lsq_linear) determined the best solution by minimising the following problem with the following bounds (0 <= ρ i < inf.
Biological sampling Copepods dominated the mesozooplankton community sampled with the MultiNet with a mean density with standard error (±SE) of 1800 ± 300 ind. m -3 (95% of the density, Fig. 3 ). Pteropods were the second most abundant taxonomic group in the MultiNet samples, with a mean density of 50 ± 30 ind. m -3. Euphausiid larvae had a low density (9 ± 2 ind. m -3 , 0. 5% of the community); most of these were represented by euphausiid larvae in furcilia stages (89% of euphausiid larvae over all MultiNet samples). Other species, such as siphonophores and meroplankton, not included in the selected taxonomic group for this study, accounted for 30 ± 5 ind. m -3 , or 2%, of the MultiNet catch in the study region. Detailed MultiNet density data are presented in Tables  S1  and S3. Like the MultiNet samples, the Tucker trawl samples were primarily composed of copepods (54% of the community, Fig.
The blue line in each panel is the median of the sensitivity analysis, the shaded region displays the extent of the 5th and 95th percentile. The red lines indicate the standard deviation of the density estimates for all the SSLs. Note the difference in scale of the y-axis. all taxonomic groups (copepods: p = 0. 08; euphausiid larvae: p = 0. 77; amphipods: p = 0. 79; fish larvae: p = 0. 31; and pteropods: p = 0. 07). In contrast, density estimates from the forward method were statistically different from estimates from the MultiNet samples for copepods (p < 0. 01), fish larvae (p < 0. 001), and pteropods (p < 0. 01), but not for the euphausiid larvae (p = 0. 18) and amphipods (p = 0. 76). The density estimates calculated from the autonomous acoustic survey data by the forward and inverse methods were statistically different for all taxonomic groups (p < 0.
We suspect that because the copepods were relatively large individuals (mainly C. finmarchicus CV with a mean length of 2. 6 mm) organised in dense swarms, the high frequency and high bandwidth (283-383 kHz) of the acoustic instrument detected most of these copepods. The agreement of the density estimates from the inverse method and MultiNet suggests that the high vertical resolution of the broadband acoustic data could be used to increase the accuracy of copepod density estimates within the epipelagic layer. In the future, satellite observations of ocean colour could compensate for the blind zone of acoustic measurements near the surface and measure the near-surface density of copepods  (Basedow et al. 2019). Variations in organism size and swimming abilities must be considered when designing surveys and selecting sampling methods.
Conclusion The inverse method was used to quantify aggregations of zooplankton and ichthyoplankton with a broadband autonomous hydroacoustic survey and detected higher densities of abundant mobile zooplankton than the net, trawl and forward acoustic method. The inverse method also detected similar densities of smaller mesozooplankton to the net samples. We conclude that it is the most accurate method to measure the density of a broad size spectrum of zooplankton, and most likely of ichthyoplankton and pelagic fish. This work built on studies on the inverse method for zooplankton layers  (Lavery et al. 2007) , autonomous hydroacoustic surveys  (De Robertis et al. 2019 ) and broadband data processing  (Basset et al. 2019; Benoit-Bird and Waluk et al. 2020 ) in recent years. We further advanced the field by offering a solution for the limitation of sparse coexisting biological sampling from autonomous acoustic surveys by using the inverse method with locally derived size measurements.
Funding statement This research was funded by  GLIDER Phase I project , which was funded by the  DEMO2000 research program  ( Norwegian Research Council  and  ConocoPhillips Skandinavia AS , project no. :  269 188 , "Unmanned ocean vehicles, a flexible and cost-efficient offshore monitoring and data management approach") and by  Glider Phase II  financed by Cono-coPhillips Skandinavia AS. The research survey was funded by " Collaborative Studies of Two Resource Ecosystems in Shelf, Slope an Oceanic Regions of the Norwegian and South China Seas (Stressor) ", funded by the  Norwegian Research Council  (project no. :  287 043 ). Maxime Geoffroy's participation is financially supported by the  Discovery Grant program  of the  Natural Science and Engineering Research Council of Canada. Geir Pedersen 's participation was co-funded by  CRIMAC (Norwegian Research Council  project no.
Data availability statement Acoustic data generated or analysed during this study are available in the "EK80 raw data collected by autonomous sailbuoy in Lofoten/Vester ålen, 2018-06-18-2018-06-30" repository (https://doi. org/10. 5281/zenodo. 6786851). Location and other sensor observations from the autonomous surface vehicle data generated or analysed during this study are available in the "Real-time oceanography captured by autonomous sailbuoy in Lofoten/Vester ålen 2018" repository (https://doi. org/ 10. 5281/zenodo. 6786919).
3, 5 By gathering global data on individuals with BSEP deficiency, the NAPPED (NAtural course and Prognosis of PFIC and Effect of biliary Diversion) consortium aims to provide data that will allow for optimized patient-specific treatment, whilst acknowledging the extensive heterogeneity in the observed genotypes. 5 The unique size of the NAPPED database allows, for the first time, the study of specific mutation combinations. In this study, we addressed the genotypephenotype relationships in individuals with compound heterozygous BSEP deficiency, with one allele affected by a BSEP1 mutation, i. e. encoding p. D482G or p. E297G, and the second allele by a BSEP3 mutation (PPTMsuch mutations are thought to lead to a complete absence of functional BSEP activity).
Data acquisition, patient inclusion and genetic categorization This cohort study was performed conforming to the 1975 Declaration of Helsinki. At the data extraction for the present analyses (May 19th 2020), the NAPPED consortium included the world-wide collaboration of 68 centers. Retrospective follow-up data were collected in most cases, which was combined with prospective data from North American centers participating in the Childhood Liver Disease Research Network (ChiLDReN, Longitudinal Study of Genetic Causes of Intrahepatic Cholestasis -NCT00571272). Demographic, clinical and outcome data were entered by the participating centers directly into the web-based NAPPED Research Electronic Data Capture (REDCap) environment, using a pre-specified case record form,  13  or prospectively into the central ChiLDReN database.
Centers using retrospective data collection identified all patients who had ever been under pediatric care (defined as age 0-18 years) since 1977. The respective ABCB11 alleles were categorized into either BSEP1, representing a p. D482G or p. E297G mutation, and BSEP3 representing a PPTM. PPTMs were defined as truncating, nonsense or splice site mutations, leading to a predicted non-functional protein. Analyses were performed on patients with a BSEP1/1 (homozygous for p. D482G or p. E297G), BSEP1/3 (compound heterozygous for either p. D482G or p. E297G and one PPTM) and BSEP3/3 (two PPTMs) genotype. Separate analyses were also performed to determine the effect of the individual mutations. Patients were split up into homozygous p. D482G (BSEP1/1), homozygous p. E297G (BSEP1/1) and p. D482G -PPTM (BSEP1/ 3), p.
Statistics Continuous variables were expressed as medians and interquartile range [IQR], unless specified otherwise. Non-parametric tests were used, including Mann-Whitney, Kruskal-Wallis and Wilcoxon signed-rank test, unless stated otherwise. Categorical data were expressed as n (%) and analyzed using Chi-square, McNemar or Mantel's test for trend. The following variables were included in the statistical analyses: birth year, sex, age at first visit in the tertiary referral center, sBA, total serum bilirubin (TSB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), gammaglutamyltransferase (GGT), platelet count, any (current or historical) use of UDCA, rifampicin, phenobarbital, cholestyramine or antihistamine therapy prior to or at first visit, HCC and pretransplant mortality.
Univariate differences in the proportion of individuals with siEHC and NLS between patient subgroups were assessed by means of age to event analysis using Kaplan-Meier curves that were compared using the log-rank test. Patients without events were censored at last follow-up. A clock-reset approach was used to visualize the association of the timedependent risk of siEHC with NLS: all patients start without siEHC. Then, patients that underwent siEHC during follow-up are censored at the age of siEHC and restart with a new risk in the siEHC curve. Multiple centers with different caseloads and geographical locations have included their data into the NAPPED database and sensitivity analyses (including geographic region and caseload of the center) were performed to assess for heterogeneity between sites.