dataesr/scientific-paragraphs-categorization huggingface dataset:
Percentage excess weight loss Five studies assessed data on%EWL 3 months postoperatively  [22 -26). They included 141 patients in the probiotic group and 138 patients in the placebo group. There was no significant difference between both groups (MD = 4. 12; 95% CI [À2. 56, 10. 81], p = 0. 23). However, there was high heterogeneity rate among the studies Tau 2 =47. 39 (I 2 = 90%, p < 0. 01). (Fig. 2 ).
Besides, the RoB 2 was assessed in the included trials, and the GRADE approach was used to assess the evidence's certainty. In addition to that, and contrary to the previous systematic review published in 2020, we looked at the change in inflammatory markers, lipids and glycemic profile in addition to anthropometric measures. However, this systematic review and meta-analysis have some limitations. One of the most relevant weaknesses was the availability of only short term follow-up data regarding the anthropometric measures. Only 3-month of follow-up did not allow us to conclude the absence of probiotics effects on anthropometric measures. In addition, only five RCTs were included, so we cannot decide for sure due to the small sample size. Moreover, when heterogeneity was noticed among the studies, it was impossible to perform a subgroup analysis for the outcomes or a meta-regression.
| Water sampling Environmental DNA was collected from water samples at 85 locations (hereafter, sites) along the main channel and the large tributaries of the Maroni, Oyapock and Sinnamary rivers (Figure  1 ). All the metadata associated with the samples are described in  Murienne et al. (2019)  and available on the CEBA geoportal (http://vmceb agndev. ird. fr) and in Table  S1. At all sites, the river was wider than 20 m and deeper than 1 m (Strahler orders 4-7; Figure  S2 ). At all rivers, the sites were sequentially sampled from downstream to upstream. Following the protocol of  Cantera et al. (2019) , we collected the eDNA by filtering two replicates during 30 min and resulting approximately in 30 L of water per site. A peristaltic pump (Vampire Sampler; Buerkle GmbH) and single-use tubing were used to pump the water into a single-use filtration capsule (VigiDNA, 0.
For the fish analyses, 10 libraries were prepared using a PCR-free library protocol (https://www. faste ris. com/metafast) at Fasteris, Geneva, Switzerland. Four libraries were sequenced on an Illumina Mid kit (Illumina). The libraries run on the NextSeq were equally distributed in four lanes. Sequencing was performed according to the manufacturer's instructions at Fasteris. The sequence reads were analyzed with the OBITools  (Boyer et al. , 2016)  according to the protocol described by  Valentini et al. (2016). Briefly, the forward and reverse reads were assembled with the illuminapairedend programme. The ngsfilter programme was then used to assign the sequences to each sample. A separate dataset was created for each sample by splitting the original dataset into several files with obisplit.
Sequences shorter than 20 bp or occurring less than 10 times per sample were discarded. Sequences labelled by the obiclean programme as 'internal' and probably corresponding to PCR errors were also discarded. The ecotag programme was used for taxonomic assignment of molecular operational taxonomic units (MOTUs). An updated version of the reference database from  Cilleros et al. (2019)  was used. This database is a local database of French Guianese freshwater fish referencing the 12S mtDNA fragments of 367 species, representing 92% of the 400 freshwater fish species described in French Guiana. Among these referenced species, 9 (2. 5%) cannot be discriminated at the species level with the 'teleo' primer pair. For the 33 (8%) species missing in the database, we were not able to collect DNA. The GenBank nucleotide database was consulted, but it contained little information on the Guianese fish species.
Most of the sequences were derived from  Cilleros et al. (2019). Species-level assignments were validated only for ≥98% sequence identity with the reference database. Sequences below this threshold were discarded. We discarded all MOTUs with a frequency of occurrence below 0. 001 per library in each sample, considered as tag-jumps  (Schnell et al. , 2015). These thresholds were empirically determined to clear all reads from the extraction and PCR negative controls included in our global data production procedure as suggested by De  Barba et al. (2014)  and  Taberlet et al. (2018). For the samples sequenced on a NextSeq platform, only species present in at least two lanes were kept. These bioinformatic analyses provided a species-by-site matrix with read number per species (Table  S2 ).
| Environmental and anthropogenic variables Satellite-based environmental variables (n = 41) were extracted from the Near Global Freshwater-specific Environmental variables for biodiversity analyses at 1 km resolution  (Domisch et al. , 2015). When there was no data available at the location of the sampling sites, the nearest pixel value was assigned. In addition, four water hydro-chemical parameters measured in the field for each site were used: conductivity, water temperature, pH and turbidity (Table  S3 ). For anthropogenic variables, we quantified deforestation using the Global Forest Change database  (Hansen et al. , 2013). This dataset identifies areas deforested between 2001 and 2017 at a 30 m spatial scale. To incorporate deforested areas before 2000, tree canopy cover data for that year were also used.
Except for river courses, all pixels with <25% canopy closure were regarded as deforested. Finally, surfaces deforested by gold mining activity in French Guiana, Suriname and Northern Brazil were also included  (Rahm et al. , 2017). We merged those datasets to create an integrative disturbance variable that quantifies the deforestation around the sampling sites. Here, deforestation intensity around each eDNA sampling site is considered an integrative measure of human-mediated environmental disturbances including gold mining, logging, agriculture and human settlements  (Cantera, Coutant, et al. , 2022). We calculated the mean percentage of deforestation upstream (along the sub-basin drainage network) and upstream and downstream of the sampling sites (Table  S3 ; Figure  S1 ). Indeed, we previously showed that 30 km represented the most relevant upstream spatial extent to investigate biodiversity responses to deforestation in our system  (Cantera, Coutant, et al.
7% of the considered fish). For these species, we assigned the average values of the morphological measures and the ecological traits of the closest related species. Using these fish functional, morphological and ecological attributes, we calculated a species dissimilarity matrix using the Gower's distance, which can handle mixed data of numeric and class variables while standardizing them. We then implemented the dissimilarity matrix into a multidimensional space using a principal coordinate analysis (PCoA) and applying the Cailliez correction  (Cailliez, 1983)  for negative eigenvalues to build a functional space. This analysis provides coordinates for all the species in a global functional space (Table  S5 ). We retained the four first PCoA axes, which was the best compromise to maximize functional space quality and minimize data loss (to compute site functional β-diversity, sites must possess a higher number of species than the number of selected axes,  Maire et al.
| Generalised dissimilarity modelling GDM is a method that relates the dissimilarities of a response variable with ecological distances  (Ferrier et al. , 2007). It is based on the regression of a dissimilarity matrix and accommodates nonlinearities often encountered in ecological datasets using I-spline coefficients (monotonic cubic splines function) for each explanatory variable. Higher coefficients indicate higher rates of change of the response variable along the gradient of a given explanatory variable. Nonlinearities occur for two reasons: (i) there is a curvilinear relationship between ecological separation and compositional dissimilarity because most dissimilarity metrics range from 0 to 1. Therefore, once no species are shared between sites, the dissimilarity value takes on an asymptotic value of 1 while the ecological separation keeps increasing.
| Variable selection The GDMs implemented with the paleo-distances had the highest explanatory power. Thus, this distance measure was used as a geographic proxy in all the GDMs (see Table  S6  for model comparisons with the three different geographic measures). To obtain the most relevant predictors, we gathered many predictor variables from different databases (Table  S3 ). Because they provided redundant information in certain cases, we conducted a preliminary analysis following a variable selection protocol using the collinearity diagnostic variance inflation factor. We used the vifstep function from the USDM package  (Naimi, 2015) , which excludes the highly correlated variables from the set through a stepwise procedure. We built two sets of predictors, each including one of the two anthropic variables (30 km upstream deforested areas or 30-km upstream and downstream deforested areas).
| Generalised dissimilarity model implementation GDMs were fitted using the gdm package  (Fitzpatrick et al. , 2022). For each biodiversity facet (taxonomic and functional) and βdiversity and its turnover component, we first formatted the data using the formatsitepair function. We then ran GDMs using the gdm. varImp function (one model for each set of predictors), including the site paleo-distances as the geographic variable (see Section 4). We did not run GDMs with the nestedness component because nestedness was too low and the models did not converge. We used the three I-spline basis function and the significance of each model was calculated by matrix permutation, by comparing the deviance explained by the original model with the distributions of the deviance calculated to all permutations while conducting a backward elimination process to select the best predictors.
| Taxonomic and functional predictions of βdiversity We predicted β-diversity and turnover over seven rivers (from west to east: Maroni, Mana, Approuague, Sinnamary, Kourou, Comte and Oyapock, see Figure  1 ) from the best GDMs. Predictions were only computed for the main tributaries as only fish communities sampled on stream orders of 4-7 were considered (Figure  S2 ). To generate spatially explicit GDM predictions, the gdm. transform function from the gdm package was first used to create layers embodying the parameters of the fitted models. More precisely, the spline functions computed for each predictor from the fitted GDMs were applied to spatialised predictors covering the seven rivers to create transformed layers displaying how β-diversity varies in space according to historical, environmental and anthropic variables.
Importantly, regional predictions of fish biodiversity indicated that survey sites of the EU WFD monitoring program failed to fully capture not only regional taxonomic diversity but also assemblages presenting unique functional diversity in response to human disturbances. This study presents a promising approach to map spatial biodiversity, to design efficient networks of survey sites and to quantify the anthropogenic impact on species-rich environments. version of the article. For the sites on the Sinnamary river, the data that support the findings (raw sequences) of this study are openly available in "figshare" at https://doi. org/10. 6084/m9. figsh are. 13739 086. v7 using the Metadata_Sinnamary_Fish file to link "Site name" and "PCR sample". For the sites on the Maroni and Oyapock rivers, the data that support the findings (raw sequences) of this study are openly available in "Dryad" at https://doi.
AUTH O R CO NTR I B UTI O N S Opale Coutant, Céline Jézéquel, Karel Mokany, Sébastien Brosse and Jérôme Murienne conceived the ideas and designed the methodology; Opale Coutant, Sébastien Brosse and Jérôme Murienne collected the data; Alice Valentini and Tony Dejean supervised the laboratory work and conducted the bioinformatic analyses; Raphaël Covain provided part of genetic material; Opale Coutant analysed the data; Opale Coutant, Céline Jézéquel, Karel Mokany, Sébastien Brosse, Jérôme Murienne and Isabel Cantera led the writing of the manuscript. All authors contributed critically to the manuscript. ACK N OWLED G EM ENTS This work benefited from Investissement d'Avenir grants managed by the Agence Nationale de la Recherche (CEBA: ANR-10-LABX-25-01; TULIP: ANR-10-LABX-0041; DRIIHM: ANR-11-LABX-0010), ANR grant (DEBIT: ANR-17-CE02-0007-01).
28 ll metrics were estimated separately for Ethiopia's nine regions and two cities and are presented with 95% uncertainty intervals (UIs). All rates presented are age-standardised rates per 100 000 population. We have summarised the GBD methods and techniques, including references for the full GBD methods (appendix pp 11-12). The GBD 2019 subnational results were reviewed with EPHI experts and GBD Ethiopia collaborators, a network of more than 700 Ethiopian researchers and health workers. GBD 2019 analyses were completed with Python version 3. 6. 2, Stata version 13, and R version 3. 5. 0 (appendix p 14). The statistical code used for GBD estimation is publicly available online.
This collaborative initiative has also helped the EPHI build a local data capacity and create in-country data repository systems. We used all available data identified through an extensive collaboration effort and involving more than 700 leading researchers and policy makers from Ethiopia. The generation of estimates and their interpretation have benefited from intensive subnational review workshops and consultative meetings with domain experts. Health progress has been uneven between regions and cities, which requires further efforts to explore the underlying factors and solutions. All regions and cities, especially the low SDI regions of Afar and Somali, could benefit from improving equity and quality of education and health-care services, improving financing and efficiency of health-care services, and reducing poverty and unemployment to improve overall health status.
This analysis underscores that quality data availability and accessibility are essential to provide reliable estimates for Ethiopia. Further, lessons learned from this effort can be leveraged for subnational analyses and improvements to data availability and accessibility throughout sub-Saharan Africa. in this study can be accessed from the Global Health Data Exchange data input sources tool (http://ghdx. healthdata. org/gbd-2019/data-inputsources). Files containing all GBD 2019 subnational estimates are available on the Global Health Data Exchange website (http://ghdx. healthdata. org/gbd-2019) and can also be downloaded from the Global Health Data Exchange results tool (http://healthdata. org/gbd-resultstool). Additional results can be explored through online interactive visualisations (https://vizhub.
We validated the performance of our approach and compared SPITFIR(e) to competitive deconvolution methods on several datasets, and we confirmed the results on real images acquired with lattice light sheet (LLSM), stimulated emission depletion (STED), multi-focus (MFM), spinning disk confocal (CM), and wide-field microscopy (WF). For the evaluation on different imaging modes, we have chosen to apply SPITFIR(e) on a limited number of structures relatively difficult to resolve or for which the photon budget is critical, i. e. , mitochondria, microtubules and biomolecules in motion in living cells.
It turns out that the decomposition into a series of 2D decoupled deconvolution problems provided very good results in a small computing time. The nearly parameterfree SPITFIR(e) algorithm has been implemented both in CPU and GPU, is interfaced with Napari (https://www. napari-hub. org/plugins/napari-sdeconv) and can be downloaded for free here: https://github. com/sylvainprigent/simglib. SPITFIR(e) is flexible and adapts to every fluorescence microscopy modality. SPITFIR(e) includes several strategies to adapt to microscopy specificities, and to particular spatial and temporal acquisition conditions (Supplementary Fig. 1 ). For instance, the practitioner can apply the conventional 3D deconvolution strategy if the PSF is perfectly known. If this is not the case, the PbyP strategy which consists in separately deconvolving each XY section of the 3D stack given a 2D Gaussian PSF model, can be applied.
As the implementation in 3D of most of aforementioned regular- izers in Table  1  is not always possible, we focused on the Hessianregularized deconvolution algorithm  13  (HR-C3D) as it was demonstrated to outperform previous software such as Huygens, Microvolution  33  , and ER-Decon (LSHV)  14. RL deconvolution is also considered and serves here as a baseline method. A first comparison of SPITFIR(e), HR-C3D and RL applied to LLSM imaging (see Supplementary Fig. 2 ) has been previously described. For the sake of objectivity, we present here the results obtained on a three-channel volume (22 planes) depicting actin (green channel, stained with Alexa Fluor 488 phalloidin), nucleus (blue challenge, stained with DAPI) and mitochondria (red channel, stained with MitoTracker Red CMXRos), acquired with a wide-field microscope and already used for algorithm evaluation by Ikoma et al.
13. In Supplementary Fig. 7 , we report the normalized root-mean-square error (NRMSE), global Pearson correlation coefficients (RSP), PSNR values, and Fourier Shell Correlation (FSC) curves  14  , between the restored wide-field images obtained with the two competing methods (SPITFIR(e), HR-C3D) and the reference spinning disk confocal image deconvolved with the Huygens software  13  , for each channel. We carefully followed the recommendations given in ref  13  to fairly normalize the deconvolved images with respect to the L2 norm of the deconvolved confocal image (with offset correction). The good performance of SPITFIR(e) is first confirmed by the PSNR/NMSE values which are slightly higher/smaller than those obtained with HR-C3D  13. The resolution estimated from FSC curves is also higher (nucleus, actin) or comparable (mitochondria).
Implementation details and code availability Images have been processed on classical workstation for CPU calculation (CPU Intel Xeon 2. 8GHz 4 threads). We use this setup since it is the most common setup in biology labs. For high speed processing we developed a GPU version of SPITFIR(e). We run the GPU on two types of GPU, a Nvidia Quadro M2000 that can be found in most workstations and a Nvidia Tesla K80 for computing grid. Typical computing times (CPU and GPU) for processing 2D-3D-4D images are reported in the table below: These computing times are small and take into account the full data processing steps (including data normalization and data copy to the GPU device). The code can be downloaded for free from the GitLab website (https://github. com/sylvainprigent/ simglib) along with accompanying documentation.
Previously acquired MFM datasets of U2OS10 cells transfected with TOM20 (translocase of outer mitochondrial membrane) fused to GFP (GFP-TOM20) (Hajj et al. 2014)   4  were used in this study. Previously acquired datasets of M10 cells, stably expressing Langerin tagged with eYFP, or Rab11a tagged with mCherry (Gidon et al. 2012)  5  ) or transiently transfected with PhLuorin Transferrin-Receptor (TfR) (Boulanger et al. 2014)  6  ) were also exploited in this study.
5× tube lens with a final pixel size of 104 nm. Lattice light-sheet imaging was performed using an excitation pattern of outer NA equal to 0. 55 and inner NA equal to 0. 493. Composite volumetric datasets were obtained using 10 ms exposure/slice/channel at a time resolution of 2. 2 s per total cell volume (about 60 planes). Fifty-six time points were acquired within 3 to 4 minutes (total raw data, only 1:45 min were analyzed for the movie). Acquired data were deskewed, a necessary step to realigned image frames, using LLSpy, a python library to facilitate lattice light sheet data processing (copyright to T. Lambert, Harvard Medical School, Boston, USA; https://github. com/tlambert03/LLSpy). Deskewed images are then considered as Raw images. Napari, a multi-dimensional image viewer for Python (https://github.
com/napari/napari), was used for 3D rendering. Maximum intensity projections were generated using ImageJ/Fiji 1. 53c (Schindelin et al. , 2012)  7. Intensity profiles were plotted using Matlab2019b. Related supplementary movies S1 and S2 were generated using Napari and napari-animation plugin (https://github. com/napari/napari-animation). Multifocus microscopy (MFM): MultiFocus microscopy (MFM)  31  allows simultaneous acquisition of a 3D stack of nine equally spaced focal 2D images with a frame rate up to 100 volumes per second, thus covering the whole volume of a nucleus or cell components in a single exposure  31. The set-up is based on a diffraction grating that forms multiple focus-shifted images followed by a chromatic correction module. This imaging technique is especially designed for high-speed imaging, and is able to capture the signal of weak fluorescent samples such as single fluorophores.
1 AU. Imaging was executed with the 9 lines accumulation. Pixel dwell time was 10 µs and a pixel size of 25 nm × 25 nm. Images were generated using ImageJ/Fiji 1. 53c3. Line profiles plots were measured with ImageJ. Then line profiles were fitted to a Gaussian model using Matlab. Finally, Full width at half maximum (FWHM) is estimated from the fitting results. Other Imaging modalities: Spinning disk confocal microscopy (CM) and Ring Total Internal Reflection microscopy (RING-TIRFM) were either used as described before (Gidon et.
Acknowledgements This work was jointly supported by the  French National Research Agency (France-BioImaging   ANR-10-INBS-04-07 ,  DALLISH DALLISH-ANR-16-CE23-0005 ), and INNOPSYS company. We would like to thank  Mathieu Morin  from  Inserm   U932  for his help with STED image acquisition, and the  PICT imaging platform of Institut Curie , member of the  France-BioImaging infrastructure  ( ANR-10-INBS-04-01 ) for maintaining spinning disk and LSM confocals used in this study.
The authors thank the anonymous referee for insightful comments, and acknowledge funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation program (grant agreement No. 834148) and from the Agence Nationale de la Recherche (ANR projects ANR-18-CE31-0006, ANR-18-CE31-0017, and ANR-19-CE31-0017). This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www. cosmos. esa. int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC; https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
Statistical analysis Precision and linearity analyses were performed with a software program termed "Eval Tools" (version 1. 3 and 3. 3) (Siemens Healthcare Diagnostics Inc. , Tarrytown, USA) which utilizes the analysis procedures outlined by CLSI  (14, 16, 19). Linearity was evaluated using weighted linear regression. Method comparison (Passing-Bablok regression) analysis was performed with Analyze-It add-in, version 4. 10 for Microsoft Excel according to CLSI procedures  (17).
Acknowledgments This work was supported by  Siemens Healthcare Diagnostics Inc. , Tarrytown, USA  who provided systems and reagents. The authors thank the healthy volunteers who participated in the study and the laboratory technicians of all participating sites for their valuable contributions. In addition, the authors would like to thank  Vania Tinterri ,  Ramona Aleotti ,  Rocco Pio D'Andrea , and  Lorena Tondelli  at  Azienda Unità Sanitaria Locale -IRCCS, Reggio Emilia, Italy , and  Rubén Gomez-Rioja  and  Jose Manuel Iturzaeta Sánchez  at  Hospital Universitario La Paz, Madrid, Spain.