dataesr/scientific-paragraphs-categorization huggingface dataset:
ABSTRACT Introduction Immunonutrition (IN) is generally used before major visceral surgery with the intent to reduce postoperative complications, especially infectious ones. However, the conclusions of published meta-analyses are conflicting. The purpose of this review is to synthesise the data of published systematic reviews on the effectiveness of IN. Methods and analysis This protocol follows the Preferred Reporting Items for Systematic Reviews and Metaanalyses Protocols guidelines. This is an umbrella review of systematic reviews comparing IN (delivered orally 5-7 days preoperatively) with normal diet or isocaloric isonitrogenous feeding before visceral surgery performed on any of several viscera (colorectum, stomach, pancreas, liver, oesophagus). We search the systematic reviews included in the main bibliographic databases.
To assess the efficacy of IN, several outcomes will be considered: the main outcome is infectious complications (surgical site infections, pulmonary infections or urinary infections) and secondary outcomes are overall morbidity, hospital length of stay and mortality. Identified reviews will be screened by two independent assessors. The methodological quality of relevant included reviews will be assessed using A MeaSurement Tool to Assess systematic Reviews (AMSTAR) instrument. The data extracted from included reviews will be synthesised using the r-Metafor package considering separate groups according to the viscus of interest. Publication bias will be evaluated, and subgroup analyses will be performed according to the quality of studies and preoperative nutritional status. Ethics and dissemination An umbrella review based on published data from systematic reviews needs no ethical approval.
Search strategy The following databases will be searched: Medline, PICO, Cochrane database for systematic reviews, Scopus, PROSPERO International prospective register of systematic reviews and Google. The references listed in each retrieved article will be manually searched. The MeSH terms used will be: (immunonutrition) AND (surgery) with the filters (systematic reviews) OR (meta-analysis). The search strategy will also be adapted to each database. Generally, the keyword 'immunonutrition' will be used in the other databases. If necessary, the corresponding authors of the included systematic reviews will be contacted to inquire about missing data regarding the main endpoints. Only systematic reviews published in English, French, German, Italian or Spanish will be selected. We will contact the authors of protocols included in PROSPERO that are terminated but not yet published, to ask them about their data and results.
Data and statistical analysis We will separately consider five groups according to the surgical specialty: liver, oesophageal, gastric, pancreatic or colorectal surgery. The data of each group will be synthesised with respect to the outcomes; with subsequent analyses, whether the patients were malnourished or not. We will screen the included review against the definition of malnutrition. The types of immunonutrients will also be considered for a sensitivity analysis. If available after random models, the pooled effect sizes of included reviews will be extracted. If not, the original data will be extracted, and the effect size calculated with its 95% CI using a random model. The r-Metafor package software will be used for this umbrella review. The random effects model will be used to estimate the OR or HR and 95% CI.
Otherwise, the Hedges adjusted g (corrected effect size) will be calculated. The Q and I 2 statistics will be calculated to test the heterogeneity. The degree of heterogeneity will be defined as considerable (I 2 >75%) or substantial (I 2 >50%). Publication bias will be evaluated using the Egger test and funnel plots. In the case of absence of quantitative data in the included reviews, only the direction of the effect and statistical significance will be provided. Certainty of the evidence will be assessed using the GRADE approach,  15  by calculating the absolute risk for treatment and control groups, estimated relative risk, and ranking of the quality of evidence based on the risk of bias, overlapping, heterogeneity, precision and risk of publication bias.
Acknowledgements The authors acknowledge funding from  European Union  under the  Horizon 2020 research and innovation programmes FET-Open  grant  ULTRAQCL 665158 , the  European Research Council  (ERC) consolidator grants SPRINT (681379) and LEON (820133), the  French National Research Agency  ( ANR-18-CE24-0013-02 -"TERASEL" ), and  EPSRC   programme 'HyperTerahertz'  ( EP/P021859/1 ). E. H. L. acknowledges support from the  Royal Society  and the  Wolfson Foundation.
obs-mip. fr/ (accessed on 7 November 2021) ) in France. They were used to calculate station positions in precise point positioning (PPP) mode using the GINS software developed by CNES  [34]. We computed the daily position solutions of more than one thousand stations mainly located in Western Europe and Scandinavia. We extracted the linear velocity and seasonal signals together with their uncertainties to validate the accuracy of the estimation of the new products using different methods. We also assessed the spectral contents of the time series, especially in terms of the noise level at the interannual band. Among the current techniques used for determining the parameters of the model, we can cite the weighted least squares method (WLS), Kalman filtering, (multi-) singular spectrum analysis (M-SSA), and the Wiener filter, all reported and analysed in  [35].
CNES-CLS Multi-GNSS Orbits and Clock Products The GNSS satellite orbit and clock files used in this study were generated as part of the GRGS participation in the third International GNSS Service (IGS) reprocessing campaign effort (REPRO3) to contribute to the realisation of ITRF2020. These products (referenced here as MG3) are the result of a homogeneous reprocessing of multi-GNSS data (GPS, GLONASS, and Galileo) between 2000 and 2020 using the zero difference with integer ambiguity fixing method described in  [34, 45]. We used up-to-date models listed in Table  1 , following the IGS recommendations (http://acc. igs. org/repro3/repro3. html (accessed on 7 November 2021)). The MG3 reference frame solutions were first evaluated and then preliminarily combined by the IGS  [46]. The station network includes a set of about 300 selected IGS stations that are distributed over the globe (Figure  1 ).
The number of available satellites for each constellation and the daily number of available stations varied over the observing period as shown in Figure  2. The CNES-CLS MG3 products are available in the CDDIS (Crustal Dynamic Data Information System) archive at https: //cddis. nasa. gov/archive/gnss/products/wwww/repro3/GRG6RE3FIN*. gz (accessed on 7 November 2021). We computed the GNSS position time series of 1077 stations over Europe using the MG3 products and the GINS software in PPP mode. We used the same models as the ones listed in Table  1  in order to maintain consistency between the products used and the individual station processing. For the tropospheric delays, we use the global mapping function (GMF)  [54]  tropospheric model and the global pressure and temperature empirical function GPT2  [55].
The numerous agencies and organisations providing the raw GNSS data in RINEX format are listed in the acknowledgements section. The selected stations have a minimum time span of five years and a completeness of 50%. Some exceptions of a time span between three and four years have been considered for recent stations (ending after 2019) but with a more selective completeness criterion (minimum 70%). The distribution of stations and the statistics of the time series are shown in Figure  3. The analysed network is homogeneous and dense in Great Britain, France, Spain, and Italy, while in the remaining European countries, the station distribution is not as dense but still spatially homogeneous.
Parameter Estimation For each time series, we determined different parameters corresponding to the classical model used in GNSS position adjustment: y(t) = y 0 + v(t -t 0 ) + 2 ∑ m=1 a m cos(mω a t) + b m sin(mω a t) + 8 ∑ n=1 c n cos(nω d t) + d n sin(nω d t) + n ∑ k=1 s k H(t -t k ) + ε(t), ( 1 ) where y 0 and v are, respectively, the intercept (at epoch t 0 ) and the linear velocity of the station; ω a /2π = 1 cpy is the solar frequency; and ω d /2π = 1. 04 cpy is the draconitic frequency. The offsets in the time series, which can be linked to known events (earthquakes and antenna or receiver changes) but are also mostly of an unknown origin  [56] , were modelled with a Heaviside function H. Although automatic methods exist for offset detection  [57] , we chose to do it manually for all the stations using an initial seismic database and monumentation change data provided by sitelog files.
We calculated the residuals of the time series by removing the linear trend and offsets from this preliminary database. We first removed major outliers from these residual time series using automatic detection of the largest outliers with a criterion of 5 σ, where σ is the standard deviation. Then, we visually checked the precleaned residual time series in order to validate the outliers/offsets removed during the first step and refine the database if needed (adding or removing offset positions and outliers). The manual detection of offsets and outliers is essential even after applying any automatic procedure. Indeed, regardless of its robustness, the automatic process could eliminate data, especially those corresponding to unmodelled geophysical signals. However, manual checking remains a subjective method that depends on the criteria set by each analyst.
Principal Component Analysis of the Interannual Signal The interannual signals derived from GNSS and derived from loading models are compared using PCA decomposition. All the time series were previously detrended (using the trend estimate from cats_d for GNSS) and resampled to nearly every 10 days by taking the mean of the successive intervals of the year (01/01-10/01, 11/01-20/01, 21/01-31/01, 01/02-10/02, 11/02-20/02, 21/02-28/02,. ). The intervals that contained no data were left empty. Then, we completed the time series using linear interpolation. We chose to treat GRACE time series by keeping the original sampling of one month before interpolation. We finally removed the mean seasonal signal in the same way as  [44]. We obtained this cycle by calculating the mean of the collection of the same dates within years: for example, taking the mean of every 05/01, the mean of every 15/01, and so on.
This is arguably the best way to filter the mean seasonal cycle compared to a sinusoidal fit. We choose to select stations only based on the following completeness criterion. The selected stations have available data between 2010 and 2020, which have accumulated gaps no longer than 60 days, the largest of which is less than 30 days, and have 90% completeness such that the interpolation should not distort the signal too much. Implementing these criteria left 268 stations from the initial network presented in Figure  3. We call EOFs (empirical orthogonal functions) the spatial function and PCs (principal components) the temporal associated time series of the PCA output. The first three principal components (also called modes) of the residual time series are presented in Figure  10 , where we report the variance fraction of the total variance associated with each mode in each panel.
We see that the variance fraction of the GNSS is equal to or lower than the models for each mode and that the GNSS's first PCs are much noisier than the ones from the model, even after resampling the data (which removes a part of the GNSS white noise). The first mode is spatially quite homogeneous. The associated PCs are very different between GNSS and the loading models. Among the three models, we can find some common temporal patterns, but also some differences that can be important, especially with GRACE. The second and third modes are under 15% of the variance fraction but show common spatial patterns between the solutions. It seems that the two modes have to be interpreted simultaneously since PCs and EOFs belonging to both appear to be very similar. For example, the PCs and EOFs of panels (i), (f), (g), and (l) seem to be consistent when they belong to two different modes (the same is the case for (j), (k), and (h)).
Acknowledgments: We thank all the different agencies, networks, etc. , for providing the daily RINEX GNSS data. All the GPS measurements used in this study were obtained from IGN/RGP (Institut Géographique National/Réseau GNSS permanent, http://rgp. ign. fr,(accessed on 7 November 2021)), RENAG (Réseau National GNSS, http://webrenag. unice. fr,(accessed on 7 November 2021)),  SONEL (Système d'Observation des variations du Niveau de la mEr à Long terme , https://www. sonel. org,(accessed on 7 November 2021)),  EUREF (EUREF Permanent GNSS Network , https: //www. epncb. oma. be,(accessed on 7 November 2021)),  RING (Rete Integrata. Nazionale GPS , http://ring. gm. ingv. it,(accessed on 7 November 2021)),  BIGF  ( British Isles continuous GNSS Facility , http://www. bigf. ac. uk, (accessed on 7 November 2021)), CDDIS (Crustal Dynamics Data Information System, https://cddis.
The Glider Survey In situ data were collected across a recently detached LCR in the central GoM between 05/08 and 03/09/2016, using a Kongsberg Sea Glider. The vehicle oscillated between the surface and a maximum depth of 1000 m, at mean horizontal and vertical speeds of 0. 15 m s -1. It was equipped with an unpumped CTD probe (Seabird CTsail) with a sampling frequency of 0. 15 Hz, resulting in effective average horizontal and vertical resolutions of 2000 m and 2 m, respectively. However, large variability of the flow speed as the glider crossed the eddy resulted in large variability of the absolute horizontal speed, and thus of the horizontal resolution (500 m to 7 km). The glider's trajectory is shown in Figure  2a  along with successive edge-contours of the LCR inferred from gridded satellite altimetry.
The ARGO dataset consists of 3995 validated profiles acquired in the GoM, sampling down to depths greater than 1000 m, with a minimum vertical resolution of 10 m. The dataset, as well as the probability density functions of satellite SSH and in situ steric height are fully described in  [9]. To validate the comparability of ADT and in situ 0-1000 dbar steric height, we compared the steric height of all ARGO profiles with the local ADT from the gridded product. Despite the coarse resolution of the grid (0. 25 • ), and the time aliasing issues of gridded ADT (related to the interpolation of along-track data in both time and space), the correlation between the closest grid-point ADT value and in situ steric height is high (coefficient of determination of 0. 93). To compensate for time-aliasing issues and the lack of synopticity of gridded ADT from our validation, we also computed the closest-matching ADT value in a radius of 50 km from the in situ profile.
( 6 ) where ρ 0 is a mean density, ρ(r, z) is in situ density, z is an integration variable, and the geostrophic velocity at the surface is computed from the along-track altimetry profile as u g φ (r, 0) = g f ∂ r η a (7) Using the azimuthal symmetry assumption, relative vorticity ζ is defined as ζ = 1 r ∂ r (ru φ ), (8) Although PV is a scalar, we will refer to the two terms of Equation (  4 ) as the vertical (first term), and baroclinic (second term) PV components. We will also estimate and discuss the PV anomaly (PVA), which is the difference between PV and the ambient PV at rest along isopycnal surfaces: q * (σ) = q(σ) - f g N 2 (σ). (9) In this work, the reference PV is computed on an f-plane using the Coriolis frequency at 25. 5 • N, and the mean stratification profile was computed using GoM's ARGO float dataset described in Section 3.
APE density (APED) is defined as the APE per unit volume. Various formulations, with different degrees of approximation, were proposed to estimate APED. Here, we use  [34] 's exact and positive definite definition for incompressible stratified fluids: E p (r, z, δ) = -g δ 0 δ d ρ(r, z -δ) dz d δ, ( 14 ) where g is the gravity acceleration, ρ(z) is the reference density profile, δ is the difference between the depth of a given density measurement, and that of the the same density in the reference profile, and δ is an integration variable. By definition, the reference density profile is the profile with the minimum possible potential energy, which would occur if all isopycnal surfaces were flattened. It is built by redistributing adiabatically all density measurements of the ARGO database described in Section 3.
Acknowledgments: The authors are grateful to  Simó Cusí  for his invaluable work piloting the gliders. T. M. is thankful to  Paula Pérez Brunius ,  Yves Morel ,  Xavier Carton ,  Julio Sheinbaum ,  Pierre Damien , and  Jose Luis Ochoa de la Torre , for the constructive discussions and comments during the course of this study. The authors wish to dedicate this work to the memory of  Jose Luis "Pepe" Ochoa , dear friend and colleague. Pepe actively participated in this glider monitoring project, and made an important contribution to the understanding of the  Gulf of Mexico 's dynamics during his long career.
Data Availability Statement: The along-track altimetry data is produced by AVISO and distributed by Copernicus Marine Service. The Dataset (SEALEVEL_GLO_PHY_L3_NRT_OBSERVATIONS_008_044) can be retrieved from the web page https://marine. copernicus. eu/ (accessed on 10 May 2021). Due to a confidentiality agreement between PEMEX and the CIGoM consortium, the glider data can not be stored in an open access public repository. The data are however available upon request on GMOG web page: https://gliders. cicese. mx/ (accessed on 10 May 2021).
These reporting guidelines consist of a list of essential items (sometimes referred to as a "checklist") that should be reported to ensure optimal informativeness and transparency. This will allow for easy identification of the study in online libraries and databases and for adequate assessment of study methodology, applicability, and results. Many reporting guidelines are accompanied by an "Explanation and Elaboration" document, which provides specific and detailed guidance on how to report each essential item on the list, along with examples of good reporting practices. The recent publication of PRISMA-DTA for Abstracts and its accompanying Explanation and Elaboration document provides a new addition to the toolbox for reporting test accuracy research  [11] [12] [13]. We here set out which steps led to the development of PRISMA-DTA for Abstracts and which tools are currently available to improve and assess the reporting and the methodological quality of test accuracy research.
STARD 2015 and STARD for Abstracts Test accuracy studies evaluate the performance of medical tests by comparing their results with a reference standard, where results are expressed in estimates of diagnostic accuracy such as sensitivity and specificity. In 2003, the STARD reporting guideline was published for these studies, and an updated version was launched in 2015  [7] [8] [9]. STARD 2015 contains a list of 30 essential items. As for most reporting guidelines, some of these items are "general," applying to any biomedical study involving patients. However, test accuracy studies have a number of design features and outcomes that are very typical of this type of research. In addition, research has shown that these studies are sensitive to several sources of bias and variation  [24]. Items on STARD 2015 that are specific to test accuracy studies are, for example, the instruction to report the intended use and clinical role of the index test (item 3), which reference standard was used and how it was applied (item 10b), the definition of test positivity cut-offs or result categories (item 12), whether test readers were masked (item 13), how missing test data were handled (item 16), and estimates of diagnostic accuracy with confidence intervals (item 24).
Some items apply to any type of systematic review and were unchanged, such as the key databases searched and the search dates (item 4), the number and type of included studies (item 6), and the primary source of funding (item 11). Eventually, one item (item 8, calling for the description of effect size) was removed as it does not apply to test accuracy studies, one item (item A1, calling for reporting of statistical methods used for data synthesis) was added, and updated phrasing was used in six additional items, reflecting language and methods more typically used in test accuracy research. The PRISMA-DTA group has now published an extensive Explanation and Elaboration document, with detailed guidance along with examples on how to report each item in an abstract  [13].
Where registration of clinical trials of interventions has become commonplace and a requirement for many institutions and journals, researchers evaluating medical tests less often register their study protocol  [31, 32]. To improve this, STARD for Registration was developed, providing guidance for informative registration of primary test accuracy studies in trial registries such as clinicaltrials. gov [33]. Systematic reviews should also be prospectively registered before data extraction starts, which can be done in PROSPERO  [34] , or, alternatively, full protocols and other research materials can be uploaded on online platforms such as Open Science Framework (available at https://osf. io/). For systematic reviewers, QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) provides a tool for the assessment of potential sources of bias or applicability concerns within four domains of primary test accuracy studies  [35].
Illumina sequencing and sequence processing Amplification of the bacterial V4 region of the 16S rRNA genes was performed using the universal primers 515F/806R. High-throughput sequencing was carried out after a multiplexing step using a MiSeq 2x300bp PE technology (MR DNA lab, www. mrdnalab. com, Shallowater, TX, USA). According to the MR DNA analysis pipeline (MR DNA, Shallowater, TX, USA), pairedend sequences were merged and denoising procedures, that consisted in discarding reads containing ambiguous bases (N) or reads that were outside the range of expected length (i. e. <150bp), were carried out using Uchime  [37]. The remaining sequences were clustered at a 97% similarity threshold  [38]  with Usearch  [39]. Chimeras were detected using Uchime  [37]  and removed from the dataset. Final OTUs were taxonomically affiliated using BLASTn against a cured database derived from Greengenes, RDPII and NCBI (www.
ncbi. nlm. nih. gov, http://rdp. cme. msu. edu) [40]. Singletons were removed from the dataset. In order to compare micro-samples and to retain the largest possible number of micro-samples, a random resampling normalization step at a depth of 5474 sequences per sample was carried out. It should be noted that this procedure resulted in the exclusion of 1 sample from the Van_PC and 3 samples from the GMV soils as they did not contain 5474 sequences (Table  1 ). The sequencing data of the LD soil have been published in  Michelland et al. (2016)  and the dataset is available under the accession number PRJEB14534 in the SRA database. Data for other soils have been deposited under the following accession number ERP016178 at EBI.
Statistical analyses The soil parameters (Supplementary Table  1 , parameters in column) were analyzed by a standardized PCA. Standardization was needed because of the differences in parameter units. The data table was transformed to row percentages (row sums = 1) before doing the PCA, to reduce the impact of the high sequence count variability among OTUs in the soil microsamples. The table of diversity data (OTUs in columns) was analyzed by a PCoA on the Jaccard distance matrix. Computations were performed with the ade4 package  [41]  for the R Statistical Software (R Core Team, 2019).