dataesr/scientific-paragraphs-categorization huggingface dataset:
, 1999; Rodrigues et al. , 2007; Silva et al. , 2009; Stramma and Schott, 1999) , is pointed out as a major process contributing to seasonal BL variability  (Araujo et al. , 2011). The characterization of the thermohaline structure of an ocean region, as the SWTA, is typically based on vertical profiles measurements, where data is as discrete set of observations of temperature and salinity sampled at varying depths. However, the use of classical statistical methods for comparing or clustering such datasets (e. g. Principal Component Analysis (PCA) and Analysis of variance (ANOVA)) have important limitations. Indeed, classical methods do not explicitly contemplate the vertical nature of the profiles. For example, in a PCA matrix, vertical profiles depths are ordered in columns but PCA is invariant under permutation of columns in the data matrix.
Functional Data Analysis (FDA) are a new alternative to solve this and other problems such as the use of diverse and heterogeneous datasets  (Nerini et al. , 2010; Pauthenet et al. , 2019 Pauthenet et al. , , 2017;; Reyes et al. , 2015). FDA accounts for the functional nature of the data i. e. observed data are considered as a function rather than a vector of multiple measurements  (Bayle et al. , 2015; Pauthenet et al. , 2017). Besides that, the extension of kriging techniques to the functional setting meets the need of interpolating complex data collected in a limited number of spatial locations and thus improve spatial inference  (Menafoglio et al. , 2013). If the functional nature of a functional dataset is ignored, results typically are sub-optimal as valuable information related to the functional nature of a dataset is not properly utilised  (Hadjipantelis and Müller, 2018; Nerini et al.
, 2010). Here, we propose to use functional statistics methods to characterise the 3D thermohaline structure of the SWTA in austral spring and fall. For that purpose, we used temperature and salinity profiles from two multidisciplinary surveys performed in northeast Brazil in spring 2015 and fall 2017. To construct a canonical picture of the spring and fall conditions we also used available profiles achieved in the SWTA in the same seasons in other years. Merging these databases was possible since the results of functional ANOVA did not reveal significant difference among datasets. From this, we provide a synoptic view of seasonal states of the thermohaline structure in the SWTA and reveal the presence of two different areas and one transition region, evidencing the variety of ocean-atmosphere processes in play in the region.
Data Conductivity, Temperature, Depth (CTD) hydrographic profiles were acquired using a CTD Seabird SBE911+ during the two multidisciplinary surveys Acoustic along the BRAzilian CoaSt (ABRACOS). The surveys were performed in northeast Brazil (Fig. 1 , triangles) onboard the French R/V Antea in September and October 2015 (austral spring; ABRACOS1;  Bertrand, 2015)  and in April and May 2017 (austral fall; ABRACOS2;  Bertrand, 2017). To provide a more comprehensive view of the thermohaline structure in the SWTA in spring and fall, we also considered the use of ancillary vertical CTD profiles or profiling floats data from: (i) the World Ocean Atlas 2013 (https://www. nodc. noaa. gov/OC5/WOD13/); (ii) the REVIZEE program  (Araujo et al. 2019) ; and (iii) the PANGEA Data Publisher for Earth & Environmental Science (https://www.
pangaea. de/). These ancillary data were selected considering the months of the ABRACOS surveys and the SWTA area (3° -10°S/30° -36°W), whatever the year. A total of 182 and 176 hydrographic profiles were considered for the periods of spring (Sep. -Oct. ) and fall (Apr. -May), respectively (Table  1 , Fig. 1 ). To describe the upper ocean thermohaline structure, ABRACOS and ancillary profiles were considered from the surface to 300 m depth. To investigate the atmospheric forcing, we analysed monthly average freshwater budget (evaporation minus precipitation -E-P) and near surface wind speed. For that, we used product composites of the Hamburg Ocean Atmosphere Parameters and Fluxes (HOAPS -v. 4) with a regular 0. 5° x 0. 5° from 1988 to 2014. All variables were derived from recalibrated and intercalibrated measurements from SSM/I and SSMIS passive microwave radiometers compiled by the EUMETSAT's Satellite Application Facility on Climate Monitoring (CM SAF)  (Andersson et al.
Functional Data Analysis (FDA) FDA is a branch of statistic that provides tools for describing and modelling sets of functions (or curves) rather than vectors of discrete values  (Ramsay, 2006). The guiding idea of this approach is to describe data as parameterized functions, and to use these parameters for clustering, comparing or interpolating functions. In particular, classical statistical tools can be adapted to functional data such as functional principal component analysis (fPCA) to summarize and characterize significant variation in finite dimension among a sample curves  (Dabo-Niang and Ferraty, 2008; Ramsay, 2006). Functional analysis of variance (fANOVA) uses all the information of each mean functional curve to test the possible differences in the datasets, based on the shape and temporal (along the depth) variability of the curves  (Cuevas et al.
Comparing ABRACOS and ancillary datasets Once the hydrographic profiles have been described with a relevant functional basis, the next step was to compare the ABRACOS and ancillary data. Indeed, we aimed at determining if these datasets could be merged to be representative of spring and fall canonical states in the study region. This question has been addressed in two steps (Fig. 3 , steps 2 and 3) using the most recent ABRACOS observations as a reference. First (Fig. 3 , step 2), we characterised the ABRACOS profiles by applying fPCA and a functional hierarchical clustering (fCluster), achieved on the coefficients of the fPCA decomposition  (Febrero-Bande and Fuente, 2012). Reproducing the aim of PCA, the idea of the fPCA is to summarize multivariate dataset with principal component seen as a linear combination of the variables (the same apply for fCluster).
In comparison, the method is adapted to deal with function rather than vector and the principal components corresponds to dominant modes of variation of functional data  (Shang, 2014). fPCA allowed identifying the main patterns of variation of the vertical profiles while fCluster were used to statistically define homogeneous groups of profiles. These groups were then plotted spatially to determine if they correspond to specific areas. Second (Fig. 3 , step 3), once homogeneous areas were defined from ABRACOS profiles, we applied a fANOVA to compare ABRACOS and ancillary profiles present in each area. fANOVA is based on the so-called one-way analysis of variance for univariate functional data using L2-norm-based parametric bootstrap test for homoscedastic samples  (Cuevas et al. , 2004). This procedure considers groups of  independent random functions such that each function of the group is assumed to be a stochastic process  with mean function and tests the null hypothesis: 𝜇 𝑘 (𝑡), 𝐴 = 1…𝑙.
Data characterisation and 3D spatial interpolation In case of no rejection of the null hypothesis (Fig. 3 , step 4), profiles from the different datasets can be merged to build complete dataset representative of spring and fall canonical states. Then fPCA, fCluster and fANOVA can be applied on the complete merged dataset to characterise the profiles and their spatial variability (Fig. 3 , step 5). To interpolate temperature and salinity profiles in 3D (Fig. 3 , step 6) we applied a functional geostatistical analysis  (Giraldo et al. , 2007). Geostatistics (or kriging methods) are well-known tools for model-based spatial interpolation, taking into account the spatial autocorrelation of an estimated set of variables  (Matheron, 1963). The fundamental premise of kriging methods is that spatial data constitute a joint realization of spatially dependent random variables, collectively referred as a random function.
Ordinary kriging refers to spatial prediction under the assumption of stationarity as specified by  Cressie (1993)  and  Wackernagel (2003). More precisely, the random function is supposed stationary, meaning that the expectation of the random function is independent of the position. This framework has been generalized to be useful within the FDA context. Yet, because we are dealing with functions, the stumbling block remains in the estimation of a spatial covariance between curves. The functional geostatistical analysis has been performed according to ordinary trace kriging method  (Giraldo et al. , 2011)  in order to described the spatial autocorrelation structure of functional data. The method is implemented in "fdagstat" R package (https://github. com/ogru/fdagstat), and has involved the two following steps: (i) the analysis of spatial structure (i.
Merging the datasets For both seasons, the fPCA and fCluster analysis of the ABRACOS thermohaline profiles revealed two significant areas (classes, the dendrograms are provided in the Supplementary Figure  S1 ), in which profiles display similar shape (Fig. 4 ). The first area corresponds to the surrounding of the SWTA slope, hereafter referred to as A1. The second area hereafter referred as A2 encompasses the oceanic islands (Fernando de Noronha and Rocas Atoll) and seamounts (Fig. 4e, j ). In each area and for each variable (temperature and salinity) and season (spring and fall), no significant differences (fANOVA, p>0. 05) were observed between the profiles from ABRACOS and other data (Fig. 5 ). fANOVA results verify that the data obtained during the ABRACOS surveys, are representative of a canonical state of spring and fall for SWTA.
Typology of thermohaline patterns The application of the fPCA and fCluster on the complete dataset allowed dividing the study area into three significantly different areas during both seasons (Fig. 6 , the dendrograms are provided in the Supplementary Figure  S1 ). We observed the two classes of profiles already depicted using ABRACOS data only (A1 and A2 in Fig. 4 ) with A1 extending to the entire longitudinal extension (35°W-30°W) south of 8°S at both seasons (Fig. 6b, d, f, h ). In addition to A1 and A2, we identified a third class, A3, corresponding to the central offshore zone between A1 and A2 (Fig. 6 ). Note that some spatial overlap between profiles characterised in different area occurs, mainly in the transition zone between A2 and A3 (Fig. 6b, d, f, h ). In A1, temperature profiles were characterised by a low stratification with a thermocline extending from 70 m to 172 m in spring (Fig.
Acknowledgments We acknowledge the  French oceanographic  fleet for funding the at-sea survey and the officers and crew of the  R/V Antea  for their contribution to the success of the operations during the ABRACOS cruises. We thanks the  Helmholtz-Zentrum für Ozeanforschung Kiel (GEOMAR)  for PANGEA data availability. The  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)  by supported through a PhD scholarship grant for Assunção, Ramilla. M. A. thanks the support of the  Brazilian Research network on Global Climate Change FINEP/Rede CLIMA  (grants  01. 13. 0353-00 ). This work is a contribution to the  International Joint Laboratory TAPIOCA  (www. tapioca. ird. fr), CAPES/COFECUB program  ( 88881. 142689/2017-01 ), PADDLE  project (funding by the  European Union 's  Horizon 2020 research and innovation programme  -grant agreement No.
The primary outcome will be the mean proportion of the 10 items that are adequately reported in the published articles. Peer reviewers and manuscript authors will not be informed of the study hypothesis, design or intervention. Outcomes will be assessed in duplicate from published articles by two data extractors (at least one blinded to the intervention). We will enrol eligible manuscripts until a minimum of 83 articles per group (166 in total) are published. Ethics and dissemination This pragmatic RCT was approved by the Medical Sciences Interdivisional Research Ethics Committee of the University of Oxford (R62779/RE001). If this intervention is effective, it could be implemented by all medical journals without requiring large additional resources at journal level. Findings will be disseminated through presentations in relevant conferences and peer-reviewed publications.
Peer reviewers will be asked to check whether the items in the C-short checklist are addressed in the manuscript and to request authors to include these items if they are not adequately reported. This second email (see online supplementary appendix 1), containing the C-short checklist together with a brief explanation, is not generated automatically within the existing journal editorial systems (eg, ScholarOne or Editorial Manager); it will be sent manually by a researcher (BS) from the journal's editorial system or by a member of the journal's staff. In both cases the email will appear to have come from the editorial office (not the researcher).
Assessment of outcomes The outcomes will be assessed independently by two (blinded or at least partially blinded; see 'blinding') outcome assessors with expertise in the design and reporting of clinical trials. Any disagreement will be resolved by consensus or if necessary by consulting a third assessor. To ensure consistency between reviewers, we will first pilot the data extraction form; any disparities in the interpretation will be discussed and the data extraction form will be modified accordingly. Adequate reporting of items will be assessed in duplicate from published full-text publications following the same instructions as provided by the CONSORT C-short checklist. 11 The following checklist items have, due to their complexity, sub-items which will be extracted separately. The sub-items are highlighted in the short explanation of the intervention (see table 1 and online supplementary appendix 1): ► Outcomes (item 6a): (i) define primary outcome, (ii) how it was measured, (iii) at what time point and (iv) the analysis metric (eg, change from baseline, final value).
sample size For the sample size calculation, we hypothesised in a first scenario (table  3 ) that the intervention C-short will result in a 25% relative increase in adequate reporting compared with the control (meaning that 70% of items will be adequately reported in the intervention group and 56% in the control group). This is based on a proportion of adequate reporting of 0. 56 for the 10 most important and poorly reported items found in the control group of a previous study (meaning that a mean of 56% of the 10 most important and poorly reported items were reported). 30 The SD in the same study was 0. 23. However, we calculated our sample size to account for a slightly larger variability in our data (SD=0. 25). To demonstrate a significant difference with a power of 90% and a type 1 error at 5%, a total of 136 published articles will be required in this scenario (68 per treatment arm; based on a two sided t-test).
data management and confidentiality Outcomes from publications will be assessed and extracted in duplicate. Since this information is not confidential, we will use freely available online forms (eg, Google forms) for data extraction from published RCTs. Data entered will be validated for completeness. Data from the journal's editorial system (eg, title of manuscript, first author, randomisation ID, journal, date when manuscript was assigned to an editor, date when the final decision was made, final editorial decision, number of peer reviewers who reviewed the manuscript, the peer review reports (if available)) will be extracted (by BS or a member of the journal's staff), anonymised and entered in password protected files which are saved on a server from the University of Oxford. Data will be managed and curated according to University of Oxford regulations, which includes regular back-up (on a daily basis) of the virtual drives where the data are stored.
No auditing or data monitoring is planned (as outcomes are directly extracted from journal's editorial system or in duplicate from published RCTs). The raw data extracted from the included published manuscripts can be made openly accessible in an anonymised way (ie, giving the included RCT a number instead of identifying them). Derived/aggregated data, including anonymised information generated from the journal's editorial system, will be stored and made available to the research community when the project ends (see also 'Publication policy and access to data'). Where appropriate, the researcher who has access to the journal's editorial system (BS) and anyone else who will see the identifiable data will sign a confidentially agreement with the participating journals, confirming that they will not share identifiable data with any other party.
Results At the time of data extraction, a total of 319 patients were included in the cohort, namely 85 patients in the 'before' period (until 20 March 2020), 62 patients in the transition period (21-25 March 2020) and 172 patients in the 'after' period (26 March-14 April 2020). Of the included patients, 11 patients (12. 9%) received corticosteroid therapy in the 'before' period, 20 (32. 3%) in the transition period and 119 (69. 2%) in the 'after' period. The main characteristics of the 257 patients in the 'before' and 'after' periods are summarised in Table  1. Patients in the 'after' period were significantly more frequently nursing home residents, had a higher prevalence of dementia, had a longer time from symptom onset to hospitalisation, less frequently received LPV and/or hydroxychloroquine, and more often required oxygen therapy than in the 'before' period.
Another limitation of this study is that the COVID-19 pneumonia diagnosis was more often performed by chest CT scan in patients who received corticosteroids than in patients who did not. Positive RT-PCR is the gold standard for confirming the diagnosis of COVID-19 but its performance presents variable sensitivities ranging from 37-71%  [10]. Although chest CT scan is highly sensitive for detecting COVID-19 pneumonia, overlapping CT image features with other viral pneumonias and other respiratory diseases make an exclusion diagnosis difficult and could be therefore a source of bias in this study  [10]. Finally, the unavailability of safety data should be acknowledged as a limitation. Nevertheless, these preliminary data support the initiation of clinical trials testing corticosteroids during the inflammatory phase of COVID-19 and may potentially lead to a change in treatment recommendations.
Given the preponderance of monocyte derived macrophages in the post-MI heart and their dual role as actors of the healing process or perpetrators of tissue damage, it is crucial to precisely understand the mechanisms regulating monocyte-derived macrophage acquisition of specific states associated with pro-repair or pathogenic functional capacities. Here, we investigated the local and systemic transcriptional landscape of monocyte/macrophage transitions and tissue specification in the acute phase after MI. Using time-series single-cell transcriptomic profiling with simultaneous cell surface epitope labeling (CITE-seq  (15) ), and integration of independently generated scRNA-seq datasets, we established a comprehensive census of cardiac monocyte/macrophage transitions and gene expression dynamics in the blood and heart following MI.
Materials and methods Detailed experimental methods are available with the Online Supplement. All animal studies and numbers of animals used conform to the Directive 2010/63/EU of the European Parliament and have been approved by the appropriate local authorities (Regierung von Unterfranken, Würzburg, Germany, Akt. -Z. 55. 2-DMS-2532-2-743). Single-cell RNAsequencing data generated for this report has been deposited in Gene Expression Omnibus (GSE135310) and will be made available upon publication. The datasets can browsed in a web-accessible interface:.
Integrated scRNA-seq analysis of the monocyte/macrophage landscape over the post-MI time continuum To validate the conservation of monocyte/macrophage states across independent scRNA-seq studies, increase the number of analyzed cells, and extend our observations to later time points, we integrated our new data with published scRNA-seq datasets of cardiac macrophages from the steady state up to 11 days after infarction (2), (24) (Table  1 ). Separate analysis of these data recovered monocyte and macrophage populations consistent with our own observations (Figure  S5  and supplementary text). After curating these datasets to exclude granulocytes, DCs, non-leukocyte contamination and "hybrid cells" (i. e. cells enriched for markers of both Mφ and non-immune cells in the Farbehi et al. dataset (  24 )), we performed data integration in Seurat v3  (23) , encompassing 14,272 monocytes/macrophages across 5 time points and 4 datasets (Figure  3A-B ).
e. toll-like receptor activation. How this inflammatory priming of monocytes interacts with local cues in the ischemic tissue niche to modulate their downstream differentiation into functionally distinct macrophages populations remains to be determined. In particular, it will be of interest to investigate how innate immune training of myeloid progenitors related to cardiovascular risk factors (e. g. atherogenic conditions  (43) ) further affect such processes. Recent reports proposed that pericardial macrophages relocate to the epicardium and have protective properties, inhibiting interstitial fibrosis and preventing cardiac rupture  (44) , (45). The datasets used here were generated using a classical open-chest MI model. Future studies will be necessary to investigate the transcriptional profile of pericardial macrophages migrating to the heart and how it relates to the populations we described here.
-C). NK cells (Nkg7) were filtered out in pre-processing steps. Monocytes and macrophages were identified by expression of established surface markers and their encoding transcripts (CD64/Fcgr1, Ly6C/Ly6c2, CX3CR1/Cx3cr1, MSR1/Msr1) (Figure S3A-B), gated out and further analyzed separately (1,904 cells, Figure 2 and S4, Table S2). Distinct clusters of monocyte/macrophages were observed in varying proportions at the different time points after MI (Figure 2C-E and S4), showing clear gene expression similarities to our first dataset (Figure 2E).
Figure3: Monocyte/Macrophage transitions from the steady state to day 11 post-MI in 4 independent datasets. UMAP representation of single-cell RNA-seq gene expression data in 14,272 monocytes/macrophages from the healthy heart and at 1, 3, 5, 7 and 11 days after MI with A) time point of origin of each cell; B) dataset of origin of each cell and C) clustering analysis and identification of cell clusters. D) Proportion of the indicated clusters among total monocytes/macrophages according to time point after MI with a focus on monocytes (excluding Ly6C low monocytes, dashed line= total Ly6C + monocytes), major population of resident and MI-associated macrophages, minor populations of macrophages, and proliferating macrophage populations (dashed line= total proliferating macrophages).
JF, Belke D, Lee WY, Jorch SK, Deppermann C, Hassanabad AF, Turnbull JD, Teng G, Rozich I, Hudspeth K, Kanno Y, Brooks SR, Hadjantonakis AK, O'Shea JJ, Weber GF, Fedak PWM, Kubes P. 2019. Gata6(+) Pericardial Cavity Macrophages Relocate to the Injured Heart and Prevent Cardiac Fibrosis. Immunity 51: 131-40 e5 Murine myocardial infarction scRNA-seq datasets generated and analyzed Mo/Mφ=monocyte/macrophages. *Included in analysis after quality control filtering **Median.