dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions We present a simple, efficient, and fully operational (from the astrophysical image analysis of MUSE WFM images) PSFreconstruction algorithm based on a Fourier analysis of the GLAO residual phase statistics completed by dedicated information and measurements concerning the instrument itself. A detailed analysis of the GLAO error budget has allowed us to both simplify and optimise the algorithm. It has been thoroughly and successfully tested with respect to complete End-to-End simulations. A sensitivity analysis allowed us to determine the required accuracy in terms of input parameters. It is shown that precise knowledge (typically a few percent accuracy) on seeing, ground layer, and outer-scale values is required to fulfill the astronomer requirements. The code was then tested with respect to real onsky data obtained during the commissioning and the science verification of the coupling of MUSE-WFM and the AOF.
Gathering almost 400 independent observations of globular clusters, PSF reconstruction was performed for each set of data and a statistical analysis of the results was performed. Using a subsample of the data (only those obtained under the best observation conditions) it has been possible to estimate the MUSE internal PSF (at each wavelength). After integration of the MUSE internal PSF into the algorithm, we demonstrated that it is now capable of reconstructing the critical parameters of a PSF (represented by the FWHM and the kurtosis parameters of the Moffat function) with the accuracy required by astronomers in 90% of the observing cases. More precisely, we obtain an error on the PSF FWHM of smaller than 60 mas (less than one-third of a pixel) for 68% of the cases and 160 mas (smaller than the pixel) for 95.
4% of the cases. Similarly, for the β parameter, an error smaller than 0. 26 is obtained for 68% of the cases and smaller than 0. 6 for 95. 4%. After this successful validation, the algorithm was implemented as a python package and can be now used routinely with the MUSE 3D data. A first example of application is presented here for the MUSE deep field observations. It is now a fully operational tool available for all users of MUSE.
Data Processing Geolocated photographs were processed using SfM photogrammetry integrated in the popular software Agisoft PhotoScan ® Pro v1. 2. 3 (Figure  3c ). The first step before data processing consists in creating and formatting a camera position file compatible with PhotoScan ®. This file is composed of four columns: a label (corresponding to the name of each photograph) and the easting, northing, and altitude coordinates of the camera measured by the RTK GNSS. The altitude provided by the RTK GNSS must be corrected from the vertical offset D (as explained in Section 3. 1). As several photos were taken from the same position, they share the same coordinates. All datasets are processed using the same parameters in the processing software to avoid biases in the comparison between Reflex and smartphone camera datasets (cf.
below). The main steps of the image matching and stereorestitution process are: -Image orientation by bundle adjustment (detection and matching of homologous keypoints in overlapping photographs in order to compute the external parameters for each camera). The accuracy is set to "high", which means that the software works with the photos of the original size. Key point limit and tiepoint limit are set to their default values. -Refinement of camera calibration parameters (internal parameters) using the camera positions (parametrized with an accuracy of 5 cm in the Reference pane in PhotoScan ® , i. e. , the RTK GNSS-measured positions are taken as initial values and their variations during bundle block adjustment process are constrained in a radius of 5 cm). -Dense image matching to produce a dense point cloud using the estimated external and internal camera parameters.
Validation Data Porsmilin beach is one of the field sites regularly monitored in the framework of the long-term coastal observatory DYNALIT. For this campaign, a TLS survey was performed simultaneously to the GNSS-assisted terrestrial photogrammetry surveys. This TLS survey involved two scans from two distinct scan positions performed with a Riegl ® VZ-400, with each scan covering 360° horizontally and 100° (from 30° to 130°) vertically with an angular resolution of 0. 04° in both directions. Associated to the TLS survey, the position of reflective targets was measured via RTK GNSS. The targets are reflective cylinders (10 cm in diameter and 10 cm in height) distributed around the TLS standpoint before the survey. The processing of the TLS data was performed using the RiScanPRO ® software suite (provided by Riegl ® ) and CloudCompare ® [31], a 3D point cloud freeware processing software.
Validation Data Porsmilin beach is one of the field sites regularly monitored in the framework of the long-term coastal observatory DYNALIT. For this campaign, a TLS survey was performed simultaneously to the GNSS-assisted terrestrial photogrammetry surveys. This TLS survey involved two scans from two distinct scan positions performed with a Riegl ® VZ-400, with each scan covering 360 • horizontally and 100 • (from 30 • to 130 • ) vertically with an angular resolution of 0. 04 • in both directions. Associated to the TLS survey, the position of reflective targets was measured via RTK GNSS. The targets are reflective cylinders (10 cm in diameter and 10 cm in height) distributed around the TLS standpoint before the survey. The processing of the TLS data was performed using the RiScanPRO ® software suite (provided by Riegl ® ) and CloudCompare ® [31], a 3D point cloud freeware processing software.
2. Manual point cloud filtering to remove artefacts and undesirable data (people on the beach, data outside of the study area, etc. ) 3. Point cloud interpolation onto a mesh, based on a Triangular Irregular Network (TIN). TLS datasets are often used as reference, because, contrary to SfM photogrammetry, measurement errors that are related to the accuracy of the laser are constant across the point cloud, with errors that are inherent to georeferencing being transmitted to the whole cloud  [8]. Under similar survey conditions, TLS accuracy on recurrent cliff face surveys was estimated to be around 3 cm  [8]. Here, the cliff face that was measured by TLS was used as large-scale validation data to assess the accuracy (represented by mean error) and precision (represented by standard deviation of error) of the SfM photogrammetric point clouds.
This approach ensures a global assessment of the error, while taking into account the error spatial variability due to reconstruction artefacts or environmental parameters. This assessment was performed using the "cloud-to-mesh distance" tool, computing nearest-neighbor distances in CloudCompare ®. Using a mesh for the TLS dataset avoids computational artefacts in quality assessment, which might be introduced due to a heterogeneous point cloud density. Nevertheless, the TLS point cloud is not error-free, and having a lower spatial resolution than SfM photogrammetric reconstructions, it is not fully relevant at small scale. Therefore, the consistency at small scale of the SfM reconstructions was also assessed by inter-comparing the SfM dense point clouds that were obtained using the Nikon Reflex camera and the Huawei Y5 Smartphone camera, respectively.
Results For the tests that were conducted using the Huawei smartphone, we mentioned previously a RMSE of 3. 44 m between the camera positions measured by the RTK GNSS and by the Smartphone's internal GNSS (cf. Section 3. 3). This deviation can be explained by the low precision of the internal GNSS module (~5 m) in comparison to RTK GNSS (~cm). Unsurprisingly, this has an impact on the SfM reconstruction performed while using the Geotag of the photographs (Test 2b). Figure  5  presents a top view comparison of the cliff front position obtained with the TLS and by SfM reconstruction using the Geotagged photographs. The mean error comparing the Geotagged SfM point cloud to the TLS mesh is 2. 13 m with a standard deviation of 1. 22 m. However, for estimating this error, CloudCompare ® measures the normal distance between a point and the closest facet on the TLS mesh, even if it does not correspond to the same portion of the cliff.
The difference between the Geotagged SfM point cloud and the TLS point cloud was assessed by performing an automatic registration in CloudCompare ® , which is based on an Iterative Closest Point (ICP) algorithm. The difference is mainly a translation (with Tx = 0. 55 m, Ty = -4. 46 m and Tz = -0. 36 m), but, correcting this translation, the residual average error between the Geotagged SfM point cloud and the TLS mesh is still of 0. 10 m with a standard deviation of 0. 36 m, implying that other sources of errors (e. g. , scale, deformation) are also present. As the geotagged Smartphone dataset is not exploitable without having to recur to a reference dataset for correction, this configuration will not be used again for the rest of the study. In the following, the SfM reconstructions obtained with the RTK GNSS-assisted system combined with Nikon D800 Reflex photographs (Test 1) and Huawei Smartphone photographs (Test 2a) are compared with the TLS point cloud and the resulting TLS mesh.
Conclusions The proposed method of RTK GNSS-assisted terrestrial SfM photogrammetry and the associated acquisition system enable low-cost and high-quality photogrammetric surveys without requiring GCPs. By comparison to a synchronous TLS survey, a mean error of 0. 3 cm (standard deviation of 4. 7 cm) was obtained with a Nikon D800 Reflex camera and, respectively, a mean error of 0. 2 cm (standard deviation of 3. 8 cm) with a Huawei Y5 Smartphone camera. Avoiding the use of GCPs saves considerable time during the field survey as well as during data processing. This method will be particularly interesting when simplicity, portability, and autonomy are desirable. In the future, using an open RTK GNSS network would help to make this approach transposable to participatory science programs and citizen observatories.
Study design and participants The present work is a prospective, exploratory substudy of the ongoing LYMPHONIE trial (ClinicalTrials. gov NCT03505281), initiated in November 2018 at the University Hospital of Dijon-Bourgogne (France). Patients were eligible if they had severe community-acquired pneumonia (CAP): 1) pneumonia (≥ 2 acute signs including cough, purulent sputum, dyspnea, chest pain, temperature < 35 °C or ≥ 38. 5 °C, and new radiological pulmonary infiltrate); 2) at least two criteria of the quick-Sequential Organ Failure Assessment (SOFA) score (systolic blood pressure ≤ 100 mm Hg, respiratory rate ≥ 22, Glasgow score < 15) and/or the need for mechanical ventilation (MV) and/or vasopressors; and 3) diagnosed within 48 h following admission. Non-inclusion criteria were: < 18 years, pregnant women, persons under legal protection, decision to limit care, known immune deficiency, chronic disorder known to cause deep lymphopenia (i.
Variables of interest, clinical outcomes, and data collection Clinical and biological parameters, severity scores (SOFA  [18] , Simplified Acute Physiology Score (SAPS II)  [19]  and Pneumonia Severity Index (PSI)  [20] ) were calculated at the time of inclusion. ARDS was defined according to the Berlin definition  [21] , and septic shock was defined as persistent hypotension requiring vasopressors and a serum lactate level > 2 mmol/L despite adequate volume resuscitation. Clinical outcomes were recorded up to 30 days after admission, namely: 30-day mortality, hospital-and ICU-length of stay, duration of MV and the occurrence of ventilator-acquired pneumonia (VAP). Dedicated clinical research assistants collected all data using a standardized electronic case report form. Automatic checks were generated for missing or incoherent data.
AQUIOS Tetra-2 + Panel CD45-FITC/(CD56 + CD16)-RD1/ CD19-ECD/CD3-PC5 provides identification and enumeration of CD45 + , CD45 + Low SS, and CD3 + , CD3-/ CD19 + and CD3-/CD56 + CD16 + lymphocyte percentages and absolute counts in peripheral whole blood. Additionally, both panels provide for CD45 + absolute count and CD45 + Low SS absolute count and percentage. The AQUIOS System Software includes the algorithms and test definitions that provide automated analysis and results for AQUIOS reagents. Normal range (5%-95% reference ranges) values of absolute counts for immune cells and lymphocyte subsets are indicated as Ref. [22, 23].
A twofold dilution with calibrator was used for all samples. The acquisition was performed using Bio-Plex 200 system and analyzed using Bio-Plex Manager TM software (Bio-rad, Hercules, CA). Cytokine concentrations were automatically determined from standard curves and expressed as pg/ml. Samples with values above the ranges were tested again with a 40-fold dilution. All raw data were collected by a data-manager for further analyses in the SAS Software.
All samples were measured the same day by the same person and using the same kit. Samples with values above the ranges were tested again with further dilution. The cytokine production after stimulation was expressed as the difference of concentrations between plasma from stimulated blood and those from non-stimulated blood. As a reference, we used samples from 7 control patients included in the Pneumochondrie study (Clin-icalTrials. gov NCT03955887) and who underwent QuantiFERON Monitor ® assay in the same conditions. The control population consisted of outpatients without fever during the previous 15 days and who underwent bronchoalveolar lavage for a non-infectious condition  [24]. Samples were conditioned and measured in the same way as for the LYMPHONIE study.
To account for potential confounders, we constructed multivariable linear regressions, with the MV duration as an outcome, for each selected cytokine. adjusted for age, COVID-19 status and either SOFA score (model 1) or PaO 2 :FiO 2 (model 2). The interaction between COVID-19 status and cytokines was systematically tested. Absence of serial correlation and heteroscedasticity were assessed using the DW statistic  [28]  and the White test  [29]  respectively. The proportion of variance explained by the models was quantified using the R 2 coefficient. Measures of association are expressed as mean differences ± standard error (SE). A p-value < 0. 05 was considered statistically significant. Analyses were performed using SAS version 9. 4 (SAS Institute Inc. , Cary, NC, USA).
Characteristics of the study population Sixty-three patients were enrolled (36 in the non-COVID-19 group, and 27 in the COVID-19 group). Bacterial, viral or mixed etiologies were proven in 10 (28%), 10 (28%) and 3 (8%) patients from the non-COVID-19 group, respectively (Additional file 1: Table  S1 ). Mean age was marginally lower in the COVID-19 group as compared to the non-COVID-19 group (62. 5 ± 10. 9 vs. 68. 0 ± 13. 0; p = 0. 07). Other demographic and comorbidity data were not statistically different between the two groups (Table  1 ). Although PaO 2 :FiO 2 ratio and SOFA score were not different between groups (p = 0. 35 and p = 0. 52, respectively), fewer COVID-19 patients had septic shock (0 vs. 11 (31%); p = 0. 0015), and arterial lactates (p = 0. 01), serum creatinine (p = 0. 02), and NT-proBNP (p = 0.
g. with lenzilumab) is increasingly being considered as a promising therapy in COVID-19  [33, 36]  and is under investigation in a phase III clinical trial (NCT04351152). In addition, CXCL10 blockade (e. g. Eldelumab/MDX-1100) may also represent an attractive therapy likely to dampen the dysregulated immune response that could be driving the duration of MV. Dampening inflammation in a context of high immune suppression is not always a hazardous route. During chimeric antigen receptor T (CAR-T) cell therapy, GM-CSF inhibition reduces cytokine release syndrome and neuro-inflammation, but enhances antitumoral CAR-T cell function  [44]. In addition, an IL-6 blocker could partially rescue immune dysregulation caused by SARS-CoV-2 (2). These considerations are of utmost importance, since we reported that COVID-19 ARDS patients had a persistent alveolar SARS-CoV-2 viral load that correlates with severity  [17].
Acknowledgements The authors thank all the investigators of the  Lymphonie study group  ( Pascal Andreu ,  François Aptel ,  Marie Labruyère ,  Sébastien Prin  ( Department of Intensive Care, Dijon Bourgogne University Hospital, Dijon, France );  Guillaume Beltramo ,  Philippe Bonniaud  ( Department of Pneumology, Dijon Bourgogne University Hospital, Dijon, France );  Philip Bielefeld ,  Hervé Devilliers  ( Department of Internal Medicine and Systemic Diseases, Dijon Bourgogne University Hospital, Dijon, France );  Bernard Bonnotte  ( Department of Internal Medicine and Clinical Immunology, Dijon Bourgogne University Hospital, Dijon, France );  Marielle Buisson  ( Infectious Diseases Department, Dijon Bourgogne University Hospital, Dijon, France );  Alain Putot  ( Geriatrics Internal Medicine Department, Dijon Bourgogne University Hospital, Dijon, France ).
Funding This work was supported by grants (to MB) from the  AOIc2020  ( Appel d'Offre Interne COVID-19 ), from the  INSERM (Centre de Recherche   UMR 1231 ), the  national research agency (ANR) Investissements d' Avenir Grant  ( ANR-11 LABX-0021-01 ,  Labex Lipstic) , and by crowdfunding (https ://thell ie. org/covid -19).
Theoretical determination of speciation The speciation program consists of two software packages  (Puigdomenech, 2018) : Hydra which contains a database with log K data at 25 C to define a chemical system and Medusa which creates speciation diagrams. These are based on the algorithms namely, SOLGASWATER  (Eriksson, 1979)  and HALTAFALL  (Ingri et al. , 1967) , respectively. The speciation was done for the different metal ions present in the real solution before removal of the contaminating ions (SO 4 2À , Co(II), Ni(II), Cr(III), Fe(III), Cu(II), Al(III), Na and H þ ) and after precipitation of Al(III), Fe(III), Cr(III) hydroxides (SO 4 2À , Co(II), Ni(II), Na and H þ ).
METHODOLOGY The dataset to carry out the analysis presented in this paper was provided by the local Planning and Development Authority (EPA) and contained 2D geometric information (in dwg Autodesk AutoCAD format) about subdivisions, parcels, roads, sidewalks, and building footprints. It corresponds to the Devillers' masterplan updated to 2018. The masterplan is flexible and constantly updated whenever new land is bought by the public agency and tender outcomes are known. The end of the development is planned for 2030. ESRI CityEngine was then used to rapidly generate 3D models of the case studies under examination from the 2D information mentioned above, in a parametric way. More specifically, CityEngine makes it possible to produce 3D buildings or other objects, at any Level of Detail (LoD), thanks to geometrical construction rules, which makes it possible to interact with their parameters.
These rules are written in Computer Generated Architecture (CGA) language, which is a formal programming language that allows to build any 3D object in the study area (buildings, streets, parks, urban furniture, etc. ) and calculate 3D Key Performance Indicators (KPI). These can then be visualized in a graphic dashboard or directly in the 3D models and can provide useful information for scenario modelling. CityEngine also permits to compute measures of street network centralities (e. g. , local and global Betweenness, Closeness, and Reach)  (Porta et al. , 2006)  and 3D visibility analyses (e. g. , Viewshed, Dome 360° and View Corridor). To better understand the structure of the 3D model generated by the construction rules and the composition of the geographic objects represented at the different levels of detail (LoD), an example of ontology (taxonomy) of all the objects of the model of Nice Meridia is presented in figure  2.
) generated in 3D and adjustable thanks to the parameters of the CGA rules. In the same way, the CGA rules make it possible to manage the shape and the typology of the roof, allowing to choose a mineral (waterproof) cover, green cover, or photovoltaic panels. The resulting 3D model, made by multipatch objects, was then exported in a 3DCityDB geodatabase, compatible with the CityGML format (www. 3dcitydb. org), and subsequently imported in ESRI ArcGIS Pro. This software was then used to compute the 3D morphometrics for the two neighborhoods under examination. More specifically, these measures were based on sun shadows and skylines (3D objects created with the 3D Analyst Toolbox of ESRI ArcGIS Pro version 2. 5). The formers are modeled as closed multipatches, extruding the buildings in the direction of the sunlight, for given date and time.
Multiple shadow volumes were then generated to calculate the daily sun shadow frequency over the unbuilt areas of the two neighborhoods under examination. For what concerns the skyline, it is a closed 3D polyline that separates the buildings or the land from the sky, starting from a given observation point. This information was used to compute the distributions of the vertical and horizontal angles of the skyline vertices, the distributions of the distances between the observation points and the skyline vertices. In the following sections, we present the analyses based on this data for the two study areas (Nice Meridia and Hotel des Postes) and compare their respective values of 3D morphometrics.
Methods We scrutinized records of consecutive patients who underwent emergency open colectomy for IC at the Henri Mondor University Hospital of Creteil (France) between January 2008 and February 2019. The study protocol was approved by the Institutional Review Board (IRB: 00011558) and was conducted in conformity to the principles declared to the National Commission for Data Protection and Liberties (CNIL: 2210699) and in accordance with the ethical principles described in the Declaration of Helsinki.
Statistical analysis Statistics were performed with tee SPSS software (Statistical Package for Social Science, IBM SPSS Statistics, version 23 for Macintosh; IBM Corp. , Armonk, NY, USA). Continuous variables are expressed as mean (SD) or median (range), as appropriate. Categorical variables are expressed as frequency (n, %). Student t test, Mann-Whitney U test, Fisher exact test, or chi-square test was used for group comparisons, as appropriate. Logistic regression analysis was performed to identify clinical and operative parameters associated with postoperative mortality including the model variables that reached a p value < 0. 2 at the univariate analysis. Odds ratios (OR) and adjusted OR (AOR) are presented with 95% confidence interval (CI). A p value ≤ 0. 05 was considered as statistically significant.
In the same vein, further studies may explore the possibility to manage IC by emergency laparoscopy to obtain additional advantages in the postoperative period related to the minimally invasive approach  [42, 43]. However, this may be conceivable only in highly selected patients, depending on the IC severity and etiology (nonrelated to aorta surgery). The present study has limitations that must be considered when interpreting the results. It is a retrospective analysis of an 11-year cohort of IC patients requiring emergency surgery. Patients were treated in a single institution over a long period of time; although the health database was prospectively maintained, reporting and selection bias cannot be ruled out. Furthermore, the external validity of the findings is limited. However, the present study investigated a relatively large sample of IC patients all treated by open colectomy in an emergency setting.