dataesr/scientific-paragraphs-categorization huggingface dataset:
A filter was applied to the search to reduce false positives and matching redundancies of the same peptide in several hits. All peptide matches above 1% risks of random matching were eliminated (Po0. 01). Individual minimum peptide score was set to 15. False-positives rates were evaluated using Mascot. Under these stringent parameters, the minimum protein score was 34. Unless a specific peptide could differentiate them, the best matching protein was selected even when a subset of peptides could match another protein isoform. The probability score calculated by the software was used as a primary criterion for correct identification. BRET and CODA-RET assays. HEK293 cells (2 Â 10 5 per well of a 6-well plates) were transfected with 2 ng plasmid DNA coding for the BRET donor (CD147-Luc, CD147DD2-Luc) and increasing amounts of BRET acceptor plasmids (b 2 -YFP, CCR5-YFP; 0-400 ng per well).
HBMECs transiently transfected with a GFP-tagged CD147 construct or a YFP-tagged b 2 AR construct, together with siRNA targeting Actn4 (siActn4) or control siRNA (siCTL), were seeded on 35 mm dishes (Ibidi, Biovalley). Imaging was performed in DMEM medium, 50 mM HEPES and 10% fetal bovine serum on a iMIC imaging work station from TILL Photonics, on the back aperture of a Â 100 oil (NA 1. 49) objective plus a Â 1. 5 of internal microscope lens. The regions of interest were photobleached for 25 ms ten times at maximum 491-nm laser power (50 mW). Subsequently, time-lapse images were collected onto a EMCCD camera (iXon Andor Technology) until the bleached signal reached a stable level. FRAP curves from three independent trials with 12 to 22 cells per condition were derived by fitting the normalized fluorescence at each time point versus time into a one-phase association model plugged into the Prism Software.
Unspecific binding was subtracted from the response using blank runs performed on a surface without immobilized peptide. Output sensorgrams were analysed using the BIAevaluation 4. 1. 1 Software. Confocal immunofluorescence microscopy. HBMECs were grown to confluence on Permanox coverslips (Thermo Fischer Scientific). After the indicated transfection and/or infection, cells were fixed and labelled as described previously  56. Image acquisitions were performed with a DMI6000 microscope (Leica, Â 63). Image analysis were carried out with the ImageJ Software (NIH). Quantitative analysis of protein recruitment under bacterial colonies was determined as the proportion of colonies showing underlying proteins of interest. At least 30 colonies were observed per coverslip. Each experiment was repeated at least three times.
0. Localization of molecules: peak intensity threshold: 2*std(Wave. F1); connectivity: Subpixel localization of molecules: Method: PSF integrated gaussian, fitting radius 3 px, fitting method; weighted least squares, initial sigma: 1. 6 px. To analyse the organization of the blinking molecules, 2D and 3D STORM imaging was performed on a Leica SR GSD 3D system, with a Â 160 oil (NA 1. 43) objective. Fluorescence was collected onto an EMCCD camera iXon Andor technology, providing an effective pixel size of 100 nm and processed with the LAS X Software (Leica). Approximately 50,000 frames were recorded for each acquisition with 10 ms exposure time, EM gain of 300 and the number of photons per pixels set to 50. 2D and 3D reconstructions were obtained from stacks (16 planes) with 50 nm axial step size, using the LAS X Software (Leica).
The complex organization of CD147 was analysed using the SR-Tesseler Software using default parameters with a density factor ¼ 1. 3 (ref. 32). Quantifications were performed on seven 2D-reconstruction images (mean cluster size ¼ 37,700 ± 2,715 nm 2 ). Quantification of the number of CD147 clusters and number of non-clustered blinking CD147 receptors per 100 mm 2 at bacterial adhesion sites was performed using both SR-Tesseler and ImageJ Software programs. Structured illumination microscopy. HBMECs were seeded on Zeiss high-performance coverlips no. 1. 5, infected with meningococci for 1 h, fixed in 4% paraformaldehyde for 10 min and labelled as described for STORM. Labelled preparations were mounted in DAKO Fluorescent Mounting Medium (S3023) on StarFrost 76 Â 26 mm 2 microscope slides. SIM images were acquired on a Zeiss ElyraPS1 microscope with Â 100 oil-immersion lens (Plan-Apochromat NA 1.
8c ) or on 2 mm SiO 2 ball cantilevers (sQube ref. CP-PNP-SiO) to mimic the size of the bacteria for interaction measurements (Fig. 8e, f ). Cantilevers were cleaned using plasma O 2 , then amino-functionalized and finally covalently coated with pilins using a acetal-PEG-NHS linker  61. All the experiments were performed at 37 °C on a commercial stand-alone AFM (NanoWizard3; JPK Instruments) combined with an inverted optical microscope (AxioObserver. Z1; Zeiss) driven under the JPK NanoWizard Software 4. 3 and Zen Blue 2012. AFM was operated in contact/force curves mode in HBMEC imaging buffer (DMEM glucose 4. 5 g l À 1 þ Glutamax þ HEPES 25 mM). The spring constant was calibrated using the thermal method of the JPK Software for each cantilever. The cantilevers were neither engaged on the edge nor on the soma of the cell.
Force curves were then automatically processed using in-house developed software for the measurement of detachment forces and works. Plots and sample curves were drawn using GraphPadPrism v. 6. Scanning electron microscopy analysis. Cells grown on glass coverslips were infected with meningococci for 1 h, fixed with 4% paraformaldehyde for 10 min, treated with 1% osmium tetroxide in water, in the dark for 1 h and dehydrated with increasing ethanol concentration baths before drying with a critical point drier (Quorum Technologies K850, Elexience, France). Dry coverslips were mounted on stubs, coated with 5 nm platinum (Quorum Technologies Q150T, Elexience, France) and observed with a Zeiss SEM Merlin Compact VP (Zeiss, France). Statistical analysis. Statistical significance was assessed with the Student's t-test and one way analysis of variance by the Prism Software (GraphPad Software).
Acknowledgements We thank  Babette B. Weksler ,  Paul Mangeat ,  Franck Perez ,  Alain Duperrey ,  Mickael Bukrinsky ,  Monique Arpin ,  Georges Bismuth ,  Tamas Balla ,  Celine Gale  `s and  Jonathan A. Javitch  for providing reagents;  Pierre Bourdoncle ,  Thomas Guilbert  of the imaging facility,  Cedric Broussard ,  Franc¸ois Guillonneau ,  Marjorie Leduc  of the 3P5 proteomics facility and  Murielle Andrieu  of the cytometry facility of the  Institut Cochin, Carmelo Di Primo of the SPR facility of the Universitede Bordeaux ,  Antonino Bongiovanni  and  Nicolas Barois  from the  BioImaging Center Lille  for their expert technical help. N. M. was supported by a doctoral fellowship from  UniversiteParis Descartes  and the  Fondation pour la Recherche Medicale of France. V. C. was supported by a postdoctoral fellowship from the  Fondation pour la Recherche Medicale.
S. C. B. was supported by a doctoral fellowship from  UniversiteParis Diderot and Association pour la Recherche sur le Cancer. This work was supported by collaborative research grants the  Agence Nationale de la Recherche of France  ( ANR-14-CE14-0010-02 ) and the  UniversiteParis Descartes  ( AAP-2011 ) to S. B. , X. N. and S. M. , from the  Fondation pour la Recherche Medicale  to X. N. and S. M. and from the  ANR  ( ANR-10-EQPX-04-01 ) to F. L.
However, data on GDM derived from exhaustive national or regional databases that link maternal and neonatal outcomes are relatively rare  [5] [6] [7] [8] , and the overall risk of morbidity of neonates born to GDM mothers remains unclear, particularly for neonates with congenital malformations and respiratory distress  [9]. In fact, two problems usually make data interpretation more difficult in GDM studies: (1) the so-called immortal time bias; and (2) the possible contamination of GDM data by inclusion of women with undiagnosed pregestational diabetes, who may be reclassified after pregnancy. We conducted a large-scale observational national study in France using data for 2012 from the French hospital discharge database and the French National Health Insurance system, which include all deliveries and terminations of pregnancy after 22 weeks for medical reasons.
We used a specific algorithm based on the use of glucose-lowering agents before, during and after pregnancy and hospital diagnosis at delivery to determine maternal diabetes status. The aims of this study were to: (1) estimate the prevalence of GDM in pregnant women in 2012 in France; (2) assess perinatal outcomes after 22 weeks of pregnancy in the presence of GDM compared with pregestational diabetes or no diabetes; (3) determine whether perinatal outcomes differed according to whether GDM was treated by insulin or diet alone; and (4) increase the validity of conclusions by controlling for immortal time bias and contamination by data from women with undiagnosed pregestational diabetes.
Methods Data sources This cross-sectional study was conducted using combined data from the French hospital discharge database (PMSI [Programme de Médicalisation des Systèmes d'Information]) and the French National Health Insurance database (SNIIRAM [Système National d'Information Inter-Régime de l'Assurance Maladie]). The PMSI database provides detailed medical information on all admissions to French public and private hospitals, including discharge diagnoses using ICD-10 codes (www. who. int/classifications/icd/en/) and medical procedures performed during the hospital stay. The SNIIRAM database contains individualised and anonymous data on healthcare claims reimbursed by French National Health Insurance covering the entire French population  [10]. Information on severe and costly long-term diseases is also available and coded according to ICD-10.
Study population The study included data on all deliveries taking place after 22 weeks of pregnancy in 2012 and recorded in the PMSI database, including terminations of pregnancy for medical reasons. The delivery date was defined as the date of hospital admission for the infant's birth, when available (88. 5%); the date of the delivery procedure, when available (11. 4%); or the date of the mother's hospital admission (0. 1%). The date of conception was calculated by using gestational age at delivery as recorded in the database. A new tool implemented in 2011 links maternal and neonatal data in the PMSI database. In 2012, this tool was available for 88. 5% of delivery stays. Algorithm for identifying maternal diabetes status An algorithm for defining the maternal diabetes status was developed for this study based on the use of glucoselowering agents before, during and after pregnancy, hospital diagnosis at delivery, and information on long-term diseases.
Data analysis Logistic regression models were used to estimate the ORs and 95% CIs for associations between maternal/neonatal outcomes and maternal diabetes status (type 1 diabetes, type 2 diabetes, GDM or no diabetes), adjusted for maternal age (≤29, 30-39, ≥40 years) and birthweight and/or gestational age, depending on the outcomes investigated. Absence of diabetes was used as the reference group to calculate ORs. A complementary analysis was carried out to study maternal and neonatal outcomes associated with GDM in order to reduce a possible immortal time bias. Immortal time refers to the follow-up time during which, because of the exposure definition, the outcome under study could not occur, corresponding to the time prior to the diagnosis of GDM  [13]. As highlighted by Hutcheon et al, differences at the start of follow-up between women with and without GDM can lead to overestimation of the perinatal mortality rate in the population without diabetes  [14].
Results Data were available for 796,346 deliveries that occurred in France in 2012. The prevalence of each subtype of diabetes is presented in Table  2. The prevalence of GDM (as defined in Oral glucose-lowering agents or insulin dispensed at least 3 times in the year before pregnancy AND at least one of the following criteria: -an HbA 1c assay performed or glucose strips dispensed in the year before pregnancy -long-term disease status for diabetes before pregnancy -oral glucose-lowering agents or insulin dispensed at least once during pregnancy or in the year after delivery At least one of the following criteria: -insulin dispensed at least once during pregnancy -at least 200 glucose strips dispensed during pregnancy on at least 2 different occasions -a diagnosis of diabetes recorded during the delivery admission (ICD-10 codes E10-E14, O240-O244, O249) a Exclusion Oral glucose-lowering agents dispensed in the year before pregnancy or the year after delivery Meeting the definition of type 1 diabetes Long-term disease status for diabetes before pregnancy Insulin or oral glucose-lowering agents dispensed at least once during the year before pregnancy a ICD-10 codes include both GDM and pregestational diabetes to correct coding errors (GDM that may have been coded as pregestational diabetes), as the sensitivity for GDM observed in the PMSI database was only 73%  [16]  Table  1 ) was 7.
Discussion On the basis of the analysis of data for 2012 obtained from a large-scale, nationwide, exhaustive database in France, we confirm that GDM is associated with lower risks for maternal and neonatal complications compared with pregestational diabetes. However, we show that: (1) the risk of cardiac malformations is increased for women with insulin-treated GDM, whereas the risk of nervous system malformations is not; (2) the risk of respiratory distress is also increased for this subgroup of insulin-treated women with GDM; and (3) the risk of perinatal mortality increases with GDM for deliveries after 37 weeks. This is the first nationwide study to evaluate the effect of diabetes on pregnancy in France based on a combination of maternal and neonatal data. The strengths of our study are that we had access to data concerning 796,346 deliveries and combined maternal and neonatal data for 88.
The validity of the PMSI database has been regularly audited, especially in terms of perinatal data  [15, 16] : prematurity and Caesarean section, for example, are well documented. However, data on maternal morbidity appear to be less comprehensive, as indicated by a sensitivity of 73% for GDM. Coding errors concerning the diagnosis of diabetes in the PMSI database were therefore corrected by the algorithm based on medication use. The prevalence of GDM in France in 2012 was 7. 24%, which was lower than that reported in other countries: recently reported GDM prevalence rates of 15% in the USA  [17]  and Erb's palsy/clavicle fracture d Rate, % 0. 5 0. 7 0. 7 0. 7 OR (95% CI) e 1 13% in Australia  [18]  were similar to those observed in the HAPO study population  [19]. However, in France, experts recommend restricting GDM screening to women presenting with certain risk factors, which could explain the lower prevalence in this study  [11].
We can hypothesise that the increased risk of cardiac malformations in women with insulin-treated GDM may be partly associated with maternal obesity, although maternal BMI was not recorded in the national database used for this study. We found that GDM did not alter the risk of nervous system malformations. The incidence of these malformations was much lower than the incidence of cardiac malformations, as previously reported by other authors  [22, 25]. One hypothesis is that early hyperglycaemic exposure of the embryo has different teratogenic effects on cardiac and nervous system tissues. Nervous system malformations may also be associated with an increased risk of miscarriage or a higher rate of termination of pregnancy before 22 weeks, which was not taken into account in this study. The relationship between maternal GDM and the risk of neonatal respiratory distress has not been clearly established  [26].
Conclusion We have clearly demonstrated that GDM is a disease related to adverse pregnancy outcomes and that most of the risks are higher in women with insulin-treated GDM. By restricting analysis to deliveries after 37 weeks and excluding cases of undiagnosed pregestational diabetes, we identified a moderate increase in perinatal mortality in non-insulin-treated women with GDM. Although more investigation is needed, this study helps illuminate the controversy about timing of delivery in GDM pregnancy. Data availability We are unable to publicly provide the datasets owing to restrictions from our ethics committees, unless an agreement between all parties has been made.
In this paper, both point clouds and meshes segmentation are critically tested. For point clouds segmentation, the region-growing algorithm was evaluated. It groups points in clusters that are close enough in terms of the smoothness constraint. This algorithm works on the angles comparison between the point's normals. On meshes two different approaches have been tested, based on: i) the use of planes for separating different mesh segments; ii) the identification of groups of polygons coherent with a pre-defined heuristic. Different software packages have been tested for the first (Innovmetrics Polyworks and CloudCompare) and the second (SimSelect) approaches. Although a rather large number of tests have been done, this paper presents an initial overview of segmentation on CH with the aim of identifying the most suitable ones for the final FEA purpose.
, 2006; Ho, Chuang, 2012). For point cloud segmentation, an efficient tool is the Point Cloud Library (PLC -http://pointclouds. org/about/) that provides open sources algorithms and scripts to process 3D point cloud. The point cloud segmentation library contains algorithms for segmenting a point cloud into distinct clusters and there are tutorial for segmentation. The most used and effective are the Region growing segmentation, that works only on plane surfaces, and the Cylinder model segmentation that identifies both plane and curvilinear surfaces. The first is a parametric model-based range segmentation algorithms based on the assumption of a parametric surface model and on grouping data points so that all of them can be considered as points. The second filters data points further away than a certain threshold, estimates surface normal at each point, segment and save a plane and a cylindrical model to disk.
The cylindrical model is not perfect due to the presence of noise in the data that can be filtered out thank to the RANSAC algorithm. The RANSAC (Random Sample Consensus) is a randomized algorithm for robust model fitting and is used for 3D point shape extraction  (Schnabel et al. , 2007). The volume of data obtained from a typical 3D survey makes it unfeasible to process with the original algorithm as a single set, unless the algorithm is modified  (Luchowski et al. , 2013). Another tool is the suite CANUPO, an automatic classifier that handles multiple classes and that can be trained on small samples on a point cloud (http://nicolas. brodu. net/en/recherche/canupo/). This tool is now available as a plugin in the CloudCompare software but it was created for environmental 3D point cloud segmentation.
Mesh segmentation Considering the goal of the segmentation, meaning the creation of models subdivided in homogeneous parts related to the main architectural element, it was decided to segment directly the meshes. Three common post-processing mesh software items, one commercial and two open-source (Polyworks from Innovmetric, CloudCompare and SimSelect) were tested. The latter is barely new software that allows to interactively selecting specific elements on 3D surfaces. It classifies selections into three different types: connected components, parts and patches  (Guy et al. , 2014). The first set of tests were made on the model of the Temple of Neptune and the second on two simplified meshes of the little David sculpture. On both meshes two different decimations were applied, one with a standard sequential optimization process that guides the removal of points from the triangulation leading to a gradual increase of its overall approximation error  (Soucy and Laurendeau, 1995)  and another one applying retopology  (Gonizzi Barsanti, Guidi, 2017).
The first software that was tested is CloudCompare. For what concerns the Temple, it was decided to use only a portion of the model, to minimize the calculation time. The software is provided with its own tool for segmenting the model, constructing a section that the user manually places at the proper height where the model has to be cut. Using this section, the software creates a partition of the model that is included in the working bounding box. To have the second part of the model properly segmented, to avoid gaps, the software allows to use the same section and to move the bounding box to select the second part. The problem with this software is that it leaves the two cut parts open and, therefore, the mesh has to be closed differently (Figure  12 ). Therefore, the possibility to have discontinuities increases.
An option is to import the two parts of the model into another software and find a way to close them by using the same flap surface, as a plane, but the result is not perfect due to the jagged profiles of the cut. Figure  12. The segmentation through a section in CloudCompare. Innovmetric Polyworks was also tested to segment the models by using a plane. Such tool allows having the two sections closed by the same plane and that assures absence of discontinuities. The process was applied both on the models of the temple and the statue. For segmenting the model of the Temple in all its architecturally relevant parts, several planes were positioned in order to cut the parts that had to be segmented. Each semantic component was segmented through a dedicated plane (Figure  13 ). However, a better segmentation should consider all the constituent elements of the structure as the drums of the columns or the single blocks of the architrave, considering their connection.
In this way, it will be possible to obtain a more precise analysis of the statics and the structural behaviour of the building. Differently from the previous case, for the model of the statue only one plane was needed. The procedure gave a perfect result: the same plane, avoiding any hole in the mesh, closed both the parts. (Figure  14 ). The FEA on statues can be important for detecting weak parts, physical stresses of the stone, so as to enable practitioners to detect the most suitable and effective methods for restoration or to investigate its behaviour during earthquakes  (Podany 2006; Casciati, Borja, 2004 ). Figure  14. The "cut by plane" mode in Polyworks The last software to be tested was SimSelect, an open source software that allows interactive selection on 3D models. As a test object it was used the Neptune Temple for its complexity.
The software works on the manual selection of a specific part on the model and then the automatic selection of similar shape can be launched. The SimSelect system takes as an input a two-manifold triangle meshes (i. e. a list of polygons indexed over a list of vertices) which can have multiple components and boundaries. It provides the user with an interactive selection process, composed of two main stages: the interactive construction of a reference selection and its expansion to similar ones  (Guy et al. , 2014, pp. 2-3). However, in the software tutorials, only CAD models were used as examples. With reality-based models, the same smooth behaviour did not occur, thus generating unexpected selections that needed a significant manual work in order to be adjusted. The model of the Temple, even if decimated, was too big for the software to be handled, so the test was run on a model portion only.