dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the coalescence behaviour was also tracked on top of 100 µm thick films of PEKK provided by Arkema, from 6000 series. The film was placed just above the glass slides. The test was performed in a Linkam THMS600 microscope stage coupled with a Bruker IRScope II and connected to a DinoEye eyepiece camera. The IRScope operated in reflection mode and in the range of visual spectrum, at a magnification of 400 times. To simulate better the LS process, the test was performed dynamically from room temperature to a maximum of 450 o C, at 120 o C x min -1. The pictures were captured every 2s, hence in an interval of 4 o C per picture, with the assistance of DinoCapture 2. 0 software. The analysis was performed using ImageJ software. A total of 20 pictures were selected for each trial, starting from the image in which neck is formed.
Such image is different for each grade as T m is different (Table  1 ) and it varies slightly for each experiment. The selection of this image was based on change in brightness of the particles; they were tracked from 36 o C below neck formation to 24 o C above it in a total of 17 sequential images according to Table  3. The analysis in ImageJ software starts by converting the scale of the image into pixels, followed by the conversion of the image into black and white and applying the threshold which matches best the image with the original particles. The ImageJ analysis gives information about particle area, aspect ratio, circularity, Feret diameter, perimeter and roundness. Particle circularity is a function of particle perimeter, as shown in equation 2. Roundness is a function of particle diameter and equation 3 shows how ImageJ computes it.
The characteristics of the patients were prospectively collected and included age, sex, the presence of cardiovascular risk factors, treatments, fasting glycemia, fructosamine, insulin and C-peptide concentrations. The aneurysmal diameter was systematically determined using the CT-scan images. Data were collected using manuscript and electronic medical records, as well as a computer software program (Clinicom®). Imaging data were recorded and extracted from the Picture Archiving and Communication System (PACS®) multiple modalities software.
Statistical analysis Results for continuous variables were expressed as median (interquartile ranges) and compared with the Mann-Whitney test. Categorical variables were expressed as number of patients and percentage and compared with the Fisher's exact test. Correlations were determined by non-parametric univariate linear regression (Spearman's rank test). A twosided P value <0. 05 was considered as significant. Statistical analyses were performed using GraphPad Prism® software (version 7. 00, La Jolla California USA).
X-ray Diffraction (XRD) To determine the new mineralogical phases formed after S/S of the studied waste, an XRD analysis was performed on S/S materials powders. The Fig. 4, 5  and Table  5  show XRD patterns for S/S samples,  the spectra were analyzed by the X'Pert High Score software. As expected , the major crystalline phases present in F0 are cement hydrates, such as portlandite Ca(OH)2 and ettringite Ca6Al2(SO4)3(OH)12•26H2O (Fig. 4 ). The new solid phases formed during S/S of our slop waste are very complex; a comparison between the patterns of Fig. 6  revealed that all peaks shapes are similar for different matrix of S/S materials (5 %, 7. 5 % and 10 %) but with different intensity. We can also observe the disappearance of the peaks of the initial metal-containing phases compared to the XRD diagram of raw waste (Fig.
One cage was installed at Port Fitou (vertical homogeneity of the water column). As the mussels were returned to their respective original habitat, it is reasonable to assume that they tolerate the environmental conditions and that growth is not affected by the experimental setup. The calcein-marked mussels were periodically sampled from the cages between November 2016 and January 2018 (Table  1 ). CTD loggers (Solinst LTC Levelogger and NKE S2T600) were installed with all the mussel cages at the three sites to monitor temperature, salinity, and water level variations. Water level was corrected for atmospheric pressure. Loggers were protected with copper mesh to avoid biofouling of the sensors, and were regularly cleaned (once every 1-2 months). Precipitation, wind and atmospheric pressure data at the nearby meteorological station "Leucate" were extracted from the French meteorological service (Météo France).
Growth Analyses In order to identify the growth lines marked with calcein, the cross-sections (on the glass slide) were viewed under epifluorescent microscope at magnification 4X (OLYMPUS BX61) and digitized with an OLYMPUS DP 72 camera at the Observatoire Oceanologique de Banyuls-sur-Mer, France (BIOPIC platform). Mutvei-treated sections were analyzed under reflecting light using the same microscope and camera. Growth analyses were carried out on mounted photographs using image processing software Adobe Photoshop and Image J. The distance between the calcein mark and the ventral edge of the shell (L t ) was measured to allow an estimation of the growth rate during the period held in the cages (Figure  2c ). The number of growth increments was counted to estimate the growth periodicity. The width of growth increments was measured to assess the relation between growth and environmental parameters.
The FFT analyses revealed peaks at low frequency corresponding to periodicities of 12. 7, 11. 2, and 11. 3 increments for the shells S1 (FDS0118-3), S2 (FDD0118-4), and S3 (PF0118-3), respectively. Considering the near-daily rhythm of the growth increment, the peaks correspond thus to a period of 11-13 days (near-fortnightly period). The power spectrum for the three shells also showed peaks at high frequencies corresponding to the periods of approximately 3 and 5 days.
ACKNOWLEDGMENTS We thank  Michel and Henry Comte (Salses-le-Château) ,  L. Fonbonne  ( Rivage Leucate ),  R de Wit  ( MARBEC Montpellier ),  A. Fiandrino ,  M. David , and  D. Munaron  ( IFREMER Sete ),  V. Bailly Comte  ( BRGM Montpellier ), and  P. Cook  ( Flinders University ) for sharing their expert knowledge on regional lagoon and groundwater processes. The community from " Le domaine de Pedros " facilitated access to Fitou. We acknowledge the facilities of Biology platform of imaging and cytometry (BioPIC) from  Banyuls Oceanographical Observatory , and thank the reviewers for their thorough and constructive comments on the manuscript.
FUNDING This research was supported by the  French National Research Agency (ANR)  through  ANR @RAction chair medLOC  ( ANR-14-ACHN-0007-01 -project  leader  TS ). AA was partially supported by  Labex OT-Med  ( ANR-11-LABEX-0061 ), and VR acknowledges support from the  European Union 's  Horizon 2020 Research and Innovation Program  under the  Marie Skłodowska-Curie Grant  Agreement No  748896.
Cytometric bead array The supernatant derived from stimulated and nonstimulated PBMC cultures was collected, after which the amount of cytokines produced by immune cells was evaluated using cytometric bead array (BD Bioscience, San Diego, CA), and the data were analyzed by the FCAP Array Software. Subsequently, the following cytokines were determined: interferon gamma, tumor necrosis factor a, granzyme B, IL-4, IL-6, IL-10, IL-13, and IL-17.
Guarantor of the article: Thomas Decaens, MD, PhD. Specific author contributions: Z. M. J. , C. A. , P. N. M. , T. D. : conception and design of the study. C. S. , T. D. : sample collection. Z. M. J. , C. A. , K. K. , A. G. , N. S. , T. D. : acquisition of data. Z. M. J. , C. A. , K. K. , A. G. , N. S. , T. D. : analysis and interpretation of the data. Z. M. J. : drafting of the article. Z. M. J. , C. A. , P. N. M. , T. D. : critical revision of American College of Gastroenterology Clinical and Translational Gastroenterology LIVER Immunomonitoring of Patients With Advanced HCC the article for important intellectual content. Z. M. J. , C. A. , K. K. , A. G. , C. S. , N. S. , P. N. M. , T. D. : approval of the final version. Financial support: This work is funded by la Ligue Nationale Contre le Cancer under Grant 2016-R16145CC-Le comité de Haute-Savoie de La Ligue Contre le Cancer (CD74).
We found 15 TFs that were activated in flat nuclei but not in the nuclei of blebbistatin-treated cells (Fig  2A ). In order to identify those responsible for significant transcriptomic consequences in response to nuclear constraint, we selected genes known to be positively regulated by these 15 TFs using the TRRUST database  [29]  and we analyzed their mRNA expression (Figs  2B  and EV3C ). Interestingly, only the genes targeted by AP1, TEAD, PPAR, or SP1 showed significant increased expression in response to nuclear flattening (Figs  2B  and EV3C ). Additional transcriptional regulatory mechanisms, such as epigenetic silencing of the target promoter or coactivator requirement, may explain why we did not detect any change in mRNA expression of the genes regulated by the other TFs (Fig  EV3C ).
Our results reveal that agarose padinduced nuclear flattening is sufficient to activate AP1, TEAD, PPAR, and SP1 in the absence of myosin II-dependent contractility. However, we cannot rule out the possibility that cytoplasmic mechanotransduction pathways may also contribute to activate these TFs, considering the cellular deformation that occurs following agarose pad application. Since the nucleus undergoes significant morphological changes in response to mechanical stress  [7, 9] , we hypothesized that these 4 TFs may interact with proteins related to mechanical stress. To test this hypothesis, we performed a bioinformatics analysis using the Biological General Repository for Interaction Datasets (BIOGRID) database  [30] , which annotates protein and genetic interactions. We obtained a minimal network connecting AP1, TEAD, PPAR, and SP1 with a group of proteins involved in the cellular response to mechanical stress, such as PRKM3 (Fig  EV3D ).
Real-time PCR with reverse transcription Total RNA was purified from cells using the RNAqueous-Micro kit (Ambion-Life technologies) according to the manufacturer's instructions. RNAs were reverse-transcribed into cDNA using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). High-throughput real-time qPCR was performed by the Center for Gastrointestinal Biology and Disease Advanced Analytics (AA) Core at UNC (Chapel Hill NC 27599). The 48 TaqMan primer references are listed in Table  EV2. Expression data were normalized to a standard curve generated from a pool of control cells. GAPDH was used as the reference gene. Data are based on results from minimum four independent experiments. Only genes with increase in mRNA expression superior to 1. 5-fold change in response to nuclear flattening were selected.
Protein-Protein Interaction Network reconstruction and analysis Literature-based protein-protein interaction involving AP1, PPARA, TEAD1, SP1, and mechano-related proteins was gathered from BIOGRID database (http://thebiogrid. org/, release archive 3. 4. 156; [49] ) with PSIQUIC retrieval. The resulting interaction Table (Table  EV3 ) was curated to represent only the interactions validated in Homo sapiens, and the network was generated in cytoscape (http://www. cytoscape. org/; Shannon et al, 2003). For visualization purpose, the self-loops and multiple edges were removed. A solution of a minimal network that includes the transcription factors was determined by iteration of shortest path analysis and network parameter analysis-based pruning. The custom list of mechanorelated proteins (Tables  EV3  and EV4 ) was built over an assembly of keyword indexed proteins in UniProt (goa: "mechanical") and GO terms in QuickGO databases (GO:0050982, "Detection of mechanical stimulus"; GO:0071260, "Cellular response to mechanical stimulus"; GO:0009612, "Response to mechanical stimulus").
Acknowledgements The authors thank the cell imaging facility MicroCell and its outstanding staff, including  Alexei Grichine ,  Mylène Pezet , and  Jacques Mazzega  for their technical assistance. We thank  Keith Burridge  for his continuous support. C. G. is supported by grants from the  Agence National de la Recherche  ( ANR-13-JSV1-0008 ) and from  European Research Council (ERC)  under European Union's  Horizon 2020 research and innovation program  ( ERC Starting Grant   n_639300 ). The authors thank the  Center for Gastrointestinal Biology and Disease (CGIBD) Advanced Analytics (AA) Core  ( NIH P30 DK34987 ) at the  University of North Carolina (Chapel Hill, NC ). L. V. L. is supported by grants from  NCSU CVM (Seed Funding) ,  UNC CGIBD (Pilot/Feasibility  Grant  NIH P30 DK034987 ) and from the  University of North Carolina Lineberger Comprehensive Cancer Center  ( Developmental grant ).
Numerical Approach The numerical study consists of 2D simulation to investigate the effect of a static internal pressure on the structural response of the compliant hydrofoil as well as the impact on hydrodynamic performances. The flow model of the XFOIL solver and the finite-element method of the ANSYS-Mechanical solver are used for the FSI analysis. The FSI algorithm is based on an iterative method between the two solvers with a small perturbation hypothesis. The flow model is based on the coupling between a panel method with a boundary layer model. More details concerning Xfoil are given in  [31]. The panel method accelerates the flow calculations as compared to finite volume methods. The numerical model of the hydrofoil structure is calculated by ANSYS Mechanical using ANSYS APDL (Ansys Parametric Design Language) with solid elements according to the preliminary hydrofoil design.
The original APDL script was modified to handle distinct geometry changes and meshing. Solid elements PLANE183 (quadratic) are used to mesh the hydrofoil geometry. The coupling algorithm is developed using Python-scripts. The fluid-structure coupling algorithm is described in Figure  8. The FSI algorithm is initialized by a structural computation as the cantilevered hydrofoil is submitted to an internal pressure only. It leads to displacements which produce to a new hydrofoil shape. Then, the viscous flow around the new foil is solved. The computation returns a C p distribution and the forces of the hydrofoil. The external hydrodynamic pressure resulting from the C p distribution is applied during the structural resolution. The problem is solved using an iterative method until the convergence on the maximum structural displacement and the lift coefficient C L is reached.
Unbiased metagenomic sequencing Briefly, samples were centrifuged and filtered. Total nucleic acids were extracted followed by reverse transcription with random hexamers and second strand synthesis in separate reactions for RNA and DNA genomes. For the RNA workflow, DNase treatment was included before reverse transcription. DNA and RNA preparations were kept separate for constructing sequencing libraries with the NexteraXT protocol (Illumina, San Diego, CA). Libraries were sequenced on a MiSeq for 1 × 150 cycles. Reads were analyzed with a dedicated bioinformatic pipeline "VirMet" (github. com/medvir/VirMet/releases/ tag/v1. 1. 1) [2]. The raw viral sequencing reads have been uploaded to zenodo (doi: https://doi. org/10. 5281/ zenodo. 2545561).
Genome analysis To generate a consensus sequence of the identified TOSV, all reads were iteratively aligned using SmaltAlign (github. com/medvir/SmaltAlign). In total, 4491 reads aligned and the full coding sequences could be reconstructed. However, a gap of 23 nucleotides remained in segment S at the position of the translation stop hairpin signal found in all ambisense viruses (Fig. 1a ). Conceivably, this region was not efficiently reverse transcribed at the conditions used in our protocol. We closed the gap by sequencing a short amplicon using specific primers (Additional file 1). The full genome sequences have been deposited to GenBank (GenBank IDs MK422498 to MK422500). Phylogenetic analysis showed that the isolate belongs to the B lineage of TOSV (Fig. 1b ).
Various Artificial Intelligence based algorithms have been applied to the clinical breast cancer data set for the early detection of breast tumor. In this research work the clinical data has been collected from the UCI machine learning repository for the classification of breast cancer tumor a) Benign and b) Malignant. Support Vector machine with its variants Kernel, Gaussian Kernel and Sigmoid Kernel have been applied to the linearly separable breast cancer data set for comparative analysis. Results proved that all the variants of SVM performed better for the breast cancer classification.
Introduction Digital images based elasto tomography (DIET) was proposed for the robust evaluation of breast tumor. Vibrated breast image was captured and surface motion was recorded to track the tumor. 3D surface motion was accurately evaluated using this approach. DIET based approach could detect upto 100 mm. of tumor  [1]. Gene expression pattern data sets were used to find out the similarities and differences between breast tumor and peripheral blood monocular cells [2]. An intelligent system was proposed comprised of neural network classifier and image processing (ISIBC). The features for the classification have been obtained by applying GLCM algorithm. Standard deviation, mean and entropy were calculated. Filtering, edge detection and morphological techniques were applied to the images for the feature's extraction  [3].
Mammographic images were collected from the data bases available on the internet  [3]. Image processing method were applied to filter the noise and to enhance the mammographic images for the robust investigation of breast cancer. Adaptive histogram equalization, wavelets and data fusion methods were applied to identify microcalcifications using LabView  [4]. Reproduction of cells is the main indication of the breast cancer. Reproduction can usually be examined by the color of the cells for the antigen Ki-67. Artificial Neural network comprising of the development of multi-layer perceptron network with the combination of feed forward neural network was proposed for the accurate and precise classification of three different cancer genes which are linked with Ki-67. Results proved that unique nonrecursive method was very successful to provide the complete detailed genes network with the visualizations [5].
Mammographic filtered images were divided into small grids and features were extracted from these small grids for better classification of Malignant and Benign. Results proved that grid-based pattern evaluation for the doubtful tumor or tissue performed better with the efficiency of 91. 67%. The results were also compared with the knowledge-based data base provided by the radiologist  [7]. OTSU method was also adopted to reduce the noise and segment the mammogram images to discriminate the changes compared to the normal one  [8]. Simplest evolving connectionist system was suggested for the classification of normal, benign and malignant classification. Wisconsin dataset was collected from UCI machine learning repository and sensitivity of 96. 02% was achieved  [9]. Computer aided diagnostic based approach was developed and 82% sensitivity was achieved [10].
Gaussian Kernel Support Vector Machine (SVM) k(x, x0) = exp ³-||x -x0||2/2σ2´ (3) Gaussian Kernel support vector machine was applied to the breast cancer data set and following results were achieved. SVC(C=1. 0, cache_size=200, class_weight= None, coef0=0. 0, decision_function_shape='ovr', degree=3, ga mma='auto_deprecated', kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0. 001, verbose=False) Fig. 6. Gaussian Kernel SVM results.
(2011). The sample flow rate was calculated daily by measuring the mass of water taken up by the peristaltic pump for 2 min (this operation was repeated 3 times). After each sample analysis, the software automatically processed the raw data and generated the particle size, particle concentration, size distribution and information about concentration of dissolved metal. The size distributions were prepared in Origin 8. 5 software (Northampton, MA) and adjusted to lognormal distributions in order to obtain the median diameter. Mass fraction 0. 8139.
Acknowledgements This work was financially supported by the  National Science Centre, Poland  (grant n°  2015/18/M/ST4/00257 ) as well as by a  STSM Mobility Grant  (JW)  COST Action TD1407  (grant n°  42007 ) and the project  AQUITRACES Region Aquitaine 20131206001-13010973. Authors would like to thank  Wojciech Kurek  ( Department of Botany, Warsaw University of Life Sciences (SGGW) ) for preparation of samples for LA-ICP-MS analysis.
First, the framework presents a formal metamodel of context. A formalization of context using the first-order logic with relational operators is then presented to specify formally the context information at different abstraction levels. The metamodel, therefore, prepares the ground for building a formal modeling language and automated support tool (https://github. com/metamodeler/ CIM-CSS/). The proposed model is then evaluated using an application scenario in the smart meeting rooms domain, and the results are analyzed qualitatively.
The upper-level ontology represents the highest abstract level in this work. It defines context information and entities in terms of a taxonomy of high-level classes such as Time, Profile, Environment, Role, States and Status, Location, Activity, Resource, and Agent. The middle-level ontology is defined by a set of modules that can be reused, extended, and adapted within the same level and with the upper-and lower-level ontologies. For instance, an Activity module can be divided into concepts such as Action, Event, Task, and Process, and properties such as has-Event and has-Performance. The lower-level ontology represents domain-dependent classes and properties. The 3LConOnt context model is built by collecting and integrating context knowledge from different ontologies to support a standardized and rich representation of context.
The instantiations of the meta-relationships are links between the instances of the meta-concepts. For example, characterizes is an instance of the relationship between the context element HostsMeet-ingNow and the entity MeetingRoom, and isRelevanTo is an instance of the relationship between the context element HostsMeetingNow and the goal Achieve  [LightsSwitchedOn]. CIM-CSS can be used to build context diagrams to model aspects of the context at the design time. A context diagram is structured from instances of domain-specific concepts, relationships, attributes, and constraints. The domain-specific concepts and relationships must satisfy their constraints or well-formedness rules (i. e. , instances of the metaconstraints). M0 (data model layer) contains concrete instances of domain-specific concepts in the running application.
Each meta-concept is represented as a class. The meta-relationships are shown as association classes. The meta-attributes are shown as attributes of classes and association classes. The diagrams also show some of the meta-constraints, particularly the multiplicity constraints of the meta-relationships that apply cardinality restrictions on instances of the meta-concepts in the context model. We defined the semantics of the meta-concepts and the meta-relationships and their meta-attributes in natural language. The association ends have names, therefore OCL expressions can be specified for the corresponding meta-elements to provide a well-formed description of the metamodel. The metamodel was designed and validated using the Papyrus tool  [58].