dataesr/scientific-paragraphs-categorization huggingface dataset:
5) METAMODELING OF CONTEXT MANIPULATION The meta-elements shown in Figure  6  and described below are used to formalize the features required to manipulate the context, from acquisition to dissemination. First, the meta-elements that define the constituent parts of the context are indicated by Context Element and Property. The Context Element captures all atomic and composite data, Second, the meta-elements that define the acquisition of context. The Context Source indicates the sources of the context such as physical sensors (RFID tag, microphone, or GPS), logical/soft sensors (e. g. , digital clock), online profiles, or databases. The Acquisition meta-relationship indicates a contextual association between the Context Element and Context Source. The updateFrequency meta-attribute specifies the temporal rate at which the context data are acquired (Never, Occasionally, Frequently, or Continually).
IV. MODELING TOOL DEVELOPMENT In this section, we outline the process through which an Eclipse-based graphical editor (a tool support for the CIM-CSS approach) was developed. The tool can be downloaded from https://github. com/metamodeler/CIM-CSS/. The main frameworks used to build the tool were the Eclipse Modeling Framework (EMF)  [63]  and the Graphical Modeling Framework (GMF)  [64]. A model-driven development (MDD) approach  [65]  based on the EMF and GMF was used to generate automatically the code and to develop the tool. The formal CIM metamodel (CIM3) was used to construct a domain model of the tool. The development steps followed to implement the graphical concrete syntax of the tool are shown in Figure  10  and outlined below. Step 1 (Create a Domain Model): In the first step, the context identification and management metamodel (CIM3) was used as a foundation on which to create a domain model using the EMF.
We loaded the metamodel to create the CIM3. ecore domain model in XMI specification  [66]. This Ecore model contained the platform independent model (PIM) information. Step 2 (Derive an EMF Generator Model): The CIM3. genmodel EMF generator model was derived from the Ecore model using the GMF dashboard. This model contained the platform specific model (PSM) information and code generation options for the context model domain elements. Step 3 (Derive a Tooling Definition Model): The CIM3. gmftool tooling definition model was derived from the Ecore model using the GMF dashboard. This model defined the elements of the diagram palette, as shown in the tool bar in Figure  11. Step 4 (Derive a Graphical Definition Model): The CIM3. gmfgraph graphical definition model was derived from the Ecore model using the GMF dashboard.
Not only can this mechanism allow the designer to consider and model varying contexts in which the system will operate, it can also allow the designer to specify multiple fusion and different interpretations that can be acquired through various sources and smart objects. The resulting context model can be saved in a database for query at run-time in order to reflect sensor data recognition and perceive the current situation in which the system needs to adapt its functionalities. Indeed, the instantiations of the CIM-CSS language can provide more expressiveness and extensive semantics for modeling context information than UML class diagrams, as shown in the right-hand part of Figure  12.
Using CIM-CSS, it is possible to define the context sources, the acquisition methods (sensed, derived, or profiled), and the relationships between contexts (refinement or causal). CIM-CSS therefore allows the complexity of the context aspect to be addressed effectively. Understandability: The CIM-CSS palette allows contexts to be expressed visually to facilitate the heterogeneity of the modeled data to be conveyed with little intellectual direction. Accuracy: The CIM-CSS tool allows designed models to be validated using the integrity, consistency, and completeness constraints imposed on the metamodel to ensure the modeled system context is represented accurately. Predictiveness: Integrating formal analysis methods into CIM-CSS makes it possible to infer future contexts from current observable states and profiled states and from historical contexts.
Cost: CIM-CSS allows the context to be specified in a model that is inexpensive to construct and maintain because the original context diagrams can easily be exported, stored, and reasoned on in a model repository for the EMF, which supports the purpose of Models@Run. Time, e. g. , XML Metadata Interchange (XMI)  [66] , EMFStore, and Connected Data Objects (CDO)  [70]. It is possible to generate Java code embedding the context specification automatically using model-to-model and model-to-text transformations  [53]. Unambiguity: The semantics of the CIM-CSS language allows the context to be specified in a more precise and explicit manner than general purpose modeling languages (e. g. , class diagrams). This allows system engineers and user representatives to achieve a common and organized understanding of the aspects of context.
VI. IMPLICATIONS AND LIMITATIONS CIM-CSS provides a lightweight multi-disciplinary method for identifying the relevant context, and a formal multilayered model and tool for specifying and managing the contextual information in CSSs. An intelligent CSS, through its sensing and inferring capabilities, observes internal and external states, assesses their influences, and collects the information to determine the actual context that holds, and adapts accordingly at run-time. CIM-CSS has an impact on the advance of context-sensitive systems design by implying (1) minimal ambiguity and uncertainty by eliciting and consolidating data and knowledge from multiple sources and different stakeholders, (2) reduced development complexity and enhanced software modularity by separating the context processing and management logic from the application logic i.
e. , separation of concerns, and (3) higher reliability by specifying multiple variants and fusion when interpreting the context information, which can support the system to use contextual alternatives when a state cannot be assessed, a sensor data is inaccurate, or a sensor failure occurs. CIM-CSS models are outcomes of stakeholders' elicitation and analysts' evaluation of how human-machine interactions are influenced by (explicit and/or implicit) sensed data and context information. Therefore, CIM-CSS is specification-based rather than learning-based, and it supports the construction of models based on the knowledge of system experts. Consequently, CIM-CSS can be used to model several types of tacit facts (e. g. , known unknowns, unknown knowns, and unknown unknowns  [35] ). It can be used to infer context elements from the first and second categories using logical reasoning on context models, but it cannot be used to infer elements from the third category (i.
| Study area and data collection Using nets fired from helicopters, we captured and sampled 149 caribou of the RG herd (90 females; 59 males) between 2000 and 2014 and 251 caribou of the RAF herd (159 females; 92 males) between 1996 and 2016, following the guidelines from the Canadian Council on Animal Care. Individuals were captured up to five times over the study period, but most (85. 5%) were captured only once. We collected muscle biopsies (n = 361), hairs (n = 27) or blood (n = 12) samples on each animal and froze all samples except for some biopsies (n = 150) that were stored in ethanol (≥70%) at room temperature. We used EDTA to preserve blood samples and prevent them from coagulating. From the 400 individuals, 222 (≥2 years old) were fitted with tracking collars using different satellite networks (Argos, Iridium, Globalstar), allowing the assessment of annual survival from capture date to 2017.
| Populations structure To assess the genetic differentiation between the two herds, we filtered the data set described in 'ddRADSeq data processing' by keeping only a SNP by locus and using r =. 8 in Populations to avoid artificially increasing or decreasing differentiation between populations. This subset included 6,384 SNPs typed for 362 individuals (10. 44% missing data). Prior to analysis, input files were exported in the appropriate format using radiator (v. 0. 0. 5; Gosselin, 2017). We removed from the data set known related individuals (e. g. mothercalf pairs identified in the field) or related individuals detected with a genetic relatedness matrix obtained using the 'snpgdsGRM' function implemented in the R SNPRelate package  (Zheng et al. , 2012). This resulted in a data set including 344 individuals.
| Assessment of genomic inbreeding indices We computed individual sMLH with the function 'sMLH' implemented in the package inbreedR  (Stoffel et al. , 2016)  in R (v. 3. 4. 0; R Core Team, 2017). sMLH accounts for differences in the identity of the loci genotyped in all individuals and corrects the heterozygosity score for the mean heterozygosity observed at the typed loci in the rest of the population. Here, sMLH was computed for individuals from the two herds simultaneously. Then, using the 'r2_hf' function of the InbreedR package  (Stoffel et al. , 2016) , we computed the expected correlation between sMLH and inbreeding (F) with 100 bootstraps. We computed F grm (F III ) in GCTA  (Yang et al. , 2011) , using the command '-ibc'. In addition, to ensure the reliability of our data sets to properly detect HFCs, we assessed identity disequilibrium (covariance of heterozygosity among loci of a given individual;  Weir & Cockerham, 1973; Szulkin et al.
, 2016). Because the data sets used to assess the effect of sMLH on body mass and annual survival did not include the same individuals, HHC and g 2 were computed separately for both data sets. Although HHC and g 2 are useful tools to detect identity disequilibrium, it was shown that HFCs could still occur and be caused by inbreeding depression in populations in which identity disequilibrium could not be detected  (Kardos, Allendorf, & Luikart, 2014; Miller & Coltman, 2014).
| Relationships between genomic inbreeding indices and performance We used linear mixed-effects models with the function 'lmer' implemented in the R package lme4  (Bates, Mächler, Bolker, & Walker, 2015)  to assess the relationship between genomic inbreeding indices and body mass. Body mass was log-transformed to improve homogeneity of variances, and sMLH and F grm were centred to improve convergence of models. To investigate the possible effect of genomic inbreeding indices in interaction with sex, age or herd, we adopted a model selection approach. We first considered a baseline model that included age, √age, sex, herd and month of capture as fixed-effect variables, as these factors are known to influence caribou body mass  (Couturier, Côté, Huot, & Otto, 2009a; Couturier et al. , 2009b; Parker, 1981).
ΔAICc and AICc weights (ω i ) were computed with the R package AICcmodavg  (Mazerolle, 2017). We used the same approach to test for the effect of genomic inbreeding indices on annual survival in adults (>2 years). Annual survival was coded as a binary variable with a value of 1 if the individual was alive during a given year and 0 if the individual was dead. Malfunctioning collars were censored starting from the point of malfunction. We fitted generalized mixed-effects models with a binomial distributed error (logit link function), using the 'glmer' function implemented in the package lme4  (Bates et al. , 2015). The baseline model included age, age 2 , herd and sex  (Couturier, Otto, Côté, Luther, & Mahoney, 2010; Loison, Festa-Bianchet, Gaillard, Jorgenson, & Jullien, 1999)  as fixed-effect variables and year and individual identity as random factors.
| CON CLUS ION In this study, we tested for the relationship between sMLH or F grm , both proxies of inbreeding, and body mass and survival in caribou. Using a large number of markers (~22k SNPs), we did not detect an association of the genomic inbreeding indices with body mass or annual survival. Furthermore, sMLH and F grm remained stable over the period monitored, which suggests that the rapid and intense demographic decline of the herds did not cause inbreeding depression in those populations. Additionally, we found a strong bias in individual heterozygosity associated with depth of sequencing coverage, which, once corrected for, reduced identity disequilibrium in our data set and may have reduced our ability to detect HFCs. This result highlights that depth of coverage should be taken into consideration when assessing heterozygosity and that, more generally, some questions remain regarding suitable filtration of SNP data, especially in HFC studies.
1 (a). A Tescan Mira XMH SEM equipped with an Oxford Instruments X-Max 80 EDS detector was used for imaging and analysis of tiles' surface chemistries using 5, 10 and 20 kV accelerating voltages. The Casino software  [15]  was applied to provide a rough estimate of the corresponding interaction volumes from which characteristic X-Rays are produced, and therefore an estimate of the depth of the oxides present. Further image analysis was undertaken using Image-J to gain an estimate of the average oxide island size and distribution  [16]. Raman Spectroscopy was untaken on both the limiter and dump plate tiles in back-scattering geometry, using a Horiba-Jobin Yvon LabRAM apparatus (×100 objective, 0. 9 numerical aperture, laser wavelength of 514. 4 nm and laser power of ∼1 mWµm -2 ). The Raman Map discussed in this report was taken with a lateral resolution of 500 nm and is focused on one area, which lead to the discovery of the Be-OD bond.
Samples were transferred to a Jeol-ARM200F TEM (cold-FEG, operated at 200 kV). Scanning TEM (STEM) was used predominantly and in conjunction with Energy Dispersive X-ray (EDX) in a Jeol Centurio detector and Electron Energy Loss Spectroscopy (EELS) in a Gatan Quantim GIF. Due to the limitations of EDX in detecting beryllium, EELS can be used to good effect to understand the stoichiometry's present between Be and O with future work continuing to investigate the presence of H isotopes in the low loss region of the spectra. Thermal desorption spectroscopy (TDS) data were collected on samples from similar locations along the tiles. The results are collected on a Hiden TPD workstation type 640,100. The process back heats a sample with a 10 °C/min thermal ramp under vacuum. The gaseous species desorbed from the surface are measured via an analytical HAL/3F RC 1051-9 PIC quadrupole mass spectrometer at line-of-sight orientation for direct detection.
Experimental results and discussion SEM studies of the beryllium surfaces were initially undertaken at lower magnifications to survey and compare the level of oxidation at different positions around JET, prior to TEM analysis. SEM voltages of 5, 10 and 20 kV were used alongside the Casino software to model the depth of the interaction volume, which corresponded to 250 nm, 400 nm and 2000 nm respectively. Included in Table  1  is the comparison among the reactor positions of a 20 kV SEM beam corresponding to ∼2 µm depth. Via image analysis of EDX oxide maps, a qualitative comparison of oxide number densities which extend to 2 µm depths is given in Table  1. Table  1  compares the outer and inner limiters with the dump plates. The outer limiters experienced lower operational temperatures than the inner limiters, whilst the dump plates experienced the highest temperatures and melt events (Fig.
Finally we included mother-child pairs with complete clinical examination data and study-questionnaire, and available blood samples (n = 1396). Local ethical committees approved the studies that were conducted according to the guidelines laid down in the Declaration of Helsinki. The ethical committees for each cohort were the following: BIB: Bradford Teaching Hospitals NHS Foundation Trust, EDEN: Agence nationale de sécurité du médicament et des produits de santé, INMA: Comité Ético de Inverticación Clínica Parc de Salut MAR, KANC: LIETUVOS BIOETIKOS KOMITETAS, MoBa: Regional komité for medisinsk og helsefaglig forskningsetikk, Rhea: Ethical committee of the general university hospital of Heraklion, Crete. Informed consent was obtained from a parent and/or legal guardian of all participants in the study.
Blood collection, DNA extraction and white blood cells proportion. Buffy coat was collected in EDTA tubes. Leukocyte DNA was extracted using the Chemagen kit (Perkin Elmer) in batches of 12 samples. Samples were extracted by cohort and ultimately DNA concentration was determined in a NanoDrop 1000 UV-Vis Spectrophotometer (ThermoScientific) and with Quant-iT ™ PicoGreen ® dsDNA Assay Kit (Life Technologies). Additionally, in samples of 1146 children white blood cell type proportions (CD4+ and CD8+ T-cells, natural killer (NK) cells, monocytes, eosinophils, neutrophils, and B-cells) were estimated from raw methylation data using Houseman algorithm 57 implemented in the minfi R package  58  and the Reinius reference panel  59. Average relative telomere length measurement. Average relative telomere length was measured by a modified qPCR protocol as described previously  60.
Telomere and single copy-gene reaction mixture and PCR cycles used can be found in Martens et al. 31. All measurements were performed in triplicate on a 7900HT Fast Real-Time PCR System (Applied Biosystems) in a 384-well format. On each run, a 6-point serial dilution of pooled DNA was run to assess PCR efficiency as well as eight inter-run calibrators to account for the inter-run variability. Relative telomere lengths were calculated using qBase software (Biogazelle, Zwijnaarde, Belgium) and were expressed as the ratio of telomere copy number to single-copy gene number (T/S) relative to the average T/S ratio of the entire sample set. We achieved CV's within triplicates of the telomere runs, single-copy gene runs, and T/S ratios of 0. 84%, 0. 43%, and 6. 4%, respectively. Obesity parameters.
Statistical analysis. Continuous data were checked for normality. Average relative telomere lengths showed a skewed distribution and were log10 transformed to approximate a normal distribution. The obesity parameters were treated as both continuous (z-score unit) and categorical variables in models evaluating associations with telomere length. Generalized additive models (GAMs) were used to assess the linearity of the associations between the obesity parameters (maternal pre-pregnancy BMI, child's BMI, waist circumference, skinfold thickness and fat mass) and telomere length. If the p-value for gain was higher than 0. 05, the model was considered linear. Multivariable linear mixed models with a random cohort effect were used to address the different associations. All analyses were adjusted for a priori chosen covariates including maternal education, maternal age at birth, child's age in days, sex, qPCR batch (2 categories), child's ethnicity, maternal smoking during pregnancy, and white blood cell type proportions (% of natural killer cells, B-cell, CD4T, CD8T, eosinophils, mononuclear cells, neutrophils).
We performed multiple imputation using chained equations to account for missing values of potential confounding variables, 20 datasets were generated and pooled for analyses (Supplemental Materials). In a first sensitivity analysis we used the complete data instead of the imputed data for our initial analyses. In additional analyses we included in the child BMI model maternal pre-pregnancy BMI as a covariate. Similarly, we included child BMI as a covariate in the maternal pre-pregnancy BMI model. Further, we stratified our analysis of the associations between child BMI and leukocyte telomere length by maternal BMI group. We also stratified our BMI analyses by sex. Finally, the association between child weight and telomere length and the association between child height and telomere length were included.
Acknowledgements This work was supported by the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no 308333 -the  HELIX  project. INMA data collection was supported by grant C/ from the  Instituto de Salud Carlos III. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under Grant Agreement No  774548. Tim S. Nawrot  was funded by the  EU Program "Ideas"  ( ERC-2012-StG 310898 ). ISGlobal is a member of the  CERCA Programme, Generalitat de Catalunya. We are grateful to all the participating families in the six countries who took part in this study.
Similarly, Zhao et al. [18]  achieved a good agreement between numerical results and experimental data by studying the impact of openings on the smoke flow during a fire in a confined enclosure. Yang et al. [19]  simulated smoke flow along a tunnel in fire as a function of the extraction rate using the CFD software PHOENICS. They observed a similarity between analytical and numerical results. Similar to the results of Vauquelin  [20] , they showed that backflow of the smoke in the downstream part could occur when the extraction speed was too low. Weng et al. [21]  performed numerical simulations on the smoke flow in a subway tunnel fire equipped with an extraction system. Their results revealed that the temperature and level of smoke stratification under the tunnel ceiling in the longitudinal direction increased with the HRR.
Governing equations The simulations were carried out using the CFD code Fire Dynamics Simulator (FDS) version 6. 6. 0  [26]. It solves the Navier-Stokes equations using an explicit finite difference scheme. As a CFD code, FDS models the thermally driven flow with an emphasis on smoke and heat transport. It is a large eddy simulation (LES) model using a uniform mesh, and has parallel computing capability using Message-Passing Interface (MPI)  [28]. Reactive flows are modelled using a turbulence model based on a LES approach, a combustion model based on the Eddy Dissipation Concept (EDC) and a thermal radiation model based on a gray gas model for the radiation absorption coefficient  [27]   [29]. The models are based on the numerical solving of Navier-Stokes equations. These equations calculate mass, momentum, species and energy conservation  [27] : 𝜕𝜌 𝜕𝑡 + 𝜕 𝜕𝑥 𝑗 (𝜌𝑢 𝑗 ) = 0 (1) Eq.
Methods This was a retrospective analysis of the French national hospital discharge database. All hospital stays with a diagnostic code for AUB and an appropriate surgical procedure code between 2009 and 2015 inclusive were identified, concerning 109,884 women overall. Outcomes were compared between second generation procedures (2G surgery), first-generation procedures (1G surgery), curettage and hysterectomy. Clinical outcomes were treatment failure and complications during the follow-up period. Costs were attributed using standard French hospital tariffs.
Ethics The study was conducted in accordance with relevant international and French regulatory requirements. Since this was a retrospective study of an anonymised database and had no influence on patient care, ethics committee approval was not required. Use of the PMSI-MCO database for this type of study has been approved by the French national data protection agency (CNIL; annual authorisation #1419102 v7-2015-111111-56-18 / order M14N056 and M14L056).