dataesr/scientific-paragraphs-categorization huggingface dataset:
Classes were added iteratively to identify the best model fit. A significant LMR test (p < 0. 05) indicates that the target class solution fits better with the data than a class solution with one fewer class. In a second step, the Akaike Information Criterion (AIC;  Akaike, 1987) , the Bayesian Information Criterion (BIC;  Schwarz, 1978)  and the samplesize adjusted BIC (SSA-BIC;  Sclove, 1987)  were inspected, with lower scores representing better fitting models. The entropy criterion was also examined, which indicates how accurate people are classified into their respective profiles. Entropy values range from 0 to 1; higher values indicate a better fit for a given solution  (Aldridge and Roesch, 2008). Finally, analyses of variance tests (ANOVAs) were performed to interpret the latent profile analysis solution from a theoretical point of view.
All this has been developed using the Eclipse and IBM Websphere's APIs. Once the VE's services are finally discovered and bound, a BPEL (BP Execution Language) file is generated as the result of the VE planning, previously modeled (in BPMN). This BPEL is deployed as a SOA application and put into the execution environment (a BPEL engine based on the Apache ODE). This is done interacting with the supporting infrastructure (via a REST API and Docker) and deploying the required services: the execution environment, the involved VE members' services and the own resilience services-based system. Each deployed service may have different numbers of replicas (not implemented yet in this prototype). The replication level and deployment policies can be defined by IT analysts, meaning that the resilience "level" of the own resilience system can be configured.
Execution Phase: In this phase the services-based system generated to support the VE's BP execution starts to run in the sense that the involved VE members' services are invoked by the BPEL process. The resilience system supervises the BPEL process (in the SOA layer, mainly for trying to guarantee the end-to-end QoS) as well as the involved services (in the infrastructure layer, for trying to keep up the VE operating). The resilience system does not take care of its own resilience at all. Its modules are implemented as threads and communicate with each other using synchronized queues coded in Java. The communication with the other modules uses the SOAP protocol (point-to-point) and the Apache ActiveMQ (JMS publish-subscribe communication middleware). However, the BPEL engine is also replicated and its state is synchronized (using the Infinispan framework).
MethODs study population Our study was conducted within the Norwegian Mother and Child Cohort Study (MoBa), a prospective population-based pregnancy cohort study conducted by the Norwegian Institute of Public Health. 24 Pregnant women from all over Norway were recruited during 1999-2008 and 40. 6% of the invited women consented to participate. The cohort now includes 114 500 children, 95 200 mothers and 75 200 fathers. Follow-up of the participants, after delivery, have been conducted at 6 months, 18 months, 36 months, 5 years, 7 years and 8 years. Data used in this study are based on V. 8 of the quality-assured data files, released for research in February 2014, with linkage to the Medical Birth Registry of Norway. The data collection in MoBa was licensed by the Norwegian Data Inspectorate.
Anthropometric data Weight and length/height measurements at eleven age points (6 weeks, 3 months, 6 months and 8 months and 1 year, 1. 5 years, 2 years, 3 years, 5 years, 7 years and 8 years) were reported. Up to 18 months the reported measurements were as documented in the child's health card, while for measurements from 2 years to 8 years no specification was provided. Implausible anthropometrics were identified and excluded by separately implementing three different methods: (1) by comparing with the WHO Growth Standards, as a weight-for-age or height-for-age z-score <6 SD below or >6 SD (5 SD for weight) above the mean,  26  (2) by identifying measured values with a >|5SD| difference from the predicted value as derived from the Jenss-Bayley growth curve model, and (3) by the conditional growth centiles.
Our main analysis consists of complete case analysis of 38 338 mother-child pairs for the risk of excess growth and of 50 943 mother-child pairs for all other growth outcomes. The cohort attrition due to loss to follow-up was addressed by the use of predicted anthropometric measurements. The correlation between measured and predicted anthropometrics ranged from 0. 85 to 0. 99 for weight and from 0. 95 to 0. 98 for length/ height (data not shown). In separate sensitivity analyses, (1) we excluded SGA neonates (SGA was defined as birth weight below the 10th centile, according to population curves as described by Skjaerven et al  35  ), (2) we excluded smokers during pregnancy, (3) we adjusted for birth weight; only the overweight models and not the excess growth model, because birth weight is included in the excess growth calculation formula, (4) we explored caffeine intake by three main sources (ie, from black coffee, black tea and soda drinks), (5) we excluded very high caffeine consumers and (6) we assessed the association between maternal caffeine intake and childhood overweight, using the measured instead of predicted anthropometric data to define the outcome.
Study design This was an investigator-initiated, prospective, multicenter, international prognostication study conducted in ten European ICUs (ClinicalTrials. gov Identifier: NCT02607878). Centers were selected within the Neurointensive Care section of the European Society of Intensive Care Medicine (ESICM), based on their expertise on prognostication after CA. The study protocol was approved by the Ethical Committee at each institution, and written informed consent to participate in the study was obtained from patients' next of kin or a legal surrogate, according to local ethical recommendations. Accuracy of the collected data was monitored by principal and local investigators. Study design and methodology was in accordance with the STARD guidelines for reporting diagnostic accuracy studies  [24].
DDR1 inhibition modulates specific gene networks in experimentally-induced crescentic glomerulonephritis In order to unveil the mode of action (MoA) of DDR1i, mouse kidneys from the NTS experiment were subjected to gene expression profiling (GEP, GEO Accession Number GSE104426). Unsupervised gene signature analysis and Gene Set Enrichment Analysis (GSEA) using Gene Ontology processes as input gene sets showed networks of genes related to immune response, integrin pathway activation and fibrotic processes (TGFβ pathway), all increased in the NTS group (Fig. 5a ). In contrast, tissues from mice treated with DDR1i HD showed a significant reduction in the expression of this set of genes (Fig. 5a ). This effect was also observed, albeit to a lesser extent, in the DDR1i LD group, but was not in the Imatinib group.
Noteworthy, among the weakly perturbed gene signatures, GSEA analyses revealed that the glomerular gene signature, driven by synaptopodin, podocalyxin, NPHS1 and NPHS2 genes, was clearly preserved in the DDR1i HD group, whereas no protection was observed in the case of the Imatinib treated group (Fig. 5b ). Comparison of DDR1i GSEA results with a previous datasets generated in the NTS mouse model treated with other target inhibitors revealed a consistent pattern of biological processes associated with NTS injection that were selectively reverted by DDR1i (Fig. 5c ). These include reactivation of genes involved in glomerular epithelial cell processes and deactivation of genes related to wound healing, cell matrix, cell adhesion, and motility and inflammation processes. Thus, to identify the main driver genes specifically modulated by DDR1i treatment, we conducted additional gene level analysis.
It should also be mentioned that when designing the preclinical experiments we sought to add control compounds to validate the models adopted. For that reason, the control compounds (Imatinib and Captopril, respectively used in the NTS and NEP25 models) were administered in a prevention mode to insure better protective performance and consequently better model validation. The doses of both compounds were selected using data reported in the literature for the selected models. Lack of efficacy of Imatinib was surprising since efficacy has been reported at the selected dose  [25]. Theoretically this dose has in vivo exposure similar to the DDR1 HD group based on reported in vitro DDR1 potency  [26]  and in vivo pharmacokinetic data (due the better bioavailability and reduced clearance of Imatinib compared to the DDR1 HD).
Briefly, for each sample a 5μl sample mix was prepared with 1× GE Sample Loading Reagent (Fluidigm), 1× Taqman Gene Expression Mastermix (Applied Biosystems) and diluted, pre-amplified cDNA. For the assay mix 1× Assay Loading Reagent (Fluidigm) was mixed with each of the Taqman Assays (final concentration: 10×), respectively. Priming of the Fluidigm array with control line fluid and mixing of sample and assay reagents was done with an IFC controller. qPCR was performed using the BioMark Instrument with the following cycling parameters: 95 °C for 10 min, followed by 40 cycles at 95 °C for 15 s and 60 °C for 1 min. Data was collected and analyzed with the Real-Time PCR Analysis Software (Fluidigm Corporation, CA, USA). Normalization was performed using geometric mean expression of housekeeping genes B2M, GAPDH, GUSB: ∆Cq = Cq gene of interest-Cq geomean Housekeepers.
Microarray analysis of gene expression on NTS and NEP25 mouse tissues Harvested tissues were lysed with RNeasy lysis buffer immediately after treatment with MM or DM. Lysates were sheared with QIAshredder spin columns and total RNA was extracted using RNeasy ® kits as per the manufacturer's guidelines (QIAGEN GmbH, Hilden, Germany). Starting with 1 μg total RNA per sample, reverse transcription into cDNA and subsequent steps until hybridization onto GeneChip ® oligonucleotide microarrays (Human Genome U133 plus 2. 0) and scanning were conducted using the manufacturer's kits and instructions (Affymetrix Inc. , Santa Clara, CA). Quantile normalization using the Robust Multi-array Average (RMA) method was applied to the raw individual microarray data set. The dataset was processed using the standard Bioconductor affy package  [30].
Statistics Values are expressed as mean ± SEM. Box-and-whisker plot represents quartiles. Data were analyzed using oneway analysis of variance followed by protected least significant difference Fisher's test of the Stat-view software package. Glomerular and tubular summary scores and collagen type IV quantitative morphometry data were analyzed using the Kruskal-Wallis test followed by p value adjusted Dunett's post hoc test. Data on in vivo NEP study were analysed using t-test with GraphPad Prism except for the score of tubulointerstitial damage which was analysed by Mann-Whitney U test. Data on in vitro PEC study were analysed using Dunnett's multiple comparison test with GraphPad Prism. Results with p < 0. 05 were considered statistically significant.
In this derived variable, adults and older children aged 5 to 16 years were counted as one, younger children aged 1 to 4 years were counted as half and infants were excluded  [19]. The validity of the FFQ was tested using data from a 7-day weighed dietary intake record that was kept by 30% of the men in Phase 1, results have been described previously in detail  [20, 21]. Briefly, there was a statistically significant correction between methods for all food items ranging from 0. 3 to 0. 5 (milk: 0. 42, p < 0. 001; cheese: 0. 32, p < 0. 001; cream: 0. 30, p < 0. 001; butter: 0. 45, p < 0. 001)  [20, 21]. For consumption of dairy products (g/day), the weight of milk, cheese, butter and cream were summed.
BMI and Other Covariates Height and weight were measured at were available at each examination during the 10-year follow-up  [17]. BMI data (kg/m 2 ) was calculated by weight divided by height squared  [22]. Several lifestyle and dietary factors, which could confound the analysis of dairy and BMI relationships, were either available or calculated  [16]. These included age, social class, smoking habits, leisure activity and other dietary variables, such as total energy intake and intakes of alcohol, eggs, red meat and sugar. Social class was based upon the men's most recent occupation  [23] , and was categorised into manual and non-manual; Smoking habits were defined at three levels: Never smoked, former smokers, and current smokers  [16] ; Leisure activity was categorised into yes and no for any heavy work or exercise was done in the leisure time in the last 7 days before subjects taking FFQ  [16].
Statistical Analyses In order to ensure consistency of the subject group throughout the study, the 561 men who dropped out after Phase 1 were excluded from this analysis. In addition, subjects who previously had cardiovascular disease and/or diabetes and/or cancer (n = 101) and subjects with missing confounding factor data (n = 160) were excluded. Therefore, a total 1690 men were available for the current analysis. Data were analysed using Stata version 15. 0 (STATA Corporation, College Station, TX, USA). As the semi-quantitative food frequency questionnaire has been collected at first 15 years, thus, the associations of dairy intake (total dairy, milk, cheese, cream and butter) of the baseline with baseline BMI were investigated as cross-sectional analysis, and the associations of the dairy intake (total dairy, milk, cheese, cream and butter) of the baseline with BMI changes at 5-year and 10-year as longitudinal analyses.
Subjects were categorised into quartiles for total dairy products, cheese and butter consumption and into groups as specified on FFQ for milk. Because a high proportion of subjects that did not consume cream (n = 1105), subjects were categorised in tertiles for cream consumption. Logistic regression and general linear regression were used to investigate the associations of dairy products and categorical and continuous variables, respectively. Multivariable models for continuous and discrete data were adjusted for a number of dietary and lifestyle variables. These confounding variables were selected if significantly associated in simple correlations, or if known to be correlated from previously published studies  [24]. For the cross-sectional analysis, the first multivariate-adjusted model for all analyses included the confounding factors of age (years), social class (manual and non-manual workers), alcohol intake (as ethanol, mL/week), smokers (non-smoker, current smoker, previous smoker), leisure activity (yes and no), total energy intake (MJ/day).
Methods Data from Russian Tobacco Control Law Monitoring and Evaluation Survey (June -August 2017) were analysed. 11699 randomly selected respondents from 4320 households in 5 Subjects of Russian Federation were interviewed and a stratified sample (n= 5289 aged ≥18 years) of smokers (n= 3228), former smokers (n= 1012) and never smokers (n=1049) participated in the survey.
Methods This study used data from the EPITeen Cohort, that recruited 13-year-old adolescents born in 1990 and enrolled at public and private schools of Porto, Portugal. For this analysis, we considered 1,038 participants, followed across four waves:  2003/2004, 2007/2008, 2011/2013, and 2014/2015. We modelled whether smoking behaviours were influenced by the educational attainment. We computed the odds ratio (OR) for smoking prevalence states (never smoking, experimenter, less than daily smoker, daily smoker and former smoker) and incidence transitions between these smoking states (never smoker to smoking experimenter; non-smoker, experimenter or less than daily smoker to daily smoker; non-smoker, experimenter or daily smoker to less than daily smoker; and daily or less than daily smoker to former smoker) as function of education, age, and adding interactions for age with education.
Study procedure A cross-sectional study was carried out. The entire student population at two chiropractic programs within Australian Universities (Murdoch and Macquarie universities) were used for data collection between October and November in 2016. An anonymous class-room handout questionnaire was chosen as the instrument of measurement, as it facilitated the collection of a large amount of robust data in a timely and cost-effective manner. A team consisting of the three authors were responsible for the design of the questionnaire and four 4th year chiropractic students from Murdoch University assisted with the survey administration and data collection. Recruitment, data administration, and collection of questionnaires were provided by administrative staff at the two universities. Ethics approval was granted by Murdoch University Human Research Ethics Committee (Project No 2016/118).
Variables of interest and analysis of data Data were entered and analysed in SPSS v. 22 (IBM Corp, Armonk NY, USA) after having been cleaned. Survey items were dummy variable coded and descriptive statistics generated. Outcome variables 1. Advice given to all patients -five health-related conditions 2. Excessive belief in chiropractic spinal adjustmentsseven health-related conditions The intention was to visually examine the responses of the participants on both research questions by tabulating them on a student year of study basis. Percentages were calculated for each of the responses to the two sets of questions and reported by year of study with their 95% confidence intervals (CI). These were shown in tables. We then sought to compare the responses between each year to see if there were any trends or if they differed.
Data analysis HPV16 copy number and ALB copy number were log10 transformed for convenience because a large dynamic range was observed for these values. Conversely, non-log transformed values were kept for HPV16 load as it was normalized with the cell number. Agreement between the two centers was determined using the Bland-Altman method  [34]. For each sample, the difference between respective values (Y axis) from each experiment was plotted against their means (X axis). The assumption of normality of the differences could not be considerate as valid. A non-parametric approach was used to compare methods  [35]. The limits of agreement (LoA) were estimated with the 2. 5th and 97. 5th percentiles of the differences; the average bias was estimated by the median of the differences. Statistical analyses were performed using the MEDCALC and Systat softwares.
Acknowledgements M. Saunier  was recipient of a predoctoral scholarship from the  Conseil Régional de Franche-Comté  and  J. Briolat  was a recipient of a postdoctoral fellowship from the  Canceropôle Grand-Est. We thank  E. M. de Villiers  ( DKFZ, Heidelberg, Germany ) for kindly providing us with the HPV16 plasmid.
For first order refraction Eq 10 𝛽 𝑖 = 2 • { √ 1 + 𝑛 2 𝑟 -√2 • 𝑛 𝑟 √𝑓 𝑖+ -√ 1 + 𝑛 2 𝑟 -√2 • 𝑛 𝑟 √𝑓 𝑖-} where   is particle refractive index and  ± = cos    10. More details about the PDA technique can be found in (Albrecht, Damaschke, Borys, & Tropea, 2013), for example. The size of the measurement volume and the position of the transmitting optics define the reliability of PDA measurements. Moreover, the measurements are more reliable at the sides of the spray facing transmitting optics than at the side that is away from the transmitting optics which is mainly due to the passing of the laser beam being affected by the dense spray. The choice of the aperture mask and focal length influences also directly the measurement size    analysis and the data assessment were performed separately using the software OriginPro as software.
Uncertainty Analysis The uncertainty of the air volume flowrate was ±3% and of the liquid mass flow less than 0. 5%. The images have been acquisitioned with 256 * 592 pixel resolution and 512 * 384 pixel resolution to be able to track the high-speed liquid ligaments. The uncertainty in the breakup length measurement is estimated by changing the threshold value used in the image processing algorithm. The calculation of a breakup length exhibited a standard deviation up to 28% of the mean due to the instability of the liquid atomization process. Eventually, the manufacturing tolerances of the characteristic nozzle dimensions were ± 0. 01 mm. During PDA measurements, the valid data quantity of PDA was set to 10000. A highresolution measurement is performed to reduce the uncertainty of the measurement locations.
The relative Weber number was calculated based on the characteristic droplet diameter D0. 9. Furthermore, the maximum relative velocity is measured from the averaged velocity of droplets less than 3 μm in size (as air velocity) and the minimum velocity of droplets (as liquid droplet velocity) using droplet size distribution data at x = 50 mm. The relative Weber numbers from three different conditions have been shown in Table  11. It appears that the relative droplet Weber number in all operation conditions are adequately below the secondary droplet breakup regime. Apparently, the secondary atomization will not occur beyond a spray axial location of x = 50 mm. Sphericity of Large Droplets -Since PDA measurement technique assumes that the droplets are non-spherical particles, the droplet sphericity requires to be evaluated in the x = 100 mm, the selected location for most of the current spray characterization studies.
The detection of gravitational waves with Advanced LIGO and Advanced Virgo has enabled novel tests of general relativity, including direct study of the polarization of gravitational waves. While general relativity allows for only two tensor gravitational-wave polarizations, general metric theories can additionally predict two vector and two scalar polarizations. The polarization of gravitational waves is encoded in the spectral shape of the stochastic gravitational-wave background, formed by the superposition of cosmological and individually unresolved astrophysical sources. Using data recorded by Advanced LIGO during its first observing run, we search for a stochastic background of generically polarized gravitational waves. We find no evidence for a background of any polarization, and place the first direct bounds on the contributions of vector and scalar polarizations to the stochastic background.
First, we describe the O1 data set and its initial processing. We then discuss the stochastic analysis, including the construction of Bayesian odds that indicate the nondetection of a generically polarized stochastic background in our data. Finally, we present upper limits on the joint contributions of tensor, vector, and scalar polarizations to the stochastic gravitational-wave background. Additional details and results are presented in the Supplemental Material, available online. Data. -We search Advanced LIGO data for evidence of a stochastic background, analyzing data recorded between September 18, 2015 15:00 UTC and January 12, 2016 16:00 UTC during LIGO's O1 observing run. We do not include several days of O1 data recorded prior to September 18, but this has negligible impact on our results.
We exclude times containing the binary black hole signals GW150914 and GW151226, as well as the signal candidate LVT151012. The initial data processing proceeds as in previous analyses  [46, 48]. Time-domain strain measurements from the LIGO-Hanford and LIGO-Livingston detectors are down-sampled from 16384 Hz to 4096 Hz and divided into half-overlapping 192 s segments. Each time segment is then Hann-windowed, Fourier transformed, and high-pass filtered using a 16th order Butterworth filter with a knee frequency of 11 Hz. Finally, the strain data are coarse-grained to a frequency resolution of 0. 03125 Hz and restricted to a frequency band from 20-1726 Hz. Within each segment, we compute the LIGO-Hanford and LIGO-Livingston strain auto-power spectral densities using Welch's method  [49]. Standard data quality cuts are performed in both the time and frequency domains to mitigate the effects of non-Gaussian instrumental and environmental noise  [46, 47, 50].