dataesr/scientific-paragraphs-categorization huggingface dataset:
TSBE , 𝑚𝑚. (6) To determine TSBE area, it is necessary we obtained the data on its width of the profile and foam filling. For this reason, it is necessary to determine the height and width of TSBE. Based on the size of the glazing and the thickness of the profile, they can be determined by the Eq. (  7 ) and Eq. (  8 ) ℎ TSBE = ℎ GL + 2⋅𝑙𝑙 PROF , 𝑚𝑚, (7) where lPROF -profile width, m [47]; 𝑙𝑙 TSBE = 𝑙𝑙 GL + 2⋅𝑙𝑙 PROF , 𝑚𝑚. (8) The foam filling width is determined from  [46] , by the Eq. (  9 )   The TSBE area is determined by the Eq. (  10 )  [31]. 𝑆𝑆 TSBE = 𝑆𝑆 GL + 2⋅𝑙𝑙⋅(𝑙𝑙 TSBE + ℎ TSBE + 2⋅𝑙𝑙), 𝑚𝑚 2 ; ( 10 ) where l -TSBE opaque part width  [46] , m; The TSBE opaque part width is determined by the Eq. (  11 )  [46]. 𝑙𝑙 = 𝑙𝑙 PROF + 𝑙𝑙 F. F , 𝑚𝑚; (11) The calculations were carried out according to the algorithm shown in the flow chart (Fig.
The calculations were carried out in the Relux program according to the global illumination algorithm (Radiosity). The error did not exceed 7%  [58]. According to  [59] , the calculation error in the Relux program does not exceed ±10%. This is quite enough for various kinds of lighting calculations. The methodology for calculating DF at RP is given  [31]. The pollution and absorption coefficients of TSBE are taken to equal 0. 9. The results of DF value at RP calculation, which will be achieved when establishing the TSBE area, is shown in Table  2 , and are given in Table  5. Analysis of the Table  5  data showed that for DF values of 1. 9% and 2. 0%, in rooms with dimensions of 4×5; 5×5, and 6×5, the TSBE area equal 9. 78 m 2 , 10. 72 m 2 and 12. 19 m 2 , respectively. Since the height of the premises is assumed to be 3 m, and according to expression  (5) , for these cases it will be 3.
Tested Three-Dimensional Warp Interlock Fabrics The five different structures 3DWIFs were manufactured with high-molecular-weight polyethylene (HMWPE) yarns (Honeywell, Charlotte, North Carolina, USA) and with the same warp (10 ends/cm) and weft densities (40 picks/cm). The linear density is tested and characterized by the skein method based on ASTM D 1907/D 1907M. The density is obtained from the manufacturer. The tensile properties of HMWPE yarns with 50 twists per meter were performed by MTS Criterion Testing Systems. The main physical properties of HMWPE yarn are noted in Table  1. The definition of the structure is shown in Table  2. All cross-section weft yarns views and 3D views of 3DWIFs are designed by Wisetex software. In 3DWIF, the binding step (X) represents the number of weft yarns between two interlacing points of the binding warp yarn located in the same layer, and the binding depth (Y) represents the number of weft yarns layers linked with the binding warp yarn.
Materials and Methods: We searched databases, including PubMed, SID, Magiran, Google Scholar, and Iranmedex using the following keywords in English: H. pylori, tooth decay, oral infection, dental infection, gingival infection and periodontal diseases. Data was analyzed using a meta-analysis and random effect model. Heterogeneity of studies was assessed using the I 2 index, and data was finally analyzed with STATA (Version 11. 2).
Study Selection In the initial search, 125 studies probably related to periodontal diseases were selected. Seventy studies were excluded because they were duplicate papers (papers extracted by two researchers with identical titles, authors, and journal). From the remaining 55 studies, 29 cases were excluded after reading the summary and the full text due to non-relevance of the topic, and the lack of criteria and low quality. Of the remaining 26 studies, 16 cases were excluded. Finally, 10 studies were reviewed (Fig. 1 ). Their data could be extracted and studies whose data could not be extracted (for various reasons such as they were duplicate papers (papers extracted by two researchers with identical titles, authors, and journal), some cases were excluded after reading the summary and the full text due to nonrelevance of the topic, and the lack of criteria and low quality, etc.
Risk of Bias Across Studies According to publication linear form figure, in this diagram, the impact of the first study on other studies will be examined. In this study, by deleting the first study, its impressive impact will be on the overall results (Fig. 3 ). In this study, we used different periodontal parameters such as Periodontal Pocket Depth (PPD), the Plaque Index (PI), the Gingival Index (GI), and Clinical Attachment Loss (CAL). In other words, we have various periodontal parameters extracted from a study and we consider them as one parameter as periodontal disease parameters. Eventually, we extracted a variety of data from one study, each of which was related to one of the periodontal diseases, but all of them considered as periodontal diseases (Table  1 ).
Context. Asteroseismic measurements of the internal rotation of subgiants and red giants all show the need for invoking a more efficient transport of angular momentum than theoretically predicted. Constraints on the core rotation rate are available starting from the base of the red giant branch (RGB) and we are still lacking information on the internal rotation of less evolved subgiants. Aims. We identify two young Kepler subgiants, KIC8524425 and KIC5955122, whose mixed modes are clearly split by rotation. We aim to probe their internal rotation profile and assess the efficiency of the angular momentum transport during this phase of the evolution. Methods. Using the full Kepler data set, we extracted the mode frequencies and rotational splittings for the two stars using a Bayesian approach.
The detection of these modes with Kepler data has made it possible to precisely measure the core rotation. It was thus shown that the core spins up when stars reach the base of the red-giant-branch (RGB) and that a significant radial differential rotation develops  (Deheuvels et al. 2014, hereafter D14). This behavior is expected, considering that the core contracts while the envelope expends during this phase, but the seismically measured core rotation rates are in fact much lower than would be predicted if the layers were conserving their specific AM. As stars ascend the RGB, the core rotation rate remains roughly constant  (Beck et al. 2012 , Deheuvels et al. 2012 , Mosser et al. 2012 , Gehan et al. 2018) , despite the ongoing core contraction. All these observations point to the existence of an efficient redistribution of AM in subgiants and red giants.
Secondly, subgiants are hotter than red giants. It is now established from both observational and theoretical point of view that the damping of the oscillation modes increases very steeply with the effective temperature of the star  (Appourchaux et al. 2012a , Belkacem et al. 2012). Consequently, the oscillation modes of subgiants have shorter lifetimes, and thus larger line widths in the oscillation spectra. This makes the measurement of the mode splittings more complicated and statistical tests are needed to establish the significance of the rotational splittings. Finally, the oscillation modes excited in subgiants have frequencies well above the Nyquist frequency of the longcadence data of Kepler (278 µHz) and short-cadence data (integration time of 58. 84876 s) are thus required. This drastically limits the number of targets available in the Kepler catalog compared to the case of more evolved red giants.
Seismic analysis A seismic analysis of the two targets has already been performed by  Appourchaux et al. (2012b)  using the Kepler data available at that time (quarters Q5-Q7, corresponding to 275 days of data). They applied statistical tests to detect oscillation modes and extracted their frequencies from the oscillation spectra. The frequency resolution at that time was not sufficient to measure individual rotational splittings of the modes. We here performed a new analysis of the oscillation spectra of the two stars using the complete Kepler dataset with the aim to (i) increase the precision of the mode frequency estimates compared to  Appourchaux et al. (2012a)  and (ii) to measure the rotational splittings of the mixed modes. The power spectra of the two stars are shown in the shape of échelle diagrams in Fig.
Acknowledgements. We thank the anonymous referee for comments that improved the clarity of the paper. S. D. and J. B. acknowledge support from  the  project  BEAMING ANR-18-CE31-0001  of the  French National Research Agency (ANR)  and from the  Centre National d'Etudes Spatiales (CNES). P. E. has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  833925 , project  STAREX ). JdH acknowledges support by the  European Research Council  ( ERC-2016-CO Grant 724560 ).
Analyses Anti-GnRH antibody titres in serum samples were quantified using an indirect ELISA technique outlined in Miller et al. [39] , Levy et al. [40]  and Bender et al. [41] , adapted for the laboratory rat using rabbit anti-rat IgG, followed by goat anti-rabbit IgG conjugated with horseradish peroxidase (Sigma Chemical Co. , St. Louis, MO, USA). A post-treatment serum sample was considered positive for anti-GnRH antibodies if the optical density value was greater than the mean optical density plus two standard deviations of the control (pre-treatment sample) values for each respective dilution. Differences in the proportions of females giving birth and litter sizes were analysed using Fisher's exact, Mann-Whitney U, and Kruskal-Wallis tests in SPSS for Windows (Version 25, IBM Corp. , 2013).
One-tailed tests were used when the direction of the effect could be predicted, otherwise two-tailed tests were employed. Differences in anti-GnRH antibody titre levels between experimental groups were examined by ordinal logistic regression using the package "MASS"  [42]  in R version 3. 4. 3 [43]  (odds ratio (OR) and their 95% confidence intervals (CI) are reported). An alpha level of 0. 05 was used for all statistical tests. A receiver operating characteristic (ROC) analysis was used to quantify how accurately the anti-GnRH antibody titre level can be used to discriminate between two states, "fertile" and "infertile". A ROC curve was created based on the trade-off between sensitivity and specificity at different cut-off points (or thresholds) of a diagnostic test with a continuous outcome (our measure of anti-GnRH antibody titre level in a female) compared to a gold standard test (whether the female was later observed to give birth).
The search terms used for these database searches were reported in that study. Based on data from only two eligible studies involving 102 participants, an unenhanced CT tumour attenuation value of more than 10 Hounsfield units (HU) was shown to have high sensitivity (100%) but poor specificity (72%) for the detection of ACC. Studies assessing ¹⁸F-FDG PET, MRI with chemical shift analysis, and CT with contrast washout had even smaller sample sizes (n=25-75) and generated sensitivity and specificity estimates with very wide CIs. In 2019 we updated our previous search with a PubMed search using the same search terms and covering the dates Sept 1, 2015-Jan 31, 2019. We found no studies that altered the conclusions of the 2016 systematic review and meta-analysis, nor any newly published metaanalyses addressing the same question.
We asked centres to recruit prospectively consecutive eligible individuals willing to participate (ie, non-selective recruitment), from Jan 17, 2011, to July 15, 2016. All participating centres obtained local ethics approval for recording of pseudonymised and standardised data in the ENSAT registry relevant to the study (demographic characteristics, method of tumour identification, tumour diameter and imaging characteristics, endocrine testing results, clinical and radiological follow-up data, surgery details, and histopathology data) and for collection and use of participant-related biomaterial (appendix pp 4-5). All participants provided written informed consent before inclusion.
Urine steroid metabolomics testing Enrolled participants collected a 24 h urine sample that was used for multisteroid profiling by liquid chromatography-tandem mass spectrometry (LC-MS/MS), with quantification of 15 urinary steroid metabolites (appendix p 19) and application of a machine-learning algorithm. The algorithm was developed by applying generalised matrix learning vector quantisation  18  to steroid excretion data from a retrospective cohort of 139 patients with adrenal masses (40 ACC and 99 adrenocortical adenoma [ACA]) measured retrospectively by use of the LC-MS/ MS method used in this study (appendix pp 7-9). Based on the distances of the entirety of the steroid metabolome of ACC and ACA prototypes, the generalised matrix learning vector quantisation classifier provides a test outcome score.
Statistical analysis We aimed to include 2000 participants and expected an ACC rate of 5%, based on the results of our proof-ofprinciple study (appendix p 4), and loss to follow-up of 10%. 18 We calculated that observing 100 ACC cases would provide 95% sensitivity with a 95% CI range of less than 10% and more than 99% power to detect a difference of 3% in specificity (87% vs 90%) between standard imaging protocols and urine steroid metabolomics at the 5% significance level. Characteristics of participants are reported for each type of tumour assessed (ACC, other malignant tumours, ACA, and other benign tumours), with data presented as median (IQR) for categorical results or number (%) for continuous data. For each diagnostic test (maximum tumour diameter [≥4 cm vs <4], imaging characteristics [positive vs negative], or urine steroid metabolomics [low, medium, or high risk of ACC]), we computed the percentage of ACC cases with each test result (giving sensitivity for a positive result in the binary tests); the percentage of non-ACC cases with each test result (giving specificity for a negative result in the binary tests); and the likelihood ratio for each test result.
Results Between Jan 17, 2011, and July 15, 2016, 2169 eligible participants were recruited from the 21 participating centres and provided 24 h urine samples (figure  1 , appendix p 14). However, review of the data showed that only 14 of the 21 centres had a high median annual recruitment rate (33 participants per year) and a median centre-specific proportion of ACC of 3•9%. These 14 centres recruited a total of 2068 participants. By contrast, the seven remaining centres jointly recruited only 101 participants (median annual recruitment rate 6•7), but the median proportion of ACC cases was 35% (appendix p 21). Investigators at these seven sites confirmed that they had recruited selectively, favouring large and suspicious masses, compared with nonselective consecutive recruitment at the other 14 centres.
Contributors WA wrote the study protocol, which was edited by IB, AJS, MB, and JJD. IB did the literature search. WA supervised the conduct of the study. IB, AET, VC, CJ, LCG, CB, HEI, CHLS, CJD-P, KLang, ST, MM, AR, TD, IDP, TK, AP, TGP, GR, MH, LM, MAG, ASa, KLangton, DE, MA, SP, AK, ASp, CLR, BS, DAD, RPS, IT, TB, MR, SB-S, RAF, LC, HRH, GE, MCD, GAU, MI, AT, MT, MQ, DK, MF, FB, UA, DAV, MWO'R, and WFY contributed to data collection. CJD-P, KL, ST, MM, AR, TD, IDP, TK, AP, TGP, GR, MH, LM, MAG, ASa, KLangton, DE, MA, SP, AK, ASp, CLR, BS, DAD, RPS, IT, TB, MR, SB-S, RAF, LC, HRH, GE, MCD, GAU, MI, AT, MT, MQ, DK, MF, FB, UA, DAV, MWO'R, and WFY collected samples. AET, VC, CJ, LCG, CB, HEI, CHLS, and WA did the steroid data analysis and interpretation. MB created the machine learning-based classification algorithm.
Acknowledgments This work was supported by the  European Commission  under the  Seventh Framework Programme  ( FP7/2007-13 , grant agreement  259735 , ENSAT-CANCER, awarded to GE, MF, FB, and WA), the  UK Medical Research Council UK (Strategic Biomarker  Grant  G0801473  awarded to WA), the  Claire Khan Trust Fund at University Hospitals Birmingham Charities  (project grant to WA), the  Mayo Clinic Foundation for Medical Education and Research (Mayo Scholarship  awarded to IB), and the  Wellcome Trust  ( Clinical Research Training Fellowship   WT101671  awarded to VC). This research was also partly supported by the  National Institute of Diabetes and Digestive and Kidney Diseases of the US National Institutes of Health (NIH) , under award  K23DK121888  (IB). JJD is a  National Institute for Health Research (NIHR) Senior Investigator and AJS, JJD , and WA receive support from the  NIHR Birmingham Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation Trust  and the  University of Birmingham  ( BRC-1215-20009 ).
MATERIALS AND METHODS The present study was based on data obtained from the MEDITS experimental surveys conducted in the northern Mediterranean Sea. The MEDITS survey is carried out annually following a standardized protocol in various areas of the Mediterranean Sea, and its primary goal is to monitor changes in the abundance of demersal megafauna species. The survey design is based on a depth-stratified random sampling scheme that considers five depth strata: 10-50, 50-100, 100-200, 200-500 and 500-800 m. At each station (haul), the total number and biomass by species were estimated (see  Bertrand et al. 2002  and the MEDITS Handbook  [Anonymous 2017 ] for further details on survey design and sampling methodology). Data on species biomass by haul used in the present analysis cover all surveys carried out during the 1994-2015 period in 13 Geographic Statistical Areas (GSAs) of the Mediterranean Sea  (5, 7, 8, 9, 10, 11, 16, 17, 18, 19, 20, 22, 23) , as determined by General Fisheries Commission for the Mediterranean (Fig.
Given the lack of temporal correlation, a generalized additive modelling (GAM) approach  (Hastie and Tibshirani 1990)  was used to examine the effects of year (by GSA) and GSA on MTC and MTL separately. Hence, two GAM models were built, having the general form: Index = a + s(Year, by = GSA) + GSA + e where Index is either MTC or MTL, a is the intercept, s indicates the smoothing function of the corresponding independent variable and e is a random error term. The smoothing function used for the continuous variable (year) was a penalized cubic regression spline, and model fitting was accomplished using the mgcv library  (Wood 2006)  under the R language environment. The procedure automatically selects the degree of smoothing based on the generalized cross-validation score, which is a proxy for the model predictive performance.
However, to avoid dubious relationships, the model was constrained to be at maximum a quartic relationship. Hence, the maximum degrees of freedom for each smoothing term was set to 4 (i. e. k=5 in the GAM formulation). A Gaussian distribution model accompanied by an identity link function were used, as preliminary analysis of the diagnostic residual plots of several models indicated that they provided a pertinent fit to the data. In order to be able to contrast the spatial MTC pattern with temperature variations, data on bottom temperatures per haul recorded during the MEDITS surveys, collected by temperature sensors attached to the net, were subsequently analysed. After a preliminary analysis of the diagnostic residual plots of the models, the effects of year, depth and GSA on bottom temperature were examined by means of a GAM assuming a Gamma distribution model and a log link function.
Overall, French consumers express various forms of behavioral engagement in acting against climate change. For this study, we used convenience sampling to target real consumers. We collected data in France during the second semester of 2015. The second author and trained graduate students collected the data. The respondents came from cities all over France and were relatives, acquaintances or unknown individuals intercepted in malls or streets. The participants were 18 years or older, and trained interviewers administered the questionnaires face to face. Of the 516 respondents, 280 were men (54. 3%), and 236 were women (45. 7%). The respondents' ages ranged from 18 to 82 years (mean [M] = 32; standard deviation [SD] = 13. 5). Table  2  details the sample characteristics.
Structural model and hypothesis testing We used AMOS 24 to test our research hypotheses. We tested three alternative models to assess the superiority of our model and followed prior procedures in well-established works  (Bajaj, Robins, & Pande, 2016; Diallo & Seck, 2018) : the hypothesized structural model, a direct model, and a full mediation model. The hypothesized structural model (Model 1) fit the data well overall (χ 2 = 126. 53, df = 48, p < 0. 001; RMSEA = 0. 056, CFI = 0. 98, TLI = 0. 98, and χ 2 /df = 2. 64). The predictors explained substantial amounts of the variance in the dependent variable (behavioral intentions) (R 2 = 0. 57)  (Chin, 1998). A chi-square difference test with the direct Model 2 2 (i. e. , no mediation, all factors directly affect behavioral intentions in the struggle against climate change) indicated a better fit for Model 1 (∆χ2(df) = 108.
01; Confidence Interval CI: [0. 082; 0. 194]) as well as the relationship between NAEs and behavioral intentions (γ = 0. 07; p < 0. 05; CI: [0. 022; 0. 131]). These are partial mediations because PAEs and NAEs have a direct effect on behavioral intentions. These results support H2a and H2b. To assess the moderating effect of behavioral engagement, we did not follow Preacher and Hayes's (2008) procedures using SPSS because of the qualitative nature of the moderator. Instead, we used multiple group analyses in AMOS 24. We created a score for behavioral engagement and distinguished three groups: low/non-engaged, engaged, and highly engaged. The model offered a good fit (χ 2 = 313. 53, df = 144, p < 0. 001, RMSEA = 0. 048, CFI = 0. 96, TLI = 0. 94, χ 2 /df = 2. 18). Next, we compared a model with no constraints on the structural parameters across the samples with a constrained model in which the structural parameters were constrained to equality  (Diallo & Seck, 2018; Lertwannawit & Mandhachitara, 2012).
IL-1β/β-actin was amplified using 35 cycles of PCR at 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 30 s, and the final extension was performed at 72 °C for 7 min. TNF-α/β-actin was amplified using 35 cycles of PCR at 94 °C for 30 s, 54 °C for 30 s, and 72 °C for 30 s, with the final extension performed at 72 °C for 10 min. The PCR products were separated on a 1. 5% stained agarose gel. They were assayed using a Gel Doc ImageQuant LAS 500 System (GE Healthcare Bio-Sciences AB, Uppsala, Sweden). β-actin was used for normalizing all the target genes. The data were analyzed using ImageJ software (National Institutes of Health, Bethesda, MD, USA).
Ser 536 (1:500; Santa Cruz), PGE synthase 2 (A-2) (1:1000; Cell Signaling Technology), and COX-2 (D5H5) rabbit mAb (1:1000; Santa Cruz) antibodies at 4 °C in TBST with 3% skim milk. After washing three times with TBST, the membranes were incubated with horseradish peroxidase-conjugated anti-rabbit (1:1000; Enzo Life Sciences, Plymouth Meeting, PA, USA) and anti-mouse (1:1000; Santa Cruz) antibodies for 1 h at room temperature. The membranes were subsequently washed three times with TBST and the bands were visualized using ECL detection reagents (Promega, Madison, WI, USA). The expression of α-tubulin was used as a control. The target-protein bands were normalized relative to the control band with ImageJ software.
Articulation of vowels with postalveolar and labial constrictions In this section, we use ultrasound and video records to characterize the vowels with postalveolar and labial constrictions in greater detail, and in particular to demonstrate the existence of the extra supralaryngeal constrictions described above. This data confirms the initial impression that /i/ and /¨/ assimilate in place of articulation to preceding postalveolar consonants. We provide evidence that the fricativized allophones of /i/ and /¨/ exhibit lingual or labial constrictions not normally expected in vowel sounds, which canonically have unobstructed central channels and laminar airflow.