dataesr/scientific-paragraphs-categorization huggingface dataset:
C. DoA observation based on measurement campaign The last observation here presented in Fig. 4  is based on a measurement campaign realised by DLR (German Aerospace Center) in 2002. The measurements aimed at providing real data to build an enhanced physical-statistical LMS channel model designed for satellite navigation purposes  [7]  and later referenced by ITU  [2]. Those measurements have been performed by using a Zeppelin for transporting the transmitter and a moving car carrying the receiver. Several environment types have been investigated such as rural, suburban or urban. From those data, various channel parameters were extracted such as the PDF (Probability Density Function) of the scatterer position, scatterer lifespan, reflected mean power of scatterers or the number of significant echoes.
Western blot analysis Total proteins were extracted, in Tris-EDTA buffer (Tris NaCl pH = 7. 4, 0. 5% NP-40, 0. 5% deoxycholic acid and 5 mM EDTA) containing proteases inhibitors cocktail. Protein concentrations were determined with Bradford colorimetric method. 50 μg were resolved on NuPAGE ® Novex 4-16% Bis-Tris gradient gel (Invitrogen), transferred on PVDF membrane. After blocking in PBSmilk solution (5%), membranes were incubated with specific primary antibodies, followed by incubation with corresponding HRP-conjugated secondary antibodies. Revelation was performed using ECL Plus (Pierce). The antibodies used were: anti-SDHA (Abcam, Ab14715), anti-SDHB (Sigma, HPA002868), anti-SNAI1 (Abcam, Ab85931), and anti-TCF3 (Abcam, Ab69999).
Wound scratch assay Cells were seeded at confluence in 6-well dishes. Twenty-four hours after plating, a wound was made using a 10 μL tip and cells were allowed to migrate for 10 h. Relative cell migration was established by calculating the closure index, based on the analysis of at least 25 pictures for each time point and condition, using ImageJ software, as described  [24]. The experiments were performed in triplicates.
ACKNOWLEDGMENTS AND FUNDING We would like to thank " La Fondation ARC " ( DOC20130606831 ), and "Le  Cancéropôle Ile-de-France " ( 2013-2-AD-02-UP 5-1 ), which allowed this project to be performed thanks to its support. We are also grateful to by  La Ligue Nationale Contre le Cancer ,  Cancéropôle Ile-de-France ,  INCa  and  Pair Mélanome , which supported MD and LL.
MULTIWAVELENGTH OBSERVATIONS The Lockman Hole is a particularly appealing candidate for a multiwavelength study because there have been many observations made of it in a wide variety of wavelengths. These data can be combined to form a very thorough study encompassing objects with redshifts out as far as z ≈ 5. We have assembled a multiwavength dataset, detailed below, with which to study the host galaxies of µJy radio sources in this field. Figure  1  shows the spatial overlap of the datasets described below. 2. 1. SERVS data SERVS uses deep Spitzer data taken during the postcryogenic Spitzer Space Telescope mission over the course of 1400 hours using the Infrared Array Camera (IRAC) at wavelengths of 3. 6 and 4. 5 µm, and detects objects out to z ≈ 5. The survey covers a total of 18 deg 2 to a detection limit of ≈ 2 µJy (AB = 23.
SWIRE data The Spitzer Wide-area Infrared Extragalactic (SWIRE) survey observed the Lockman Hole during its cryogenic mission using the Infrared Array Camera (IRAC) and the Multiband Imaging Photometer for Spitzer (MIPS). IRAC observed the Lockman Hole using all four channels, 3. 6 µm, 4. 5 µm, 5. 8 µm, and 8. 0 µm; and MIPS observed the Lockman Hole with all three of its channels, 24 µm, 70 µm, and 160 µm. The SWIRE catalogs reach depths of ≈ 10 µJy (AB ≈ 21. 4) at 3. 6 and 4. 5 µm. SWIRE detected galaxies out to z ≈ 3  (Lonsdale et al. 2003). Details of the data release used may be found in the SWIRE data release 2 document 1.
Simulated samples Samples of simulated Monte Carlo (MC) signal and background events are used to guide the analysis optimization and to estimate signal yields. Several event generators are used to produce the MC events. The samples of VBF and GF signal processes are generated using the nextto-leading order perturbative QCD program POWHEG 1. 0  [21] , interfaced to TAUOLA 2. 7  [22]  and PYTHIA 6. 4. 26  [23]  for the hadronization process and modeling of the underlying event (UE). The most recent PYTHIA 6 Z2* tune is derived from the Z1 tune  [24] , which uses the CTEQ5L parton distribution functions (PDF), whereas Z2* adopts CTEQ6L  [25]. The signal samples are generated using only H → bb decays, for five mass hypotheses: m H = 115, 120, 125, 130, and 135 GeV. Background samples of QCD multijet, Z +jets, W+jets, and tt events are simulated using leadingorder (LO) MADGRAPH 5.
1. 3. 2  [26]  interfaced with PYTHIA. The single top quark background samples are produced using POWHEG, interfaced with TAUOLA and PYTHIA. The default set of PDF used with POWHEG samples is CT10  [27] , while the LO CTEQ6L1 set  [25]  is used for other samples. The production cross sections for W+jets and Z +jets are rescaled to next-tonext-to-leading-order (NNLO) cross sections calculated using the FEWZ 3. 1 program  [28] [29] [30]. The tt and single top quark samples are also rescaled to their cross sections based on NNLO calculations  [31, 32]. To accurately simulate the LHC luminosity conditions during data taking, additional simulated pp interactions overlapping in the same or neighboring bunch crossings of the main interaction, denoted as pileup, are added to the simulated events with a multiplicity distribution that matches the one in the data.
Triggers The data used for this analysis were collected using two different trigger strategies that result in two different data samples for analysis. First, a set of dedicated trigger event selection (paths) was specifically designed and deployed for the VBF qqH → qqbb signal search, both for the L1 trigger and the HLT, and operated during the full 2012 data taking. Then, a more general trigger was employed for the larger part of the 2012 data taking that targeted VBF signatures in general. The first (nominal) set of triggers collected the larger fraction of the signal events, while the second trigger supplemented the search with events that failed the stringent nominaltrigger requirements. The integrated luminosity collected with the first set of triggers was 19. 8 fb -1 , while for the second trigger it was 18.
All the jets found in an event are ordered according to their p T , and the four leading ones are considered as the most probable b jet and VBF-tagging jet candidates. The distinction between the two jet types is done by the means of a multivariate discriminant that, in addition to the b-tag values and the b-tag ordering, takes into account the η values and the η ordering. In the VBF H → bb signal simulation it is found that the b jets have higher b-tag values and are more central in η than the VBF-tagging jets. A boosted decision tree (BDT), implemented with the TMVA package  [41] , is trained on simulated signal events using the discriminating variables previously described and its output is used as a b-jet likelihood score; out of the four leading jets the two with the highest score are identified as the b jets, while the other two are identified as the VBF-tagging jets.
Z boson signal vs. background discrimination As discussed above, the selection of events is based on set A, with the additional requirement of having at least one CSVM jet; the tightening of the b-tagging condition was found to improve the expected sensitivity. A Fisher discriminant (FD)  [41]  is implemented with the TMVA package and trained to discriminate between the Z +jets signal and the background. For this purpose, seven variables are used: (i) the absolute η difference |∆η qq | of the VBF jets; (ii) the absolute η of the b-jet system |η bb |; (iii) the CSV value of the jet with highest CSV value (with best b tag); and (iv)-(vii) the QGL values of the four leading jets. Due to the very small correlations between the variables, the FD performs almost as well as more advanced, nonlinear discriminators.
The shift of QGL only affects the shape of the discriminant, and thus the distribution of signal events in the analysis categories. The trigger uncertainty is estimated by propagating the uncertainty in the data vs. MC simulation scale factor for the efficiency. This is achieved by convolving the two-dimensional efficiency scale factor with the signal distribution. As a result, the uncertainty in the signal yield ranges from 1% to 6% for the VBF process, and from 5% to 20% for the GF. Theoretical uncertainties affect the signal simulation. First, the uncertainty due to PDFs and strong coupling constant α S variation is computed to be 2. 8% (VBF) and 7. 5% (GF)  [52]. A residual uncertainty from these sources is estimated for the particular kinematical phase space of the search: following the PDF4LHC prescription  [53, 54]  the PDF and α S uncertainty ranges from 2% to 5%, while the renormalization and factorization scale variations in the signal simulation induce an uncertainty of 1% to 5% in the analysis categories, on top of a global cross section uncertainty of 0.
ACKNOWLEDGMENTS We are grateful to the  Clinifutur group , the entire Biochemistry Department team at the  Félix Guyon Hospital ,  Reunion Island ,  Johny Serveaux  (from the anatomical pathology department of the  Félix Guyon Hospital, Reunion Island ), and  Jérôme Triay  for the illustration. We thank all patients who consented to the collection of tissue samples and thus made this study possible. This study conforms to the  Declaration of Helsinki.
Background: Evidence relating childhood cancer to high birthweight is derived primarily from registry and casecontrol studies. We aimed to investigate this association, exploring the potential modifying roles of age at diagnosis and maternal anthropometrics, using prospectively collected data from the International Childhood Cancer Cohort Consortium. Methods: We pooled data on infant and parental characteristics and cancer incidence from six geographically and temporally diverse member cohorts [the Avon Longitudinal Study of Parents and Children (UK), the Collaborative Perinatal Project (USA), the Danish National Birth Cohort (Denmark), the Jerusalem Perinatal Study (Israel), the Norwegian Mother and Child Cohort Study (Norway), and the Tasmanian Infant Health Survey (Australia)]. Birthweight metrics included a continuous measure, deciles, and categories (≥4.
The I4C I4C was established in 2005 to address the lack of prospective, adequately powered studies investigating the aetiology of childhood cancer. The initial collaboration involved 11 international birth/infant cohorts ranging from ∼11 000 to 100 000 participants at various stages of recruitment or follow-up. 27 Additional cohorts have since joined. This report involves the pooling of data from (alphabetically): the Avon Longitudinal Study of Parents and Children (ALSPAC, UK), the Collaborative Perinatal Project (CPP, USA), the Danish National Birth Cohort (DNBC, Denmark), the Jerusalem Perinatal Study (JPS, Israel), the Norwegian Mother and Child Cohort Study (MoBa, Norway), and the Tasmanian Infant Health Survey (TIHS, Australia). Data from all cohorts were transferred to the I4C International Data Coordinating Center (IDCC) at the Murdoch Childrens Research Institute (Australia).
Study design and population We performed a pooled cohort study, identifying and including all cancer cases from 380 000 livebirths in the six participating cohorts. The dataset includes all livebirths for ALSPAC, CPP, and TIHS. As per Consortium agreements with the I4C, a random 10% sample of non-cases from MoBa and DNBC rather than the entire cohorts were included. Offspring from the JPS cohort were included if their GA was recorded (from mothers' pre-or postpartum interviews), comprising all those born 1974-1976, and a subset born 1964-1973 (total n = 20 944). The pooled dataset thus comprises 112 781 livebirths, after excluding multiple births (due to their high rate of low birthweight)  28  and children with Down syndrome (due to their particularly high risk of childhood leukaemia)  29  (Table  1 ).
Missing data Missing covariate data among the cohorts ranged from 0% to nearly 40% (see Table  1 ). In order to construct multivariable models with maximal sets of covariates, we used chained multiple imputation to impute 20 complete datasets. 31 Cox regression was performed separately on each imputed dataset and the results pooled into a single multiple imputation result. We used truncated linear regression to impute missing continuous variables (paternal age, maternal height, pregnancy weight gain, and pre-pregnancy BMI) where the imputations are limited to lower and upper boundaries set at the minimum and maximum values of non-missing observations. Logistic regression was used to impute missing dichotomous variables (first born and maternal smoking). Variables used to impute missing data were maternal age, GA, birthweight, sex of child, and cohort.
Statistical analysis We report hazard ratios (HR) and 95% confidence intervals (CI) from Cox proportional hazards regression models. All models were stratified by cohort. Model 1 was unadjusted (birthweight was the only independent variable). Model 2 adjusted for GA and child's sex. Model 3 was a parsimonious multivariable model adjusted for GA, child's sex, as well as different combinations of covariates for each cancer outcome, chosen as follows: Starting with all confounders in the model, we removed variables one at a time (beginning with the variable with the largest P-value, so long as that variable no longer changed the coefficients for birthweight or the other covariates in the model by >15% in either direction) using the multiple imputation dataset. Once removed, a variable could not re-enter the model.
Schoenfeld residuals were used to assess the proportional hazard assumption with all covariates entered into the model, first, on the original data containing missing observations and, second, after imputing the missing data. Proportionality assumptions were met in both. 32 We assessed the linearity of continuous variables in the log-hazard using the method of fractional polynomials. 33 Paternal age, determined to be non-linear, was transformed to a quadratic expression. To assess the possibility of effect modification by maternal anthropometric measurements, we introduced interaction terms of birthweight × maternal pre-pregnancy BMI using a cut-off of normal or underweight (<25 kg/m 2 ) vs. overweight (≥25 kg/ m 2 ). 34 In separate models, we introduced an interaction term of birthweight × pregnancy weight gain, dichotomised according to the Institute of Medicine recommendation (based on a healthy BMI) of ≤16 kg vs.
Comment In this pooled analysis, we provide evidence from prospectively collected data that birthweight, adjusted for GA and sex, is positively associated with increased cancer and leukaemia risks in children. In addition, higher birthweight is particularly associated with nonleukaemia cancer diagnosed at or after age 3 years. Although maternal obesity is associated with high birthweight,  22, 23  heightened cancer risks in high birthweight offspring of overweight women or those with excessive pregnancy weight gain were not observed in our exploratory analyses. For every kilogram increment in birthweight, the HR for cancer was 1. 26, similar to that reported in large registry-based studies. The Norwegian Medical Birth and Cancer Registries reported a HR of 1. 23 (1. 14-1. 32)/kg birthweight increase adjusting for GA,  14  with no modification by age at diagnosis.
08 (UK) and 1. 11 (US), the latter adjusted for GA. Reference group for birthweight ≥4. 0 kg is birthweight <4. 0 kg. f The reference group for the top 10% of each cohort is the bottom 90% of each cohort. g For continuous birthweight, the hazard ratio represents a 1 kg increase in birthweight. In our dataset, high birthweight was strongly associated with non-leukaemia cancers diagnosed at or after the age of 3 years. However, leukaemia risks were not modified by age at diagnosis, echoing findings from a large meta-analysis. 5 The time-varying pattern of birthweight effects may be due to the fact that many cancer subtypes vary by age of onset and their relation with birthweight may vary. As the I4C cohorts mature, more detailed analyses on specific solid tumours and lymphomas, including those which are usually diagnosed in older children will be possible.
Few investigators have explored the effects of maternal anthropometrics on the birthweightleukaemia or birthweight-cancer association. McLaughlin and colleagues,  26  in a case-cohort study, noted an association between birthweight and leukaemia only among infants whose mothers weighed <80 kg. That study lacked data on maternal height, thus overweight per se, as measured by BMI, was not addressed. They observed an effect of pregnancy weight gain on ALL risk (RR 1. 31), using a cut-off of 14. 1 kg; however, no interaction with birthweight was noted. Most women in the I4C cohorts were nonobese, with pregnancy weight gain within the recommended range. However, given worldwide trends in maternal adiposity,  38  this relation deserves further scrutiny, particularly as our analysis was limited by small numbers.
40 ur study's strengths include prospectively collected data from a wide variety of geographic and temporal settings. All birth and maternal characteristics were ascertained at birth or during pregnancy, minimising recall bias. Most of the contributing cohorts were representative of their respective source populations and cancer cases were derived from the same populations as non-cases. This contrasts with case-control studies, in which (because of low response rates and consequent selection bias) controls may differ substantially from the case population, including in their birthweight distribution. 41 iven that the correlation between birthweight and birthweight-adjusted-for-GA is not high (kappa = 0. 45),  16  accounting for GA, as we did, is important. In our analysis, adjustment for GA generally resulted in improved precision of the HR estimates.
Cancer, leukaemia, ALL, and non-leukaemia cancer hazard ratios in the pooled dataset using a time-varying coefficient for birthweight across two time periods (age at diagnosis <3 vs. ≥3 years old) adjusting for sex and gestational age Stratified Cox proportional hazard regression with a time-varying coefficient for birthweight based on an indicator function for time defined at the age of diagnosis cut-point adjusted for gestational age and sex of the child. Birthweight and childhood cancer: I4C Cohort Consortium 343.
Acknowledgements This work was supported by:  NIH (NCI, NICHD) -USA; National Children's Study -USA ; the  Childrens Cancer Centre Foundation -Australia; Bluey Day Foundation -Australia ;  Baxter Family Foundation -Australia ; The  Rotary Club of North Brighton -Australia ;  Tour de Cure -Australia ; Private philanthropic donations -Australia; and  Murdoch Children's Research Institution  ( M1300049 ),  Australia. The  UK Medical Research Council  and the  Wellcome Trust  (Grant ref:  092731 ) and the  University of Bristol  provide core support for ALSPAC. The  Maria Ascoli Foundation , Jerusalem, Israel, provided support for data pooling of the JPS.
14 0. 77, 1. 68 1. 18 0. 80, 1. 75 1. 14 0. 75, 1. 71 Continuous birthweight, kg g Cancer (377) 1. 10 0. 91, 1. 31 1. 26 1. 02, 1. 54 1. 26 1. 02, 1. 56 Leukaemia (115) 1. 25 0. 89, 1. 75 1. 42 0. 98, 2. 06 1. 35 0. 90, 2. 02 ALL (98) 1. 16 0. 81, 1. 67 1. 29 0. 85, 1. 93 1. 29 0. 83, 1. 99 Non-leukaemia (262) 1. 04 0. 83, 1. 28 1. 19 0. 93, 1. 52 1. 18 0. 91, 1. 54 pregnancy weight gain, and any maternal smoking during pregnancy. • Non-leukaemia cancers: birthweight hazard ratio is adjusted for gestational age, child's sex, paternal age (rescaled as quadratic), total pregnancy weight gain, first born, and maternal pre-pregnancy BMI. a Hazard ratios (95% CI) from a stratified Cox proportional hazard regression using all observations in the pooled dataset. In models 2 and 3 missing observations are imputed using a chained multiple imputation method.
Literature search We searched MEDLINE, EMBASE, and Web of Knowledge for publications reporting agestratified dengue serological surveys. Studies published before 1980 were not included in the analysis as we were interested in contemporary dengue transmission. Studies reporting age-specific seroprevalence for at least 5 age groups were included and categorised according to the assay type used. Studies reporting less than 5 age groups were excluded as these studies tended to have wide age groups where the mean seroprevalence did not accurately reflect the variability in seroprevalence within that age group. Data were extracted from published datasets where age-specific seroprevalence was tested by IgG ELISAs, inhibition ELISAs (IEs) or PRNTs. IgG and IE data are both non-serotype specific and we refer to them interchangeably.
Estimating the force of infection (λ) In the context of dengue, seroprevalence measures obtained with IgG ELISAs only give an indication of whether an individual has 'ever' been infected and do not differentiate between infecting serotypes or identify the number of past infections. Since infection with one serotype only provides homologous immunity, a seropositive individual may still be susceptible to secondary heterotypic infection  [39]. We fitted the single cross-sectional IgG datasets using a simple catalytic model (model A). The model assumes a constant infection hazard λ, with infection causing individuals of age a to move from a seronegative x(a) to a seropositive z(a) state  [19]. Since some datasets appeared to have declining seroprevalence with age, we extended model A by assuming that protection could decay with age at a rate α (model B).
Whenever yearly cross-sectional IgG data were available from the same location, these data were fitted using a time-varying catalytic model (model C) which allowed estimation of the periodicity (T), seasonal amplitude (δ) and within-year timing (θ) of dengue outbreaks, and the critical age (A crit ) and scale (S) at which exposure levels change. See the Supporting Information for full details (S1 Text). In order to fit serotype-specific PRNT data, we applied the multi-strain catalytic model developed by Ferguson et al. [40]. Different model variants were assessed, which explored different assumptions on serotype interactions. Model D1 assumed no serotype-interaction. Model D2 assumed that cross-protection or enhancement did not vary by serotype. Model D3 assumed that the magnitude of cross-protection or enhancement varied by the primary infection serotype.
Last, model D4 assumed that the magnitude of cross-protection or enhancement depended on the serotype of the secondary infection. Moreover, for comparison purposes, we fitted model A to PRNT data, having defined individuals with PRNT titres below the detection limit for all four dengue serotypes as seronegative and individuals with at least one PRNT titre over the detection limit as seropositive. Since assays differed between surveys, here the detection limit also varied from study to study. We defined a beta-binomial likelihood for models A-C and a multinomial likelihood for models D1-D4. Models were fitted to the data using the Metropolis-Hasting Markov Chain Monte Carlo (M-H MCMC) algorithm using the R Statistical Package (version 3. 1. 0, R Development Core Team, Vienna, Austria)  [41].