dataesr/scientific-paragraphs-categorization huggingface dataset:
The variants in the cohorts' VCFs have been decomposed and normalized with VT  23 (v0. 5)  prior to submission to G2P. VEP-G2P plugin. The VEP-G2P plugin is designed to utilise LGMDET data in a simple text format to identify plausibly disease-causing variants from WES or WGS data in VCF files; it enables the facile and flexible integration of extensive allele frequency data in addition to mutation consequence. The default VEP predictions and annotations are invaluable for filtering variants to find those relevant for further analysis based on consequence type and allele frequencies. The VEP-G2P plugin uses the default VEP annotations, the individual's genotype information and knowledge from the G2P datasets to find genes, which have a sufficient number of potentially deleterious variants according to their allelic requirements and are therefore likely disease causing (Fig.
resources. Gene2Phenotype https://www. ebi. ac. uk/gene2phenotype/ VEP-G2P https://www. ebi. ac. uk/gene2phenotype/g2p_vep_plugin PanelApp https://panelapp. genomicsengland. co. uk AMELIE https://AMELIE. stanford. edu DIVINE https://github. com/hwanglab/divine VVP https://github. com/Yandell-Lab/VVP-pub GAVIN http://molgenis. org/gavin.
Acknowledgements Ensembl receives majority funding from  Wellcome  (grant numbers  WT095908 ,  WT098051 ,  WT108749/Z/15/Z ). This project has received funding from  Wellcome  ( WT200990/Z/16/Z ) and the  European Molecular Biology Laboratory. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement n°634143 (MedBioinformatics). The DDD study presents independent research commissioned by the  Health Innovation Challenge Fund  [grant number  HICF-1009-003 ], a parallel funding partnership between  Wellcome  and the  Department of Health , and the  Wellcome Sanger Institute  [grant number  WT098051 ]. The views expressed in this publication are those of the author(s) and not necessarily those of Wellcome or the  Department of Health.
The study has UK Research Ethics Committee approval ( 10/H0305/83 , granted by the  Cambridge South REC , and  GEN/284/12  granted by the  Republic of Ireland REC). The research team acknowledges the support of the  National Institute for Health Research, through the Comprehensive Clinical Research Network. This study makes use of DECIPHER (http://decipher. sanger. ac. uk), which is funded by the  Wellcome. H. F. is supported by the  Wellcome Trust  [award  200990/Z/16/Z ] 'Designing, developing and delivering integrated foundations for genomic medicine'. The views expressed in this publication are those of the author(s) and not necessarily those of the  Wellcome Trust  or the Department of Health. The research team acknowledges the support of the  National Institute for Health Research, through the Comprehensive Clinical Research Network.
Funding for UK10K was provided by the  Wellcome Trust  under award WT091310. D. R. F. funded as part of the  MRC Human Genetics Unit grant  to the  University of Edinburgh. M. H. is supported by an  IGMM Translational Science Award. Generation Scotland received core support from the  Chief Scientist Office of the Scottish Government Health Directorates  [ CZD/16/6 ] and the  Scottish Funding Council  [ HR03006 ]. Genotyping of the GS:SFHS samples was carried out by the  Genetics Core Laboratory  at the  Wellcome Trust Clinical Research Facility, Edinburgh , Scotland and was funded by the  Medical Research Council UK  and the.
Data availability The DDD VCF files used in this manuscript are available (managed access) from the European Genome-Phenome Archive (EGA) under the Dataset ID EGAD00001003340 (DDD DATAFREEZE 2016-10-03: 7831 trios). The cognate DDD phenotypic and family descriptions are available as EGAD00001003350. The Generation Scotland VCF files are available (managed access) from EGA under Dataset ID EGAD00001002715. All other relevant data are available upon request.
The present study aims at modeling the real random rough surface of a microchannel with structured rough channel of known geometric parameters. The surface of the microchannel is created by sinusoidal function using MATLAB code and 2D simulation of the model is carried out with commercial software ANSYS Fluent. The height of the channel is varied from 100 to 250 µm and length of the channel is 12. 5 mm. The range of Reynolds number selected for analysis is 100 to 500 with water as the fluid medium. The roughness height is selected within the range of actual manufacturing roughness level of microchannels. The results show that channel geometry has significant influence on flow characteristics. A new non-dimensional roughness parameter β, is proposed to represent the dependence of friction factor on geometric parameters in the laminar region.
 In the present study, commercial software package ANSYS Fluent is used to simulate the flow through microchannel with water as working medium. The assumptions used for the analysis are (1) steady, laminar, incompressible flow, (2) constant physical properties and (3) no-slip condition at the wall. The numerical study of  Guo et al. (2015)  reveals that noslip velocity condition is a reasonable assumption for modeling fluid flow over the structured rough surface. The governing equations used to solve the model are, Conservation of mass:. 0 U  (3) Conservation of momentum: 2 (. ) U U P U       (4) where, U is the velocity vector, P is the pressure,  and  are density and dynamic viscosity of the fluid respectively. The boundary conditions specified are, constant uniform velocity at inlet and ambient pressure at the outlet.
MATLAB code is developed to generate the geometries with parameters specified by Table  2  in ANSYS Fluent design modular interface and is meshed with ANSYS mesh module. Figure  2  represents the schematic of the mesh geometry for channel height 250 µm and roughness height 20 µm. The grid independence study of the model is conducted for all geometries and optimum number of nodes for the analysis is obtained. Residual of 10E-12 set for convergence criteria for conservation of mass and momentum equation. First order upwind scheme along with SIMPLE algorithm for velocity pressure coupling is used to simulate the model. Table  3  depicts the details of grid independence study performed for aligned configuration for H =250 µm and e =25 µm. The mesh quality super fine is used for the analysis. The flow parameters are calculated in fully developed laminar region.
RESULTS AND DISCUSSIONS The numerical models are generated with specified geometric parameters defined in Table  2  and are meshed with commercial software ANSYS Fluent. The meshed models are simulated using prescribed boundary conditions, and the results are obtained from the converged solution. The 2D analysis of the models is carried out for different Reynolds number ranging from 100 to 500. The channels have a length of 12. 5 mm and pitch is taken as height of the channel. The inlet velocity for different configurations is found from Eq. (  7 ) and friction factor is calculated using Eq. (  8 ). Figures  5  and 6  represent the variation of pressure drop per unit length with Reynolds number for roughness height 5 µm for different channel height with phase angle 0 and π/2 respectively. It has been observed that as Reynolds number increases the pressure drop also increases for all configurations.
CONCLUSION 2D analysis of the flow of water through microchannel is performed by modeling structured sinusoidal rough surface with different geometric parameters. The simulation of the models was carried out using commercial software package ANSYS Fluent at optimum mesh quality. It is found that roughness height and Reynolds number have a significant effect on pressure drop and resistance to flow. The aligned cases of surface roughness give higher pressure drop for the same Reynolds number compared with offset configuration. It is observed that when the phase angle increases the pressure drop also increases. However, at low Reynolds number and roughness heights it is not so significant. The friction factor is also calculated at different Reynolds number and roughness heights. Based on the numerical analysis a correlation for friction factor is developed by introducing a new non-dimensional roughness parameter,.
, 2018). Over all, there is a general call for precaution from the atmospheric research community to validate the performance of such low-cost sensors  (Lewis and Edwards, 2016; WMO, 2018). Because of the potentially large datasets produced and the influence of numerous pollutant interferences, machine learning approaches such as artificial neural networks, boosted regression trees and Gaussian processes emulation are being proposed for data evaluation  (Lewis et al. , 2016; Smith et al. , 2017; Spinelle et al. , 2017; WMO, 2018; Zimmerman et al. , 2018). In this framework, this work aims to test the performance under field conditions of two custom-made types of ozone sensing devices, based on metal-oxide and electrochemical sensors. The sensors were built for H2020 project CAPTOR (www. captor-project.
Captor nodes 33 Captor nodes were tested. Captor nodes were developed by the Universitat Politecnica de Catalunya (UPC) and consist of a box with 4 SGX Sensortech MICS 2614 metal-oxide ozone sensors, 1 air temperature (T) and 1 air relative humidity (RH) sensor (DHT1 Grove). The sensors were soldered on to an electronic board circuit which is connected to an Arduino Yun platform. 3G USB connection with a global telecom operator is the main communication channel, although they can also use Wifi connection. The electronic board and communication chipsets are fixed inside the box (Fig. S1 ). Resistance values (kOhm) reflecting ozone concentrations were recorded with a 1-minute time resolution, and averaged over 30-minute periods. The raw data (kOhm) were stored in a csv file and transmitted to a central server.
Field testing Data quality is assessed as a function of sensor sensitivity, selectivity, temporal resolution, reproducibility, and stability over time  (WMO, 2018). These assessments are carried out by comparing sensor data with those from reference instrumentation, under conditions which may range from laboratory to environmental, and using different degrees of signal processing. Each of these options has different requirements and results in varying degrees of data quality (Fig. 2 ). In this work, the performance of 33 Captor and 11 Raptor nodes (132 metal-oxide ozone sensors and 11 electrochemical ozone sensors) was tested between May and October 2017 by comparison with ozone reference data from 5 local air quality monitoring stations in Spain and 4 in Italy (Fig. 1 , Tables  1  and S1   S1 ).
Data processing After the collocation period, a dataset was obtained for each node including date stamp, ozone concentration from the reference station, raw data (resistance values, kOhm) from the four ozone metal-oxide sensors (referred to as s1, s2, s3, s4), T and RH, for the Captor nodes. A similar dataset was obtained for the Raptor nodes, although with only one electrochemical ozone sensor raw data and in addition with nitrogen dioxide from the reference station and raw data from the nitrogen dioxide electrochemical sensor. Each individual sensor inside each node (in total, 132 metal-oxide and 11 electrochemical ozone sensors) was calibrated for each of the calibration periods. Each dataset was divided into two, defined as the training and the validation (or testing) sets. Prior to this division the data were shuffled in order to randomly include high and low ozone concentrations in both subsets of data.
The sensors were then calibrated with the results from the training set, and the goodness of the fit with regard to reference data was quantified by applying the calibration coefficients obtained with the training set to the validation set  (Hastie et al. , 2001). Calibration was carried out applying Multiple Linear Regression (MLR) to the training datasets of each individual ozone sensor, by regressing the raw data (in units of electrical resistance) against the simultaneous reference ozone concentrations (μg/m 3 ). Temperature and RH were included in the model as dependent variables, and also nitrogen dioxide in the case of Raptors. In order to do this, all datasets were previously normalized with respect their mean and standard deviation. MLR then produced normalized coefficients (referred to as "beta" coefficients) for each individual ozone, T and RH sensor in the Captor nodes (and ozone, nitrogen dioxide, T and RH for the Raptor nodes).
In total, four ozone regression coefficients were calculated for the Captor nodes, and 1 for Raptor nodes, for each of the calibration periods (first and second calibration). Subsequently, ozone concentrations for each individual sensor were calculated by denormalizing the data using the following equations: X O3Rnorm ¼ X O3R -μ O3R ð Þ =σ O3R ð1Þ X NO2Rnorm ¼ X NO2R -μ NO2R ð Þ =σ NO2R ð2Þ X Tnorm ¼ X T -μ T ð Þ =σ T ð3Þ X RHnorm ¼ X RH -μ RH ð Þ =σ RH ð4Þ Y O3RSnorm ¼ Y O3RS -μ O3RS ð Þ =σ RS ð5Þ Y O3RSnorm ¼ β 0 þ β O3 X O3Rnorm þ β T X Tnorm þ β RH X RHnorm ð6Þ Y O3RSnorm ¼ β 0 þ β NO2 X NO2Rnorm þ β O3 X O3Rnorm þ β T X Tnorm þ β RH X Rhnorm þ ε ð7Þ where parameters μ O3RS , μ O3R , μ NO2R , μ T and μ RH are the means of the reference station data (μg/m 3 ), resistor (metal-oxide) or voltage (electro- and RH (%) respectively.
Parameters σ O3RS , σ O3R , σ NO2R , σ T and σ RH are the standard deviation for the same parameters. Parameters X O3R , X NO2R , X T , X RH are the data measured in the low-cost sensors, Y O3RS is the reference station ozone concentration and ε is the error, Gaussian distributed with mean zero and variance σ 2 , i. e. , ε ~N(0, σ 2 ). As a result of this calibration process, four time-series of ozone concentrations were obtained for each Captor node during each of the calibration periods, and one time-series per calibration period for each Raptor node. All of these time series were then compared with the simultaneous reference ozone concentrations, and the root mean square error (RMSE) was calculated for each individual sensor during each co-located period (143 sensors; Table  S2 ). The RMSE was used to identify the best performing sensor during each calibration period, to assess potential differences in performance between both calibration periods (e.
S4  and S5 ). These findings are consistent with the literature  (Moltchanov et al. , 2015; Spinelle et al. , 2015a, b; Kizel et al. , 2018; WMO, 2018) , where the non-linear response of the metal-oxide sensors is already described. While the literature reports this limitation based on studies which apply linear models for sensor calibration (WMO, 2018; and references therein), the large dataset available in the present work (N4 month time series; 132 metal-oxide sensors) could be sufficient to evidence that this limitation could be overcome by applying non-linear regression models to calibrate metal-oxide sensors. Work is currently underway in this research direction. A preliminary evaluation of the potential of non-linear regression was carried out by comparing the results from the linear calibration (MLR; Fig.
Summary and conclusions The performance of two types of custom-made sensor nodes for ozone monitoring in a citizen science approach was assessed in this work. The sensors aimed to fulfil the purpose of awareness raising, and were thus not meant for compliance checking or air quality reporting. The sensing nodes were equipped with metal-oxide (referred to as Captor nodes, 33 units tested, 132 individual ozone sensors) and electrochemical (referred to as Raptor nodes, 11 units tested, 11 individual ozone and 11 individual NO 2 sensors) sensors. They were tested in the field by collocation at reference air quality monitoring stations, and were exposed to different ambient ozone concentrations and environmental conditions in Italy and Spain. The individual sensor datasets were calibrated applying multi-linear regression (MLR) analysis, which resulted in mean R 2 between calibrated sensor and reference data of 0.
Acknowledgements The authors would like to thank the anonymous reviewers, whose comments improved the manuscript significantly. This work was funded by  H2020  project  CAPTOR. The authors gratefully acknowledge the collaboration of the staff at the  Department of the Environment of the Generalitat de Catalunya  and at  ARPA Lombardia (Agenzia Regionale per l'Ambiente) , who kindly provided support for the deployment of the sensing nodes at reference stations and provided access to the reference data. Support is also acknowledged from AGAUR  SGR44 , the  Agencia Estatal de Investigación (AEI ;  CGL2017-82093-ERC ), and the  Spanish Ministry of Economy, Industry and Competitiveness  ( EUIN2017-85799 ).
Opportunities at the W + W -and t t threshold The safest and most sensitive way to determine the W boson and top quark masses and widths is to measure the sharp increase of the e + e -→ W + W -and e + e -→ t t cross sections at the production thresholds, at centre-of-mass energies around twice Precision on αs derived from the electroweak fit today (blue band from  [35] ) and expected at the FCC-ee (yellow band, without theoretical uncertainties and with the current theoretical uncertainties divided by a factor of four). All fits were performed with the GFitter software (http://cern. ch/gfitter/Software/index. html). the W and top masses (Fig. 1. 7). In both cases, the mass can be best determined at a quasi-fixed point where the cross section dependence on the width vanishes: √ s 162. 5 GeV for m W and 342.
All fits were performed with the GFitter software (http://cern. ch/gfitter/Software/index. html). The red ellipse is obtained from the FCC-ee measurements at the Z pole, while the blue ellipses arise from the FCC-ee direct measurements of the W and top masses. One of the two blue ellipses is centred around the central values measured today, the other is central around the values predicted by the standard model (pink line) for mH = 125. 09 GeV. The two dotted lines around the standard model prediction illustrate the uncertainty from the Z mass measurement if it were not improved at the FCC-ee. The green ellipse corresponds to the current W and top mass uncertainties from the Tevatron and the LHC, as in Figure  1. 4. The potential future improvements from the LHC are illustrated by the black dashed ellipse.
Synchrotron radiation Two independent approaches have been used for evaluating the SR from dipoles and the final focus (FF) quadrupoles, in order to define the IR beam pipe dimensions and to place masks and shielding at the correct locations. The MDISim  [199]  code is used to evaluate SR from near and far dipoles, whilst a modified version of SYNC BKG  [200]  (SYNC B KG was originally developed by Al Clark of LBNL) is used to evaluate SR from the FF quadrupoles and design the IR with masks and shielding. In this second method, macro-particles are traced through sliced magnets, MDISim combining the standard tools MAD-X, ROOT and GEANT4. See reference  [200]  for a detailed description of the two methods and studies. The upper plot of Figure  2. 21 shows a 3D MDISim display of a Gaussian positron beam at 175 GeV for 5000 particles tracked from 510 m to the IP in GEANT4 with the standard electro-magnetic processes.
Interaction-region vacuum system For most of the FCC-ee straight sections the vacuum chamber will be of the SuperKEKB type, which is similar to the arc system, except that, in the IR straight, the two beams run in separate single-yoke magnets. The SuperKEKB-type chamber profile ends immediately before the focusing doublet, where it is connected to a custom absorber, which protects, or masks, the subsequent chambers up to the IP  [297]. The ray-tracing Monte-Carlo code SYNRAD+ has been applied to the entire FCC-ee interaction region  [298]. A length of approximately 680 m around the IP has been modelled. The Molflow+ code was used to predict the pressure profile along the 680 m long interaction region based on the photon flux and planned pumping system  [298]. Special requirements exist close to the collision point, where a 500 mm long Be pipe sits at the centre of the detector, and incident synchrotron radiation must be minimised.
Once the scenario is validated by the office of the President of the Republic, which is where the regional and national stakeholders come together, the preparation of the legally required process for the public to participate in the project ("débat public") [553] can begin. The goal of this process is to develop an acceptance and appreciation of the project amongst the population, which forms the basis for a governmental clearance for the project owner to continue with the preparation. A commission defines which topics are relevant for the population and where there can be reasonable involvement in this process. The formal procedure lasts six months, but it requires about two years of preparation and is concluded with a one-year phase for its implementation. After this internal preparatory phase, the project owner in close cooperation with the public debate commission, prepares information material (documents, databases, Web contents, booklets, videos, travelling information stands, town meetings), organises the nation-wide process, employs consultants and companies experienced with this process, sets up the required technical infrastructure and finally, defines a schedule that will allow all relevant public representatives to be involved.
BFPP Bound-free pair production is one of the new types of processes that occur in relativistic collisions of atoms and ions. It is the production of an electronpositron pair with the electron not produced as a free state but as a bound state of one of the ions. Bhabha scattering The electron-positron scattering process: e + e -→ e + e -There are two leading-order Feynman diagrams contributing to this interaction: an annihilation process and a scattering process. BHWIDE A Monte Carlo event generator software simulating Bhabha-scattering, developed for linear collider detector luminosity studies. BIM Building information modeling is a process involving the generation and management of digital representations of physical and functional characteristics of places. BLM Beam loss monitor. BPM Beam position monitor.
Comsol A cross-platform, finite element analysis, solver and multiphysics simulation software developed by private company COMSOL Inc. (Sweden). It allows conventional physics-based user interfaces and coupled systems of partial differential equations. Cooper pair Two electrons that appear to "team up" in accordance with theory, BCS or other, despite the fact that they both have a negative charge and normally repel each other. Below the superconducting transition temperature, paired electrons form a condensate, a macroscopically occupied single quantum state, which flows without resistance. However, since only a small fraction of the electrons are paired, the bulk does not qualify as being a "Bose-Einstein condensate". COTS Commercial-off-the-shelf. CP Charge conjugation parity symmetry is the product of two symmetries: C for charge conjugation, which transforms a particle into its antiparticle, and P for parity, which creates the mirror image of a physical system.