dataesr/scientific-paragraphs-categorization huggingface dataset:
Computational modelling Two different models were constructed to examine different hypotheses on how TAF accumulate in cells. The models were encoded in the Systems Biology Markup Language (SBML) modelling standard  (Hucka et al, 2003)  with the Python SBML shorthand tool (Wilkinson, 2011). Model simulations were carried out in COPASI  (Hoops et al, 2006) , and the results were analysed and plotted in R using ggplot2  (Wickham, 2009). We used stochastic simulation (Gillespie direct method) in order to be able to account for the variability in the experimental data. The models were deposited in BioModels  (Chelliah et al, 2015)  and assigned the identifiers MODEL1608250000 and MODEL1608250001.
Acknowledgements This work was funded by  BBSRC  grants  BB/H022384/1  and  BB/K017314/1  to JFP,  BHF  project grant  PG/15/85/31744  to GR and JFP,  NIH  grant  AG013925  and funding from the  Connor Group and Noaber Foundation  to JLK, and grant from  Région Occitanie, France  and a grant from foundation Cariplo  2014-0672 , to JMP, DM and VDE. CJP and JFP were funded by the  MRC-Arthritis Research UK Centre for Integrated research into Musculoskeletal Ageing (CIMA)  grant  MR/K0063121/1. We thank  Prof. Roger Greenberg  for kindly providing us with the TRF1-FOKI plasmid. We thank  Gabriele Saretzki  for expert assistance in TRAP assays,  Thomas von Zglinicki  for providing mice tissues and  Glyn Nelson  for assistance in live-cell microscopy. We thank  Gaelle Legube  for technical advice regarding the use of the I-PpoI endonuclease.
Data accessibility The RNA-seq data from this publication have been submitted to the GEO database (http://www. ncbi. nlm. nih. gov/geo/), accession number: GSE95822. The models were deposited in BioModels and assigned the identifiers MODEL1608250000 and MODEL1608250001 (https://www. ebi. ac. uk/biomodels-main/). Expanded View for this article is available online.
Some German samples were collected during equine infectious anemia surveillance. Samples from Ukraine were collected during a scientific surveillance study conducted on glanders. No further demographic data are available. There were no adverse reactions recorded for blood collection. All serum samples were dispensed in 96 deep-well plates for each test and stored at -20˚C to avoid multiple freezing-thawing cycles. Serological testing, as described in the following sections, was done independent of knowledge of infection status of the donor.
Results Data were analyzed from a short surround-suppression paradigm recorded as part of a larger study of 28 PD patients and 30 healthy age-matched controls. In this paradigm, subjects passively viewed a series of 2. 4-s long trials in which four "foreground" grating stimuli flickered on-and-off at 25Hz, embedded within a static (non-flickering) surround grating pattern, with both the foreground (FG) and surround (SS) contrasts varying randomly from trial to trial. Background (no-stimulus) Frequency spectrum profiles. Since differences in background spectral EEG amplitude have previously been reported in PD  [31] [32] [33] [34]  and would impact the estimation of ssVEP amplitudes driven by the external flicker-stimulation, it was important first to characterize such background spectral differences.
Materials and Methods Subjects. EEG data were collected on the surround suppression paradigm as part of a larger multi-test study of 28 patients with PD (7 women) and 30 healthy control subjects (20 women), which adopted inclusive recruitment criteria that only required subjects to be right-handed and to show no signs of dementia. As described below, all subjects also received a complete neuropsychological assessment. Subjects were examined by a movement disorder neurologist using the standardized rating scales for cognitive and motor dysfunction: the Unified Parkinson Disease Rating Scale, UPDRS, and the Modified Hoehn and Yahr scales. All EEG recordings were conducted while patients were on their usual dopaminergic medication schedule. All subjects had normal or corrected-to-normal vision.
EEG recordings. During each block, high-density electroencephalography data (HD EEG, Electrical Geodesics Inc. , Eugene, OR) were recorded from 256 electrodes at a sample rate of 1,000Hz. We applied an online notch filter at 60Hz. Impedances were stable below 50kΩ. We selected 183 channels on the scalp and downsampled to 500Hz. Eye gaze was monitored continually using an EyeLink1000 (SR-Research) eye tracker to ensure gaze was within 1 degree of visual angle around the fixation spot during stimulus presentation.
Data analysis. Analysis was carried out offline using in-house Matlab scripts in conjunction with topographic mapping functions of EEGLAB, an open source toolbox for EEG analysis. 49 dividual epochs were extracted in the interval [-600:2600]ms relative to the visual stimulus onset at 0ms. Blinks were detected using an in-house blink detection script based on the frontal electrodes with a threshold of 60μV. Trial epochs were rejected if they contained a blink or an artifact greater than 300μV within the 2240-ms window beginning 160ms after stimulus onset, avoiding most of the early component of the transient visual evoked response. We baselinecorrected each trial epoch by subtracting the mean value over the points [-50:0]ms and rereferenced all channels to average mastoids by simple subtraction.
Then the complex coefficients , ,  and  can be calculated by data fitting of the wave pattern at each frequency, for both the forward and backward case. Thus the scattering matrix is determined by 𝑆 = 𝑡 𝑟 𝑟 𝑡 = 𝛾 𝛾 𝛽 𝛽 • 𝛼 𝛼 𝛿 𝛿 where subscripts "" and "" correspond to forward and backward incident waves, respectively. Simulation. All full-wave simulations performed in the paper are obtained using COMSOL Multiphysics. To compute the scattering matrix , perfect matched layers (PML) are used on both end of the beam, and plane waves are generated in the forward and backward directions respectively to generate the field pattern. Then similar processes as experiments are used to calculate the scattering matrix. It is noted that the.
The microphotographs of the powder particles were taken using a TM-3000 Tabletop Scanning Electron Microscope Hitachi (Tokyo, Japan). At least 200 particles of every sample powder were outlined in the microphotographs using the MultiScan program (Computer Scanning Systems, Warsaw, Poland), and the sieve diameters of the particles were provided automatically by the software. The glass transitions of the powders were measured using differential scanning calorimetry with a DSC Q200 (TA Instruments, New Castle, USA). The calibration was carried out on indium, an empty pan was used as a reference. 4-5 mg samples were weighed into pressure pans which were then heated according to the following program: (1) 20°C 2 min -1 , (2) 20-150°C -10°C min -1 , (3) 150°C 1 min -1 , (4) 150-20°C -10°C min -1 , (5) 20°C 1 min -1 , (6) 20-150°C - 10°C min -1.
The glass transition of the first heating cycle T g1 and of the second cycle T g2 , were evaluated using Universal Analysis software (TA Instruments, New Castle, USA). Powders reconstituted in water were evaluated using the duo-trio method. The analysis was carried out by a sixperson qualified staff of the "Pollena-Aroma" company. The results were statistically analysed using STATIS-TICA v. 13 software (Dell Inc. , USA). A one-way analysis of variance and Tukey HSD test were used (at significance level α = 0. 05). In the case of a failure to fulfil the assumptions of variation connected with the heterogeneity between the compared groups, a t-Student test (in the case of the exclusion of one group) or Kruskala-Wallis test (in the case of the exclusion of more than one group) was used. In order to analyse the interrelationships between the determined parameters of the multivariate statistical methods: a principal component analysis (PCA), and a hierarchical cluster analysis (HCA) were also performed.
hundreds per year globally) is extremely small relative to other gear types (e. g. , gillnet, trawl and pelagic longline,  Lewison and Crowder 2007; Gilman 2011 ). When captured, more than 90% are released alive  (Williams et al. 2009; Amandeet al. 2008 Amandeet al. , 2010;; Gilman 2011; Dagorn et al. 2013; Hall and Roman 2013). Sea turtle mortality also occurs from entanglement in FAD appendages and surface structures but data are unavailable to estimate mortality levels from these interactions. Turtle catch rates are higher in combined (drifting FAD and log) associated sets than in school sets in the Atlantic, Eastern Pacific and Indian Oceans  (Amandeet al. 2008 (Amandeet al. , 2010;; Hall and Roman 2013). However, based on data from the western and central Pacific Ocean, turtle capture rates are higher in school sets than drifting FAD sets (Table  2 )  (Williams et al.
2013). Minimally invasive tissue biopsy samples could be collected and analyzed to determine which populations are caught in a fishery. Even when information on which populations are captured in a fishery is available, data requirements for robust population models are frequently not met. For many bycatch species of conservation concern, improved information is required on key life history characteristics, information to determine population structure, and accurate estimates of all sources of mortality, including total fishing mortality by age class and sex (e. g. ,  NMFS 2012; Camargo et al. 2016; Gilman et al. 2017b). Similarly, ERAs on the effects of bycatch require improvements in understanding of effects on genetic diversity within and between species. Increased knowledge is needed of the phenotypic ranges of intraspecific diversity of traits that explain susceptibility to fisheries capture  (Hollins et al.
The droplets generated from each sample were transferred to a 96-well plate and PCR amplification was conducted in an iCycler PCR instrument (Bio-Rad) with the following conditions: 95°C for 10 minutes, 40 cycles of 94°C for 30 sec with a ramping of 2°/sec, 59°C for 1 minutes with a ramping of 2°/sec, followed by 98°C for 5 minutes and a hold at 4°C. After amplification, the 96well plate was loaded onto the QX100 droplet reader (Bio-Rad) that automatically measures the fluorescence intensity in individual droplets. The generated data were subsequently analyzed with QuantaSoft™ software (Bio-Rad) 2 Hepat Mon. 2019; 19(3):e85775. based on positive and negative droplet populations. For TP53, genomic DNA from the PLC/PRF5 HCC cell line that harbors the R249S mutation was included as a positive control in each experiment, while a genomic DNA from a tumor mutated in -124 of TERT promoter was used as positive control.
Acknowledgments We express our gratitude to  Marion Benabou  and  Thomas Bourgeron  ( Unité de Génétique humaine et fonctions cognitives, Institut Pasteur, Paris)  for their help in the use of the droplet digital PCR machine. We feel indebted to  Prof. Anne Dejean  for her constant support in the lab. This work was supported by the  French Ligue Nationale Contre le Cancer  ( Equipe Labélisée d'Anne Dejean ) and the  Tunisian secretariat for Scientific Research and Technology (SERST , contract:  LR11SP09 ).
However, these studies were highly heterogeneous in terms of AL assessment (biomarkers and formulas of calculation varied substantially between studies), making results not directly comparable among them and with ours  13, 22. Despite methodological differences, most investigations found a significant assocation between neighbourhood socioeconomic structure and AL, even after adjustment for confounding variables such as individual SEP, gender and age. As such harmonized data were not available for the studied cohorts, we could not explore the potential pathways that may explain the observed association. We were only able to assess to what extent adjustment for well-known health-related behaviours (smoking, alcohol consumption and physical inactivity) affected the measured association. We observed that adjusting for these factors attenuated the observed association, but these behaviours did not fully account for them.
Finally, the Whitehall II cohort, in contrast to the other two studied cohorts, is limited to individuals who were originally civil servants and therefore not representative of the general population living in London. Yet, there is a wide range of neighbourhoods in all three cohorts allowing us to detect associations with allostatic load. Furthermore, the inclusion of Whitehall II (composed by higher SEP individuals) allowed us to achieve a more socioeconomically balanced population sample. Our study has important strengths and implications too. We utilized prospective and harmonized data from three well-established cohorts representing different societies. It resulted in a large sample size, which allowed us to generate solid and comparable estimates of the association between neighbourhood deprivation and AL.
For EPIPorto we used baseline information (1999-2003, n = 2485), for CoLaus data from wave 2 (2009-2013, n = 5064), and for Whitehall II data from wave 3 (1991-1993, n = 8815), corresponding to the evaluations with the largest number of available biomarkers and which permitted us to compute the AL score. Therefore, this study included a total of 16,364 individuals.
The harmonization procedures of SEP data in these cohorts, as part of LIFEPATH study procedures, are fully described elsewhere  3. Geocoding and neighbourhood socioeconomic deprivation. In CoLaus, addresses were geocoded using QGIS (Quantum GIS Development Team, 2013) with the MMQGIS Python plugin (http://michaelminn. com/linux/mmqgis/) facilitating the use of the Google Maps API  12. In EPIPorto, addresses were geocoded using ArcGIS Online World Geocoding Service and Google Maps  36. In Whitehall II, participants were geocoded using postcodes. Then, point-in-polygon overlay operations were conducted to ascertain each participant neighbourhood and the corresponding level of socioeconomic deprivation  36. Neighbourhood socioeconomic deprivation was measured using different multivariable indexes of socioeconomic deprivation: the Townsend index of deprivation in CoLaus and Whitehall II  38  and the European Deprivation Index (EDI) in EPIPorto  39, 40  fully described in Supplementary Table  2.
To account for demographic differences, regression models were adjusted for age, sex and marital status. Then, individual SEP (i. e. educational attainment) was added to the models to assess if inclusion attenuated the previously obtained associations. Finally, previously-mentioned individual-level behaviours (smoking, alcohol consumption, physical activity) were included to test whether these explained the differences in AL across neighbourhood deprivation quintiles. Subsequently, interactions between individual SEP and neighbourhood deprivation were investigated. Associations were presented as Relative Risks and 95% Confidence Intervals, which express the relative change in AL score in each neighbourhood deprivation quintile, as compared with the reference quintile (Q1-least deprived). To reduce possible biases caused by missing data and attrition, missing values were imputed for the variables included in the models  18.
We performed a multiple imputation model using chained equations implemented in the R software, more precisely in the package 'mice' for missing imputation. This technique allows imputing missing information for several variables at a time, through an iterative process (the chained equations)  47. Imputed variables and the number of missing data are depicted in Supplementary Table  3 , totalizing roughly 5% of the data used in our study. Analyses were conducted in R 3. 1. 1. using the 'lme4' , 'mice' and 'ggplot2' packages.
Robustness checks. For the cohorts with information on the unit of aggregation ('neighbourhood of residence') -EPIPorto and CoLaus -a random effect at neighbourhood level was also added to the models, accounting for the fact that individuals were nested within neighbourhoods. Previous analyses were reproduced using this additional random effect and results remained mostly unaffected (see Supplementary Table  4 ). To guarantee that our results were not driven by the process of multiple imputation, we fitted the models using the original dataset imputation. Results remained mostly unchanged (see Supplementary Table  5 ). We also computed associations using a fixed-effect meta-analysis. The results revealed small-to-moderate heterogeneity and associations matched those obtained using Mixed-effects Poisson regression (see Supplementary Tables  6, 7 ).
Acknowledgements This study was supported by the  European Commission (Horizon 2020  grant number  633666 ) and by  FEDER  through the  Operational Programme Competitiveness and Internationalization  and national funding from the  Foundation for Science and Technology -FCT (Portuguese Ministry of Science, Technology and Higher Education)  under the  Unidade de Investigação em Epidemiologia -Instituto de Saúde Pública da Universidade do Porto (EPIUnit)  ( POCI-01-0145-FEDER-006862; Ref. UID/DTP/04750/2013 ); and the  FCT  contract  DL 57/2016/CP1356/CT0001  ( Silvia Fraga ), co-funded by the  FCT  and the  POPH/FSE Program. MK is supported by the  UK Medical Research Council  ( K013351 ,  MR/R024227 ),  NordForsk , the  Nordic Programme on Health and Welfare , the  Academy of Finland  ( 311492 ), and a  Helsinki Institute of Life Science fellowship.
This configuration corresponds to multiple spots on disc brake system (found in the railway brake systems). Experimental and numerical studies of disc brake systems for different configurations and applications have been carried out  [20] -  [24]. An experimental study of hot spots occurrence in railway disc brakes is reported in reference  [20]. Belhocine et al. [21]  present a numerical modeling in three dimensions to analyze the thermal behavior of the full and ventilated disc brake. In this study, the thermal calculation based on the finite element method is carried out using code ANSYS 11. Ghadimi et al. [22]  investigated the thermal analysis of a wheel-mounted brake disc. The brake disc and the fluid zone are simulated as a 3D model (FLUENT) with a thermal coupling boundary condition.
Numerical simulation The numerical solution is obtained using COMSOL multiphysics software. The number of elements of the mesh is detailed in the Table  5. The mesh has been refined in the contact areas as it can be seen in Figure  6  (a). The corresponding thermal map is giving by Figure  6  (b). These results obtained by numerical calculation will serve as a reference to compare those obtained by the analytical solution. In order to precisely compare the results, the temperature profiles for 0 r r = will be extracted from this solution to compare them to the temperatures calculated by the analytical model.
Spectral Data Measurements Infrared (IR) spectrum of the molecule (Z)-3-N-(ethyl)-2-N'-((3-methoxyphenyl)imino)thiazolidine-4-one was recorded in the range 500-4000cm -1 on a Nicolet FT-IR 6700 spectrometer using sample prepared as KBr pellets. 1  H NMR (500 MHz) and  13  C NMR (125 MHz) spectra of the molecule using CDCl 3 as the solvent were recorded on Bruker AC250 spectrometer at 298 K.
3. X-Ray Data Collection and Processing A transparent-yellowish parallelepiped crystal was selected and separated from the mother liquor, immediately cooled to 130 K and mounted on a Bruker Kappa APEX 2 diffractometer, equipped with a Triumph monochromator using MoKa radiation. The crystal presented no decay during the data collection. The frames collected (running ϕ and ω scans) were integrated with the Bruker SAINT Software package,  15  using a narrow-frame algorithm. Data were corrected using the SADABS program. 16 he structure was solved by the SUPERFLIP package. 17 rystals program package version 14. 40  18  has been used for the refinement and all the rest subsequent calculations through full-matrix least-squares on F 2. All non-hydrogen atoms have been refined anisotropically. Hydrogen atoms were found at their expected positions and refined using proper riding constraints to the pivot atoms.
DFT Calculations In this theoretical study, the hybrid functional B3LYP with the 6-311G (d, p)  20  basis set were used in all calculations by using the Gaussian 09 program,  21  the X-ray structure was used as starting geometry to optimize a molecular structure of the investigation compound. Vibrational frequencies were calculated and the Gauss-view molecular visualization program  22  and VEDA software  23  were used for the assignment. Additionally, 1 H and  13  C NMR chemical shifts were calculated using the same level of theory. The theoretical calculation also allowed us to compute the energy values of the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO). Furthermore, the molecular electrostatic Megrouss et al. : Synthesis, X-Ray Structure Determination and.
Supplementary Material Crystallographic data for the structure reported in this article have been deposited with Cambridge Crystallographic Data Center, CCDC 1871013. Copies of this information may be obtained free of charge from the Director, CCDC, 12 Union Road, Cambridge, CB2 1EZ, UK. Facsimile (44) 01223 336 033, E-mail: deposit@ccdc. cam. ac. uk or http//www. ccdc. com. ac. uk/deposit.
Participants Data for the current analysis were derived from a case-control study designed to examine the association of novel biomarkers including leptin with renal and retinal diseases (Singapore Kidney Eye Study; n = 2944)  [18]. The study was nested within two independent population-based cross-sectional studies: the Singapore Indian Eye Study  (SINDI, 2007 (SINDI, -2009)) , and Singapore Chinese Eye Study  (SCES, 2009 (SCES, -2011)). Both studies followed the same study protocol, were conducted in the same study clinic (Singapore Eye Research Institute), and recruited Indian and Chinese adults aged 40-80 years residing in the southwestern part of Singapore though an age-stratified random sampling method. Details of the methodology and population characteristics of SINDI, SCES, and the Singapore Kidney Eye Study were published previously  [18] [19] [20].
Data were grouped for circulating leptin concentration ≥20 ng/mL, as the number of participants was low. Second, leptin levels were categorized based on quintilization: quintile 1 less than or equal to 2. 64 ng/mL, quintile 2 between 2. 65 and 5. 1 ng/mL, quintile 3 between 5. 2 and 8. 6 ng/mL, quintile 4 between 8. 7 and 17. 96 ng/mL, and quintile 5 greater than or equal to 18 ng/mL.
e. , odds ratio = 1). Separate models were used to explain the total AMT score as well as all domain-specific AMT subscores (i. e. , anterograde episodic memory, retrograde episodic memory, semantic memory, working memory, orientation in space, and orientation in time). p-values < 0. 05 were considered significant. All statistics were performed using SPSS (v15. 0, IBM Corporation, Chicago, IL, USA).
S4, ESI † ). The same space group is shown by the paramagnetic phase of SFMO and reflects the loss of tilting around the c axis present in I4/m. Neutron diffraction patterns have been collected for Sr 2 Fe-MoO 4. 9 N 1. 1 from 1. 5 K to 450 K and for Sr 2 FeMoO 6 at 90 K. Ferromagnetic ordering of the oxide secondary phase in the oxynitride sample is reflected in a change in intensity of some diffraction peaks (e. g. 101, Fig. S5 , ESI †) for patterns collected below 400 K. A detailed examination of the diffraction data at lower temperatures does not reveal any further enhancement of any other diffraction peak, or the appearance of purely magnetic reflections. In fact, low temperature (1. 5 K) neutron data can be satisfactorily refined assuming that the magnetic reflections arise solely from the SFMO phase (ordered moment of Fe E 4.