dataesr/scientific-paragraphs-categorization huggingface dataset:
They jointly fitted the photometric data points and the FORS2 spectra at their native resolution. The multidimensional likelihood space was sampled by using PYMULTINEST  (Buchner et al. 2014 ), a python wrapper for the MULTINEST code. For direct comparison with the results obtained from our model SFHs, we reproduce the results of  Fossati et al. (2018)  in Fig. 10  to 15. For our purpose we use the modeling of  Fossati et al. (2018)  to obtain the synthetic photometry and the optical spectra for our model SFHs. The model photometry and spectrum is then compared to observations using MULTINEST and the total evidence is calculated. The goodness is evaluated using the Bayesian evidence. This value is formally defined as the integral of the likelihood of a given model over the entire parameter space.
The model filaments are thicker than the observed filaments because of the coarse spatial resolution of the diffuse gas particles. It should be noted that pronounced filamentary structures are naturally produced in simulations whenever magnetic fields are taken into account  (Ruszkowski et al. 2014; Tonnesen & Stone 2014). It has to be shown for other galaxies undergoing ram pressure stripping if this effect is ubiquitous. 6. 2. Ram pressure stripping of cosmic ray electrons NGC 4330 does not show an asymmetric ridge of polarized emission as other ram pressure stripped galaxies  (Vollmer et al. 2012). This is due to the NGC 4330's projection which places the region of compressed gas along the line of sight. On the other hand,  Vollmer et al. (2012)  discovered a tail structure bending southward in their 6 cm radio continuum data (upper panel of Fig.
Acknowledgements. BV would like to thank  R. Beck  for useful discussions. We are grateful to the  CFHT team  who assisted us in the observations:  T. Burdullis ,  D. Devost ,  B. Mahoney ,  N. Manset ,  A. Petric ,  S. Prunet ,  K. Withington. The  VESTIGE  project is financially supported by the  "Programme National de Cosmologie and Galaxies" (PNCG). MF has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  757535 ). We would like to thank the anonymous referee for her/his comments which helped to improve the article.
Population A single-center continuous prospective study included patients aged >15 years, treated between January 2014 and December 2016 for unilateral femoral shaft fracture by anterograde intramedullary nailing. Joint fractures, floating knee and bilateral fractures were excluded, as were patients followed up elsewhere. Ninety-six patients were eligible. Forty-eight were included: 12 female, (25%), 36 male (75%); mean age, 31. 4 ± 14. 8 years (range, 15. 4-72. 4 years). The inclusion flowchart is shown in figure  1. Mean body-mass index (BMI) was 23. 6 ± 3. 2 kg/m² (15. 9-31,8). Fracture and surgery data are shown in Table  1.
Assessment Patients were followed up at 45 days, 3 months, 6 months and 1 year, with standard AP and lateral femoral X-ray. At 6 months, clinical and radiological assessment including systematic EOS stereoradiography was performed by an independent observer. Data comprised epidemiological parameters, fracture characteristics, accident-to-surgery time, operator experience (senior or trainee), day or night surgery (emergency surgery after 6 pm), and clinical and radiological data comprising hip and knee ranges of motion, clinical measurement of morphological parameters including limb-length discrepancy, return to work, pain on VAS, and postoperative low back pain. Radiologic assessment used EOS stereoradiography (EOS Imaging, Paris, France)  [6, 8]. The dedicated sterEOS software, version 1.
Meteorological forcing Three components are considered for the meteorological forcing stage. The first two, named ECMWF and GEFS, are ensemble forecasts of precipitation and temperature issued by individual forecast centres. These are, respectively, the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP), whose ensemble forecasts are more commonly referred to as GEFS (Global Ensemble Forecast System). While ECMWF is often considered the most skilful global ensemble product, GEFS is widely used, partly because of its easy and free access. They contain 51 and 21 members, respectively, which are here considered exchangeable (their 50 and 20 perturbed members, respectively, are not distinguished from the control member). The archives of past operational forecasts were extracted from the TIGGE database  (Park et al.
(2009)  and  Pappenberger et al. (2008) , who leverage the full TIGGE database and form grand ensembles of 216 members. However, the financial cost (to access the operational forecasts) and the computational cost (for downloading, storing and running all these members into the hydrological models) is likely to remain unaffordable for many hydrological forecasting centres. Thus, our 72-member grandEns is a more realistic alternative, and comparing it to ECMWF and GEFS  permits us to study the problematic of whether the performances of a given meteorological ensemble can be improved -or not -by adding a potentially less skilful ensemble. In  Bellier (2018) , when evaluated against observed precipitation and temperature at individual catchments and lead times, these three meteorological forcings were found to rank as follows: grandEns > ECMWF > GEFS (with A > B meaning that A performs better than B).
Hydrological modelling A multi-model (multiMod) is here composed of three different hydrological models: TOPMODEL  (Beven, 2012) , a variable contributing area model, GRP  (Berthet et al. , 2009) , a parsimonious reservoir model, designed specifically for flood forecasting, and finally a fully statistical autoregressive exogenous model (ARX,  Remesan & Mathew, 2015)  based on regression equations considering previous streamflow and precipitation as inputs, and with coefficients segmented by model state parameters. As implemented here, the three models are driven by mean areal precipitation and mean areal temperature forecasts, and assimilate the latest streamflow observations with the objective of reducing the forecast errors on the first lead times. All three models are coupled with the snow-accounting module Cemaneige  (Valéry et al.
Study design This cross-sectional data was collected between December 2012 and December 2013 within a pilot study for a intervention study evaluating an electronic psychooncological adaptive screening program. Individuals were eligible if they were (i) diagnosed with any malignancy according to ICD-10, (ii) 18 years or older and (iii) able to read and speak German. Patients were excluded if they had any physical and/or cognitive impairments potentially impeding the ability to give informed consent. Eligible patients who were identified via review of their medical records and who approved to be contacted by medical staff were consecutively approached by research assistants at the in-and outpatient facilities of the Hubertus Wald Tumorzentrum of the University Cancer Center of Hamburg-Eppendorf (UCCH).
Statistical analysis Descriptive statistics were used to provide medical and sociodemographic sample characteristics. For the 32 items of the SCCC, we provided mean and standard deviation as well as skewness and kurtosis. Values of skewness > 2 and kurtosis > 7 were used to indicate substantial deviations from normal distribution  [16]. To further assess applicability of the items, we investigated (i) the distribution of response choices for each item to identify any floor and ceiling effects (according to Defossez et al. [12]  indicated if > 50% filled in the same response option) and (ii) the respective number of missing data. Dimensions were extracted via principal axis factoring (PAF). Varimax rotation was used to ease interpretation of the results. Since multivariate outliers may bias factor and reliability analysis  [17, 18] , we identified such outliers among the 182 cases with complete questionnaire data via Mahalanobis distance exceeding the critical value of the chi-square distribution (p < 0.
Patients with more than 50% missing data in the SCCC were excluded. Means of outcomes were calculated if at least 50% of the items had valid values. Effect sizes were interpreted according to Cohen (small: r ≥ 0. 1; medium: r ≥ 0. 3; large: r ≥ 0. 5)  [24]. Given the high amount of missing data in the 7 items containing the response option "not applicable", we applied pairwise correlations for both PAF and correlation analyses. We used two tailed tests with an alpha of 0. 05. Analyses were conducted with IBM SPSS Statistics (Version 26) and R (version 3. 5. 0, the R Foundation for Statistical Computing).
Registry population The analysis was conducted in 52,080 patients enrolled in GARFIELD-AF between March 2010 and July 2016. The data were extracted from the study database on 19 November 2018. To minimize recruitment bias in GARFIELD-AF, investigator sites were selected randomly from representative care settings in each participating country (apart from 18 sites, out of >1000) and consecutive patients were enrolled, regardless of whether or not they received antithrombotic treatment. Eligible patients comprised adults (aged ≥18 years) who had been newly diagnosed with AF (not related to mechanical valves or severe valve disease), within the previous 6 weeks and had at least one unspecified risk factor for stroke as judged by the investigator.
Study procedures and outcome measures The methods employed in GARFIELD-AF have been published  (7, 8). In brief, baseline characteristics included: patient characteristics, medical history, care settings, type of AF, date and method of diagnosis, symptoms of AF, type of anticoagulant treatment (VKAs, factor Xa inhibitors [FXas] and direct thrombin inhibitors [DTIs], as well as antiplatelet treatment [AP]). Data on components of the CHA2DS2-VASc (9) and HAS-BLED (10) risk stratification schemes were also collected to assess the risks of non-haemorrhagic stroke and major bleeding. Collection of follow-up data occurred at 4-monthly intervals based on telephone interviews and hospital records up to 24 months. The incidence of ischaemic stroke, transient ischaemic attack (TIA), systemic embolism (SE), acute coronary syndrome (ACS), hospitalisation, death (cardiovascular and non-cardiovascular), Congestive heart failure (CHF) (occurrence or worsening), and bleeding (severity and location) was documented.
An audit and quality control programme was applied  (11) , and data were examined for completeness and accuracy by the coordinating centre (TRI, London, UK). By design, 20% of all electronic case report forms (eCRFs) in the GARFIELD-AF registry were monitored against source documentation at sites over the recruitment period and follow-up. Loss to follow-up was found to be 4. 2% of all prospectively enrolled patients. Any events that occurred after two years follow-up were censored at two years. Patients with unavailable follow-up information were excluded from all the analyses.
Risk tool Design The new risk stratification tool was derived from prospective data from the GARFIELD-AF registry. Models were trained on indicators for three events (all-cause mortality, nonhaemorrhagic stroke/systemic embolism, and any major bleed) that occurred within 2 years of enrolment. As with the previous GARFIELD-AF risk models, the derivation of the GARFIELD-AF risk models followed the TRIPOD process for the development of predictive models  (4, 12). Comparisons of the performance of the new GARFIELD-AF risk models were made with (a) CHA2DS2-VASc score (for all-cause mortality, non-haemorrhagic stroke/SE), and (b) HAS- BLED score for major bleeding. The performance of the new risk tool was tested in the whole GARFIELD-AF population as well as in patients treated and untreated with OACs for stroke prevention at baseline.
Statistical modelling Predictors of mortality, non-haemorrhagic stroke/SE, and major bleeding were identified using the least absolute shrinkage and selection operator (LASSO) regression. The predictors were selected from the list of potential predictors (Supplement List 1). A Cox model was fitted with the selected parameters. Thirty-fold cross-validation was applied during the modelling process. Both a Kolmogorov-type supremum statistical test and a graphical examination of the Schoenfeld residuals were used to assess the Cox model proportional hazards assumption. All continuous covariates were tested for linearity and appropriate transformations were applied as needed. One imputed dataset was used for the model generation. The final model was established with multiple imputation. Combined hazard ratio estimates with 95% CI from five imputations were presented.
Acknowledgements We thank the physicians, nurses and patients involved in the GARFIELD-AF registry. We thank  Prof Gunnar H. Gislason ,  Department of Cardiology, Hertlev & Gentofte Hospital, Hellerup, Copenhagen, Denmark  who provided Danish data for validations. Medical writing Downloaded from https://academic. oup. com/ehjqcco/advance-article/doi/10. 1093/ehjqcco/qcab028/6248089 by  St George's University of London  user on 07 May 2021.
Based on these observations, we could construct the consumption patterns of health and education facilities in RAMP and allocate it to the (latent) demand of clusters where similar facilities are located. Information on operational healthcare facilities in Kenya is based on open-data on the location and characteristics of public  7  healthcare facilities  [51]. Similarly, open-data for the position and size of schools is retrieved  [52]. We classify healthcare facilities into five tiers combining results from the observations and the facility type explicated in the original dataset  [51]. Once information about the location and typology of healthcare and education facilities is compiled, we calculate the density of facilities of each tier in each cluster. Based on this information, we estimate the total local sectoral demand exploiting the monthly load demand profiles calculated with RAMP.
Based on this review the energy requirements are associated to crop-specific cropland extent and average yield in each cluster, as discussed in SI-A7. In addition, M-LED can identify hotspots where investment can be prioritized to onset rural development. As an example, we demonstrate that the platform can estimate the increase in the revenues from the potential boost in the per-hectare yield due to artificial irrigation, which in turn might compensate for the low ability-to-pay for energy services of rural dwellers  [63]. In our analysis, we consider a recent database of wholesale prices for a basket of crops in Kenya relative the location of each wholesale market. Prices are multiplied to the local potential for yield increase of each crop, net of transportation and total (installation, operation, and maintenance) pumping costs, as detailed in SI-A8.
Data uncertainty and modelling limitations Irrespective of the large amount of work involved in the development of the M-LED platform and in the formulation of its assumptions, limitations remain. The M-LED platform is open-source and fully customisable to let the user define the bulk of the input data sources (both for the Kenya case study presented in this paper and for future applications), including technical and economic parameters, the devices ownership and usage patterns, and the overall infrastructure. Yet, the data-intensiveness (table  SI3 ) of the analysis implies growing uncertainty over the reliability of the database, as (despite a careful data selection and wrangling) some sources such as infrastructure and facilities location and characteristics might be outdated or biased.
For instance, gridded population data products are based on census data downscaling based on dasymetric methods applied on building footprint data  [69] , which can result in systematic undercounting of certain population groups  [70]. Electricity access is estimated with nighttime lights, which has own limitations  [6, 43]. Cropland information is limited by quality of official data and downscaling methods  [71]. Groundwater availability maps are produced by interpolating a set of in-situ measurements under given hydrological constraints  [72]. Moreover, the water and agricultural analysis relies on the assumption of an optimal irrigation scheduling (as described in  [73] ) and local crop processing based on current cropping patterns. Healthcare and education facility databases have their own data availability and quality limitations  [51].
The discrete paradigm, adopted in this work, utilizes a finite set of basic emotions that, when combined, can express more complex feelings  (Ekman, 1992). Our system relies on state-of-the-art deep learning architectures, and it requires two different datasets (one for EEG signals and one for paintings) sharing the same emotional labels. We provide a conceptual pipeline in Figure  1 , in which we see the role of both datasets as inputs to two different blocks. The first block of the pipeline (EEG emotion encoding) relies on a Regularized Graph Neural Network  (Zhong, Wang and Miao, 2020)  dedicated to processing the emotion in the EEG signals. This block produces latent vectors synthesising emotions in EEG signals. We keep the dimension of these vectors high, preserving more information than the single predicted emotion in each signal.
This design choice is motivated by an interest in dealing with the influence between different emotions as, in many cases, a single word may not be enough to describe a feeling. The image generation block relies on a generative adversarial network, StyleGAN2ADA  (Karras et al. , 2020) , trained on the dataset of paintings. This block receives the EEG latent vector produced by the EEG emotion encoding block and a random vector. The introduction of randomness in generative processes is necessary to simulate the variability and unpredictability of a creative process  (McCormack et al. , 2014). For more details on the technical implementation of this system, we refer the readers to  (Riccio, 2021).
Since EEGs represent an inner physiological reaction, it can be assumed that any existing bias would be less strong. This consideration has led to the choice of recognizing emotions from EEG signals. We have performed two kinds of experiments: one using the SEED-IV EEG dataset  (Zheng et al. , 2018)  and one performing recordings with an easyto-use and off-the-shelf device, the OpenBCI headband kit with eight dry-comb electrodes, paired with the Cyton board. The SEED-IV presents high-quality signals, recorded on a homogeneous set of people. The choice to record signals with an off-the-shelf device derives from the desire to create an accessible system, potentially trainable on a diverse set of subjects. Recording EEG signals ex-novo requires understanding how to elicit emotions in laboratory settings.
Algorithmic biases in image generation The dataset on which generative adversarial networks (GANs) are trained creates a latent space containing the possible features of the generated elements  (Goodfellow et al. , 2014). Algorithmic biases in content generation can arise unintentionally, creating misalignments between the distributions of the features of the training data versus those of the generated data  (Salminen, Jung and Jansen, 2019). In this context, art practices represent a significant opportunity to explore and understand such biases  (Booth et al. , 2021). We have trained StyleGAN2ADA on the WikiArt Emotions dataset  (Mohammad and Kiritchenko, 2018) , a public collection of heterogeneous paintings experimentally labelled with conveyed emotions. Given that these artworks are mainly conceived by western artists, the generated images will inevitably follow the aesthetic norms of western arts, causing a technical limitation.
RESULTS In this section, we provide a selection of obtained paintings from different experiments at different resolutions. We comment on some of their characteristics, when grouped according to the emotional class of their respective EEG. We trained the pipeline on the SEED-IV (EEG dataset) and WikiArt Emotions (paintings dataset), with labels in four classes: anger, sadness, fear, and happiness. With image resolution 512x512 pixels, the training took approximately ten days, utilizing the GPU Tesla V100-SXM2-16GB. The training time becomes much longer with less powerful GPUs, and shorter at lower resolutions. The resulting images were upscaled using a super-resolution technique  (Sun and Chen, 2020). Although acknowledging that connecting images to emotions is a cultural and learned experience, we mention some features in the paintings.
Particle-phase analysis Particle concentrations and size distributions are measured with a scanning mobility particle sizer (SMPS, TSI) composed of an advanced aerosol neutralizer (3087), a differential mobility analyzer (DMA, 3081) column, and a condensation particle counter  (CPC, 3775). The sampling flow is 0. 3 L min -1. Classification is based on the particle electrical mobility, and the measurement range goes from 14 to 615 nm. The particle mass is computed, with the assumption that particles are spherical, with an arbitrary density of 1. 2  (Barone et al. , 2011; Totton et al. , 2010). Data are given with a 5 min timescale, with a 5 % analytic uncertainty. The black carbon concentration is measured using an AE33-7 aethalometer from Magee Scientific. Air is sampled at 2 L min -1 on filter tape.
Indeed, individual domain searches revealed hits for the coiled-coil and PEHE regions, albeit at rather low fidelity (Table  S1 ; note that even mammalian and Drosophila MSL1 show <20% amino acid similarity despite providing the same scaffolding function to the complex). Assessing the expression level of the core MSL complex members by RT-qPCR from adult Anopheles, we found a modestly biased expression of mof and mle in males  (Fig 1E, statistical  analyses in Table  S1 ). Since entire adult mosquitos contain the gonads, which could be introducing a bias, we also analyzed published RNA-seq data  (Papa et al, 2017)  of dissected carcasses (soma) and reproductive tissues (germline) (Fig S1B ; modEncode and RT-qPCR data from Dmel as a comparison in Fig  S1C  and D ). In this RNA-seq dataset, Agam msls were all classified as not sex-biased.
MSL2 confers gene-by-gene regulation on all chromosomes The early embryonic and pronounced germ line msl-2 KO phenotypes could be a consequence of an additional function of the MSL complex in mosquitos compared with Drosophila, which could mask a phenotype related to regulating the Anopheles X chromosome. To analyze this, we characterized the transcriptome alterations upon loss of msl-2 in Anopheles embryos, in particular with regards to the chromosomal location. We analyzed two different time points: 6-7 h after egg laying with n = 3 biological replicates and 15 h after egg laying with n = 2 biological replicates (Fig S3A ; we also generated a single dataset at 24 h after egg laying, which was only considered for qualitative analyses). Because it is not possible to phenotypically separate sexes at this stage, we performed RNA-seq experiments from mixed embryo populations.
To validate that this approach per se permits capturing effects on DC of the X, we performed an equivalent experiment from a mixed male and female Dmel embryo population  (Fig 3A). We scored differentially expressed (DE) genes using DESeq2 with a falsediscovery rate (FDR) cutoff of <0. 05 in control versus msl-2 KO (Fig  S3B  and Tables S2  and S3 ). As expected from the role of Dmel MSL2 in regulating the X chromosome, we made the following observations in the Drosophila dataset: First, X-linked genes are significantly overrepresented among all down-regulated genes in the msl-2 KO embryos (Fig 3B, 717 of 1,304 genes, Fisher's exact test P = 1. 65645 × 10 -273 ). Second, genes on the X chromosome are globally down-regulated, irrespective of whether a gene is scored as DE or not  (Fig 3C, statistical  analyses in Table  S1 ).