dataesr/scientific-paragraphs-categorization huggingface dataset:
The statistical characterization of the diffuse magnetized interstellar medium (ISM) and Galactic foregrounds to the cosmic microwave background (CMB) poses a major challenge. To account for their non-Gaussian statistics, we need a data analysis approach capable of efficiently quantifying statistical couplings across scales. This information is encoded in the data, but most of it is lost when using conventional tools, such as one-point statistics and power spectra. The wavelet scattering transform (WST), a low-variance statistical descriptor of non-Gaussian processes introduced in data science, opens a path towards this goal. To establish the methodology, we applied the WST to noise-free maps of dust polarized thermal emission computed from a numerical simulation of magnetohydrodynamical (MHD) turbulence in the diffuse ISM.
2014) , SPIDER  (Fraisse et al. 2013) , LiteBIRD  (Ishino et al. 2016) , PICO  (Sutin et al. 2018) , the Simons Observatory  (Ade et al. 2019) , and CMB-S4  (Abazajian et al. 2019) , with a detection limit goal of r 10 -3. However, any claim to the detection of the cosmological B-mode signal will have to be critically assessed against alternative explanations involving Galactic foregrounds. An overarching challenge for studying both interstellar turbulence and CMB foregrounds is the need for a new approach to data analysis, which is able to efficiently capture statistical couplings across scales. This information is encoded in the data, but most of it is lost when one resorts to such classical tools as one-point statistics and power spectra. To characterize magnetized interstellar turbulence, as well as to produce realistic synthetic dust polarization sky maps for CMB data analysis, we aim at a statistical description of the polarized Galactic emission maps that encompasses non-Gaussian characteristics.
Building simulated Stokes maps Throughout this paper we work with Stokes maps which are directly computed as integrations along a given direction of 3D simulated cubes of data extracted from a numerical simulation of magnetized interstellar turbulence (this given direction of integration corresponds to the line of sight for observations). As a first step, working with MHD simulations instead of observational data is a stepping-stone to relate our statistical descriptions to physics where we avoid the difficulty of dealing with data noise. We use a MHD simulation designed to study the biphasic nature of the diffuse interstellar medium  (Bellomi et al. , in prep. ). The simulation uses the adaptive mesh refinement code RAMSES  (Teyssier 2002; Fromang et al. 2006 ) to solve the equations of ideal MHD as described in  Iffrig & Hennebelle (2017) , neglecting self-gravity and taking into account heating and cooling processes of the gas.
Random syntheses of Q + i Ũ polarization maps The RWST coefficients are not only statistical descriptors of the data but can also be used as a set of constraints for generating multiple random realizations of polarization maps. By comparing the original and synthetic maps one can assess the exhaustiveness of the statistical description. A visual agreement would give more confidence in the relevance of the statistical information captured by a RWST description. However, this qualitative assessment has its limits as it is unclear what kind of statistics human eyes are sensitive to  (Julesz 1981). A quantitative comparison using summary statistics other than the RWST is thus also needed. The synthesis is also a means of simulating noise-free maps artificially and realistically augmenting demanding MHD simulations, or producing realistic foregrounds for component separation methods for CMB data analysis.
In the framework of CMB data analysis, polarization is more usually characterized through E and B modes. Thus, it would be interesting to apply the RWST to E and B maps. This will lead to a physically motivated statistical model that would include observational constraints such as the E/B power asymmetry and the correlation between E-modes of the polarization and total intensity  (Planck Collaboration XI 2019). Once this is achieved, the RWST may be used to synthesize dust polarization maps that would help to assess and optimize component separation methods for CMB data analysis.
Acknowledgements. We thank the anonymous referee for a very helpful report. We gratefully acknowledge  V. Jelić  for pointing out to us the complex-valued Article number, page 15 of 20 A&A proofs: manuscript no. Main description of polarization maps. We also acknowledge fruitful discussions with  T. Marchand ,  J. -F. Cardoso ,  A. Frolov ,  S. Zhang , and  S. Mallat  that enriched this work in various ways. This research was supported by the  Agence Nationale de la Recherche  (project  BxB :  ANR-17-CE31-0022 ). F. Levrier  and  F. Boulanger  acknowledge support from the  European Research Council  under the European Union's  Horizon 2020 Research & Innovation Framework Programme / ERC  grant agreement  ERC-2016-ADG-742719. B. Regaldo-Saint Blancard  acknowledges support from the  Centre National d'Etudes Spatiales (CNES).
, 2018). Air quality modelling, from global to street scale, requires detailed representations of local and regional emission fields. However, generating accurate and up-to-date emissions data is a considerable challenge, owing to difficulties in obtaining the necessary activity, emission factor, and production/control technology data for each emission source sector  (Hong et al. , 2017; Qi et al. , 2017). Additionally, in China, the rapid decrease in emissions of major air pollutants over recent years needs to be accounted for  (Sun et al. , 2018; Zheng et al. , 2018). This reduction in emissions has followed the nationwide implementation of a number of clean air policies since 2013 as part of the Air Pollution Prevention and Control Action Plan (APPCAP) and more locally through the Beijing Action Plan  (Ni et al.
The additional modelling of local fast chemistry processes on pollutant emissions with ADMS-Urban, involving the simplified Generic Reaction Set (GRS) chemistry scheme, including NO x -O 3 reactions, enables sharp concentration gradients adjacent to major urban sources to be captured  (Hood et al. , 2018). Previous applications of ADMS-Urban in China have largely focussed on evaluating the impact that emission control schemes targeting individual sources have on the immediate environment. For instance,  Chen et al. (2009)  combined pollutant concentrations simulated by ADMS-Urban with population data to investigate the impact of traffic control policies on human exposure levels in Shanghai. Similarly,  Cai and Xie (2011)  used the ADMS-Urban model to quantify the effect that the odd-even traffic scheme (restricting vehicles with odd or even number plates), enforced during the 2008 Olympics, had on emissions from a selection of major roads, finding that some of the previously most polluted areas subsequently complied with the Chinese National Air Quality Standards (CNAAQS).
Unlike the data-intensive methodologies adopted by Y. Zhang et al. (2018)  and  Yang et al. (2019) , spatiotemporal variations in traffic volume and vehicle type are not considered here. However, this work provides a robust framework suitable for similar street-scale air quality modelling across large urban areas with limited data availability that future human health studies can build on. Furthermore, both the MEIC v1. 3 and an optimised version of the same inventory are used to assess the performance of proxybased inventories for street-scale modelling. Aggregated sectoral emissions (industrial, power and residential) are also included. We perform simulations for the Atmospheric Pollution and Human Health in a Chinese megacity (APHH-China) winter measurement campaign period, which took place in November-December 2016 at the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences  (Shi et al.
Background pollutant concentrations Background pollutant concentrations represent the regional pollution levels on which the local emissions build. For this study, background levels for NO 2 , O 3 , PM 2. 5 , PM 10 , SO 2 and CO are derived directly from hourly air quality measurement data and are assumed to be uniform across the study domain. Measured concentrations at 12 national air quality monitoring stations, run by the China National Environmental Monitoring Center (CNEMC), the IAP field site and an additional site 60 km south-east of Beijing, situated in the built-up Guangyang district of Langfang in Hebei province, are used to estimate this background concentration field. The locations of these 14 sites are given in Table  1. For particulate matter (PM 2. 5 and PM 10 ), an hourly upwind background concentration is derived based on wind direction with concentrations selected from sites 3 (270-360 • ), 10 (0-90 • ) and 14 (90-270 • ) located to the NW, NE and SE of urban Beijing, respectively.
Liu et al. (2015)  describe the unitbased technique, adopted to generate the power sector emissions, which utilises the Coal-fired Power Plant Emissions Database (CPED), including information on the technologies, activity data, operation situation, emission factors and locations of individual units. Industrial and residential sector emissions are calculated from provincial-level activity data and emission factors  (Zheng et al. , 2017). Industrial emissions are downscaled to the county level using GDP (National Bureau of Statistics, 2014), with both industry and residential emissions further distributed to grid-level resolutions based on high-resolution (∼ 1 km) population density data (Oak Ridge National Laboratory, 2013)  (Zheng et al. , 2017). To model conditions during the APHH-China winter campaign, the MEIC v1.
3 emissions inventory is re-scaled for this study to account for the 2013-2016 emission reductions in Beijing (Sect. 1). According to  Cheng et al. (2019) , total emissions of NO x (and NO 2 ), SO 2 , VOCs and PM (PM 2. 5 and PM 10 ) in Beijing were estimated to decrease by 30 %, 63 %, 27 %, 35 % and 30 %, respectively, between 2013 and 2016. This adjusted MEIC v1. 3 emissions inventory is hereafter referred to as MEIC Std. An alternate optimised version of MEIC v1. 3 (hereafter referred to as MEIC Opt) was created  (Li et al. , 2018) , for November and December 2016, with the aim of addressing the over-allocation of emissions to urban areas that occurs when downscaling MEIC v1. 3 to fine scales based on proxy data  (Zheng et al. , 2017). This MEIC Opt inventory was created using the Nested Air Quality Prediction Modeling System (NAQPMS) to perform iterative minimisation of a cost function comparing NAQPMS simulations with winter campaign observations  (Li et al.
3 power or industrial sector grids. MEIC Std and MEIC Opt campaign period mean NO 2 , NO x , PM 2. 5 , PM 10 , SO 2 and VOC emission rates from 3-D grid sources, aggregated across all, urban and suburban grid cells, are shown in Table  2. An explicit network of road emissions for Beijing has been constructed based on the MEIC transportation sector emissions. Figure  3  illustrates the pseudo top-down approach adopted here in the absence of detailed information on traffic activity and fleet composition. Figure  3a  shows the spatial distribution of the November and December mean MEIC Std transportation sector surface NO 2 emissions. The transportation sector emissions of all pollutants are apportioned to individual road segments on a grid cell-by-grid cell basis, using the ArcGIS geographic information system software.
The spatial road network of Beijing, presented in Fig. 3b , is provided by the OpenStreetMap dataset (https://openstreetmap. org/, last access: 6 June 2019) and includes individual road segment type and geometry information. Emissions are mapped onto the road network based on each road segment length and an emissions weighting factor, producing the distribution shown in Fig. 3c , following Eq. (  1 ): Emis i,j,k = l i,j • w k n i=1 (l i,j • w k ) × E j , (1) where l i,j represents the length of road segment i in grid cell j of road type k. The weighting factor of road type k is given by w k. E j and n denote the total traffic emissions and number of road segments in grid cell j , respectively. A weighted mean emission rate, based on road segment length, is calculated along segments traversing multiple grid cells in order to avoid discontinuities.
Data availability. All modelled output data presented here may be accessed by contacting the authors. Measurement data are available via the Centre for Environmental Data Analysis (CEDA) depository at https: //catalogue. ceda. ac. uk/uuid/7ed9d8a288814b8b85433b0d3fec0300 (Centre for Environmental Data Analysis (CEDA), 2020) by contacting our APHH-China partners.
The transferability of this model to other, more distantly related species was not examined. Here, we leverage extensive data gathered using our crowdsourcing platform CrowdCurio to develop and evaluate an instance segmentation approach using Mask R-CNN to train and test a model to identify and count phenological features of a larger number of species. Specifically, we investigated digitized specimens from six common spring-flowering herbs of the eastern United States: Anemone canadensis, A. hepatica, A. quinquefolia, Trillium erectum, T. grandiflorum, and T. undulatum. As with any feature detection model, accurate human-collected data are required to train, test, and refine these models. We thus gathered phenological data from these species using CrowdCurio to provide expert annotation data of buds, flowers, and fruits to train and test our models.
Dataset Our experiments are based on a subset of the data used in  Park et al. (2018 Park et al. ( , 2019) )  comprising six species in two genera of common spring-flowering herbs, Anemone and Trillium. This subset includes 3073 specimens of: Anemone canadensis (N = 108), A. hepatica (N = 524), A. quinquefolia (N = 686), Trillium erectum (N = 862), T. grandiflorum (N = 226), and T. undulatum (N = 667). Each specimen (herbarium sheet) was previously examined using the CrowdCurio-Thoreau's Field Notes platform by, on average, three citizen-scientists. For the purposes of this study, these specimens were additionally scored by expert botanists to provide the most accurate training and testing data possible. Annotators added markers in the center of each visible reproductive structure (bud, flower, or fruit), and determined its type, number, and spatial location.
For our experiments, we randomly split this dataset into two parts: one (N = 2457) for training the deep-learning models and one for testing them (i. e. , for evaluating their predictive performance; N = 615). Apart from the comparative experiment described in Machine-Learning vs. Crowd-Sourcing section, only the annotations of experts were used to train and test the deeplearning models. We also only used the annotations of one of the experts for each specimen (selected in a pre-defined order). The final dataset contains 7,909 reproductive structures (6,321 in the training set and 1,588 in the test set) with the following imbalanced distribution: 492 buds (6. 2%), 6,119 flowers (77. 4%), and 1,298 fruits (16. 4%). Fruits were counted without any knowledge of seeds.
g. , overlapping structures) likely made both model training and phenophase detection more prone to error. This result supports the recent hypotheses that successful application of machine-learning to phenophase assessment will be dependent on species-specific morphological details  (Goëau et al. , 2020). Along these lines, plant morphological trait databases could help facilitate the identification of suitable taxa to be analyzed with machine-learning methods. Precise quantification of different reproductive structures, as demonstrated here, allows the determination of finer-scale phenophases (e. g. , early flowering, peak flowering, peak fruiting). For this exercise, the lowest mean absolute error (MAE) was for bud counts, most likely due to the morphological consistency of buds and their rarity on specimens (Table  1 ).
In contrast, MAE for counting flowers was significantly worse than for buds or fruits. We attribute this result to the greater number of flowers, ontogenetic variability in floral morphology, and variation in appearance of dried, pressed specimens. Variation in appearance of reproductive features among dried and pressed specimens of a single species also could add complexity to automated detection of phenological features and merits further investigation. Perhaps more consequentially, large variation in the number of reproductive organs resulted in unbalanced datasets (Table  1 ). Numerous data augmentation approaches can be implemented to improve comparisons and model selection for such data sets (e. g. ,  Tyagi and Mittal, 2020) , but these approaches have been used more frequently in classification or semantic segmentation  (Chan et al.
Objectives: To describe population PK and optimized dosing regimens for flucloxacillin in critically ill patients. Methods: First, we developed a population PK model, estimated between-patient variability (BPV) and identified covariates that could explain BPV through non-linear mixed-effects analysis, using total and unbound concentrations obtained from 35 adult critically ill patients treated with intermittent flucloxacillin. Second, we validated the model using external datasets from two different countries. Finally, frequently prescribed dosing regimens were evaluated using Monte Carlo simulations. Results: A two-compartment model with non-linear protein binding was developed and validated. BPV of the maximum binding capacity decreased from 42. 2% to 30. 4% and BPV of unbound clearance decreased from 88.
Patients and samples PK data were obtained from 35 patients treated with intermittent flucloxacillin for a (suspected) infection in the 30 bed tertiary referral ICU at the Royal Brisbane and Women's Hospital in Brisbane, Australia. This study comprised data from two different sources. The first dataset was from a previously published prospective clinical study, performed between May and December 2009. 5 This dataset consisted of 10 patients with hypoalbuminaemia (serum albumin concentration 32 g/L). After at least 24 h of treatment, timed samples were collected at 30, 45, 60, 90, 120, 150 and 180 min (q4h dosing regimens) or 300 min (q6h dosing regimens) after the 30 min infusion. In all samples, both total and unbound flucloxacillin concentrations were measured. Data for the other 25 patients originated from a prospective observational study that was conducted as part of the b-lactam therapeutic drug monitoring (TDM) programme, between May 2012 and July 2014.
Bioanalytical method The samples obtained for the prospective study were frozen at #80 C and analysed within 8 months after the sample was drawn, in accordance with the results of the long-term stability investigation. 10 Samples collected during the TDM programme were centrifuged within 1 h of collection and analysed directly. All samples were analysed using a validated HPLC-UV method. Detailed information on the bioanalytical methods is available as Supplementary data (Appendices 1-3) at JAC Online.
External validation Two external datasets were used for the external validation of the predictive performance of the PK model. The first external dataset, the Brisbane dataset, consisted of 28 unbound flucloxacillin concentrations from 20 critically ill patients admitted to the ICU of the Royal Brisbane and Women's Hospital. The second external dataset, the Nijmegen dataset, consisted of 34 total and unbound flucloxacillin concentrations from 14 critically ill patients admitted to the ICU of the Radboud University Medical Center in Nijmegen, The Netherlands. Detailed information on the external patient cohorts is available in the Supplementary data. Total and unbound flucloxacillin plasma concentrations were predicted by fixing the population PK parameters to the final estimates of the previously developed model and setting maximum evaluations (MAXEVAL) to 0.
Statistical analysis Spearman correlation tests were used to test the correlation between the protein binding (%) of flucloxacillin, calculated as (1 # unbound fraction) % 100, and (i) the total flucloxacillin concentration and (ii) the serum albumin concentration. The Shapiro-Wilk test was used to assess whether the data were normally distributed. Wilcoxon matched-pairs tests were used to assess the differences between the predictive performance of the structural model and the predictive performance of the final model in the external datasets. All abovementioned tests were performed using GraphPad Prism version 8. 0. 2 for Windows, GraphPad Software, San Diego, CA, USA, www. graphpad. com.
Ethics Approval for the prospective study, from which 10 patients for the method development dataset originated, was obtained by the ethics committee of the Royal Brisbane and Women's hospital (protocol HREC/09/QRBW/85) and written informed consent was obtained for each patient prior to entering the study. For the other 25 patients included in the modelbuilding dataset and the 20 patients in the Brisbane external dataset, a waiver for informed consent was granted by the ethics committee of the Royal Brisbane and Women's Hospital, since blood sampling was performed as part of the local TDM programme. For the Nijmegen external dataset, the study protocol was evaluated by the local ethics committee and the need for written informed consent was waived due to its observational nature.
External validation Table  1  shows the characteristics of the patients in the different datasets. Table  3  shows that, for both external datasets, the predictive performance of the final model was statistically significantly better than the predictive performance of the structural model, with lower median percentage errors and lower median absolute percentage errors. For both external datasets, the structural as well as the final model proved to be unbiased for predicting the unbound and total flucloxacillin concentrations. The VPCs (Figures  S2  and S3 ) show that the final model, with covariates, better predicted the observed concentration-time data of both external datasets than the model without covariates.
As a consequence, unnecessarily high flucloxacillin doses may be selected when dosing regimens are based on measured total concentrations. We validated the impact of eGFR and serum albumin concentrations on the flucloxacillin PK in two external datasets from two different countries. Since there is no defined therapeutic window for flucloxacillin and TDM is not routinely applied, we chose to compare the population-predicted concentrations (PRED) from the model with and without the covariates. There are no standardized requirements for bias and precision when applying a PK model to an external dataset, therefore we used a statistically significant endpoint to evaluate the predictive performance. For both datasets, we found a significant improvement in predictive performance when the covariates were added to the model.
JAC Our study has several limitations. First, the limited number of patients and total and unbound flucloxacillin concentrations that were used for model building has likely resulted in a limited power to identify covariates that have a significant impact. This might, for example, explain why no statistically significant relationship was found between renal replacement therapy (RRT) (n = 4 patients) and flucloxacillin. However, we believe that with this dataset we were able to identify the most clinically relevant covariates, which was also confirmed in two external patient cohorts. Also, although our dataset consisted of a limited number of patients, it is the largest ICU patient dataset used for population PK modelling of flucloxacillin to date. Second, our PK model is solely based on patients receiving intermittent, and not continuous, infusion of flucloxacillin.