dataesr/scientific-paragraphs-categorization huggingface dataset:
Determination of thermal expansion coefficients Expansivities were calculated using the Python NumPy library 47 following the method described by Cliffe & Goodwin. 48 A number of them were compared to the values obtained by using the on-line calculator PASCAL described in the Cliffe & Goodwin paper, but also with the unit strain calculations implemented in PLATON  49  and those on the Bilbao Crystallographic Server. 50 he linear expansion coefficients were thus calculated along three orthogonal directions dened by the eigenvectors of the unit strain matrix. In the case of right angles between the cell parameters the uniaxial expansion coefficients coincide with those calculated along the crystallographic axes. When this is not the case, the coefficients are related to directions which are determined by the orthogonalization matrix of the cell metrics.
N. This can be done in two different ways, either by calculating the expansion between subsequent points T i and T i+1 :  where a ij is eigenvalue j between temperatures T iÀ1 and T i of the strain matrix. Alternatively, the expansion at each temperature can be calculated with respect to the lowest temperature. E j (T i ) ¼ À1. 0 + (1. 0 + E iÀ1,j ) Â (1. 0 + a ij Â (T i À T iÀ1 )) E j (T i ) ¼ a 0 ij Â (T i À T 0 ) where a 0 ij is eigenvalue j of the unit strain matrix between temperature T i and T 0. For N ¼ 1 the two methods are identical. The latter method is adopted by the algorithm in the PASCAL program and gives minor differences with the results when the expansion is calculated subsequently. A problem may arise by the calculation of the eigenvalues and eigenvectors of the strain matrices at more than two temperatures.
The dependency of the distributions on R 2 is relatively low (Table  S1  and Fig. S2 -S6 †), and even the global data set containing 4719 observations has a distribution which is not completely different from the high-quality reduced data set. The obtained mean volumetric thermal and uniaxial expansion coefficients in this way are 156. 0 and 51. 6 MK À1 , respectively, with standard deviations of 68. 7 and 73. 8 MK À1 for 4719 different volumetric coefficients and 14 157 axial values. These mean values are somewhat smaller than those obtained using the reduced subsets and the standard deviations are somewhat larger for the volumetric coefficients, but smaller for the axial coefficients. The values from the global dataset are nevertheless relatively close to the values of the reduced subset, giving condence that even cell parameters from studies in different laboratories performed on different machines can give valuable thermal expansion information.
Negative volumetric expansion coefficients Amazingly, 139 structures have been detected with negative volumetric thermal expansion. The different temperature data for each of the compounds have been measured in different laboratories, and most probably on diffractometers of different brands. Additional tests were carried out on these entries to extract the most probable candidates for true negative thermal volumetric expansion. Table  S2  † gives the 127 compounds remaining aer this selection with information about aV, the temperature interval over which it occurs, the number of temperature entries for each compound and the NTE capacity parameter c a which is the product of the relative volume contraction of the compound and the temperature range over which it is observed. 63 For each compound it was checked whether <U eq > s showed a positive tendency with temperature.
Related to this is the accurate determination of the temperature at the position of the crystal and its stability during the measurement. The cif-les of more recent structure determination contain oen a standard uncertainty for the data collection temperature, since the temperature can be logged continuously during the measurement, but this gives only a hint about the stability of the temperature, not on its accuracy. The second bias is related to inaccurate or missing information in the cif-les that are deposited at the CSD. A major source of error is the presence of structures measured at nonambient pressure, usually at room temperature but not only, and which are not agged as such in the cif-le. The structure of benzoic acid is reported 21 times in the CSD of which 11 have been recorded at room temperature.
Conclusions and outlook Data mining studies rely on the availability, diversity and redundancy of data. The Cambridge Structural Database is an extremely important source of available data from very different compounds, and from many different sources and at different times. The importance of redundant data inside this huge data base cannot be underestimated, since this helps in the worst case to discard faulty data and in the best case to increase precision. Therefore, scientists should be even more encouraged than they are now to deposit data from structures that have been published before. All practical crystallographers make very oen the decision to skip the full data collection once they discover from the preliminary cell screening and 'helped' by fast reduced cell checkers as CellCheckCSD 80 hat can be enabled during data collection, that the structure has already been published.
Background: Poor treatment adherence in COPD patients is associated with poor clinical outcomes and increased healthcare burden. Personalized approaches for adherence management, supported with technology-based interventions, may offer benefits to patients and providers but are currently unproven in terms of clinical outcomes as opposed to adherence outcomes. Methods: Maximizing Adherence and Gaining New Information For Your COPD (MAGNIFY COPD study), a pragmatic cluster randomized trial, aims to evaluate the impact of an adherence technology package (interventional package), comprising an adherence review, ongoing provision of a dual bronchodilator but with an add-on inhaler sensor device and a connected mobile application. This will compare time to treatment failure and other clinical outcomes in patients identified at high risk of exacerbations with historic poor treatment adherence as measured by prescription collection to mono/dual therapy over one year (1312 patients) versus usual care.
25 ere, we describe the protocol of the MAGNIFY (Maximizing Adherence and Gaining New Information For Your COPD) study, an ongoing (ISRCTN10567920), pragmatic, cluster RCT. MAGNIFY is the first of its kind that aims at evaluating the impact of an adherence technology package (interventional package) on time to treatment failure and other outcomes in patients with COPD at high risk of exacerbations and with historic poor treatment adherence to mono/dual therapy over one year. The intervention package includes Ultibro ® (indacaterol/glycopyrronium) Breezhaler ® (Novartis, Basel, Switzerland) with a Conformité Européenne (CE) marked add-on inhaler sensor device (add-on device) and app connected mobile application. Cluster randomization was preferred to avoid cross contamination within practices and keep the trial findings relevant for real-world practice by combining real-world evidence and randomization and utilizing a heterogeneous population to inform treatment effectiveness and healthcare decisions.
The exploratory objectives are to assess: (1) respiratory-related healthcare resource utilization and costs per patient per year, defined by assessing the costs from the Optimum Patient Care Research Database, (2) the intervention package acceptance rate at the ASA sites, and (3) primary and secondary endpoints, and respiratory-related healthcare resource utilization and costs, in the secondary and exploratory populations (see Methods section for definition on different populations), and in the group of patients in the ASA versus a subgroup of patients on indacaterol/glycopyrronium Breezhaler ® (standard) in the control and ASA arms.
Study Design MAGNIFY is a one-year, pragmatic, cluster randomized, multicenter, single-country trial to be run at an estimated 176 primary care practices in the UK starting January 2020. Considering the design and requirements of the study, the most efficient option was to perform the study in a setting where a single database with integrated prescribing data including refill rates and health outcomes of interest is available. This could be best obtained only in the UK, leading to design the study as a single-country trial. The primary care practice is the unit of randomization and the practices will be randomized in a 1:1 ratio to either the ASA or the control arm  27, 28  (Figure  1 ). Anonymized electronic medical record (EMR) data will be used at baseline to identify suitable patients at each site prior to the study start.
Electronic medical record data will then be extracted for defined data analyses in all sites on study completion (one year, i. e. 365 days). To allow feasibility assessment of practices as study sites and to collect outcome data, all participating study sites will have to participate in the Optimum Patient Care (OPC) quality improvement program for COPD. 29 OPC contributes data to the Optimum Patient Care Research Database (OPCRD), a UK based longitudinal electronic health record database of over 900 practices and 11 million patients with fully characterized chronic disease data. From these 900 practices, 176 practices will be selected to participate in the study based on their prescribing of the appropriate medicines to patients. The patients at practices in the ASA arm will get a full COPD quality improvement program including the OPC COPD review questionnaire and the intervention package, whereas the patients at practices in the control arm will continue to receive their usual routine clinical care.
Randomization Randomization code will be generated via a computer code and randomization will be stratified according to practice size, deprivation score and the percentage of patients with COPD with a record of FEV 1 in the preceding 12 months. The data for all three stratification factors can be obtained for all National Health Service primary care practices through Public Health England (and equivalent for the devolved nations). 30 For each factor, median value from nationally available data will be used as a cutoff to categorize each factor into two strata.
Sample Size Considerations The sample size was calculated based on existing data from over 800 primary care practices within the Optimum Patient Care Research Database (OPCRD). 32 ver 5555 patients in the OPCRD were identified as meeting the trial eligibility criteria. Of these, 3010 had outcome data available for 1 year and a further 2530 had a treatment failure within this outcome period. For the primary endpoint, assuming that the median time to treatment failure in the control arm is 90 days as observed on the OPCRD and based on a clinically meaningful increase of 20%  33  in the ASA (i. e. 108 days), we calculated that 82 practices per arm are required (total 164). This equates to a hazard in the control arm of 0. 007 per day and 0. 0064 in the ASA, giving a hazard ratio of 0. 83. Assuming that over 50% of suitable patients accept the add-on device and app technology (on average, 17 suitable patients will be identified at 200 practices to qualify as suitable for the trial; of the suitable patients, it is estimated that an average of 8 will accept the technology), a total sample size of 1312 patients will provide 80% power, based on a 5% level of significance and an Intraclass Correlation Coefficient (ICC) of 0.
038. To allow for a practice drop-out rate of approximately 6%, 88 practices per arm will be enrolled. The ICC was calculated in the OPCRD dataset of 2530 patients from 508 practices, using a one-way random effects analysis of variance model, with the model fit in Stata V14 (Stata Corp, College Station, TX, USA) using the "loneway" command specifying "failure time" as the dependent variable, and practice ID as the independent variable. 34 atistical Methods.
Additional details on the analysis, models and assumptions are described in Supplementary File S2. The secondary endpoints will be analyzed in the primary population. For the adherence endpoint, we will use a linear mixed model to compare the mean medication possession ratio between the two arms. Random effects for the practice and fixed effects for the stratification factors used in the randomization algorithm will be included in the model. Level of significance will be set at 5% for both endpoints. Data will be analyzed in Stata V14 (Stata Corp, College Station, TX, USA). Details on exploratory and sensitivity analyses are described in Supplementary File S3. include index date, treatment failures and impact on prescribing decisions. The description for the sources and the measures taken to address these, are described in Table  2.
38 Notably, they minimize the risk of cross contamination between intervention and control group patients within the same practice. Pragmatic study design sets the study in real clinical practice as far as possible. With the widespread use of electronic health records, there are increasing opportunities to integrate pragmatic cluster trial designs with real-world datasets to improve patient safety, data accuracy, and clinical trial efficiency. Various approaches are being considered to monitor medication adherence because improving adherence to therapy has shown substantial health and economic benefits. 39 A personalized treatment approach aided with digital inhaler technologies is one such approach. 19, 40 echnology-enabled inhalers offer a range of support such as patient reminders, feedback on inhaler technique and timing, and sensors to analyze inspiratory flow rates, and characterize inhaler technique.
ACKNOWLEDGMENTS We thank  Fabien Alet ,  Zhengcheng Gu ,  Andreas Läuchli ,  Wenyuan Liu ,  Pierre Pujol ,  Anders Sandvik , and  Sandro Sorella  for helpful discussions. This work was granted access to the HPC resources of CALMIP supercomputing center under the allocation  2020-P1231. This project is supported by the  TNSTRONG ANR-16-CE30-0025  and the TNTOP  ANR-18-CE30-0026-01  grants awarded by the  French Research Council.
Context. The Vela complex is a region of the sky that gathers several stellar and interstellar structures in a few hundred square degrees. Aims. Gaia data now allows us to obtain a 3D view of the Vela interstellar structures through the dust extinction. Methods. We used the FEDReD (Field Extinction-Distance Relation Deconvolver) algorithm on near-infrared 2MASS data, crossmatched with the Gaia DR2 catalogue, to obtain a 3D cube of extinction density. We applied the FellWalker algorithm on this cube to locate clumps and dense structures. Results. We analysed 18 million stars on 450 deg 2 to obtain the extinction density of the Vela complex from 0. 5 to 8 kpc at ∈ [250 • , 280 • ] and b ∈ [-10 • , 5 • ]. This cube reveals the complete morphology of known structures and relations between them.
There are also some 3D extinction maps which cover the Vela direction. Capitanio et al. (2017)  and  Lallement et al. (2019)  applied the 3D inversion technique of  Vergely et al. (2001)  on composite dust proxies using respectively the Gaia DR1 parallaxes and the 2MASS crossmatch with Gaia DR2. Chen et al. (2019)  used a random forest algorithm on a dataset built with Gaia DR2, WISE and 2MASS to compute the extinction density. Hottier et al. (2020)  analysed 2MASS and Gaia DR2 data with the FEDReD algorithm  (Babusiaux et al. 2020 ) and derived the extinction density in the Galactic plane. However, as far as we know, there is no study focussing on the 3D extinction structure of the Vela complex. In a previous work  (Hottier et al. 2020) , we noticed that the Vela complex presents large structures that reach distances up to 5kpc.
We also discussed in  Hottier et al. (2020)  the possibility that the Vela complex could belong to the local arm. This belonging seems to be confirmed by the spiral arms locations found by  Khoperskov et al. (2020). Studying the Vela complex could therefore give the opportunity to study a spiral arm viewed from the inside. In this work we used FEDReD to probe the 3D extinction density distribution towards Vela. In section 2 we present the analysed dataset. Section 3 sums up the FEDReD algorithm and develops the differences from  Hottier et al. (2020). In Sec 4 we present the method of clump extraction. Section 5 describes and analyses the three dimensional density map and the clumps and cavities extracted from it.
Data In this study, we use the infrared photometry in bands J, H and K from the 2MASS survey  (Skrutskie et al. 2006 ) and we combine them with the photometry in bands G, G BP and G RP and the astrometry both coming from the Gaia DR2 (Gaia  Collaboration et al. 2018). We use the same methodology as  Hottier et al. (2020)  to filter and merge data of these surveys, so we will just briefly review them here. We use the 2MASS near-infrared photometry as principal data, that is our dataset completeness will be the 2MASS one. In practice, every star included in our dataset has ph_qual≥D in all three J, H and K bands. Once we have selected the stars in 2MASS, we use the  Marrese et al. (2019)  crossmatch to potentially add Gaia DR2 parallaxes and photometry. To filter the Gaia photometry  (Evans et al.
Extinction density towards Vela In Fig. 3  we present the extinction density a 0 cut along constant Galactic latitudes. Fig. 4  shows views of the extinction density at several distances. We also add on those two figures, the contours of clumps that we segmented with the FellWalker algorithm. On both figures, we do not provide the extinction density close to the Sun because of the red clump visibility criterion (see Sect 3. 1). To see the closeby Gum Nebula and the VSNR, we used instead the  Lallement et al. (2019)  extinction cube. They used the same data as this work (2MASS and Gaia DR2) restricted to stars with relative error on the parallax less than 20%, and they apply the same extinction law (see Appendix. A for a more detailed comparison). In Fig. 5  we plot the extinction density that they obtain at the canonical distance of the Gum Nebula and VSNR.
Acknowledgements. The authors thank the referee for comments and suggestions that improved the paper. C. H. thanks the  Centre National d'Études Spatiales (CNES)  for the  Gaia post-doctoral grant. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This work makes use of data products from the  2MASS , which is a joint project of the  University of Massachusetts  and the  Infrared Processing and Analysis Center/California Institute of Technology , funded by the  National Aeronautics and Space Administration  and the  National Science Foundation.
One of the essential diagnostic tools of cardiac arrhythmia is activation mapping. Noninvasive current mapping procedures include electrocardiographic imaging. It allows reconstructing heart surface potentials from measured body surface potentials. Then, activation maps are generated using the heart surface potentials. Recently, a study suggests to deploy artificial neural networks to estimate activation maps directly from body surface potential measurements. Here we carry out a comparative study between the data-driven approach DirectMap and noninvasive classic technique based on reconstructed heart surface potentials using both Finite element method combined with L1-norm regularization (FEM-L1) and the spatial adaptation of Time-delay neural networks (SATDNN-AT). In this work, we assess the performance of the three approaches using a synthetic single paced-rhythm dataset generated on the atria surface.
Another recent study introduced a physics-informed neural networks for cardiac activation mapping  (Sahli Costabal et al. , 2020). In continuity with our previous works, we conduct a comparative study to evaluate quantitatively the performance of the data-driven methods: DirectMap  (Karoui et al. , 2019a)  and the Spatial Adaptation of Time-Delay Neural Network (SATDNN-AT)  (Karoui et al. , 2019b)  compared to the classic inverse method: Finite Element Method combined with L1-norm regularization (FEM-L1)  (Karoui et al. , 2018). The choice of these two methods is based on their performance results reported in  Karoui et al. (2018)  and  Karoui et al. (2019b). The study is conducted using atrial paced in-silico data. This manuscript is organized as follows: in section 2. 1, we introduce the 3 methods, the synthetic data we use and the evaluation metrics.
Database We build a synthetic paced-rhythm dataset of 101 simulations of BSPs and their correspondent activation time (AT) maps on the atrial outer surface. Each sample of BSPs and AT map corresponds to a single stimulation site randomly distributed on the atrial surface. We use the monodomain reaction-diffusion model to simulate the electrical wave propagation inside the heart. In order to simulate the BSPs, we first need to compute the extracellular potential in the heart (EGMs). Then, we use a Laplace's equation in the torso with a Dirichlet boundary condition on the heart-torso interface to compute the BSPs. For more details, see  Zemzemi et al. (2013). Activation times are derived from the simulated EGMs by determining the IDT (Intrinsic Deflection Time) at each point of the atrial mesh.
(12) Here, M 1,2,3 are network layer weights and b 1,2,3 are biases. The method consists of using the ECGs of a heartbeat sequence as input to the artificial neural network in order to estimate the corresponding activation time map. A study is conducted over the neural network layers size. The aim is to determine the optimal model architecture for a given dataset. In order to guarantee a predefined accuracy on activation times reconstruction with the lower possible dataset size, we conducted a grid search procedure, allowing to determine simultaneously the maximal sparsity in the training data set and the optimal architecture of the neural network. We defined a threshold equal to 15 ms in terms of absolute error to choose the model with the minimal dataset size achieving a performance under this threshold.
Implementation Data-driven models are implemented using Pytorch  (Paszke et al. , 2019). To train our models over labeled data, we use the mean squared error as an optimization criterion and the stochastic gradient descent as an optimization algorithm. K-fold cross validation  (Refaeilzadeh et al. , 2009)  is used to evaluate the model performance on unseen data. It generally results on a less biased estimation of the target. The procedure consists on splitting the dataset on a training-validation dataset and a testing dataset. Then, K-fold cross validation is applied on the training-validation dataset. In fact, this latter is splitted into K groups. Each unique group is once kept as a validation dataset and all the remaining ones are used for training the model. In the end, the trained models are evaluated over the testing dataset.
The training phase ends when the optimization criterion stops improving over the validation dataset. Hyper-parameters of the models are tuned empirically based on the performance of the models on the validation dataset. Learning rate and momentum are, respectively, 0. 00001 and 0. 8. The cross validation parameter K is equal to 4. Training and validation subsets are shuffled at each epoch of the training process. The time-window size d of SATDNN-AT is equal to 2. Concerning the physics-based method, we developed the numerical tools into MUSIC software (Multi-modality Platform for Specific Imaging in Cardiology)  (Cochet et al. , 2014). More information about the MUSIC platform could be found in the following link: https://www. ihu-liryc. fr/en/music. MUSIC is intended for cardiac imaging processing, cardiac mapping analysis and electrocardiographic imaging inverse problem resolution.