dataesr/scientific-paragraphs-categorization huggingface dataset:
Doctoral school Complexitedu vivant (ED515) (contract number  2449/2016 ) (http:// www. ed515. upmc. fr/fr/index. php); INCEPTION  project ( PIA/ANR-16-CONV-0005 ) (https:// research. pasteur. fr/en/program_project/inception/)The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Our method was compared with the Global Registry of Acute Coronary Events (GRACE) Score 2. 0 on a test dataset. Results. For in-hospital mortality, our model achieved a c-statistic of 0. 89 while the GRACE score 2. 0 achieved 0. 90. For six-month mortality, the results of our model and the GRACE score on the test set were 0. 77 and 0. 73, respectively. Red cell distribution width (HR 1. 23; 95% CL 1. 16-1. 30; P < 0 001) and neutrophil to lymphocyte ratio (HR 1. 08; 95% CL 1. 05-1. 10; P < 0 001) showed independent association with all-cause mortality in multivariable Cox regression. Conclusions. Hematological markers, such as neutrophil count and red cell distribution width have a strong association with all-cause mortality after acute coronary syndrome. A machine-learned model which uses the abovementioned parameters can provide long-term predictions of accuracy comparable or superior to well-validated risk scores.
Methods We retrospectively examined electronic medical records of patients admitted to a cardiology department between January 2012 and December 2016 to select all patients hospitalized because of an ACS. The analyzed group comprised of patients who had their diagnosis confirmed by a cardiologist according to ESC guidelines  [2]. 5053 individual patients were qualified (1522 with STEMI, 857 with NSTEMI, and 2674 with unstable angina). We analyzed the descriptions of the electrocardiograms in the patient's medical records to identify patients who had an ST-segment elevation (n = 1522) or any ST-segment deviation-elevation or depression (n = 4420) according to current guidelines. We obtained information on all-cause death or survival and on the exact date of death from the national death registry one year after the end of data collection.
During the period of data collection, both Troponin I and Troponin T were used. Therefore, we expressed troponin elevation as a ratio (actual value divided by the norm). Statistical analyses were performed using the RStudio Software. The Shapiro-Wilk test was used to test the variables' distribution for normality. Most of the analyzed variables did not have a normal distribution. Median and interquartile ranges were selected as measures of central tendency. The univariable two-tailed Mann-Whitney U test was used to compare numerical features. We created a multivariable Cox regression model using variables with statistically significant differences (P value <0. 05) in univariate analysis. 310 observations were excluded from the analysis because of missing values. We did not use automated stepwise backward elimination.
0 death probability based on relevant clinical and laboratory features. As the information about Killip class and creatinine level was available for almost all patients, the full version of the   1. The remaining missing values which did not affect the calculation of the GRACE score were imputed using mean of all observations. The gradient-boosted tree algorithm was implemented using the xgboost  [18]  software package. One-fifth of the available data (n = 994) was put aside as a test set and not used for training. Observations for the test set were chosen randomly, but in a way that preserved the ratio of positive to negative class (death and survival). The ML classifier was optimized using the training data only (n = 3975), using the 5-fold cross-validation. In this process, the training data was divided into 5 parts, and each of these parts was used to train the classifier and to measure its performance.
Study Limitations In our study, we retrospectively analyzed the electronic medical records of patients hospitalized over several years. This allowed for rapid development on an ML algorithm but is also a significant limitation. Data stored in medical records are often incomplete, complex, messy, and can be biased  [31]. The naive use of raw medical records as input for either inferential statistics or machine learning models can lead to false conclusions. A good example of such situation is the study of Fine et al. , in which patients who were admitted with severe communityacquired pneumonia and died in the emergency department had very little information stored in medical records. As a result, some deceased patients appeared healthier than those who survived  [32]. The most concerning limitation of our study is related to variables that were stored in medical records as unstructured data in the form of physicians' notes (e.
Finally, although the study included patients hospitalized over many years, this dataset is still modest in terms of machine learning model development. The performance of our classifier varied slightly, depending on which observations were chosen randomly for the test set. In contrast, GRACE score was validated on over 100000 patients worldwide, thus the evidence that supports its usefulness is strong. We do not aim to prove that our method is better than any existing well-validated risk score, but to present a new approach to long-term risk prediction in ACS based on different analytic methods and different variables than existing scores.
Acknowledgments The Python implementation of the GRACE 2. 0 death probability calculation, as well as the Python implementation of our classifier, will be published on GitHub repository https://github. com/konradpieszkoupon publication of this manuscript. The Center for Outcomes Research did not evaluate nor formally approve our implementation of the GRACE 2. 0 algorithm. However, they were informed about it and had no objections to us sharing the source code of our implementation.
Simulations We have carried out a set of twenty forced-dissipative simulations of the SW equations in a quadratic domain with periodic boundary conditions using the standard open-source pseudo-spectral code FluidSim  (Augier, Mohanan & Bonamy 2019; Mohanan, Bonamy & Augier 2019a,b). In these twenty simulations we used an eighth-order diffusion operator to act as the agent of dissipation. On the request from the reviewers we have also carried out a set of nineteen simulations with Laplacian diffusion. The results from the latter set, which are similar to the results from the first set, are presented in appendix A. The simulations were started from zero initial conditions and were run till a statistically stationary state was reached. The equations were reformulated in terms of the three eigenmodes of the linearised equations in Fourier space.
Acknowledgements The authors would like to thank  G. Falkovich  and  S. Nazarenko  for fruitful criticism of an early version of the manuscript,  S. Galtier  for making a check of the final manuscript and three anonymous reviewers for useful comments. Financial support from the  Swedish Research Council (VR)  is gratefully acknowledged (grant no. D0519101 ). The simulations were performed on resources provided by the  Swedish National Infrastructure for Computing (SNIC)  at PDC. https://doi. org/10. 1017/jfm. 2019. 375Downloaded from https://www. cambridge. org/core. IP address:.
Acknowledgments This work was granted access to the HPC resources of CINES, under the allocation 2018-A0040510482 made by  GENCI  and of  Aix-Marseille Université  financed by the project  Equip@Meso  ( ANR-10-EQPX-29-01 ) of the programme " Investissements d'Avenir " supervised by the  Agence Nationale pour la Recherche. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement number  633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission.
This reference is kept until the axial motion correction procedure is deactivated. After the measurement, a corresponding voltage is sent to the voice-coil translation stage, which moves the whole reference arm of the FF-OCT to compensate for the axial motion, re-matching the reference arm with the retinal layer of interest. A custom-developed real-time calculator, comprising core libraries written in MATLAB, controls the axial motion stabilization closed-loop with a 50 Hz loop rate. Control strategy is a PI (proportional-integral) controller with 0. 5 gain and the main steps of the chronogram are: SD-OCT B-scan acquisition (7 ms), SD-OCT B-scan processing (2 ms), axial eye motion measurement (1 ms) and voice-coil translation stage reaction and positioning (10 ms).
During the imaging session, subjects were wearing their prescription glasses to increase the signal level of both SD-OCT and FF-OCT  [10]. Image acquisition was realized in a dark room, maximizing the pupil dilation. Four sets of data were recorded simultaneously during imaging sessions: FF-OCT images, SD-OCT B-scans, retinal axial position and the position of the voice-coil translation stage (equivalent to the position of the FF-OCT reference mirror). Phase modulation was performed by the residual axial motion after optical stabilization (i. e. the residual tracking error). FF-OCT images were acquired at 300 Hz using custom-built software  [17]. The FF-OCT camera worked close to saturation to use the whole full well capacity, decreasing relative importance of shot noise  [18]. Concerning the SD-OCT, we empirically chose to scan 1 o FOV of the retina with 256 A-scans, providing a good trade-off between acquisition speed and SNR.
Finally, useful images were registered using a custom-built normalized cross-correlation algorithm, where the image presenting the highest signal level was chosen as the reference. C ) and (D) illustrate again the axial motion as a function of time, but now applied to the acquired averaged A-scans, where the red rectangle indicates the coherence gate thickness of the FF-OCT system, i. e. depth positions where interference takes place. The continuous motion drastically reduces the chances of acquiring an FF-OCT image of a layer of interest (here the inner/outer segment junction -IS/OS). From the collected data set of retinal axial position temporal evolution (excluding blink occurrences), we computed a peak-to-valley amplitude of 244. 9 ± 49. 2µm, a mean deviation from zero position of 62. 5 ± 29.
In this paper, a systematic model-development strategy using model-based experimental design, consisting of initial data acquisition, model development, model identification, model validation and model refining, is developed. Model development is based on the reaction network proposed within the reaction supernetwork containing all feasible chemical reactions and mass transfers. For model identification, validation and refining, model-based experimental design is performed by taking into consideration several reactors, which enlarges the explored experimental windows. Based on this strategy, a computer-aided software, facilitating the user's work, is developed for model development of liquid-phase reaction systems. The application of the strategy is demonstrated with an experimental case study involving NaOH-catalyzed ethanolysis of sunflower oil.
Model development module and initial data acquisition module The first steps consist of developing one or several candidate sets of model structures (step 1) (see Section 2. 2), defining the operating space and a set of variables to be actually measured (step 2). Given these information, model structure analysis is performed for each set of model structures to evaluate whether its parameters are identifiable and whether it can be distinguished from the others (step 3). The default method is the direct one, but if the SMUTs possess the same or similar parameter estimation criteria, the stepwise one seems better to be used.
Computer-aided software The systematic model-development strategy for liquid-phase reaction systems is implemented as a computer-aided software using MATLAB R2014a. In order to reduce software development difficulty, following simplifications have been done without losing the fundamental functions: • The reaction supernetwork is developed through empirical method; • The reaction network under test is proposed by the user on the basis of his expertise; • The kinetic orders cannot be considered as the parameters to be estimated; • Model structure analysis is not integrated into the software; • Model adequacy is qualitatively evaluated; • If several SMUTs are available, the most suitable SMUT is directly identified. The developed software provides user-friendly interfaces and guides the user for model development through the modules of the workflow (illustrated in Figure  3 ).
Submodule D1: Design data acquisition experiments The initial data acquisition experiments (i. e. the PEs) are designed by user, and the experiment design results are shown in Table  9  in terms of practical operating variables, from which the operating conditions in general form are derived and input into the software. Experiments under different operating variables can be carried out during one single run of the setup: for instance, 8 experiments under the same initial operating variables with different experiment durations (i. e. sampling times) using the BSTR are carried out in the 1 st run. For the PEs using the BSTR, samples are taken at incremental time intervals, not uniform time intervals, since the reaction progress is rapid, necessary samples should be taken at the beginning.
Submodule D2: Input experimental data The measured responses of the PEs are input into the software. The measurement error of each measurable variable, which will be used as the threshold to evaluate model prediction accuracy at F2 (see Sections 3. 2. 3. 5, 3. 2. 5. 4 and 3. 2. 7. 4) and F3 (see Section 3. 2. 9. 1), has to be defined here. For this case study, that for the measurable variable of type concentration is set as 5 mol/m 3.
It is not easy to interpret quantitatively why the software proposes such operating conditions, since the interaction among the reactions defined in the RN1 is complex. For this case study, the reactions, whose parameters log k j,T max and log k j,T min are both lower than -7. 5, are considered as reactions with relatively long characteristic times. It is noticed that the inverse reactions contain more such reactions in comparison to the forward reactions (see Table  10 ). Data acquisition efficiency is related to the sensitivity coefficient, which is defined as the time-varying derivatives of model responses with respect to the model parameters (see Eq. (  16 )). The parameters involved in the inverse reactions with the long characteristic times are more sensitive than the parameters of the other reactions at the operating conditions identified above.
a The oil used is sunflower oil; the catalyst used is NaOH; the practical operating variables are: (i) reaction temperature of 45, 60 °C, (ii) ethanol-to-oil molar ratio of 6:1, 12:1, (iii) NaOH amount of 0. 5, 1. 0 g per 100 g of oil. b The oils used are canola, palm, peanut, soybean and sunflower oils; the catalysts used are KOH and NaOH, the practical operating variables are: (i) reaction temperature of 40, 50, 60 °C, (ii) ethanol-to-oil molar ratio of 6:1, (iii) NaOH amount of 0. 8 g per 100 g of oil. Furthermore, there are a number of limitations to the computer-aided software in its current version. Following the failure of the SMUTs, a new reaction network used for model structure development is currently proposed through empirical method by the user, but a better prediction quality of the new candidate set of model structures is not guaranteed without model parameter estimation, which takes much computation time.
All the above steps can be described in the same manner as conventional chemical reactions by considering the adsorbed reactant/product as pseudo-species, refering to the representation of mass transfer between two phases in this study. The model structure for a given gas-solid catalytic reaction system in a given reactor can be constructed after: (i) developing of reaction network within the reaction supernetwork including all feasible chemical reactions, adsorptions and desorptions; (ii) defining the rate law for each of the involved chemical reactions, adsorptions and desorptions; (iii) indicating the (characteristics of) given reactor type. Therefore, in order that the strategy as well as the strategy-based software can be applied in more reaction systems, it is necessary to expand the database of reaction types, rate laws, and reactor models.
Biodiversity is being lost at an unprecedented rate, and monitoring is crucial for understanding the causal drivers and assessing solutions. Most biodiversity monitoring data are collected by volunteers through citizen science projects, and often crucial information is lacking to account for the inevitable biases that observers introduce during data collection. We contend that citizen science projects intended to support biodiversity monitoring must gather information about the observation process as well as species occurrence. We illustrate this using eBird, a global citizen science project that collects information on bird occurrences as well as vital contextual information on the observation process while maintaining broad participation. Our fundamental argument is that regardless of what species are being monitored, when citizen science projects collect a small set of basic information about how participants make their observations, the scientific value of the data collected will be dramatically improved.
However, these projects vary greatly in the types of information that they collect, with important consequences for the ability of each to meet its intended outcomes for science and society. In this article, we argue that citizen science projects aimed at robustly monitoring species distributions should follow several basic data-collection principles to provide a solid foundation for data analysis. We build on existing recommendations for biological monitoring programs that collect sufficient information on observation processes such that resulting data can be used for statistical analyses  (Yoccoz et al. 2001). Although, in this article, we focus on birds and use eBird as an exemplar, our intent is to emphasize that anyone designing, or redesigning, a citizen science project that is gathering observational data on any organism or any taxa should follow these principles.
Counts of all butterflies are recorded in a 5 meters wide band along each transect, and if a species is not reported, it is considered undetected or absent. The participants are highly motivated and trained in the survey methodology. Although the method is labor intensive, the project has allowed the calculation of robust site-level trend estimates for more than 50 species of butterflies  (Fox et al. 2011)  and has contributed to more than 100 peer-reviewed publications. At the opposite end of the continuum unstructured projects have few requirements for data collection, and the participants report only when they opportunistically encounter a species (table 1). For example, iNaturalist is successful in recruiting volunteers to submit observations and images of species they detect. However, the observations have limited application for monitoring populations of organisms because the project collects little information about how the participant made their observations, which are crucial for subsequent scientific analyses  (Guillera-Arroita et al.
Whereas a highly structured project attempts to eliminate variation in the data collection with strict protocols, a semistructured project gathers just enough data to control variation and bias during analysis  (Fink et al. 2010). One example of a semistructured survey project that allows open participation and observer-selected sites is eBird  (Sullivan et al. 2014). What characterizes eBird as a semistructured survey is that it provides the option to collect information on how the participant made their observations; for example, the duration of data collection, the distance an observer traveled while collecting observations, inferring the nondetection of a species (e. g. , from a complete checklist), and other facets of the data-collection event that can affect the probability that a species will be detected, identified, and recorded.
more homogeneous observation behavior, helping further analysis. What is observed? Most citizen science monitoring projects identify a specific taxonomic or ecological scope, because developing protocols that effectively gather observations of one taxon (e. g. , mammals) would be different from another (e. g. , stream invertebrates). We recommend clearly defining the taxonomic scope to ensure that the observation process is well described and as straightforward as possible. Where are the observations collected? Most semi-or unstructured projects allow the observer to select their preferred observation location. Regardless of how a location is selected, we recommend accurately recording where data are collected. Spatial aggregation to lower resolution is always possible at a later stage, e.
2009). Regardless of the monitoring project, knowing who collected each observation is important. In this way bias related to variability in observer behavior and expertise can be estimated and taken into account. We recommend retaining clear information about who collected the observations in the data management framework, by using coded identifiers that are unique to individual observer. How are the observations collected? A high level of data quality can be maintained in the absence of a rigid sampling design and protocols if the observation event is well described. Not only should this description include information on what, where, when, and who made the observations, but also how observations of species are made. Recording all species generates a "complete checklist, " which directly provides information on species detected and inferred information on species not detected.
2015). Other key aspects of how the observations were collected may include the distance travelled or any variables that describe variation in the area surveyed or the method of surveying, which affect the detectability of individuals. eBird, a semistructured citizen science project eBird is a semistructured citizen science project with an open project design and minimal protocol requirements  (Sullivan et al. 2014 ). To date, eBird's more than 400,000 observers have volunteered 40 million hours of effort in reporting bird observations. No restrictions are placed on who can participate, where and when they participate, or how they participate. However, observers are encouraged to submit "complete checklists. " eBird has maintained 20% or more annual growth in data collection for more than a decade, and as of October 2018, eBird data include 31 million complete checklists containing 567 million observations of more than 10,000 bird species from every country in the world.
An important part of these complete checklists is the description of the observation process. eBird answers the six questions above as follows: Why is eBird being conducted? eBird collects data that will be used to estimate the distribution and abundance of bird populations by taking advantage of the global network of bird enthusiasts who submit their observations to a central data repository. What is observed? eBird gathers observations and counts of all wild bird species. Where are the observations collected? eBird participants can select where they make their observations from any global location. All locations are georeferenced either through mapping tools provided on the eBird website or through the GPS system available on mobile phones and used within the freely available eBird App. The mobile App also records the track and distance the observer travelled while making observations.