dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collected included information on bacterial identification and virologic, bacteriologic, mycologic, and parasitologic laboratory results. Currently, PA-CASurvE is particularly geared toward the monitoring of bacterial species. We defined a case as illness in a patient from whom >1 bacterial species was isolated and confirmed. Two different bacterial species isolated from samples collected from the same patient resulted in 2 reported cases. The samples without bacterial identification were considered to be negative. Laboratories were free to use the microbiologic methods of their choice to identify bacterial species, including PCR, matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry, or conventional phenotypic methods (e. g. , Gram coloration and API galleries).
Data Flow, Analyses, and Feedback All the steps of data flow, analyses, and feedback were mapped (Figure  2 ). Every week, biologists at participating institutions sent a report of new cases, in the form of anonymized data contained in Excel spreadsheets or encrypted PDFs, to the system coordinator. The first step before analysis was validating and standardizing the reported data, which were checked, cleaned, and deduplicated. The search for duplicates was performed weekly according to the unique patient identifier and the isolated microorganism, and data were then automatically compared with a thesaurus of all identified bacterial species  (10). All analysis steps (e. g. , deduplication, merging of data from different laboratories, statistical analysis, and visualization tools production) were performed automatically by using a specific algorithm written in a Visual Basic (Microsoft) script for Excel.
To detect outbreaks as early as possible, an alarm was triggered when the weekly count of cases for a bacterial species was higher than 2 SDs of the mean of historical data since the beginning of surveillance for each laboratory  (10). After 6 months, once the collection procedures were stabilized, the C1-mild epidemics detection method used by the Early Aberration Reporting System  (18, 19)  was performed. This method enables the detection of outbreaks on a dataset with limited historical data (>7 weeks). Both of these statistical methods operate in parallel at the Institut Hospitalo-Universitaire Méditérannée Infection and CESPA. A statistical alarm is triggered if the observed value is significantly different from the expected value  (16). After checking biologic criteria, alarms were assessed as confirmed or unconfirmed by senior biologists during the weekly AP-HM epidemiologic surveillance meeting.
Discussion Since July 2013, we have been operating a biosurveillance system based on a network of clinical microbiology laboratories in the PACA region to monitor infectious diseases, especially those attributable to bacterial species. To date, to our knowledge, the PACASurvE network is unique in France. It is a collaborative system (one relying on the participation of private and hospital laboratories) which is different from BALYSES (a hospital system)  (10). Similar biosurveillance networks were developed in Belgium  (14, 24) , the United Kingdom  (13) , and the United States, where the Laboratory Response Network was implemented by the Centers for Disease Control and Prevention in 1999  (25). In Belgium, a sentinel network using microbiology laboratory data was created for the weekly monitoring of selected pathogens  (24, 26).
The first and main strength of the system is that it can easily be replicated, thanks to its low implementation cost and its use of Excel software. The use of this software allowed the system to be set up rapidly, and any necessary modifications can be made easily, compared with the software used by other surveillance systems, such as Real Time Outbreak and Disease Surveillance  (27). This simplicity could allow it to be implemented in developing countries. Continuous improvement also is a major strength of PACASurvE. The number of laboratories increases regularly, which improves the representativeness of the PACA region. Improving geographic representativeness is important for the purposes of extrapolating our results or extending the system to other regions. Currently, the coverage of the system includes 214 laboratories in the PACA region, representing 81% of major urban areas of the PACA region (e.
Our biosurveillance system has some limitations. The first relates to the statistical analyses used at the beginning for the detection of abnormal events. The use of a threshold of 2 SDs higher than the historical weekly mean is not necessarily appropriate, although it is easy to set up rapidly and enabled detection of an abnormal event. At the beginning of the surveillance system data collection, in the absence of strong historical data, this basic algorithm seemed to be the most appropriate and easy to use given the circumstances. After studying the methods used to address seasonal variations and sporadic emergence of rare bacterial species as described by Enki et al. (13)  in 2013, Farrington et al. (28)  in 1996, Buckeridge et al. (29)  in 2004, and Frickers et al. (30)  in 2008, we decided to implement another method and this introduced the C1-mild epidemics detection method  (18)  with R software into the surveillance package.
This method is now used routinely in CESPA. The second limitation concerns the laboratories' willingness to participate, which could lead to problems in reporting. For example, we have to encourage laboratories to report their data automatically by using the Internet platform. After only 2 years in operation, the results achieved by our network are already promising. The economic cost of this system will be calculated, being a major criteria for the first planned evaluation of the system. In the future, we will improve the completeness of transmitted data and will try to extend our network to other regions in France. This type of regional biosurveillance network could be linked to data from existing networks implemented by the National Institute for Public Health Surveillance (Santé Publique France) to enable comprehensive surveillance of all French territory.
Acknowledgments We would like to thank  Gaetan Texier  for his help with the organization of his paper. We would also like to thank the additional members of PACASurvE who contributed data:  Jean Paul Casalta ,  Pierre Yves Levy ,  Jean Pierre Arzouni ,  Philippe Halfon ,  Alexandre Toro ,  Patricia Roussellier ,  Raymond Ruimy ,  Philippe Seyral ,  Jean Dominique Poveda ,  Éric Garnotel ,  Patrick Brunet ,  Pierre Hance ,  Nathalie Brieu ,  Christophe Maruejouls ,  Olivier Ridoux ,  Evelyne Jean.
Following the release and initial testing period of the NEA's Nuclear Data Sensitivity Tool [1], new features have been designed and implemented in order to expand its uncertainty analysis capabilities. The aim is to provide a free online tool for integral benchmark testing, that is both efficient and comprehensive, meeting the needs of the nuclear data and benchmark testing communities. New features include access to P1 sensitivities for neutron scattering angular distribution [2] and constrained Chi sensitivities for the prompt fission neutron energy sampling. Both of these are compatible with covariance data accessed via the JANIS nuclear data software, enabling propagation of the resultant uncertainties in k eff to a large series of integral experiment benchmarks. These capabilities are available using a number of different covariance libraries e.
g. , ENDF/B, JEFF, JENDL and TENDL, allowing comparison of the broad range of results it is possible to obtain. The IRPhE database of reactor physics measurements is now also accessible within the tool in addition to the criticality benchmarks from ICSBEP. Other improvements include the ability to determine and visualise the energy dependence of a given calculated result in order to better identify specific regions of importance or high uncertainty contribution. Sorting and statistical analysis of the selected benchmark suite is now also provided. Examples of the plots generated by the software are included to illustrate such capabilities. Finally, a number of analytical expressions, for example Maxwellian and Watt fission spectra will be included. This will allow the analyst to determine the impact of varying such distributions within the data evaluation, either through adjustment of parameters within the expressions, or by comparison to a more general probability distribution fitted to measured data.
Revised input and output features Several new features revising the overall "look" of the NDaST software have been developed for the 2016 release version. Here we describe and illustrate the most important of these new-look input and output features: In addition to the Database for the International Handbook of Evaluated Criticality Safety Benchmark Experiments (DICE), the new version allows access to benchmarks from the International Reactor Physics Handbook Database and Analysis Tool (IDAT). This uses the same familiar search interface as the main IDAT version, with some bespoke modifications. The data accessible is currently limited to criticality measurements, as these are the only data to which sensitivity profiles are associated within the NEA online database. Users' own benchmarks and sensitivities for other integral parameters may still be input via the 'New Benchmark' feature.
Sorting of output figures by individual and common criteria (for instance evaluation ID, spectrum, calculation code etc. ) now takes into account DICE and IDAT properties in a single plot. Similarly to the DICE and IDAT software tool plots, NDaST now features 'tool-tips'; these floating windows appear when the cursor rests on a bar within a plot, giving the underlying quantitative values. This will now allow users to quickly access data on the number of points constituting each 'grouping' and mean and standard deviation values for keff, original and perturbed keff C/E values and the propagated nuclear data uncertainty. There have also been several fundamental improvements to the output window presentation. Firstly, in addition to the k eff and data variance, the output table now shows additional columns for the (mean) original and perturbed k eff C/E values of each benchmark.
Automated JANIS computations In order to more easily facilitate the input of perturbation series to NDaST, an automated link has been introduced to the JANIS nuclear data viewing software. From a search dialogue within the 'Perturbations' panel of NDaST, the user may search their public / private JANIS databases for a given nuclide and reaction combination. Selection of two nuclear data evaluations returned from this search as numerator and denominator prompts the software to generate the relevant perturbation ratios. This ratio is  shown graphically in a plot generated at the bottom of the dialogue window (Fig. 3 ). These are represented within any energy group structure required -either single group, equally spaced logarithmically, or more generally as supplied by the user in text file format.
A number of common analytical spectrum weightings may also be applied from a fixed list: Constant / PWR / General / Maxwellian / Fission; all of which may be characterised by their E and Theta values as chosen by the user (default values are also given). More specificity can be obtained through use of simple X-Y text file input spectra, where either linear or logarithmic interpolation may be applied to either dimension. NDaST is also being designed to provide a 'file upload' feature, so as to avoid the necessity to already have the desired data libraries held in a JANIS database prior to launching the software. This is primarily aimed at those users testing new evaluations, and wanting to project the effect of intermediary cross-section changes that might be suggested, to the testing suite of integral experiments.
Another option it will present is a capability to run a 'Total Monte Carlo' (TMC)-like bulk series of perturbation calculations; in an analogous way 'Total NDaST' could be used to project the effect of many (in the order of hundreds or thousands) randomly perturbed nuclear data evaluations to specified integral parameters. This could circumvent the Monte Carlo computational burden otherwise necessary. The method may be limited by the assumption of linearity in the first-order sensitivity parameters, but testing indicates satisfactory results for the selected applications. Automated calculation of the mean k eff (for example in the case of criticality cases) and related statistics such as standard deviation and skewness / kurtosis could also be built into the code for analysis of the resulting distribution.
Energy dependence breakdown NDaST was previously able to display in its output the breakdown of either a perturbation (i. e. , k eff ) or uncertainty (covariance in k eff ) calculation as a function of the contributing nuclide-reaction pairings (including cross-reaction covariances where included). These data are accessible via separate tabs in the 'detail popup' windows which are produced upon double-clicking the tabulated result for a given benchmark case from the main output window. However, limitation to this degree of detail alone does not allow for a deeper analysis of which regions of the energy spectrum constitute the greatest or smallest values within the total for that nuclide-reaction. The need for a means of viewing the results as a further function of  energy was clear, but the problem to overcome was the memory size of such a fine level of detail (∼ thousands of energy groups, for ∼ hundreds of benchmarks, for ∼ tens of nuclide-reaction pairs; noting particularly that energy covariance matrices are large, square and often not very sparse).
Additional uncertainty capabilities In order for the NDaST tool to be effective for general nuclear data testing, it is desirable that it should be able to analyse as completely as possible all important nuclear data properties. In this regard, efforts have been made to provide sensitivity / uncertainty capabilities for neutron multiplicity data (nubar), prompt fission neutron spectrum (PFNS / Chi) and even angular dependence of scattering reactions, with respect to the underlying Legendre series coefficients (presently having been tested for the P1 term only). In addition, NDaST will also incorporate the ability to compare evaluated data from nuclear data libraries with fundamental analytical expressions, such as the basic Maxwellian or Watt fission energy distributions. In order to verify perturbation techniques such as these, and to test the accuracy of sensitivity data based on a 238 multi-group energy structure, a series of simple tests were completed.
Patients Consecutive HF patients fulfilling the following criteria were enrolled in the study: (1) cardiomyopathy of any pathogenesis with LV ejection fraction of <35%; (2) LV endocardial map acquired during sinus rhythm with the use of an electroanatomic mapping system (CARTO XP and CARTO 3, Diamond Bar, CA); and (3) complete LV geometry with homogeneously distributed activation points. The protocol was approved by the CHU Bordeaux ethics committee (CARTO-CRT trial; clinicaltrials, NCT01270646), and all patients gave written consent. Patients referred for LV tachycardia (n=43) had ablation performed after completion of the mapping study. HF patients were included regardless of the QRS duration. Based on the 12-lead EKG, patients were divided into 3 subgroups: (1)  narrow QRS (QRS duration [QRSd] <120 ms); (2) complete LBBB (QRSd>120 ms); (3) NICD (QRSd>120 ms).
Surface Activation Slope Construction Surface ECG, LV geometries, local activation times, and positions of corresponding data points obtained via the CARTO 3 electroanatomic mapping system were analyzed offline using a custom software created in MATLAB (The Mathworks, Inc. , Natick, MA). Geometry was smoothed (pillars/trabeculae were excluded) using Poisson surface reconstruction to ensure that surface activation was more representative of myocardial mass activation. Dense activation maps were created by interpolation from data points (mean 387±349 points). The onset of the QRS complex was determined manually from the 12-lead ECG and taken as time reference. Activated surface as a function of time was calculated with a temporal resolution of 1 ms and normalized to the total endocardial surface.
Scar Imaging A proportion of patients underwent adjunctive preprocedural imaging of scar with real-time integration of noninvasive imaging data during the procedure. The adjunctive imaging modality was chosen based on the presence of an implantable device. Delayed enhancement • Patients with narrow QRS or nonspecific intraventricular conduction delay share several activation characteristics, including early, Purkinje-mediated, multifocal septal activation and heterogeneously distributed areas of slow conduction related to myocardial disease. magnetic resonance imaging was preferred in absence of an implantable device, while a high-resolution computed tomography imaging was performed in patients with a device. The magnetic resonance imaging study was conducted on a 1. 5 Tesla scanner (Magnetom Avanto; Siemens Medical Systems, Erlangen, Germany) equipped with a 32-channel cardiac coil.
Late gadolinium enhancement imaging was performed at high spatial resolution (1. 25×1. 25×2. 5 mm) using a respiratory-navigated method initially dedicated to atrial fibrosis imaging. CT was performed on a 64-detector scanner (Somatom Definition; Siemens Medical Systems, Forchheim, Germany). Images were acquired during the arterial phase after iodine contrast administration and reconstructed at mid-diastole. The complete methodology of our image acquisition and processing, as implemented in MUSIC Software (IHU LIRYC-Université de Bordeaux/Inria-Sophia Antipolis), has been described previously. 14 riefly, myocardial structural substrate was automatically mapped from CT as areas of wall thinning <5 mm or myocardial hypoattenuation <0 HU and from magnetic resonance imaging as areas of late gadolinium enhancement.
Acknowledgements This work was partly supported European Union's  Horizon 2020 research and innovation program  under the  Sklodowska-Curie  grant agreement No. 675676. Simulations were carried out at TGCC (The Curie supercomputer, owned by  GENCI  and operated into the TGCC by CEA), and at  CRIHAN (Centre de Ressources de Haute Normandie ).
Morphological analysis is the standard method of assessing embryo quality; however, its inherent subjectivity tends to generate discrepancies among evaluators. Using genetic algorithms and artificial neural networks (ANNs), we developed a new method for embryo analysis that is more robust and reliable than standard methods. Bovine blastocysts produced in vitro were classified as grade 1 (excellent or good), 2 (fair), or 3 (poor) by three experienced embryologists according to the International Embryo Technology Society (IETS) standard. The images (n = 482) were subjected to automatic feature extraction, and the results were used as input for a supervised learning process. One part of the dataset (15%) was used for a blind test posterior to the fitting, for which the system had an accuracy of 76.
Methods Embryo image capture and embryologists' classifications. For the software development, a database containing 482 images captured at the company In Vitro Brasil (Mogi Mirim, SP, Brazil) of bovine blastocysts produced in vitro (7 days post-insemination) was used. The images were obtained using an inverted microscope Olympus IX71 at 32x magnification and the software Lucam Capture v6. 30; images were stored in JPG format in 8-bit colour (RGB) at a resolution of 1280 × 1024 pixels. Each image contained only one embryo, which was approximately centred in the visual field. To capture the image, the position of the blastocyst was standardized such that the plane of focus was in the largest diameter of the embryo and the inner cell mass (ICM) was perpendicular to the focal plane (e. g. , Fig.
Image standardization. Each laboratory applies a different standard to capturing images; therefore, standardizing these methods has become indispensable. In addition to the resolution and formatting differences, different batches of images can have distinct lighting characteristics (Fig. S2 ). For the segmentation and extraction of information to find similar conditions independently of the images' original features, all the factors must be considered. All of the algorithms were developed using the MATLAB ® platform  28, 29  and enabled the automated analysis of the images without the need for user intervention. For standardization, the software consecutively followed the steps of image import, conversion to greyscale, resolution and proportion adjustment, and intensity adjustment (Supplementary Section 1).
Artificial neural network. For the ANN learning process, the Backpropagation algorithm was used  16, 18. The database (n = 482 blastocysts images) was divided into training, validation and test, with, respectively, 70, 15 and 15% of the images and following an already established validation standard  [36] [37] [38] [39] [40]. Several transfer functions that perform the passage of information between neurons were used (logsig, purelin, tansig, hardlim, tribas, radbas and satlin  28  ), training functions (trainrp, trainscg, traincgf, traingdx and traingdm  28  ), hidden layers (between 1 and 3), and the number of neurons in each layer (between 10 and 120, ref. 17). The accuracy of the obtained ANN was verified according to the error between the real values (the mode of the embryologists' evaluation) and the values obtained by the ANN  16, 17, 41, 42.
After selection, replication, and replacement, there was a new population from which the best ANNs were saved. This cycle was termed a generation. On average, 1000 generations were produced for each population created. When we determined the number of individuals in the initial population (100, 200 or 300 individuals), this number of individuals remained unchanged until the end of the 1000 generations. The entire procedure was performed 500 different times for 100, 200 and 300 individuals. Further statistical analysis. Specific techniques were used to test whether the data obtained during the information extraction steps and database classification were correctly interpreted. Receiver Operator Characteristic (ROC): One way to analyse the ANN results for pattern recognition is through a ROC curve.
Kappa index: The Kappa statistic, or coefficient, is often used for measuring the agreement between two or more evaluators when there is a degree of subjectivity in their interpretations. A Kappa index of 1 indicates a perfect agreement between the evaluators, and a value of 0 indicates that the evaluation by the observers is virtually random  [46] [47] [48]. To evaluate the Kappa index, a sample of 10% of the image database (n = 48) was used twice in separate evaluations, and three embryologists reclassified the previously classified images in a blind test. Thereby, we measured the intra-evaluator disagreement rate, which, together with the rate of disagreement among evaluators (classification of the total images database, n = 482), provided the profile of the embryologists' subjectivity.
6% of 72 images); these errors occurred when the embryologist classified the embryo as grade 1 and the ANN classified the embryo as grade 3 (and vice versa). Nevertheless, the technique demonstrated robustness in the consistency of the classification. The ROC curve obtained for the test data set (Fig. 2 ) showed excellent performance for the ANN; that is, the curve is well placed on the northwest region of the graph. The performance of the ANN architecture 1 on the totality of the generations is shown in Fig. 3. This work resulted in two different interfaces, which were an interface developed using the MATLAB ® plat- form and an interface developed using a multiplatform approach for online purposes. For both interfaces, we called the program "Blasto3Q", which refers to the three qualities of the blastocyst evaluation.