dataesr/scientific-paragraphs-categorization huggingface dataset:
2. Section 2 explains all the blocks of the framework shown in Figure  1. Starting from a small set of real scans, we show how 1 Available at: https://inria-asclepios. github. io/simul-atrophy/. longitudinal images with different atrophy patterns and realistic intensity variations can be simulated. Section 3 shows some simulation results using Simul@trophy, and also illustrates some potential applications of the simulator. In Section 4, we present some example simulations to illustrate some of the important points to consider when using Simul@trophy for different applications, such as evaluation of atrophy estimation algorithms, validation of data-driven disease progression models, training of brain morphometry algorithms based on machine learning etc.
I s 2 has the same atrophy as that of I s 0 but with the intensity characteristics of I r. Figure  3  illustrates how we obtain I s 0 , I s 1 , and I s 2. These three simulated images have the volume changes as encoded by sim , but have intensity characteristics coming from three different real images of the same patient. Figure  4  illustrates how the approach described in this section can be used to generate multiple sets of longitudinal simulated sequences having identical morphological evolution but different variations of intensities. The three shaded regions in Figure  4  are the sets of longitudinal sequences with identical volume changes but with different variations of intensities. 2016 ). Starting from the publicly available OASIS dataset  (Marcus et al. , 2010) , the images in the database had undergone intensity inhomogeneity correction using ANTs-N4BiasFieldCorrection  (Avants et al.
ToolKit (ITK) and (ii) PETSc  Balay et al. (2013). All the input and output images of the brain deformation model shown in Figure  1  can be in any format that ITK supports. ITK has strongly promoted reproducible science in the medical imaging domain, and has been widely used in computational science applied to medical imaging  (McCormick et al. , 2014; Avants et al. , 2015). Similarly, implementation of the model solver is based on opensource PETSc, a library based on C programming language. It has also been very widely used in a very diverse set of applications that also include the medical field. It is a very powerful library that supports wide range of iterative solvers and preconditioners for large systems of equations. The solvers implemented in PETSc can scale very well to large distributive computer systems.
Standalone Utility Tools and Scripts for Pre-processing and Post-processing There are some standalone tools and scripts available for various pre-and post-processing operations that are detailed in the documentation of the released software. Some of these tools for pre-processing and post-processing operations are C++ executables based on ITK, while others are python scripts. In this work, all the input segmentation of the model were obtained by using FreeSurfer. As explained in  Khanal et al. (2016a) , these segmentation maps were processed to obtain in the format required by the model. Although the provided scripts are developed for FreeSurfer segmentation maps, they can be easily modified to adapt to other pre-processing tools. Finally, the registration and simulation deformations were composed using ComposeMultiTransform of Advanced Neuroimaging Tools (ANTs)  (Avants et al.
, 2011). The core component of Simul@trophy is the implementation of the brain deformation model. Resampling of the intensity is straightforward once the deformations from the model and from registration are available. The simulator is not dependent on any one particular registration algorithm. Although, we used LCC-LogDemons for illustrative purposes, this can be replaced with any other non-rigid registration algorihtms. Similarly pre-processing is also independent of Simul@trophy. We used FreeSurfer in the simulation examples shown in this work, but any other skull stripping and segmentation algorithms can be used. Simul@trophy provides some example scripts and some utility scripts, which could be modified when using other tools for the pre-processing step.
For each subject, we could generate large number of synthetic images by perturbing these atrophy patterns in different ways. Even with the same atrophy pattern, we can generate multiple sets of longitudinal sequences of varying intensity characteristics using the approach illustrated in Figure  4. Thus, by changing the atrophy patterns and the image intensities, Simul@trophy could be used to generate a database of very large number of simulated images. Such a database might be useful for training of machine learning algorithms. In Figure  6 , smoothly varying atrophy pattern was prescribed by taking the negative of the divergence of a stationary velocity field obtained by registering the input baseline image with a follow-up image of the same subject. The objective of the experiment was to illustrate the ability of Simul@trophy to simulate smoothly varying patterns of atrophy in addition to the piecewise continuous atrophy maps.
Although the ground truth atrophy can be measured from the combined deformation fields, the users must be aware of both limitations when they use Simul@trophy for the evaluation of registration algorithms. The ability to prescribe atrophy at any time point allows the user to introduce volume changes at different regions of the brain at different times. Thus, another interesting application of the simulator is to train and/or validate disease progression models such as the models proposed in  Chen et al. (2012) ,  Fonteijn et al. (2012) ,  Jedynak et al. (2012) ,  Dukart et al. (2013), and Schmidt-Richberg et al. (2016). Having a database of longitudinal MRIs with known spatiotemporal distribution of atrophy can be useful to validate such algorithms. Furthermore, since the algorithms use a data driven approach, the simulator could be useful to train or fine-tune such models.
Another possible application is in filling up unavailable timepoint MRIs of some of the subjects, when performing groupwise longitudinal analysis. In such studies, usually the available time-point images of each subject are used to estimate subjectspecific volume changes. These subject-specific measurements are then used to perform group-wise statistics to check whether there are significant differences amongst different groups in some particular regions of the brain. Databases used in such analyses, might not always have all the required time-point images for all the subjects. This could lead to bias if all the subjects are not aligned properly in the temporal dimension of disease progression. Simulating new time-point images for some subjects and using them in the analysis might allow evaluating the impact of such mis-alignments.
Simul@trophy could also be used in studying the role of morphology and intensity on atrophy estimation algorithms, and in machine learning based AD classification algorithms. Simul@trophy enables to perform such studies as it allows creating a large number of images by simulating atrophy patterns commonly observed in AD patients but with intensities taken from normal subjects and vice versa. We hope to promote two directions of research in the community with open-source release of Simul@trophy. First, the public availability of Simul@trophy enables researchers to build their own simulated databases as needed. This might also hopefully lead to a large public database of ground truth simulated images, that could be used for benchmarking and evaluation of various image based morphometry tools.
Second, we hope that Simul@trophy allows other researchers to build upon the biophysical model we presented in  Khanal et al. (2016a) , and investigate further, providing more accurate models of brain atrophy. Finally, Simul@trophy is general enough to be used for other imaging modalities such as CT scans. It could also be used with images of any other organs, where one requires simulating specified volume changes. In this case, the pre-processing should be changed accordingly to generate a segmentation image and atrophy maps. Thus, once the software is public, other researchers might find it useful in applications that we have not foreseen yet.
CONCLUSIONS We proposed a simulation framework that can generate realistic longitudinal MRIs with specified volume changes. The framework allows generating large number of subjectspecific multiple time-point images based on a biophysical model of brain deformation due to atrophy. We developed an open-source software Simul@trophy to implement the proposed framework. The core part of Simul@trophy is the implementation of our brain deformation model presented in  Khanal et al. (2016a). Simul@trophy is based on widely used state of the art libraries PETSc (for solving large systems of equations) and ITK (for medical image processing). Since the software is publicly available in an open-source repository, we hope that researchers can use it to create databases of ground truth images. The framework could be used to generate a common public database, which in turn could be used to validate and evaluate a large number of available atrophy estimation algorithms.
This section presents simulation examples of synthetic longitudinal MRIs with prescribed atrophy patterns and realistic intensity variations 2. The real input MRIs used for the simulations presented in this section come from the database made available by Hadj-Hamou et al. (2016). The images had already undergone the Pre-Processing and Position Correction steps of the Longitudinal Log-Demons Framework (LLDF) detailed in Hadj-Hamou et al. (.
This paper describes a protocol for quantification of heritage copper alloys by energydispersive X-ray fluorescence spectroscopy (ED-XRF). The protocol, nicknamed CHARMed PyMca, is designed for users who wish to maximize inter-laboratory reproducibility of quantitative ED-XRF results for the wide range of copper alloys found in heritage materials. By maximizing reproducibility, this protocol should facilitate collaboration and allow the rigorous use of shared data and databases. The protocol uses free, open-source, fundamental parameters software called PyMca. PyMca allows for a consistent and transparent application of the fundamental parameters approach independent of the ED-XRF instrumentation used. The proposed protocol calls for calibration of standardless PyMca results against a set of certified reference materials designed specifically for use with heritage copper alloys, the so-called copper CHARM set.
2016). Rehren and Freestone (2015) , writing about a parallel evolution in the compositional study of glass, state that progress towards deeper understanding (asking 'why  ' and 'how' rather than just 'what', 'where' and 'when')  relies on 'expansion and refinement of the [shared] data base'. It seems evident that the study of copper alloys would also benefit tremendously from the continued growth of a shared body of quantitative compositional data  (Rehren and Freestone 2015). There is, however, an elephant in the room. If one wishes to analyse a database of research results that combine quantitative compositional data from different collaborating laboratories, good (or at least, known) inter-laboratory reproducibility is a fundamental prerequisite. This goal has not been simple to achieve for copper alloys in the art and archaeology domain  (Heginbotham et al.
This paper presents a protocol, nicknamed 'CHARMed PyMca' for the quantification of heritage copper alloys by ED-XRF that is designed specifically to maximize inter-laboratory reproducibility for the wide range of alloy types found in heritage materials. The name refers to the two essential components of the protocol, namely PyMca fundamental parameters software, used in conjunction with the so-called copper CHARM (Cultural Heritage Alloy Reference Material) set of certified reference standards. The protocol assumes that the metal to be analysed is in a state appropriate for study by ED-XRF (uncorroded, flat, homogeneous and representative). Under such circumstances, it is designed to provide accurate results with well-characterized precision, and to do so for a broad range of elements over a large concentration range, independent of instrument type.
The protocol calls for the use of a free, open-source, fundamental parameters software for spectral analysis called PyMca  (Solé et al. 2007 ). The fundamental parameters approach was selected for this protocol because it is generally favoured over empirical methods for quantification where significant matrix variability may be present and where large concentration ranges are to be addressed  (Lachance and Claisse 1995, 356-7; de Vries and Vrebos 2002; Mantler et al. 2006). The fundamental parameters approach has also recently been shown to be strongly associated with good inter-laboratory reproducibility in practice  (Heginbotham et al. 2011). PyMca software was selected for the protocol primarily because it is available to any interested user at no cost, and it allows for a consistent and transparent application of the fundamental parameters approach independent of the ED-XRF instrumentation used.
DESCRIPTION OF THE PROTOCOL The CHARMed PyMca protocol for quantitative analysis of heritage copper alloys has been developed and tested using several tube-based ED-XRF instruments from different manufacturers, typically operated between 40 and 50 kV with moderate filtration in an air path environment (although a vacuum or helium flush protocol could also be used). Several tube anode materials have been used (Cr, Re and Rh) and both PIN and silicon drift detectors have been employed. The protocol is presented below in five sections: the use of PyMca software; the use of the copper CHARM standard set; the calibration strategy; the error modelling strategy; and the validation procedure.
Today, however, the computational power required to apply fundamental parameters to ED-XRF analysis is readily available to any analyst at relatively low cost and the use of the robust FP approach can be readily adopted. In addition to the theoretical arguments for the use of fundamental parameters, a round-robin inter-laboratory study conducted in  2009 -10 (Heginbotham et al. 2011) )  suggests that FP methodology, combined with the use of reference standards, appears to deliver superior reproducibility compared to empirical methods in the analysis of copper alloys. This enhanced reproducibility was observed even though the six laboratories using 'FP with standards' methodology used six different instrument types and six different FP software packages. Given this observation, one might well ask why advocate for the use of PyMca software specifically when any FP software might perform as well? There are a number of arguments to be made in favour of PyMca, which can be condensed into three classes: accessibility, transparency and functionality.
In terms of accessibility, PyMca is a freely downloadable, open-source software that can be used on Solaris, Linux, Windows and Mac OS X platforms. The software is institutionally supported by the European Synchrotron Radiation Facility (ESRF) and is maintained and updated frequently. The software is able to process spectra from many instrument types; to date, spectra have been processed from Bruker's Artax and Tracer spectrometers, Thermo's Niton and ARL spectrometers, Olympus' Delta spectrometers and XGLab's Elio spectrometers. Additional spectrum formats can be quickly made readable by request to the program's administrator. Transparency is an extremely important factor that also weighs in favour of PyMca. Most proprietary quantification software packages (FP or otherwise) do not allow the user access to, much less full control over, the many parameters that must be configured for a successful quantification program.
The software also gives the user control over the modelling of energy output from polychromatic X-ray sources, which is very important for most investigators in art and archaeology, who predominantly use X-ray tube-based XRF instruments  (Solé et al. 2007). Recent improvements in PyMca have had the effect of greatly improving its performance in the quantitative analysis of complex high-density materials such as copper alloys. In particular, starting with version 5. 0, PyMca models both secondary and tertiary excitation phenomena for every assigned peak in the spectrum. The modelling strategy is based on the work of D. K. G. de Boer; for details of the implementation, see de  Boer (1990)  and  Solé et al. (in preparation). A second, major improvement in PyMca, beginning with version 5. 0, is the implementation of reiterative matrix modelling in which the initial matrix composition can be automatically refined.
In the case of copper alloys, the initial composition can be as simple as pure Cu, but PyMca's initial estimate of the composition will then be used as the matrix description for a second iteration of the quantification process. The second estimate of composition can then be used for a third iteration and so on. The number of iterations and the specific elements to be considered in the matrix and their chemical form are user-configurable. For all of the sophisticated capabilities that PyMca has for the analysis of XRF spectra, quantitative results generated by the software are still prone to some degree of systematic error. The likely causes of such error fall into three general categories.
Theoretical Uncertainties The theoretical database used by PyMca for calculations contains thousands of values, drawn from the published literature, for constants such as binding energies, fluorescence and Coster-Kronig yields, photoelectric absorption cross-sections, radiative emission probabilities and mass attenuation coefficients. These values have some uncertainty associated with them and this is likely to lead to some error in the spectrum modelling  (Caussin 2013; Schoonjans et al. 2013 ). In addition, PyMca does not account for fluorescence induced by ejected photoelectrons or Auger electrons within the matrix, which may be significant, particularly for low atomic number elements and with higher energy excitation  (de Vries and Vrebos 2002; Fernandez et al. 2013 Fernandez et al. , 2014)).
On a separate tab, the MECS also presents a calibration report with an element-by-element summary table of all the calibration parameters and the constants used for the calculation of all the regression models and the error models that comprise the total calibration scheme. The MECS described here greatly simplifies and automates calibration and quantification according to the CHARMed PyMca protocol; a complete calibration model can be built from PyMca results in a matter of a few hours and, subsequently, new results can be processed, plotted and tabulated from batched PyMca results in a matter of minutes. The use of an Excel® spreadsheet for this work has its limitations, however, and it is hoped that this MECS might, therefore, be used as a model for the development of new open-source software that could be integrated with PyMca and used more widely by the art and archaeology community.
ACKNOWLEDGMENTS The authors are sincerely indebted to all those who have graciously volunteered to trial various iterations of this protocol and offered valuable feedback over the past several years, including  Jane Bassett ,  Federico Caro ,  Julia Day ,  Jan Dorscheid ,  Joseph Godla ,  Katie Holbrow ,  Lynn Lee ,  Arie Pappot  and  Beth Price. Thanks are also extended to  David Bourgarit ,  Gareth Davies  and  Aaron Shugar , for their thoughtful commentary.
" We will compare drug administrations with the doctor's prescriptions as noted in the CPOE system in the pharmacy database. We will exclude intravenous and nonintravenous preparation errors because these errors are not preventable by BCMA and are thus unlikely to be influenced by workarounds in the BCMA process. We will classify the MAEs using the classification of van den Bemt et al  [42]  (Table  3 ). We will divide the number of erroneous medication administrations (containing 1 or more errors) by the number of observed drug administrations plus the number of omissions, thus using the concept of opportunities for errors as in other MAE research  [43].
Statistical Analysis Data will be analyzed using IBM SPSS Statistics version 22 (IBM Corporation). We will analyze the potential association between workarounds and the occurrence of MAEs using univariate multilevel logistic regression, with the proportion of medication administrations with 1 or more errors as the dependent variable and the occurrence of workarounds as the independent variable. The nurse and the patient will be the levels in the multilevel analysis. We will analyze the occurrence of workarounds as a categorical variable, with the following categories: no workarounds (reference category), 1 workaround, 2 workarounds, and 3 or more workarounds. We will adjust for potential confounders by using multivariate multilevel logistic regression. The parameters in the multivariate multilevel logistic regression model will be hospital, ward type, day of the week, time schedule of medication administration rounds, ATC code, the number of drugs per patient per round, and the route of administration.
The objectives of these systems are to analyze clinical data produced by the microbiologic laboratories of 4 public hospitals in Marseille. The first program implemented, the Epidemiologic Surveillance and Alert Based on Microbiological Data, has monitored more than 293 infectious disease-related items on a weekly basis since November 2002 (9), including 38 clinical samples, 86 pathogens, 79 diagnosis tests, and 39 antimicrobial-resistance patterns. After the introduction of this system, several other systems based on a previously described historical database (11) were set up, such as the Bacterial Real-Time Laboratory-Based Surveillance System (BALYSES) and the Marseille Antibiotic Resistance Surveillance System (MARSS) (10). The latter 2 systems have routinely operated in the AP-HM network since 2013.
Biosurveillance System We created a biosurveillance system, the PACA Surveillance Epidemiologic System (PACASurvE), capable of collecting, standardizing, and computing the laboratory results produced by public (i. e. , hospital-affiliated) and private-sector microbiology laboratories located in the PACA region every week. The system's objectives are to provide early detection capability and an initial description of possible infectious disease threats  (10, 16) ; accordingly, the system is designed to issue alarms if an outbreak is detected or if a single case of a rare but severe infectious disease or an unknown infectious agent is discovered. PACASurvE is Internet-based and uses Excel software (Microsoft, Redmond, WA, USA) for data collection and management and R version 3. 0. 1 software  (17)  for analysis.
The system was implemented and has been routinely used since July 2013. Public hospital and private sector laboratories of the PACA region were invited to participate in the surveillance network. Fifteen institutions were first selected among the leading laboratories in terms of volume of activity to rapidly achieve a better geographic representation of PACA (Figure  1 ). Their participation was based on several criteria proposed in the literature and relevant to the implementation of our network, including those described by Walckiers et al. (  14 ): participation of laboratories on an unpaid and voluntary basis, participation of microbiologic laboratories, anonymity of data, and a standard, predefined frequency for data collection  (14). After identifying the participating institutions, the second task was to define which events should be monitored and their respective definitions, which were transmitted to all laboratories.