dataesr/scientific-paragraphs-categorization huggingface dataset:
GPs may make treatment adjustments according to their clinical opinion. Repeat prescriptions of study medication are issued by GPs as usual and collected by patients from their usual pharmacy. Pharmacy data were initially collected by faxing copies of study treatment prescriptions to the study coordination center, but these are now collected electronically. Prescription collection data are used to assess treatment adherence. Hospital admissions are primarily to two local hospitals, Salford Royal Hospital and the University Hospital of South Manchester. Relevant admissions are identified electronically and assessed by a separate safety team. The integrated system was set up by NorthWest EHealth, a not-for-profit organization formed by a partnership between The University of Manchester, Salford Royal Foundation Trust and Salford Clinical Commissioning Group, and links patients' records across their GP surgeries and hospitals.
Furthermore, it might lead to changes in treatment, if training relates to information relevant for treatment decisions and thus interfere with the usual care provided to patients, possibly resulting in a decrease in the generalizability of trial results  [1]. Another option that might interfere less with clinical routine would be to implement data quality checks in the system that would detect incorrect or missing data during data entry, and specify procedures for correction. When a health care database is the source of study data, any change made to the database after data extraction needs to be captured in an audit trail  [22, 23].
Quality and completeness of data varies within and among databases The collected data as such and their level of detail, completeness, and correctness can vary, both within and between existing databases  [13, 16]. Generally, ''completeness'' of data in health care databases depends on what data exactly are routinely collected in health records and the exact data needs for a trial, and one should be explicit about both when establishing the appropriateness of health care databases as a data source for a pragmatic trial  [24]. In addition, before selecting a data-collection approach solely based on routinely collected data, one needs a very good understanding of the process of data entry and management in the individual database  [25]  as well as of the database landscape within and across settings and countries in which a pragmatic trial will be performed  [26].
As a starting point for finding the optimal database for ones demands, the International Society for Pharmacoeconomics and Outcomes Research provides an international digest of databases  [27]. This list is neither exhaustive nor does it assess the quality, completeness, and accessibility of data and its potential for customization for a specific study, which would assist the assessment of the suitability of data for inclusion in pragmatic trials. However, guidelines exist for how to select a database for a study, which are yet oriented toward noninterventional studies  [28]. The Medicines & Healthcare products Regulatory Agency has recently published a position paper in which compliance issues of EHRs are identified and user requirements for EHR owners are formulated to ensure good clinical practice compliance  [29].
Compliance with safety requirements on adverse event reporting As described previously, routine data are typically collected infrequently or irregularly, which poses problems Box 5 SCOTda pragmatic trial using record linkage for identifying potential outcome events Aim: To evaluate the cardiovascular (primary) and gastrointestinal (secondary) safety of celecoxib vs. traditional nonsteroidal anti-inflammatory drug therapy in patients with osteoarthritis or rheumatoid arthritis. Outcomes: First hospitalization for nonfatal myocardial infarction or nonfatal stroke or cardiovascular death. Other outcomes were hospitalization or death for various reasons (gastrointestinal, specific cardiovascular, and renal) Approach for data collection: Participants were recruited in general practices (Scotland, England, Denmark, and Netherlands) and randomized by telephone or a web site.
Outcome events were not directly collected during follow-up but potential events identified through record linkage: the trial database was linked every 3 months to national population health care and mortality databases containing all hospital discharge diagnoses or causes of death in a particular country. For each potential outcome event, original case records were requested. Each potential event was adjudicated by a blinded, independent adjudication committee. Serious adverse events were recorded in the electronic case report form. particularly for the collection of adverse events (AEs), for which frequent or regular scheduled visits may be deemed necessary. Especially delays in recording data in relevant databases and time between data entry and information becoming available to the sponsor/in the study database can interfere with obligatory timelines for reporting AEs as well as result in missing data.
Data privacy and access A further challenge with using routinely collected data is protection of the privacy and confidentiality of research participants  [12]. Providing access to a health care database system and therefore to patient data beyond what is required for a study, as opposed to recording and accessing studyspecific data in an eCRF raises right-to-privacy concerns  [31]. In a recent series of papers exploring ethical issues in pragmatic trials, McGraw et al. point out that patients have concerns about privacy protection and may engage in ''privacy-protecting behavior'' such as withholding information to health care providers. The authors argue that neither privacy nor the value of pragmatic trials should be ''compromised for the sake of the other''  [32]. Traditionally, anonymity and informed consent have protected patient information, but it has been argued that neither can ensure privacy in the era of big data  [32, 33].
Technological solutions are sought to fill the protection gap, but it is unlikely that these solutions will defeat all challenges. To allow the use of personal data, modifications of informed consent procedures have been Abbreviations: eCRF, electronic case report form; EHR, electronic health record. proposed  [34]. The concept of a learning health care system also has emerged in which knowledge generation is ''a natural outgrowth and product of the health care delivery process''  [35]. In such a system, accessing patient data for research purposes is likely to be facilitated. However, because a tradeoff between utility and security remains and guidance on how to balance privacy and research interests is currently lacking, a small risk of identification might remain  [36]. Privacy concerns inevitably constrain access to data by investigators.
Currently, access is often granted to database owners only, decreasing the possibility of using routinely collected data. In some countries, national, local, or regional data privacy regulations prevent the use of routinely collected patient-level data and the linkage of information on individuals between databases such as death or disease registries. Apart from the data privacy issue, technical challenges exist, for example, efficient and timely data extraction from systems can be particularly challenging when data must be de-identified but the possibility of individual patient identification needs to be retained for safety reasons. In some settings, algorithms for probabilistic linking have been developed to address this  [37]. 4. Opportunities and current developments for the use of health care databases for pragmatic trials 4.
1. International initiatives support the utilization of health care databases Several initiatives worldwide are dedicated to improving both the standardization and quality of routinely collected data. As routinely collected data were not intended for research purposes, it is important to create a joint understanding on the do's and don'ts of its collection and use. Therefore, several guidance documents have been or will be published to ensure standards and best practices when using routinely collected data  [29, 38]. In addition, several initiatives work on technical solutions for challenges that arise when using EHRs or other health care databases for research. Supported by the EU's 7th Framework Programme, the TRANSFoRm project aims to develop a ''rapid learning healthcare system'' that can improve both patient safety and the conduct and volume of clinical research in Europe.
A dynamic interface is integrated with EHRs to identify patients eligible for research and collect outcomes and safety data. Improving the interoperability of EHR data and other data sources is another aim of TRANSFoRm  [39]. The Electronic Health Records for Clinical Research (EHR4CR) project of the EU's Innovative Medicines Initiative developed a technological platform that combines hospital data across countries, with a focus on the identification of sites and patients for trials  [40]. Another approach to tackle the challenge of heterogeneity of multiple sources is being developed in the United States under the FDA's Sentinel Initiative, which aimed to develop a linked system that will draw on existing health care data from multiple database sources to actively monitor the safety of medical products in real time  [41].
PCORnet again creates clinical data and patient-powered research networks to further enhance effectiveness research  [42]. To enhance the quality of EHRs and make their level of quality more visible, the eClinical consortium will release a tool under the EHRCR initiative, which will allow doctors to evaluate the quality and security of their own EHR systems and provide study teams information on the quality and content of EHR systems of interest  [43]. Achieving homogeneous quality of databases on an international scale will always be challenging and a formal check of data quality and completeness will be needed to examine the suitability of EHR systems or sites for pragmatic trials.
The use of existing registries A special source of routinely collected data is existing registries  [44]. For the purposes of this article, we define a registry as any system that collects uniform data  [45]. Registries are becoming increasingly common, aiming at high if not 100% coverage of patients with a target disease or treatment. If high coverage is achieved, registries may be considered (public health) surveillance systems. Participation in a registry is often embedded within clinical practice either via an additional eCRF or by use of a standard IT system. If a registry for a specific patient population of interest exists, it may be particularly suited to a ''nested'' pragmatic trial or so-called ''registry-based'' randomized trial [46e48]. For example, within the TASTE trial, which is nested within the Swedish Coronary Angiography and Angioplasty Registry, one out of several registries that run under the SWEDEHEART registry, all data on end points were collected through the ''routine registry data collection'' (Box 4)  [47].
The use of a hybrid approach ''Hybrid approach'' means that routinely collected data are complemented with additional data collected for a specific study, either through the implementation of additional IT-solutions to the existing EHR, linking multiple databases, collecting patient reported outcomes  [51] , or by implementing additional eCRFs  [6]. These may be solutions to the challenges that arise when using either eCRFs or existing medical records solely for data collection, including the limited availability of certain variables, insufficient detail, and the timing of data collection but also an increased interference with routine clinical practice. A hybrid approach may enable an appropriate datacollection framework which takes advantage of both worlds while reducing overall workload.
The eLung and Retropro study are two examples where the implementation of IT systems within existing EHRs made it possible to flag eligible patients, efficiently evaluate eligibility, randomize patients, collect outcome data, and monitor adverse events (Box 2)  [5]. In contrast, the Salford Lung study used near real-time linkage of primary as well as secondary care electronic health records next to limited eCRFs for data collection (Box 3)  [6]. The SCOT trial used linkage to national population health care and mortality databases to ascertain outcome regarding hospitalizations and a separate eCRF for adverse events (Box 5)  [52]. To integrate additional data, eCRFs or add-on solutions into an existing system, access to the back-end data of and IT information pertaining to an EHR or health care database is a prerequisite.
Conclusions and recommendations Decisions regarding data collection and management are important in any clinical trial. When designing pragmatic trials, it needs to be decided on how much interference with routine clinical practice through the data-collection process one is willing to accept to obtain high-quality data. Such interference should be as limited as possible and as such, the use of existing EHRs or other routinely used electronic systems for data collection and management should always be considered. To determine the feasibility of using standard systems, one needs to evaluate the process of data entry and the level of detail and completeness of the data of these systems to compare the quality of their data with that needed for the trial. If an (additional) eCRF-based datacollection process is chosen, its structure and implementation should be as easy and as much in line with routine clinical practice as possible.
In general, high-quality purposes and may be more of using routinely collected data valid data, but entry/ variable, display more missing depending on specific approach transcription errors, may occur data and entry errors (both random and systematic) may be expected. Level of detail might be insufficient for trial All relevant variables Yes as eCRF is made specifically Possibly not. However, one Probably yes as the hybrid collected for the study advantage is that variables that approach is designed to obtain are not collected as part of the sufficient quality data on all trial but are relevant in hindsight relevant variables for the study, might be present in the making use of routinely database collected data as much as possible. Compliance with safety Yes Possibly not Probably yes: safety requirements requirementsdreporting i.
Security Guidelines The OWASP Foundation (OWASP, a) introduces a set of guidelines and rules to be followed in order to protect data at rest. However, the guidelines are presented in an informal style, and their interpretation and implementation require security expertise, as stressed in  (Zhioua et al. , 2016). In the OWASP Storage Cheat Sheet (OWASP, b), OWASP introduces the guideline "Store unencrypted keys away from the encrypted data" 1  explaining the encountered risks when the encryption key is stored in the same location as encrypted data. This guideline recommends to store encryption key and the encrypted data in different locations. As the reader can see, the guideline involves 2 information flows (encryption key and encrypted data) that are dependent. Their specification and identification on the code level require an advanced information flow analysis capable of handling complex information flows, such is the case for this guideline.
Sample Code Let's analyze the sample code in Figure  1  to verify whether the guideline "Store unencrypted keys away from the encrypted data" is met or not. The developer Bob encrypts the secret data credit card number, and stores the cipher text into a file. At line 115, Bob creates a byte array y used as parameter for the instantiation of a SecretKeySpec named k (line 116). At line 119, Bob stores key k in a file, through the invocation of method save to file (Figure  2 ). Once created, key k is provided as parameter to the method save to file(String data, String file) (Figure  2 ) Bob then encrypts the secret variable creditCardNumber using method private static byte[] encrypt(Key k, String text) which uses key k as parameter. The encrypted data is then stored using method save to file(String data, String file) (Figure  2 ).
1). This phase results in generic security guidelines that can be instantiated on different programs logics. The developer writes the code and invokes the framework that verifies whether the rules specified by the security expert are correctly applied in his software. First, the framework constructs the program model (Section 3. 2), that is used as a basis to lead the formal verification of the security guidelines (Section 3. 3). We stress that the developer does not have to deal with the specified formulas. We aim at reducing the intervention of the security expert for the identification on the code of the critical data that are at the heart of the security guidelines, such as encryption key in our example. However, in some other cases, the identification of sensitive data requires knowledge and awareness about the code logic and semantics.
File Location Dependency Detection In the guideline Store unencrypted keys away from encrypted data, we encounter the imprecise and implicit notion of location that can be expressed in different manners such as file location, insert in database, add to an array, etc. We worked towards closing this gap through the arrangement of labels into classes. For example, the set save contains labels such as save to file, save to database, save to array, etc. We performed advanced information flow analysis with the objective of capturing the implicit file location dependency; we captured the parameters (file names) of the specific method PrintWriter. print(String) invocations, and we compared their values. This comparison indicated that the two files are in the same file system. This results in the creation of a new edge of kind DEPEND between the nodes matching the invocations of Print-Writer.
LIMITATION The framework we proposed in this paper constitutes a proof-of-concept regarding the feasibility of our approach. However, our framework falls short in specifying guidelines involving imprecise and ambiguous notions such as guideline IDS15-J:Do not allow sensitive information to leak outside a trust boundary (CERT, a) from CERT Coding Standard source (CERT, b). The notion of trust boundary requires well defined system boundaries, which is not trivial when it comes to distributed applications. Other guidelines involve quantitative information flow theories, and this is also some other limitation of the formal specification we cover in our framework. For instance, the guideline Limit quantity of data encrypted with one key recommends the use a new encryption key when the amount of encrypted data goes beyond a certain threshold.
It is obvious to the reader than our specification and verification method falls short in covering this guideline for the dynamic aspect it involves, and that can't be captured statically. For the guidelines involving the semantic aspect, such as password security rules, the automatic detection of the password data on the program cannot be performed automatically. The annotation of the password requires a deep knowledge on the program logic, and the intervention of the developer/security expert to annotate the password is required. sources such as reading from a database. In addition, there might be the case where multiple sensitive data are provided as input, the monitoring of multiple sets of traces is then required, which can turn to be too expensive, and may lead to loss of precision. In their work "Detecting Temporal Logic Predicates in Distributed Programs Using Computation Slicing"  (Sen and Garg, 2003) , Alper Sen and Vijay K.
https://github. com/zeineb/Java-classes-parser this given class (html code, javadoc), and we extract all the relevant details, such as the description, the attributes, the constructors, the methods signatures and their parameters. Then is performed a semi-automatic semantic analysis to detect key elements from the Java methods details (return type, method description, etc. ), such as the key-word secure, key, print, input, etc. Then, the security expert establishes the mapping of those key words used to build the formulas to the possible Java language instructions. For example, the method (the constructor) SecretKeySpec(byte[] key, String algorithm) that constructs a secret key from a byte array, will be mapped to the abstract label "create key"; this label is described in our Security Knowledge Base as "encryption key", a sensitive information that should be kept secret.
DGP results The test set consists of a set of 46 Protein Data Bank (PDB)  [7]  files, to be realized in R 3. The protein graph has an edge {i, j}, weighted by the inter-atomic distance between atoms i and j, whenever this distance is smaller than 5 Å (compatibly with Nuclear Magnetic Resonance  [36]  data). Note that, since all the proteins we tested actually exist, all of these instances are YES instances of the DGP. Based on the computational results, we partition these instances in two groups: Easy (27 instances) and Hard (19 instances). We configured both MWU and MS solvers with T = 20 iterations, and use the IPOpt solver  [14]  as the local NLP solver in both methods. The result tables 1 and 2 report, for each of the instance sets Easy and Hard, the instance PDB name, the number n of atoms, the number m of given distances, the minimum, maximum and average distance values in the instance, and the solver performance statistics.
Acknowledgments We are very grateful to  Dr. Pascale Bendotti  ( EDF ) for useful suggestions about the HUC problem. Luca Mencarelli  is sponsored by a  Ph. D. Fellowship from the  FP7 Marie Curie ITN  " MINO " project. Youcef Sahraoui  is sponsored by a  CIFRE Ph. D. Fellowship with Éléctricité De France (EDF). Leo Liberti  was partly sponsored by the  ANR Bip : Bip project  under contract  ANR-10-BINF-0003 , and completed this work during a visiting term at  IMECC, University of Campinas (SP), Brazil , sponsored by the  Chaires Françaises dans l'état de São Paulo (CFSP) program , a collaboration between the  French Consulate in São Paulo  and the three main universities in São Paulo:  UNICAMP ,  USP  and  UNESP.
Behavioral data analysis Incorrect trials and correct trials with reaction times faster than 200 ms or slower than 800 ms (representing 2. 3% of trials) were excluded from all analyses on reaction times. Moreover, when studying trial-to-trial conflict adaptation, the first trial of each block and trials following an error were excluded. For error rate analyses, all trials were included. Behavioral data were analyzed with MATLAB R2011a (The MathWorks, Inc. ) and the Statistical toolbox to perform t-tests and repeated-measures ANOVA when comparing the different conditions. The normality assumption required by these parametric tests was checked with a chi-square goodness of fit test. It was met for all conditions (all χ 2 values < 0. 42, all p < 0. 05). One subject was excluded due to excessive artefacts on the EEG signal.
EEG recordings and analyses EEG activity was continuously recorded using a 256-channel EEG net (Electrical Geodesics Inc, Eugene, OR). The signal was sampled at 250 Hz. All analyses were conducted using Fieldtrip toolbox  [53]. EEG data were filtered between 0. 5 and 30 Hz with a two-pass Butterworth filter and then epoched from -400 ms to 900 ms relative to the onset of the target. Then, the data were visually inspected for artefacts not related to blinks. Bad channels were interpolated with the weighted average of all their neighbors. Independent components analysis was computed and components containing blinks or oculomotor artefacts clearly dissociable from brain activity were subtracted from the data. Finally, the data were re-referenced to common average and baseline corrected over 200 ms before the prime, between -400 and -200 ms relative to the onset of the target.