dataesr/scientific-paragraphs-categorization huggingface dataset:
Diagnostic Support Application Diagnostic support software configuration (Figure  6 : diagnostic support configuration) consists of tools for mining new rules from health data sources and managing their deployment into the knowledge base, upon which an evidence service is operating to drive a diagnostic support tool embedded into a local EHR system. The primary function of the tool is to suggest to clinicians diagnoses to consider at the start of the clinical encounter based only on the existing information in the patient record and the current reason for encounter  [18]. It also allows bottom-up input of observed patient cues (symptoms and signs), independent of associated diagnosis, or top-down drilling into and selection of cues supporting specific diagnoses. The rules used in the diagnostic process are generated by data mining tasks (step 0), which get manually curated and fed through the evidence service into the Clinical Evidence Repository.
TRANSFoRm output Exploitation plan (1) Privacy model: a "zone" model with an explicit method of graphically depicting the zones and operation of filters between zones Published method  [15]  (2) Provenance infrastructure: based on the Open Provenance Model [REF], each infrastructure component captures a provenance trace that enables reconstruction of an audit trail for any given data element Published method  [16]  (3) Clinical prediction rule ontology based web service The diagnostic ontology has been made available as a public download in OWL format on the TRANSFoRm website (http://www. transformproject. eu/). A future project is required to extend the data beyond the three initial reasons for encounter (4) Research data model CDIM  [12]  and CRIM  [13]  have been published. A full description of the use of CDIM and CRIM in the construction of data node connectors will be published and made available on the TRANSFoRm website (5) eCRF Extension of CDISC ODM and SDM by the incorporation of archetypes with references to the CRIM and CDIM models will be published and discussions are ongoing with CDISC regarding future incorporation into the standards.
Search authoring tools will be available open source (7) DSS integration The DSS is currently integrated with the InPS Vision 3 system. Further work is required to move this to a data node connector/CDIM-based flexible system the Clinical Evidence Repository (step 2), before sending the potential diagnoses back, annotated with levels of support and confidence for the presenting case. Upon exiting the tool, the coded evidence cues and current working diagnosis can be saved back to the patient EHR (step 3).
Conclusions TRANSFoRm demonstrated how a Learning Health System can be implemented in European clinical research and practice. The full list of project outputs and the exploitation plan for each are shown in Table  1  and promoted via an open source model. TRANSFoRm will be a full participant in the European Institute for Innovation through Health Data and will make its tools and models available via the institute. In addition, we are internationally active as participants and promoters of the Learning Healthcare System. Via the LHS, we are publishing models, standards, and tools to the world research community. The UK serves as an exemplar of our business model, with multiple EHRs participating in the project as well as the Medicines and Healthcare Products Regulatory Agency, Clinical Practice Research Datalink (CPRD).
Local user-centric identity management The OffPAD represents technology for improving the user experience and for strengthening security, which already makes it user centric. Since the OffPAD in addition is located on the user side, it is physically local to the user, and thereby represents technology for local user-centric identity management. The OffPAD can be used for a number of different security services  [16] , but this article only focuses on how it enables trusted interaction through mutual entity (user and server ) authentication as well as data origin authentication. The OffPAD can also support incremental authentication modalities, i. e. syntactic, semantic or cognitive authentication, as shown in Figure  4. In the sections below is described how the OffPAD enables mutual user-server entity authentication as well as data authentication.
This method can in theory provide strong data authentication, but it puts a relatively high cognitive load on the user for verifying the transaction data. In a study, it has been shown that about 30% of users fail to notice when transaction data in an SMS message have been changed, which means that 30% of attacks would succeed even in the presence of SMS authorisation  [13]. The problem with SMS-authorisation is poor security usability, which fortunately can be solved with Lucidman as is explained below. Figure  8  illustrates a simple ceremony for data origin authentication, i. e. to ensure that what is displayed on the VDU corresponds to what is being transmitted to other parties in online transactions. The method assumes that the user has an OffPAD with integrated camera, OCR (Optical Character Recognition) and communication functions.
The user first captures a screenshot from the VDU with the OffPAD camera, then uses the OffPAD to recover the displayed data from the image through OCR, and finally to compute a MAC (Message Authentication Code) which is sent along with the original transaction data. The MAC enables the recipient server to authenticate the received original data. The actions/messages of the ceremony are described in Table  5. Even though it is assumed that the client platform is infected, it is easy to see that attackers will not be able to falsify the transaction data undetected. Falsified transaction data would produce a MAC mismatch, which would be discovered by the server in  (8). In order to successfully falsify data, the attacker would have to compromise both the client platform and the OffPAD simultaneously.
Security analysis of the OffPAD The OffPAD is considered as a trusted device, which means that it is assumed to provide the authentication services described in the sections above, more specifically 1) cognitive server authentication, 2) user authentication with never present cleartext passwords, and 3) cognitive data authentication. Should the OffPAD fail, then the mentioned authentication services would be invalidated. In this section we discuss possible threats against the OffPAD device and its integration into the security ceremonies described above. The discussed threats are summarized in Table  6.
Unauthorized use of device If an unauthorized person were able to use the functionality of the OffPAD device, the attacker might be able to activate the user authentication function as well as the data authentication function. This could have serious consequences, e. g. the masquerading as the user and executing fraudulent transactions. Furthermore, the attacker could breach the device integrity by making unauthorized modifications and configurations changes on the device, such as defining assigning existing petnames to fraudulent websites. In case the legitimate owner is unaware of the integrity breach and continues to use the OffPAD, the consequence could e. g. be that the owner is tricked to access fraudulent websites in the belief that they have been authenticated as legitimate websites by the petname system on the OffPAD.
Loss or destruction of device Incidents of loss, theft or destruction of mobile devices occur relatively frequently, rendering the functions of the device unavailable. In such situations it is important that replacement devices can be obtained and configured relatively easily. For the OffPAD, this would require that a new OffPAD device can be configured with e. g. user passwords and petnames in an user friendly and secure manner. This requirement must be based on back-up procedures which e. g. to a storage device via a USB connection. The back-up data can be stored in cleartext format or in encrypted form, where both methods need to be studied and analysed further with regard to security assurance and usability. The design and analysis of back-up procedures are outside the scope of this article.
Data injection attacks Data injection consists of sending malformed data to the OffPAD with the aim of causing harm to the OffPAD. Data injection could be generated by malware on the client computer with which the OffPAD communicates, or by malicious computers in physical proximity to the OffPAD. In case of software vulnerabilities on the OffPAD malformed data could damage the integrity of the OffPAD or cause data leakage, similarly to SQL injection attacks. It is therefore crucial that the format of received and transmitted data is well defined and validates, and that any malformed data can be properly handled and rejected. The data format will have XML at its core, but the detailed specification and the requirements for interpretation are outside the scope of this article.
Under the development of the fake sites and during the experiment Google Chrome was used as web browser. Most of the sites that we made copies of used JavaScripts to check if the domain name was the correct and redirect if not. So all of the images were saved locally and the JavaScripts removed. We made a change to the web server and the Chrome extension so that only this browser on this machine could open the phishing sites, thus making it impossible for anybody else to discover the phishing sites through the Internet.
The proposed ceremonies are designed to cross-check the processes of server authentication, user authentication and data authentication so that malware on the client platform can not break the integrity of these processes without detection. Malware could of course disrupt the processes and thereby cause a denial of service attack, but to protect against this form of attack is not within the scope of our proposed solutions. Carrying an OffPAD as a separate device could represent a barrier for many people, so it is worth investigating whether the OffPAD could be integrated in a smart phone in a secure way. Modern mobile phones, or smart phones, are packed with advanced features and must be considered a 'general purpose computing platform'. This certainly provides great flexibility and support for many new business models, but it also opens up many new attack vectors.
However, a compromised smart phone could enable attackers to trick the user into believing that she is interacting with the OffPAD sub-system. It is therefore crucial that the user can be assured that she is interacting with one or the other sub-system, and not confuse the two sub-systems. To achieve this goal the user must be able to select each sub-system specifically, and the platform must clearly indicate which sub-system the user is interacting with at any time. In this article we described local user-centric identity management as an architecture for providing robust authentication services of various types and modalities. In particular we have described the OffPAD as a personal device that supports mutual authentication between user and server, as well as authentication of data sent from the user to the server.
Patients and Methods We performed a multi-institutional retrospective observational study in which we selected all cases of primary cutaneous melanoma registered in databases at four hospitals: Hospital Universitario "Virgen de la Victoria" (HUVV) in Malaga, Spain; Instituto Valenciano de Oncología (IVO) in Valencia, Spain; Hospital Clínic Universitari de Barcelona (HCUB) in Barcelona, Spain; and Gustave-Roussy (GR) in Villejuif-Paris, France. It is noteworthy that all databases included patients' data in a prospective way. Relevant ethical standards regarding the use of databases were applied in all cases. The study included patients with a single primary melanoma (clinical stage I or II) who had undergone sentinel-node biopsy within 120 days of excision of the primary tumor and who were still alive at the end of this period (n=1977).
Patients who developed a recurrence during this time (n=14) were excluded. The final number of patients evaluated was 1963. The inclusion dates and number of patients evaluated at each of the hospitals were as follows: October 1, 2001 to December 31, 2012 at the HUVV (n=189); January 1, 2000 to December 31, 2012 at the IVO (n=415); February 1, 1997 to December 31, 2012 at the HCUB (n=847); and January 1, 2003 to December 31, 2012 at GR (n=512). The dates corresponded to the periods during which the patient data had been prospectively recorded. The study was approved by local ethic committee. The main variable was the interval between the date of the excisional biopsy of the primary melanoma and the date of the sentinel-node biopsy. This interval, defined as delay time, was used as continuous variable and also categorized using the minimum p-value approach,  [4]  which consisted of performing multiple log-rank tests to compare survival curves and determine the optimal cutoff point for separating the patients into two groups.
The log-rank test was used to perform univariate analyses. Multivariate analyses were performed with Cox proportional hazard models. The assumption of proportionality was evaluated graphically using "log-log" plots in the two-sample comparison case. An analysis of missing values was also performed. These values were imputed using a complete-case (multiple imputation) model  [9]  for which we ran five iterations and combined estimates and standard errors using Rubin's rules. Prior to developing the model, we tested if the data were randomly missing using the missing values add-on module in the SPSS statistical package. A P value of less than 0. 05 was considered to indicate statistical significant. SPSS software version 20. 0 (SPSS) was used for all statistical analyses. Kaplan-Meier curves were drawn using SAS software version 9.
We identified 594 patients (30. 2%) lost to follow-up until 31 December 2012. Vital status was systematically reviewed in all cases and it was ascertained that 5 patients (1%) had died. A sensitivity analysis was performed on the reviewed data and the results were similar (data not shown). A delay time of more than 40 days was associated with better 5-year disease-free survival (80. 1% vs. 73. 8%, P<0. 0839) (Figure  1A ) and better 5-year melanoma-specific survival (89. 5% vs. 82%; P=0. 0002) (Figure  1B ). On analyzing cases of regional lymph node involvement during follow-up, we also found no differences between the rate of false-negative sentinel-node in the two groups (21,3% vs. 18. 1%). False-negative sentinel-node was calculated as the amount of false negative results divided by the amount of false-negative + true positives as suggested van Akkooi et al.
To our knowledge, only two studies to date, published by Parrett et al. ,  [3]  and at the same time, in this volume, the study of Oude Ophuis et al. [16] , have studied the impact of the timing of sentinel-node biopsy on survival in melanoma patients. In Parrett's study, surprisingly, they observed an increased frequency of recurrence and melanoma-specific mortality in patients with a delay time of less than 40 days. The authors attributed the higher mortality and recurrence rates observed in the early group to the fact that sentinel-node biopsy tends to performed sooner in patients with thicker or ulcerated melanomas. In a similar way, Oude Ophius et al. , have analyzed the same topic on a database of the EORTC Melanoma Group exclusively in patients with a positive SLN. Observing that, the timing of sentinel-node biopsy for any interval is not an independent prognostic factor in this group of patients  [16].
Acknowledgments Founding: This work was supported by partially (Grants  03/0019 ,  05/0302 ,  06/0265 ,  09/1393  and  12/00840 ) from  Fondo de Investigaciones Sanitarias, Spain ; by the  CIBER de Enfermedades Raras of the Instituto de Salud Carlos III, Spain ; by the  AGAUR   2009 SGR 1337  of the Catalan Government, Spain; by the  European Commission  under the 6th Framework Programme, Contract no:  LSHC-CT-2006-018702  (GenoMEL); and by the  National Cancer Institute (NCI)  of the  US National Institute of Health (NIH)  ( CA83115 ). Role of the Sponsors: The sponsors had no role in the design and conduct of the study; in the collection, analysis and interpretation of data; or in the preparation, review, or approval of the manuscript.
Results: We have developed IntactEye, a software package that uses two orthogonal images of the intact retina to locate focal injections of a dye. The two images are taken while the retina is still inside the eye. This bypasses the dissection step, avoiding unnecessary damage to the retina, and speeds up data acquisition. By using the native spherical coordinates of the eye, we avoid distortions caused by interpreting a curved structure in a flat coordinate system. Our method compares well to the projection method and to the Retistruct package, which both use the flattened retina as a starting point. We have tested the method also on synthetic data, where the injection location is known. Our method has been designed for analysing mouse retinas, where there are no visible landmarks for discerning retinal orientation, but can also be applied to retinas from other species.
In the projection method the location of the nasotemporal axis is first estimated based on the vestigial nictitating membrane at the nasal pole, and then the location of the injection site is projected onto the nasotemporal (NT) axis. The injection location is reported as a fraction of the NT axis, measured from the nasal pole (Fig. 2 ). The dorsoventral axis is calculated by orthogonal projection of the NT axis. This projection method uses a Cartesian coordinate system to describe a curved surface, which leads to distortions. To address this issue, the Retistruct program  [11]  was developed recently independently from earlier work using relaxation techniques  [12]. Retistruct refolds the retina back onto a sphere to try and recover the original geometry. Internally it minimizes an energy function such that the stretching of the surface is as small as possible.
Installation IntactEye is free to download from  [13]. An archived version of our program is also available from http://dx. doi. org/10. 6084/m9. figshare. 1605574. The zip file contains the source code and some example images. This article acts as the main documentation for the program. Unpack the zip file, then start MATLAB. In the MATLAB GUI click the "Set path" icon, and add the IntactEye directory to the path. Alternatively this can be done from the command line by executing addpath('/your/path/to/ IntactEye'); savepath.
Preparation of images To localise a retinal injection using IntactEye two images of the intact retina, with the injection site visible, must be prepared. We suggest one taken from the top looking down at the iris, and a second one taken from the side. For mouse we recommend a small cut at the vestigial nictitating membrane as a nasal marker. Make sure this cut is visible in at least one of the two images. The two images can be loaded separately or as a part of a composite image. The software reads images in tiff, png and jpeg format. The images need to have the same magnification and either have the same height or width so they can be automatically merged.
How to use the software A video of the following four-step procedure can be found on YouTube  [14]. Step 1 Open up Matlab and type IntactEye to start the program. In the user interface click "Load Image". This brings up a dialog box to select which composite file to load. Alternatively, "Load Images" allows you to load the two views from two separate files. The images of the eye are displayed with two wire-frame spheres overlaid. The "Left Eye"/"Right Eye" button lets the user select if it is a left or right eye that is being analysed; this switches the dorsoventral (DV) axis accordingly. Step 2 Using the computer mouse, the wire-frame spheres can be interactively resized and repositioned (Fig. 3 ). The left mouse button is used when the sphere is resized (by clicking and dragging on the axis handles) or moved (by clicking and dragging the centre).
This is done by calculating (where r x , r y , r z are the lengths of the semiaxes of the ellipsoid) for 2000 points on the line, and picking the one with the smallest v. The fraction of the total area of the eye is reported as the injection size. The injection area is calculated using MATLAB's built in alpha hull  [16]  function (alphaShape); this functionality requires matlab version 2014b or newer; older versions of matlab will not calculate the area of injections, but can still calculate the location.
Results and discussion We have created a software package named "IntactEye" to calculate the location of a retinal injection from two orthogonal pictures of an intact retina. IntactEye lets the user manually place two reference wire-frame spheres on the images of the eye. The program reports the nasotemporal and dorsoventral coordinates of the injection as a (9) v = abs x 2 /r 2 x + y 2 /r 2 y + z 2 /r 2 z -1 fraction along the respective axes using our wedge coordinate system (Fig. 4 ). The injection size is reported as a fraction of the area of the retina. To compare IntactEye with the projection method and Retistruct, a set of wild type mouse retinas (N=11) which had been imaged both before and after flattening was analysed. Fig. 5  compares the position estimates from the three methods, for the nasotemporal axis (A) and the dorsoventral axis (B).
6 ). We also created synthetic images of eyes with label injections where the exact location was known. This allowed us to assess the variability with the IntactEye method that comes from fitting the spheres to the image of an idealised eye. Three observers each marked a unique set of synthetic data. Figure  7  shows the known NT (DV) coordinates on the x-axis and the corresponding estimates on the y-axis. We see a good correspondence between the true location and the estimated position in the synthetic data (Pearson correlation coefficient 0. 98   6  Estimating the precision of the IntactEye method by locating the optic disc. The optic disc is located in the centre of the eye and was visible in 6 out of 11 retinas. For each of the six retinas, the optic disc location was estimated in polar coordinates using IntactEye (triangles) and Retistruct (filled circles).
Limitations Each point that is localised using IntactEye must be uniquely identified in both images of the retina. Because of this limitation the IntactEye method is best suited for finding single injections and other distinct landmarks. If more than one distinct region is labelled with the same dye, it may be difficult to uniquely identify the same region in the two different images. This is known as the correspondence problem in stereo vision  [18]. This may limit the use to anterograde injections where the retinal marking is focused. For retrograde injections in animals where the label is spread over a large region of the retina (e. g. when topographic maps are perturbed, such as Figure  4  of  [19] ), it is currently better to dissect the retina and then use a program like Retistruct, which analyses the entire retina and can generate density estimates.
Future work By using the IntactEye method we can locate the centre of a single injection from images taken from any two distinct views where the injection is visible. The program currently only tracks one injection, but could be extended to handle multiple injections of different coloured dyes. This, together with the area estimation, might allow the processing of focal retrograde injections as well, as long as they are wholly visible in the images, potentially by allowing for additional views of the retina. The manual placement and alignment of the wireframes takes a few minutes, and is a candidate for automisation. However currently the time-intensive part is the data acquisition; for example dissecting out the retina and flattening procedures takes 10-15 min. An alternative method to our current problem of locating an injection site might be to image the entire eye to generate e.