Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

Effects of antiplatelet therapy after stroke due to intracerebral haemorrhage (RESTART): a randomised, open-label trial

Background: Antiplatelet therapy reduces the risk of major vascular events for people with occlusive vascular disease, although it might increase the risk of intracranial haemorrhage. Patients surviving the commonest subtype of intracranial haemorrhage, intracerebral haemorrhage, are at risk of both haemorrhagic and occlusive vascular events, but whether antiplatelet therapy can be used safely is unclear. We aimed to estimate the relative and absolute effects of antiplatelet therapy on recurrent intracerebral haemorrhage and whether this risk might exceed any reduction of occlusive vascular events. Methods: The REstart or STop Antithrombotics Randomised Trial (RESTART) was a prospective, randomised, open-label, blinded endpoint, parallel-group trial at 122 hospitals in the UK. We recruited adults (≥18 years) who were taking antithrombotic (antiplatelet or anticoagulant) therapy for the prevention of occlusive vascular disease when they developed intracerebral haemorrhage, discontinued antithrombotic therapy, and survived for 24 h. Computerised randomisation incorporating minimisation allocated participants (1:1) to start or avoid antiplatelet therapy. We followed participants for the primary outcome (recurrent symptomatic intracerebral haemorrhage) for up to 5 years. We analysed data from all randomised participants using Cox proportional hazards regression, adjusted for minimisation covariates. This trial is registered with ISRCTN (number ISRCTN71907627). Findings: Between May 22, 2013, and May 31, 2018, 537 participants were recruited a median of 76 days (IQR 29–146) after intracerebral haemorrhage onset: 268 were assigned to start and 269 (one withdrew) to avoid antiplatelet therapy. Participants were followed for a median of 2·0 years (IQR [1·0– 3·0]; completeness 99·3%). 12 (4%) of 268 participants allocated to antiplatelet therapy had recurrence of intracerebral haemorrhage compared with 23 (9%) of 268 participants allocated to avoid antiplatelet therapy (adjusted hazard ratio 0·51 [95% CI 0·25–1·03]; p=0·060). 18 (7%) participants allocated to antiplatelet therapy experienced major haemorrhagic events compared with 25 (9%) participants allocated to avoid antiplatelet therapy (0·71 [0·39–1·30]; p=0·27), and 39 [15%] participants allocated to antiplatelet therapy had major occlusive vascular events compared with 38 [14%] allocated to avoid antiplatelet therapy (1·02 [0·65–1·60]; p=0·92). Interpretation: These results exclude all but a very modest increase in the risk of recurrent intracerebral haemorrhage with antiplatelet therapy for patients on antithrombotic therapy for the prevention of occlusive vascular disease when they developed intracerebral haemorrhage. The risk of recurrent intracerebral haemorrhage is probably too small to exceed the established benefits of antiplatelet therapy for secondary prevention

Effects of antiplatelet therapy after stroke due to intracerebral haemorrhage (RESTART): a randomised, open-label trial

Background: Antiplatelet therapy reduces the risk of major vascular events for people with occlusive vascular disease, although it might increase the risk of intracranial haemorrhage. Patients surviving the commonest subtype of intracranial haemorrhage, intracerebral haemorrhage, are at risk of both haemorrhagic and occlusive vascular events, but whether antiplatelet therapy can be used safely is unclear. We aimed to estimate the relative and absolute effects of antiplatelet therapy on recurrent intracerebral haemorrhage and whether this risk might exceed any reduction of occlusive vascular events. Methods: The REstart or STop Antithrombotics Randomised Trial (RESTART) was a prospective, randomised, open-label, blinded endpoint, parallel-group trial at 122 hospitals in the UK. We recruited adults (≥18 years) who were taking antithrombotic (antiplatelet or anticoagulant) therapy for the prevention of occlusive vascular disease when they developed intracerebral haemorrhage, discontinued antithrombotic therapy, and survived for 24 h. Computerised randomisation incorporating minimisation allocated participants (1:1) to start or avoid antiplatelet therapy. We followed participants for the primary outcome (recurrent symptomatic intracerebral haemorrhage) for up to 5 years. We analysed data from all randomised participants using Cox proportional hazards regression, adjusted for minimisation covariates. This trial is registered with ISRCTN (number ISRCTN71907627). Findings: Between May 22, 2013, and May 31, 2018, 537 participants were recruited a median of 76 days (IQR 29–146) after intracerebral haemorrhage onset: 268 were assigned to start and 269 (one withdrew) to avoid antiplatelet therapy. Participants were followed for a median of 2·0 years (IQR [1·0– 3·0]; completeness 99·3%). 12 (4%) of 268 participants allocated to antiplatelet therapy had recurrence of intracerebral haemorrhage compared with 23 (9%) of 268 participants allocated to avoid antiplatelet therapy (adjusted hazard ratio 0·51 [95% CI 0·25–1·03]; p=0·060). 18 (7%) participants allocated to antiplatelet therapy experienced major haemorrhagic events compared with 25 (9%) participants allocated to avoid antiplatelet therapy (0·71 [0·39–1·30]; p=0·27), and 39 [15%] participants allocated to antiplatelet therapy had major occlusive vascular events compared with 38 [14%] allocated to avoid antiplatelet therapy (1·02 [0·65–1·60]; p=0·92). Interpretation: These results exclude all but a very modest increase in the risk of recurrent intracerebral haemorrhage with antiplatelet therapy for patients on antithrombotic therapy for the prevention of occlusive vascular disease when they developed intracerebral haemorrhage. The risk of recurrent intracerebral haemorrhage is probably too small to exceed the established benefits of antiplatelet therapy for secondary prevention

Effects of antiplatelet therapy after stroke due to intracerebral haemorrhage (RESTART): a randomised, open-label trial

BACKGROUND: Antiplatelet therapy reduces the risk of major vascular events for people with occlusive vascular disease, although it might increase the risk of intracranial haemorrhage. Patients surviving the commonest subtype of intracranial haemorrhage, intracerebral haemorrhage, are at risk of both haemorrhagic and occlusive vascular events, but whether antiplatelet therapy can be used safely is unclear. We aimed to estimate the relative and absolute effects of antiplatelet therapy on recurrent intracerebral haemorrhage and whether this risk might exceed any reduction of occlusive vascular events. METHODS: The REstart or STop Antithrombotics Randomised Trial (RESTART) was a prospective, randomised, open-label, blinded endpoint, parallel-group trial at 122 hospitals in the UK. We recruited adults (≥18 years) who were taking antithrombotic (antiplatelet or anticoagulant) therapy for the prevention of occlusive vascular disease when they developed intracerebral haemorrhage, discontinued antithrombotic therapy, and survived for 24 h. Computerised randomisation incorporating minimisation allocated participants (1:1) to start or avoid antiplatelet therapy. We followed participants for the primary outcome (recurrent symptomatic intracerebral haemorrhage) for up to 5 years. We analysed data from all randomised participants using Cox proportional hazards regression, adjusted for minimisation covariates. This trial is registered with ISRCTN (number ISRCTN71907627). FINDINGS: Between May 22, 2013, and May 31, 2018, 537 participants were recruited a median of 76 days (IQR 29-146) after intracerebral haemorrhage onset: 268 were assigned to start and 269 (one withdrew) to avoid antiplatelet therapy. Participants were followed for a median of 2·0 years (IQR [1·0- 3·0]; completeness 99·3%). 12 (4%) of 268 participants allocated to antiplatelet therapy had recurrence of intracerebral haemorrhage compared with 23 (9%) of 268 participants allocated to avoid antiplatelet therapy (adjusted hazard ratio 0·51 [95% CI 0·25-1·03]; p=0·060). 18 (7%) participants allocated to antiplatelet therapy experienced major haemorrhagic events compared with 25 (9%) participants allocated to avoid antiplatelet therapy (0·71 [0·39-1·30]; p=0·27), and 39 [15%] participants allocated to antiplatelet therapy had major occlusive vascular events compared with 38 [14%] allocated to avoid antiplatelet therapy (1·02 [0·65-1·60]; p=0·92). INTERPRETATION: These results exclude all but a very modest increase in the risk of recurrent intracerebral haemorrhage with antiplatelet therapy for patients on antithrombotic therapy for the prevention of occlusive vascular disease when they developed intracerebral haemorrhage. The risk of recurrent intracerebral haemorrhage is probably too small to exceed the established benefits of antiplatelet therapy for secondary prevention. FUNDING: British Heart Foundation

Management and exploration of big linked datasets

This document presents the research contribution regarding the exploration and visualization of very large linked datasets. First, the technologies and innovations that led to the increase of the available big data are discussed. Then the challenges that people interested in the exploration and analysis of the available information are discussed. Emphasis is given in differentiating the challenges related to the nature and characteristics of the available dataset from the ones coming from specific use cases and target audience. Specific, real-world examples are presented to show the needs of the users and the specification for the solutions. Next, a solution that supports users with querying SPARQL endpoints, visualizing the results, in the optimal way based on a knowledge base and a decision support system, and facilitating the exploration of the information through an innovative functionality toolkit is presented. The solution is proposing a client-server architectural model, that allows the users to perform SPARQL queries over any available endpoint, receive the results visualized based on the specific characteristics of the query and explore the visualized information through multiple abstraction and filtering criteria. In addition, a fully-fledged innovative system that supports the representation of any RDF dataset as one continuous graph at the two-dimensional space. The system has been carefully designed to manage any dataset independently of its specific characteristics. The system stores the information in a distributed key-value storage system and indexes the information with a XZ-index ensuring the smooth and timely provision of the information to multiple users regardless the spatial criteria used or the area requested. A dedicated user interface, allows the user to access the information, explore the complete graph, visualize the dataset thought multiple abstraction and filtering criteria, navigate paths of interest or isolate parts of the dataset that wants to further explore.Understanding that the value of the available dataset is closely related to their quality, a technique to improve the quality of the available conversational datasets is proposed. The technique builds on top of semantic relationships, such as synonyms and hyponomy, to calculate the semantic similarity and the semantic relatedness between the topic that the dataset is to be used for and the available information. Taking into consideration the use case that the output dataset is going to be used for, its thematic relation with the source of the input dataset and the language formality needed for the task, the two scores are merged using a weight-based score function into a matching percentage. The dataset is then ranked based on this percentage and only the information above the required threshold is present in the output file. Extended experimental analysis showed that machine learning solutions perform better when trained with smaller but properly created dataset than when trained over complete initial dataset.Finally, the data quality control needed when collecting big datasets is discussed. The specific example of the data collected within the context of the SCENT EU founded project is presented. There volunteers were tasked to use mobile applications and smart sensor to collect images, video and sensor measurement at area of hydrological interest. The collected data were processed in order to collect information about the land cover of the area, the water level and the water velocity of the water body as well as air temperature and soil moisture values. The data were collected from volunteers with no training regarding the proper way to collect scientific measurements, in conditions that were challenging regarding the weather phenomena and the accessibility and in areas that had many technological challenges such as the lack of accurate GPS signal. The collected data are to be used in order to update hydrological models, meaning that there is a need for high accuracy in the measurements used. Innovative techniques that filter out invalid measurements were developed in order to provide the proper data for the models. The techniques were proven to work properly and they were able to support the creation of improved, more accurate flood models.Η παρακάτω διατριβή παρουσιάζει την ερευνητική μελέτη πάνω σε ζητήματα διαχείρισης και εξερεύνησης μεγάλων συνόλων διασυνδεδεμένων δεδομένων. Στα πλαίσια της διδακτορικής διατριβής έχει ερευνηθεί σε βάθος τόσο η υπάρχουσα βιβλιογραφία όσο και η σχετική ερευνητική εργασία σε παγκόσμιο επίπεδο. Έχουν διερευνηθεί ενδελεχώς τα ζητήματα αυτά τόσο από την πλευρά των μηχανικών που καλούνται να σχεδιάσουν συστήματα που να διαχειρίζονται τα χαρακτηριστικά αυτών των δεδομένων, όσο και από την πλευρά των χρηστών που επιθυμούν ομαλή και ανεμπόδιστη πρόσβαση στα δεδομένα με εύκολους, ως προς την χρήση και την κατανόηση, τρόπους. Επιπλέον, έχουν προταθεί πλήρεις λύσεις για την αντιμετώπιση αυτών των ζητημάτων με βάση τα σενάρια χρήσης. Συγκεκριμένα, έχει προταθεί ένα ολοκληρωμένο σύστημα οπτικοποίησης της πληροφορίας βασισμένη σε χαρακτηριστικά του SPARQL ερωτήματος. Η προτεινόμενη λύση περιλαμβάνει ένα σύστημα υποστήριξης λήψεων αποφάσεων που συμβάλει στην επιλογή της κατάλληλης οπτικοποίησης για κάθε ερώτημα SPARQL που μπορεί να δημιουργήσει ο χρήστης, βασισμένο σε μια βάση γνώσεων που περιλαμβάνει τα αποτελέσματα μια εκτεταμένης πειραματικής μελέτης κατά την οποία αναλύθηκαν συγκεκριμένα χαρακτηριστικά πολλών SPARQL συνόλων δεδομένων. Προτείνεται ακόμα μια λύση η οποία στοχεύει στο να βοηθήσει χρήστες που δεν είναι εξοικειωμένοι με τα μεγάλα σύνολα δεδομένων και τον Σημασιολογικό Ιστό στο να εξερευνήσουν σύνολα δεδομένων τα οποία δεν ενημερώνονται συχνά αλλά περιέχουν σημαντικές πληροφορίες που πρέπει να εξερευνηθούν σε βάθος.Για την αξιοποίηση των συνόλων δεδομένων ζευγών ερώτηση-απάντηση που είναι διαθέσιμα με τέτοιο τρόπο που να εξαλείφονται τα μη-αξιοποιήσιμα και υποκειμενικά δεδομένα σε συστήματα αυτόματων διαλόγων, αναπτύχθηκαν τεχνικές σημασιολογικής ανάλυσης των δεδομένων. Προτάθηκε μια τεχνική που ορίζει μια αυστηρή ροή δεδομένων και εξασφαλίζει ότι τα σύνολα δεδομένων που δίνονται ως είσοδο επεξεργάζονται με τον καλύτερο δυνατό τρόπο τόσο με βάση τον σημασιολογικό προσανατολισμό του συστήματος όσο και με βάση την περίπτωση χρήσης. Όπως είναι αναμενόμενο σε κάθε μεγάλο σύνολο δεδομένων έτσι και για δεδομένα που συλλέγονται από τους πολίτες η ποιότητα και η αξιοπιστία των μετρήσεων που συλλέγονταί είναι αμφισβητούμενη. Για τον λόγο αυτόν αναπτύχθηκε ένας μηχανισμός ελέγχου της ποιότητας των δεδομένων που βασίστηκε σε μια σειρά από κανόνες και πρακτικούς περιορισμούς

Disk-based visualization of large graphs

50 σ.σχεδόν τελείως περιορισμένη στην κοινότητα του Σημασιολογικού Ιστού. Η ραγδαία αύξηση των διασυνδεδεμένων δεδομένων που βρίσκονται διαθέσιμα στο διαδίκτυο όμως, καθώς και η αποδεδειγμένη χρησιμότητα τους για τους απλούς χρήστες, αυτούς που δεν έχουν τις τεχνικές γνώσεις ώστε να μπορέσουν να καταλάβουν τις δομές που διέπουν τα δεδομένα, καθιστούν επιτακτική την ανάγκη για μία εφαρμογή που θα ξεπερνάει τις δυσκολίες μέσα από την οπτικοποίηση των δεδομένων. Η οπτικοποίηση των διασυνδεδεμένων δεδομένων πρέπει να γίνει με ένα κατανοητό και εύχρηστο τρόπο, που θα επιτρέπει στον απλό χρήστη να αντιληφθεί την δομή τους, να πλοηγηθεί σε αυτά και να τα κατανοήσει με τελικό στόχο να εντοπίσει την ζητούμενη πληροφορία. Για να καλύψουμε αυτήν την ανάγκη, ξεκινήσαμε με είσοδο ένα σύνολο διασυνδεδεμένων δεδομένων, δομημένα με το RDF μοντέλο, τα επεξεργαστήκαμε κατάλληλα και δημιουργήσαμε μία web εφαρμογή που παρουσιάζει τα δεδομένα στον χρήστη με την μορφή ενός κατευθυνόμενου γράφου. Πλαισιώσαμε τον γράφο με μια σειρά από εργαλεία με στόχο να κάνουμε πιο εύκολη για τον χρήστη τόσο την εξερεύνηση των δεδομένων όσο και την κατανόηση της πληροφορίας που αυτά περιέχουν Λέξεις Κλειδιά: <<RDF, οπτικοποίηση, γράφος, διασυνδεδεμένα δεδομένα, Σημασιολογικός Ιστός>>The uptake and consumption of Linked Data is currently restricted almost entirely to the Semantic Web community. The dramatic increase of the amount of the semantic data available on the Web, as well as their proven usefulness for the non-tech savvy web users -who are unable to understand the structure behind the data- have resulted in a need for an application that would overcome these hurdles by visualizing of the data. Visualizing and interacting with Linked Data should be implemented with a coherent and legible manner that would allow the user to understand their structure, browse the data and retrieve new pieces of information. In answer to this challenge, we processed a Linked Data dataset, in RDF format, and we created a web application that presents the data to the user as a directed graph. We also implemented a series of modules with the purpose of helping the user explore the data and grasp a better understanding of their meaning. Keywords: >Μαρία Κ. Κρομμύδ

Smart Tags: IoT Sensors for Monitoring the Micro-Climate of Cultural Heritage Monuments

The building materials of Cultural Heritage monuments are subjected to continuous degradation throughout the years, mainly due to their exposure to harsh and unexpected weather phenomena related to Climate Change. The specific climatic conditions at their vicinity, especially when there are local peculiarities such as onshore breeze, are of crucial importance for studying the deterioration rate and the identification of proper mitigation actions. Generalized models that are based on climate data can provide an insight on the deterioration but fail to offer a deeper understanding of this phenomenon. To this end, in the context of the EU-funded HYPERION project a distributed smart sensor network will be deployed at the Cultural Heritage monuments in four study areas as the solution to this problem. The developed system, which is demonstrated in this paper, includes smart IoT devices, called Smart Tags, designed to provide environmental measurements close to monuments, a middle-ware to facilitate the communication and a visualization platform where the collected information is presented. Last but not least, special focus is given to the device’s NB-IoT connectivity and its power efficiency by conducting various tests and extract useful conclusions

A Comparative Study of Autonomous Object Detection Algorithms in the Maritime Environment Using a UAV Platform

Maritime operations rely heavily on surveillance and require reliable and timely data that can inform decisions and planning. Critical information in such cases includes the exact location of objects in the water, such as vessels, persons, and others. Due to the unique characteristics of the maritime environment, the location of even inert objects changes through time, depending on the weather conditions, water currents, etc. Unmanned aerial vehicles (UAVs) can be used to support maritime operations by providing live video streams and images from the area of operations. Machine learning algorithms can be developed, trained, and used to automatically detect and track objects of specific types and characteristics. EFFECTOR is an EU-funded project, developing an Interoperability Framework for maritime surveillance. Within the project, we developed an embedded system that employs machine learning algorithms, allowing a UAV to autonomously detect objects in the water and keep track of their changing position through time. Using the on-board computation unit of the UAV, we ran and present the results of a series of comparative tests among possible architecture sizes and training datasets for the detection and tracking of objects in the maritime environment. We tested architectures based on their efficiency, accuracy, and speed. A combined solution for training the datasets is suggested, providing optimal efficiency and accuracy

A Video Analytics System for Person Detection Combined with Edge Computing

Ensuring citizens’ safety and security has been identified as the number one priority for city authorities when it comes to the use of smart city technologies. Automatic understanding of the scene, and the associated provision of situational awareness for emergency situations, are able to efficiently contribute to such domains. In this study, a Video Analytics Edge Computing (VAEC) system is presented that performs real-time enhanced situation awareness for person detection in a video surveillance manner that is also able to share geolocated person detection alerts and other accompanied crucial information. The VAEC system adopts state-of-the-art object detection and tracking algorithms, and it is integrated with the proposed Distribute Edge Computing Internet of Things (DECIoT) platform. The aforementioned alerts and information are able to be shared, though the DECIoT, to smart city platforms utilizing proper middleware. To verify the utility and functionality of the VAEC system, extended experiments were performed (i) in several light conditions, (ii) using several camera sensors, and (iii) in several use cases, such as installed in fixed position of a building or mounted to a car. The results highlight the potential of VAEC system to be exploited by decision-makers or city authorities, providing enhanced situational awareness