15 research outputs found

    Combined Deep Learning and Traditional NLP Approaches for Fire Burst Detection Based on Twitter Posts

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    The current chapter introduces a procedure that aims at determining regions that are on fire, based on Twitter posts, as soon as possible. The proposed scheme utilizes a deep learning approach for analyzing the text of Twitter posts announcing fire bursts. Deep learning is becoming very popular within different text applications involving text generalization, text summarization, and extracting text information. A deep learning network is to be trained so as to distinguish valid Twitter fire-announcing posts from junk posts. Next, the posts labeled as valid by the network have undergone traditional NLP-based information extraction where the initial unstructured text is converted into a structured one, from which potential location and timestamp of the incident for further exploitation are derived. Analytic processing is then implemented in order to output aggregated reports which are used to finally detect potential geographical areas that are probably threatened by fire. So far, the part that has been implemented is the traditional NLP-based and has already derived promising results under real-world conditions’ testing. The deep learning enrichment is to be implemented and expected to build upon the performance of the existing architecture and further improve it

    5G-PPP Software Network Working Group:Network Applications: Opening up 5G and beyond networks 5G-PPP projects analysis

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    As part of the 5G-PPP Initiative, the Software Network Working Group prepared this white paper to demystify the concept of the Network Applications. In fact, the Network Application ecosystem is more than the introduction of new vertical applications that have interaction capabilities. It refers to the need for a separate middleware layer to simplify the implementation and deployment of vertical systems on a large scale. Specifically, third parties or network operators can contribute to Network Applications, depending on the level of interaction and trust. Different implementations have been conducted by the different projects considering different API types and different level of trust between the verticals and the owner of 5G platforms. In this paper, the different approaches considered by the projects are summarized. By analysing them, it appears three options of interaction between the verticals and the 5G platform owner: - aaS Model: it is the model where the vertical application consumes the Network Applications as a service. The vertical application deployed in the vertical service provider domain. It connects with the 3GPP network systems (EPS, 5GS) in one or more PLMN operator domain. - Hybrid: it is the model where the vertical instantiates a part of its Vertical App in the operator domain like the EDGE. The other part remains in the vertical domain. A similar approach has been followed in TS 23.286 related to the deployment of V2X server. - Coupled/Delegated: it is the model where the vertical delegates its app to the operator. The Network Applications will be composed and managed by the operator. This approach is the one followed in the platforms like 5G-EVE. In addition, the paper brings an analysis of the different API type deployed. It appears that the abstraction from network APIs to service APIs is necessary to hide the telco complexity making APIs easy to consume for verticals with no telco expertise and to adress data privacy requirements

    Machine learning and data mining frameworks for predicting drug response in cancer:An overview and a novel <i>in silico</i> screening process based on association rule mining

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    Chapter Combined Deep Learning and Traditional NLP Approaches for Fire Burst Detection Based on Twitter Posts

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    The current chapter introduces a procedure that aims at determining regions that are on fire, based on Twitter posts, as soon as possible. The proposed scheme utilizes a deep learning approach for analyzing the text of Twitter posts announcing fire bursts. Deep learning is becoming very popular within different text applications involving text generalization, text summarization, and extracting text information. A deep learning network is to be trained so as to distinguish valid Twitter fire-announcing posts from junk posts. Next, the posts labeled as valid by the network have undergone traditional NLP-based information extraction where the initial unstructured text is converted into a structured one, from which potential location and timestamp of the incident for further exploitation are derived. Analytic processing is then implemented in order to output aggregated reports which are used to finally detect potential geographical areas that are probably threatened by fire. So far, the part that has been implemented is the traditional NLP-based and has already derived promising results under real-world conditions’ testing. The deep learning enrichment is to be implemented and expected to build upon the performance of the existing architecture and further improve it

    The emerging role of microdialysis in diabetic patients undergoing amputation for limb ischemia

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    Lower limb ischemia in diabetic patients is a result of macro- and microcirculation dysfunction. Diabetic patients undergoing limb amputation carry high mortality and morbidity rates, and decision making concerning the level of amputation is critical. Aim of this study is to evaluate a novel microdialysis technique to monitor tissue microcirculation preoperatively and predict the success of limb amputation in such patients. Overall, 165 patients with type 2 diabetes mellitus undergoing lower limb amputation were enrolled. A microdialysis catheter was placed preoperatively at the level of the intended flap for the stump reconstruction, and the levels of glucose, glycerol, lactate and pyruvate were measured for 24consecutive hours. Patients were then amputated and monitored for 30 days regarding the outcome of amputation. Failure of amputation was defined as delayed healing or stump ischemia. Patients were divided into two groups based on the success of amputation. There was no difference between the two groups regarding gender, ASA score, body mass index, comorbidities, diagnostic modality used, level of amputation, as well as glucose, glycerol, and pyruvate levels. However, local concentrations of lactate were significantly different between the two groups and lactate/pyruvate (L/P) ratio was independently associated with failed amputation (threshold defined at 25.35). Elevated preoperative tissue L/P ratio is independently associated with worse outcomes in diabetic patients undergoing limb amputation. Therefore, preoperative tissue L/P ratio could be used as a predicting tool for limb amputation&apos;s outcome, although more clinical data are needed to provide safer conclusions

    Neurorehabilitation Through Synergistic Man-Machine Interfaces Promoting Dormant Neuroplasticity in Spinal Cord Injury: Protocol for a Nonrandomized Controlled Trial

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    Background: Spinal cord injury (SCI) constitutes a major sociomedical problem, impacting approximately 0.32-0.64 million people each year worldwide; particularly, it impacts young individuals, causing long-term, often irreversible disability. While effective rehabilitation of patients with SCI remains a significant challenge, novel neural engineering technologies have emerged to target and promote dormant neuroplasticity in the central nervous system. Objective: This study aims to develop, pilot test, and optimize a platform based on multiple immersive man-machine interfaces offering rich feedback, including (1) visual motor imagery training under high-density electroencephalographic recording, (2) mountable robotic arms controlled with a wireless brain-computer interface (BCI), (3) a body-machine interface (BMI) consisting of wearable robotics jacket and gloves in combination with a serious game (SG) application, and (4) an augmented reality module. The platform will be used to validate a self-paced neurorehabilitation intervention and to study cortical activity in chronic complete and incomplete SCI at the cervical spine. Methods: A 3-phase pilot study (clinical trial) was designed to evaluate the NeuroSuitUp platform, including patients with chronic cervical SCI with complete and incomplete injury aged over 14 years and age-/sex-matched healthy participants. Outcome measures include BCI control and performance in the BMI-SG module, as well as improvement of functional independence, while also monitoring neuropsychological parameters such as kinesthetic imagery, motivation, self-esteem, depression and anxiety, mental effort, discomfort, and perception of robotics. Participant enrollment into the main clinical trial is estimated to begin in January 2023 and end by December 2023. Results: A preliminary analysis of collected data during pilot testing of BMI-SG by healthy participants showed that the platform was easy to use, caused no discomfort, and the robotics were perceived positively by the participants. Analysis of results from the main clinical trial will begin as recruitment progresses and findings from the complete analysis of results are expected in early 2024. Conclusions: Chronic SCI is characterized by irreversible disability impacting functional independence. NeuroSuitUp could provide a valuable complementary platform for training in immersive rehabilitation methods to promote dormant neural plasticity

    D4.6 REUSABLE MODEL & ANALYTICAL TOOLS: SOFTWARE PROTOTYPE 3

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    This document is the third and last software demonstrator deliverable of PolicyCLOUD at M34, October 2022, of the project and is intended for the reviewers of the software deliverables. This deliverable provides the description of the final software demonstrator for the components of the Integrated Data Acquisition and Analytics (DAA) Layer, which provides the analytical capabilities of the PolicyCLOUD platform. The components include the DAA API Gateway (responsible for the overall orchestration and the layer API), the built-in analytical tools such as the new Trend Analysis tool as well as the analytical tools already presented in D4.4 [6]: Data cleaning and interoperability, Situational Knowledge, Opinion Mining & Sentiment Analysis and Social Dynamics & Behavioural Data analysis, and the Operational Data Repository. The full integration of the DAA API Gateway (responsible for the overall orchestration and the layer API) was demonstrated during the review of June 2021 for two use cases. As of the publication of this deliverable, this has been extended in multiple ways: Additional ingest analytics capabilities such as Trend Analysis were added as detailed in section 2.1.3 and 2.1.4 The new integration of the Politika framework with PolicyCloud as detailed in section 2.2.5 of D4.5 [3] and as further detailed in sections 2.1.5.4 and 2.1.6 of this deliverable. The integrated Politika framework was itself used for two use cases, as detailed in section 2.2.6.2 The novel seamless architecture which was introduced in D4.4 [6] and which presents single logical datasets to users that can be explored with SQL had further been enhanced in the past year by performance enhancements of the SQL JOIN as detailed in section 4.4.4 of D4.5. The impact of using the seamless technology is minimal, as the users do not even know in what storage tier their datasets are stored. The only impact is a change of the SQL statement that now makes use of atable function that accepts standard SQL statements though, as detailed in section 2.2.8.3.This deliverable is submitted to the EC, not yet approved

    The Dual Role of Oxidative-Stress-Induced Autophagy in Cellular Senescence: Comprehension and Therapeutic Approaches

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    The contemporary lifestyle of the last decade has undeniably caused a tremendous increase in oxidative-stress-inducing environmental sources. This phenomenon is not only connected with the rise of ROS levels in multiple tissues but is also associated with the induction of senescence in different cell types. Several signaling pathways that are associated with the reduction in ROS levels and the regulation of the cell cycle are being activated, so that the organism can battle deleterious effects. Within this context, autophagy plays a significant role. Through autophagy, cells can maintain their homeostasis, as if it were a self-degradation process, which removes the “wounded” molecules from the cells and uses their materials as a substrate for the creation of new useful cell particles. However, the role of autophagy in senescence has both a “dark” and a “bright” side. This review is an attempt to reveal the mechanistic aspects of this dual role. Nanomedicine can play a significant role, providing materials that are able to act by either preventing ROS generation or controllably inducing it, thus functioning as potential therapeutic agents regulating the activation or inhibition of autophagy

    Assembling Ecological Pieces to Reconstruct the Conservation Puzzle of the Aegean Sea

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    The effective conservation of marine biodiversity through an integrated ecosystem-based management approach requires a sound knowledge of the spatial distribution of habitats and species. Although costly in terms of time and resources, acquiring such information is essential for the development of rigorous management plans and the meaningful prioritization of conservation actions. Located in the northeastern part of the Mediterranean, the Aegean Sea represents a stronghold for marine biodiversity. However, conservation efforts are hampered by the apparent lack of spatial information regarding marine habitats and species. This work is the first to address this knowledge gap by assembling, updating, and mapping information on the distribution of key ecological components. A range of data sources and methodological approaches was utilized to compile and complement the available data on 68 ecological features of conservation interest (58 animal species, six habitat categories, and four other vulnerable ecological features). A standardized data evaluation procedure was applied, based on five semi-quantitative data quality indicators in the form of a pedigree matrix. This approach assessed the sufficiency of the datasets and allowed the identification of the main sources of uncertainty, highlighting aspects that require further investigation. The overall dataset was found to be sufficient in terms of reliability and spatiotemporal relevance. However, it lacked in completeness, showing that there are still large areas of the Aegean that remain understudied, while further research is needed to elucidate the distribution patterns and conservation status of several ecological features; especially the less charismatic ones and those found in waters deeper than 40 m. Moreover, existing conservation measures appear to be inadequate to safeguard biodiversity. Only 2.3% of the study area corresponds to designated areas for conservation, while 41 of the ecological features are underrepresented in these areas. Considering the high geomorphological complexity and transnational character of the Aegean Sea, this study does not offer a complete account of the multifaceted diversity of this ecoregion. Instead, it represents a significant starting point and a solid basis for the development of systematic conservation plans that will allow the effective protection of biodiversity within an adaptive management framework

    Functional interplay between the DNA-damage-response kinase ATM and ARF tumour suppressor protein in human cancer

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    The DNA damage response (DDR) pathway and ARF function as barriers to cancer development. Although commonly regarded as operating independently of each other, some studies proposed that ARF is positively regulated by the DDR. Contrary to either scenario, we found that in human oncogene-transformed and cancer cells, ATM suppressed ARF protein levels and activity in a transcription-independent manner. Mechanistically, ATM activated protein phosphatase 1, which antagonized Nek2-dependent phosphorylation of nucleophosmin (NPM), thereby liberating ARF from NPM and rendering it susceptible to degradation by the ULF E3-ubiquitin ligase. In human clinical samples, loss of ATM expression correlated with increased ARF levels and in xenograft and tissue culture models, inhibition of ATM stimulated the tumour-suppressive effects of ARF. These results provide insights into the functional interplay between the DDR and ARF anti-cancer barriers, with implications for tumorigenesis and treatment of advanced tumours.</p
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