In-vitro Validation of Intratumoral Modulation Therapy for Glioblastoma

Intratumoral modulation therapy (IMT) is a novel electrotherapy used to treat brain cancer tumours using electric fields applied directly to the tumours through implanted electrodes. Previous research has validated IMT\u27s effectiveness and provided computer-simulated optimizations for IMT electric fields. This work validates these computer optimizations in-vitro, using a PCB construct to deliver electric fields, and bioluminescence imaging to assess cell viability. We found electric field strength to correlate with cell viability, and found that rotating (phase-shifted) electric fields did not produce significant improvements in IMT efficacy. Future work will investigate different IMT frequencies and other parameters, while providing biological replicates to strengthen our results

DelBugV: Delta-Debugging Neural Network Verifiers

Deep neural networks (DNNs) are becoming a key component in diverse systems across the board. However, despite their success, they often err miserably; and this has triggered significant interest in formally verifying them. Unfortunately, DNN verifiers are intricate tools, and are themselves susceptible to soundness bugs. Due to the complexity of DNN verifiers, as well as the sizes of the DNNs being verified, debugging such errors is a daunting task. Here, we present a novel tool, named DelBugV, that uses automated delta debugging techniques on DNN verifiers. Given a malfunctioning DNN verifier and a correct verifier as a point of reference (or, in some cases, just a single, malfunctioning verifier), DelBugV can produce much simpler DNN verification instances that still trigger undesired behavior -- greatly facilitating the task of debugging the faulty verifier. Our tool is modular and extensible, and can easily be enhanced with additional network simplification methods and strategies. For evaluation purposes, we ran DelBugV on 4 DNN verification engines, which were observed to produce incorrect results at the 2021 neural network verification competition (VNN-COMP'21). We were able to simplify many of the verification queries that trigger these faulty behaviors, by as much as 99%. We regard our work as a step towards the ultimate goal of producing reliable and trustworthy DNN-based software

Dietary and Activity Habits in Adolescents Living in the United Arab Emirates: A Cross-Sectional Study

Background: The Global School Health Survey 2010 reported that 40% of pupils aged 12-15 years are overweight or obese; double what was reported in 2005. Following such concerns the government introduced mandatory school health education sessions to students, and produced strict guidelines on school food and drink provision (September 2011). The aim of this survey was to obtain information about adolescents’ dietary and activity habits, and their association with the increased prevalence of obesity.Methods: A cross-sectional study of 1,022 students (539 boys; 483 girls) aged 12-16 years, from 17 government schools in Dubai, UAE. Dietary practices and physical activity was collected using a short self-completed questionnaire.Results: Non-Emirati pupils, especially the girls appear to eat more healthily than their Emirati counterparts. Overall, 16% of students reported never eating breakfast, 31% reported drinking sugar sweetened beverages everyday 18% said they never drank milk and 15% never ate fruits. 67% reported buying food from school every day; Emiratis spending more than non-Emiratis. 37% of pupils reported exercising or playing sport daily, whereas 60% reported they daily watch more than 2 hours of TV.Conclusion: Despite the recent changes in school policies, pupils are still failing to eat a healthy diet and engage in physical activity. There needs to be further interventions promoting changes in lifestyle amongst adolescents, and enhancing provision of healthy food in schools to be more appealing to students

Adjuvant treatment in colorectal cancer

British Journal of Cancer (2002) 86, 1525–1526. DOI: 10.1038/sj/bjc/6600280 www.bjcancer.co

Scholars 4400Y: Cultivating Resilience: Nourishing Communities through Urban Agriculture and Food Security in London

In February 2024, Urban Roots London took on the challenge of conducting a digital community survey. This survey was conducted to gain insights from individuals in the community about how urban agriculture fits into the current climate crisis and food insecurity challenges currently facing the City of London. Through a series of carefully crafted questions, participants were invited to share their lived experiences, challenges, and suggestions for fostering a more sustainable and resilient food system in London. A total of 81 individuals lent their voices to the conversation. This report contains the collective insights and concerns regarding food security and environmental sustainability in the community that have been drawn from the survey

A reference architecture for federating IoT infrastructures supporting semantic interoperability

: The Internet-of-Things (IoT) is unanimously identified as one of the main pillars of future smart scenarios. However, despite the growing number of IoT deployments, the majority of IoT applications tend to be self-contained, thereby forming vertical silos. Indeed, the ability to combine and synthesize data streams and services from diverse IoT platforms and testbeds, holds the promise to increase the potential of smart applications in terms of size, scope and targeted business context. This paper describes the system architecture for the FIESTA-IoT platform, whose main aim is to federate a large number of testbeds across the planet, in order to offer experimenters the unique experience of dealing with a large number of semantically interoperable data sources. This system architecture was developed by following the Architectural Reference Model (ARM) methodology promoted by the IoT-A project (FP7 “light house” project on Architecture for the Internet of Things). Through this process, the FIESTAIoT architecture is composed of a set of Views that deals with a “logical” functional decomposition (Functional View, FV) and data structuring and annotation, data flows and inter-functional component interactions (Information View, IV)

Reply: Thymidylate synthase polymorphism and survival of colorectal cancer patients treated with 5-fluorouracil

British Journal of Cancer (2002) 86, 1366. DOI: 10.1038/sj/bjc/6600230 www.bjcancer.co

A proof-of-concept for semantically interoperable federation of IoT experimentation facilities

The Internet-of-Things (IoT) is unanimously identified as one of the main pillars of future smart scenarios. The potential of IoT technologies and deployments has been already demonstrated in a number of different application areas, including transport, energy, safety and healthcare. However, despite the growing number of IoT deployments, the majority of IoT applications tend to be self-contained, thereby forming application silos. A lightweight data centric integration and combination of these silos presents several challenges that still need to be addressed. Indeed, the ability to combine and synthesize data streams and services from diverse IoT platforms and testbeds, holds the promise to increase the potentiality of smart applications in terms of size, scope and targeted business context. In this article, a proof-of-concept implementation that federates two different IoT experimentation facilities by means of semantic-based technologies will be described. The specification and design of the implemented system and information models will be described together with the practical details of the developments carried out and its integration with the existing IoT platforms supporting the aforementioned testbeds. Overall, the system described in this paper demonstrates that it is possible to open new horizons in the development of IoT applications and experiments at a global scale, that transcend the (silo) boundaries of individual deployments, based on the semantic interconnection and interoperability of diverse IoT platforms and testbeds.This work is partially funded by the European projectzFederated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) from the European Union’s Horizon 2020 Programme with the Grant Agreement No. CNECT-ICT-643943. The authors would also like to thank the FIESTA-IoT consortium for the fruitful discussions

Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia

Dementia is a neurological and cognitive condition that affects millions of people around the world. At any given time in the United Kingdom, 1 in 4 hospital beds are occupied by a person with dementia, while about 22% of these hospital admissions are due to preventable causes. In this paper we discuss using Internet of Things (IoT) technologies and in-home sensory devices in combination with machine learning techniques to monitor health and well-being of people with dementia. This will allow us to provide more effective and preventative care and reduce preventable hospital admissions. One of the unique aspects of this work is combining environmental data with physiological data collected via low cost in-home sensory devices to extract actionable information regarding the health and well-being of people with dementia in their own home environment. We have worked with clinicians to design our machine learning algorithms where we focused on developing solutions for real-world settings. In our solutions, we avoid generating too many alerts/alarms to prevent increasing the monitoring and support workload. We have designed an algorithm to detect Urinary Tract Infections (UTI) which is one of the top five reasons of hospital admissions for people with dementia (around 9% of hospital admissions for people with dementia in the UK). To develop the UTI detection algorithm, we have used a Non-negative Matrix Factorisation (NMF) technique to extract latent factors from raw observation and use them for clustering and identifying the possible UTI cases. In addition, we have designed an algorithm for detecting changes in activity patterns to identify early symptoms of cognitive decline or health decline in order to provide personalised and preventative care services. For this purpose, we have used an Isolation Forest (iForest) technique to create a holistic view of the daily activity patterns. This paper describes the algorithms and discusses the evaluation of the work using a large set of real-world data collected from a trial with people with dementia and their caregivers