Prevention of cisplatin-induced hearing loss in children: Informing the design of future clinical trials.

Cisplatin is an essential chemotherapeutic agent in the treatment of many pediatric cancers. Unfortunately, cisplatin-induced hearing loss (CIHL) is a common, clinically significant side effect with life-long ramifications, particularly for young children. ACCL05C1 and ACCL0431 are two recently completed Childrens Oncology Group studies focused on the measurement and prevention of CIHL. The purpose of this paper was to gain insights from ACCL05C1 and ACCL0431, the first published cooperative group studies dedicated solely to CIHL, to inform the design of future pediatric otoprotection trials. Use of otoprotective agents is an attractive strategy for preventing CIHL, but their successful development must overcome a unique constellation of methodological challenges related to translating preclinical research into clinical trials that are feasible, evaluate practical interventions, and limit risk. Issues particularly important for children include use of appropriate methods for hearing assessment and CIHL severity grading, and use of trial designs that are well-informed by preclinical models and suitable for relatively small sample sizes. Increasing interest has made available new funding opportunities for expanding this urgently needed research

The Impact of Virtual Environments for Future Electric Powered-Mobility Development Using Human-in-the-Loop: Part A - Fundamental Design and Modelling

The use of virtual tools will be discussed across two complimentary chapters, Part A explores the fundamental concepts of electric vehicle systems modelling and a design procedure for human-in-the-loop virtual environments; Part B demonstrates how this architecture can be applied to assess energy optimization strategies. In Part A, this research investigates the design and implementation of simulation tools used to predict the energy consumption and strategic tool for the development of an electric vehicle. The case study used is an electric prototype race car for Ene-1 GP SUZUKA competition. Engineering effort is re-directed from physical product design, optimisation and validation to digital tools, processes and virtual testing. This virtual platform is characterised by the integration of two different simulation models—mathematical model of the electric vehicle systems represented by Matlab/Simulink, which accounts for the representation of the powertrain performance prediction that taking into account the resistance motion; and a virtual environment represented by Cruden Software, which accounts recreate topography of real world environment in a driving simulator and incorporate human driver behaviour

The Impact of Virtual Environments for Future Electric Powered-Mobility Development Using Human-in-the-Loop: Part B - Virtual Testing and Physical Validation

Electric vehicles are increasing in popularity worldwide, and there have been numerous advances in technology to increase the energy efficiency of the vehicle and reduce the range anxiety for the user. For example, the latest electric vehicle (Tesla model S, equipped by 100kWh battery) available in the market in 2019 is able to drive around 375 miles. However, human behavior such as driving strategy is an important issue that impacts on energy optimization and ultimately vehicle range. Human behavior is rather complex and is difficult to replicate with computer algorithms. Therefore, to fully assess the impact of a particular technology, the interactions between humans, vehicle, and the environment need to be examined simultaneously, through a Human-in-the-Loop approach. In this chapter, the results of investigating a human-in-the-loop test platform, which incorporate human-driving behavior and the vehicle characteristics, are presented. In addition, this chapter analyzes a driving strategy, using a Human-in-the-Loop approach, applied to optimizing the energy usage for an electric vehicle competition

Multitask Active Learning for Graph Anomaly Detection

In the web era, graph machine learning has been widely used on ubiquitous graph-structured data. As a pivotal component for bolstering web security and enhancing the robustness of graph-based applications, the significance of graph anomaly detection is continually increasing. While Graph Neural Networks (GNNs) have demonstrated efficacy in supervised and semi-supervised graph anomaly detection, their performance is contingent upon the availability of sufficient ground truth labels. The labor-intensive nature of identifying anomalies from complex graph structures poses a significant challenge in real-world applications. Despite that, the indirect supervision signals from other tasks (e.g., node classification) are relatively abundant. In this paper, we propose a novel MultItask acTIve Graph Anomaly deTEction framework, namely MITIGATE. Firstly, by coupling node classification tasks, MITIGATE obtains the capability to detect out-of-distribution nodes without known anomalies. Secondly, MITIGATE quantifies the informativeness of nodes by the confidence difference across tasks, allowing samples with conflicting predictions to provide informative yet not excessively challenging information for subsequent training. Finally, to enhance the likelihood of selecting representative nodes that are distant from known patterns, MITIGATE adopts a masked aggregation mechanism for distance measurement, considering both inherent features of nodes and current labeled status. Empirical studies on four datasets demonstrate that MITIGATE significantly outperforms the state-of-the-art methods for anomaly detection. Our code is publicly available at: https://github.com/AhaChang/MITIGATE.Comment: Preprint. Under review. Code available at https://github.com/AhaChang/MITIGAT

Residents’ quality of life in smart cities : a systematic literature review

DATA AVAILABILITY STATEMENT: Material is available upon request.Despite its popularity in urban studies, the smart city (SC) concept has not focused sufficient attention on citizens’ quality of life (QoL) until relatively recently. The aim of this study is, therefore, to examine the concept of QoL in SCs using a systematic review of 38 recent articles from 2020–2022. This includes definitions and concepts, indicators and domains that are used to measure QoL, and the typical research methods that are used to collect data. The review analyses some of the main themes that emerge from the field of SCQoL which include smart urban governance, sustainability, smart living, participation, and social inclusion. The findings from this SC and QoL research can help city planners to prioritize which domains are the most important or meaningful for citizens and which services to invest in. It has been suggested that smart living is the most important domain of a SC. However, various studies have found that citizens experience SC initiatives holistically and that QoL is quite dependent on context in terms of priorities. Therefore, citizen participation strategies should be tailored and adapted to each respective context. SC governance also needs to be more long-term and strategic with real evidence that citizens are involved in decision making and problem solving and are not just passive recipients.https://www.mdpi.com/journal/landHistorical and Heritage StudiesSDG-11:Sustainable cities and communitie

Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups. This could have important clinical impact, notably by allowing for a better personalization of chemo-radiation treatments for head-and-neck cancer patients from different risk groups.Comment: (1) Paper: 33 pages, 4 figures, 1 table; (2) SUPP info: 41 pages, 7 figures, 8 table

The Anti-hepatitis B Virus Activity of Boehmeria nivea Extract in HBV-viremia SCID Mice

Boehmeria nivea extract (BNE) is widely used in southern Taiwan as a folk medicine for hepato-protection and hepatitis treatment. In previous studies, we demonstrated that BNE could reduce the supernatant hepatitis B virus (HBV) DNA in HBV-producing HepG2 2.2.15 cells. In the present study, we established an animal model of HBV viremia and used it to validate the efficacy of BNE in vivo. In this animal model, serum HBV DNA and HBsAg were elevated in accordance with tumor growth. To evaluate the anti-HBV activity of BNE, HBV-viremia mice were built up after one subcutaneous inoculation of HepG2 2.2.15 tumor cells in severe combined immunodeficiency mice over 13 days. The levels of serum HBV DNA were elevated around 105–106 copies per milliliter. Both oral and intraperitoneal administration of BNE were effective at inhibiting the production of HBsAg and HBV DNA, whereas tumor growth was not affected by all test articles. Intraperitoneal administration of BNE appeared to have greater potential to inhibit serum HBV DNA levels compared with oral administration under the same dosage. Notably, reduced natural killer cell activity was also observed after high dosage of BNE administration, and this correlated with reduced serum HBV DNA. In conclusion, BNE exhibited potential anti-HBV activity in an animal model of HBV viremia

Organization of Valence-Encoding and Projection-Defined Neurons in the Basolateral Amygdala

The basolateral amygdala (BLA) mediates associative learning for both fear and reward. Accumulating evidence supports the notion that different BLA projections distinctly alter motivated behavior, including projections to the nucleus accumbens (NAc), medial aspect of the central amygdala (CeM), and ventral hippocampus (vHPC). Although there is consensus regarding the existence of distinct subsets of BLA neurons encoding positive or negative valence, controversy remains regarding the anatomical arrangement of these populations. First, we map the location of more than 1,000 neurons distributed across the BLA and recorded during a Pavlovian discrimination task. Next, we determine the location of projection-defined neurons labeled with retrograde tracers and use CLARITY to reveal the axonal path in 3-dimensional space. Finally, we examine the local influence of each projection-defined populations within the BLA. Understanding the functional and topographical organization of circuits underlying valence assignment could reveal fundamental principles about emotional processing. Basolateral amygdala (BLA) neurons distinctly encode cues predicting rewards or punishments, but how does form give rise to function? Beyeler et al. overlay anatomical projection target, location of neurons in a 3D map, and encoding properties during cue discrimination. The influence on local networks differs across projection-defined BLA populations. Keywords: reward; aversion; topography; tracing; connectivity; network; channelrhodopsin; phototagging; photoexcitation; photoinhitionNational Institute of Mental Health (U.S.) (Grant R01-MH102441)National Institute of Diabetes and Digestive and Kidney Diseases (U.S.) (Award DP2-DK-102256

Knowledge, attitudes, and practices of Florida physicians regarding dengue before and after an educational intervention.

BACKGROUND: Failure to recognize and appropriately manage dengue early in the clinical course may result in late initiation of supportive treatment for severe disease. In Florida, travel-related and autochthonous dengue occur and are likely under-recognized. The objective of this study was to evaluate physician knowledge of dengue and its management before and after an educational intervention in Florida. METHODS: From 2012-13 we conducted 14 grand-rounds style lectures on dengue clinical management attended by 413 physicians, and analyzed data from the pre- and post-tests. RESULTS: Of those attending, 231 and 220 completed the pre-and post-tests, respectively. Overall, the mean pre-test score for knowledge-based questions was 74.3 and average post-test score was 94.2 %, indicating a mean increase of 19.9 % (P \u3c 0.0001, 95 % CI 17.7-22.4). Reported confidence in dengue recognition and management also increased. Non-US trained physicians and those who had treated more than ten dengue cases performed significantly better in the pre-test. Post-test scores did not differ by subgroup. CONCLUSIONS: The train-the-trainer approach with grand-rounds style presentations appear to be an effective intervention to improve knowledge of dengue among physicians

Knowledge, attitudes, and practices of Florida physicians regarding dengue before and after an educational intervention.

BACKGROUND: Failure to recognize and appropriately manage dengue early in the clinical course may result in late initiation of supportive treatment for severe disease. In Florida, travel-related and autochthonous dengue occur and are likely under-recognized. The objective of this study was to evaluate physician knowledge of dengue and its management before and after an educational intervention in Florida. METHODS: From 2012-13 we conducted 14 grand-rounds style lectures on dengue clinical management attended by 413 physicians, and analyzed data from the pre- and post-tests. RESULTS: Of those attending, 231 and 220 completed the pre-and post-tests, respectively. Overall, the mean pre-test score for knowledge-based questions was 74.3 and average post-test score was 94.2 %, indicating a mean increase of 19.9 % (P \u3c 0.0001, 95 % CI 17.7-22.4). Reported confidence in dengue recognition and management also increased. Non-US trained physicians and those who had treated more than ten dengue cases performed significantly better in the pre-test. Post-test scores did not differ by subgroup. CONCLUSIONS: The train-the-trainer approach with grand-rounds style presentations appear to be an effective intervention to improve knowledge of dengue among physicians