Prodromal neuroinflammatory, cholinergic and metabolite dysfunction detected by PET and MRS in the TgF344-AD transgenic rat model of AD - a multi-modal and multi-centre study

Mouse models of Alzheimer's disease (AD) are valuable but do not fully recapitulate human AD pathology, such as spontaneous Tau fibril accumulation and neuronal loss, necessitating the development of new AD models. The transgenic (TG) TgF344-AD rat has been reported to develop age-dependent AD features including neuronal loss and neurofibrillary tangles, despite only expressing APP and PSEN1 mutations, suggesting an improved modelling of AD hallmarks. Alterations in neuronal networks as well as learning performance and cognition tasks have been reported in this model, but none have combined a longitudinal, multimodal approach across multiple centres, which mimics the approaches commonly taken in clinical studies. We therefore aimed to further characterise the progression of AD-like pathology and cognition in the TgF344-AD rat from young-adults (6 months (m)) to mid- (12 m) and advanced-stage (18 m, 25 m) of the disease

An Approach for Time-aware Domain-based Analysis of Users Trustworthiness in Big Social Data

In Online Social Networks (OSNs) there is a need for better understanding of social trust in order to improve the analysis process and mining credibility of social media data. Given the open environment and fewer restrictions associated with OSNs, the medium allows legitimate users as well as spammers to publish their content. Hence, it is essential to measure users’ credibility in various domains and accordingly define influential users in a particular domain(s). Most of the existing approaches of trustworthiness evaluation of users in OSNs are generic-based approaches. There is a lack of domain-based trustworthiness evaluation mechanisms. In OSNs, discovering users’ influence in a specific domain has been motivated by its significance in a broad range of applications such as personalized recommendation systems and expertise retrieval. The aim of this paper is to present an approach to analysing domain-based user’s trustworthiness in OSNs. We provide a novel distinguishing measurement for users in a set of knowledge domains. Domains are extracted from the user’s content using semantic analysis. In order to obtain the level of trustworthiness, a metric incorporating a number of attributes extracted from content analysis and user analysis is consolidated and formulated by considering temporal factor. We show the accuracy of the proposed algorithm by providing a fine-grained trustworthiness analysis of users and their domains of interest in the OSNs using big data Infrastructure

High-Q-factor Al [subscript 2]O[subscript 3] micro-trench cavities integrated with silicon nitride waveguides on silicon

We report on the design and performance of high-Q integrated optical micro-trench cavities on silicon. The microcavities are co-integrated with silicon nitride bus waveguides and fabricated using wafer-scale silicon-photonics-compatible processing steps. The amorphous aluminum oxide resonator material is deposited via sputtering in a single straightforward post-processing step. We examine the theoretical and experimental optical properties of the aluminum oxide micro-trench cavities for different bend radii, film thicknesses and near-infrared wavelengths and demonstrate experimental Q factors of > 10[superscript 6]. We propose that this high-Q micro-trench cavity design can be applied to incorporate a wide variety of novel microcavity materials, including rare-earth-doped films for microlasers, into wafer-scale silicon photonics platforms

Expression and localization of NUB1 in Tauopathy and Alzheimer’s disease models

Alzheimer’s disease (AD) is characterized at a subcellular level by intracellular neurofibrillary tangles (NFTs), aggregates of hyperphosphorylated Tau, and by senile plaques, extracellular aggregates of amyloid beta peptides. Previous data revealed that Nedd8 ultimate buster 1 (NUB1) plays a role in reducing the aggregation and phosphorylation of Tau in an in vitro model. To clarify the role of NUB1 in AD, the spatiotemporal expression and localization of NUB1 was analyzed in T301L, a mouse model for Tauopathy characterized by NFTs, and in TASTPM mice, characterized by early development of senile plaques. The analysis revealed no change in the level of NUB1 expression in T301L mice and a significant decrease at 12 months in TASTPM mice. In brain cryosections, NUB1 expression was detected in the hippocampus and entorhinal cortex. Subcellularly, NUB1 was localized predominantly in the neuronal nuclei, but also in neuronal processes. In both mouse models at 12 months, NUB1 signal was observed to co-localize with AT8 positive cytoplasmic aggregates. Moreover in TASTPM mice, a NUB1 cytoplasmic signal was observed in non-neuronal cells. Our data confirm that NUB1 could be a therapeutic target in AD and also help to establish the appropriate window of opportunity for therapeutic intervention

A Deep Feedforward Neural Network and Shallow Architectures Effectiveness Comparison: Flight Delays Classification Perspective

Flight delays have negatively impacted the socio-economics state of passengers, airlines and airports, resulting in huge economic losses. Hence, it has become necessary to correctly predict their occurrences in decision-making because it is important for the effective management of the aviation industry. Developing accurate flight delays classification models depends mostly on the air transportation system complexity and the infrastructure available in airports, which may be a region-specific issue. However, no specific prediction or classification model can handle the individual characteristics of all airlines and airports at the same time. Hence, the need to further develop and compare predictive models for the aviation decision system of the future cannot be over-emphasised. In this research, flight on-time data records from the United State Bureau of Transportation Statistics was employed to evaluate the performances of Deep Feedforward Neural Network, Neural Network, and Support Vector Machine models on a binary classification problem. The research revealed that the models achieved different accuracies of flight delay classifications. The Support Vector Machine had the worst average accuracy than Neural Network and Deep Feedforward Neural Network in the initial experiment. The Deep Feedforward Neural Network outperformed Support Vector Machines and Neural Network with the best average percentage accuracies. Going further to investigate the Deep Feedforward Neural Network architecture on different parameters against itself suggest that training a Deep Feedforward Neural Network algorithm, regardless of data training size, the classification accuracy peaks. We examine which number of epochs works best in our flight delay classification settings for the Deep Feedforward Neural Network. Our experiment results demonstrate that having many epochs affects the convergence rate of the model; unlike when hidden layers are increased, it does not ensure better or higher accuracy in a binary classification of flight delays. Finally, we recommended further studies on the applicability of the Deep Feedforward Neural Network in flight delays prediction with specific case studies of either airlines or airports to check the impact on the model's performance