Deep Long Short-term Memory Structures Model Temporal Dependencies Improving Cognitive Workload Estimation

Using deeply recurrent neural networks to account for temporal dependence in electroencephalograph (EEG)-based workload estimation is shown to considerably improve day-to-day feature stationarity resulting in significantly higher accuracy (p \u3c .0001) than classifiers which do not consider the temporal dependence encoded within the EEG time-series signal. This improvement is demonstrated by training several deep Recurrent Neural Network (RNN) models including Long Short-Term Memory (LSTM) architectures, a feedforward Artificial Neural Network (ANN), and Support Vector Machine (SVM) models on data from six participants who each perform several Multi-Attribute Task Battery (MATB) sessions on five separate days spread out over a month-long period. Each participant-specific classifier is trained on the first four days of data and tested using the fifth’s. Average classification accuracy of 93.0% is achieved using a deep LSTM architecture. These results represent a 59% decrease in error compared to the best previously published results for this dataset. This study additionally evaluates the significance of new features: all combinations of mean, variance, skewness, and kurtosis of EEG frequency-domain power distributions. Mean and variance are statistically significant features, while skewness and kurtosis are not. The overall performance of this approach is high enough to warrant evaluation for inclusion in operational systems

Cross-Participant EEG-Based Assessment of Cognitive Workload Using Multi-Path Convolutional Recurrent Neural Networks

Applying deep learning methods to electroencephalograph (EEG) data for cognitive state assessment has yielded improvements over previous modeling methods. However, research focused on cross-participant cognitive workload modeling using these techniques is underrepresented. We study the problem of cross-participant state estimation in a non-stimulus-locked task environment, where a trained model is used to make workload estimates on a new participant who is not represented in the training set. Using experimental data from the Multi-Attribute Task Battery (MATB) environment, a variety of deep neural network models are evaluated in the trade-space of computational efficiency, model accuracy, variance and temporal specificity yielding three important contributions: (1) The performance of ensembles of individually-trained models is statistically indistinguishable from group-trained methods at most sequence lengths. These ensembles can be trained for a fraction of the computational cost compared to group-trained methods and enable simpler model updates. (2) While increasing temporal sequence length improves mean accuracy, it is not sufficient to overcome distributional dissimilarities between individuals’ EEG data, as it results in statistically significant increases in cross-participant variance. (3) Compared to all other networks evaluated, a novel convolutional-recurrent model using multi-path subnetworks and bi-directional, residual recurrent layers resulted in statistically significant increases in predictive accuracy and decreases in cross-participant variance

MedZIM: Mediation analysis for Zero-Inflated Mediators with applications to microbiome data

The human microbiome can contribute to the pathogenesis of many complex diseases such as cancer and Alzheimer's disease by mediating disease-leading causal pathways. However, standard mediation analysis is not adequate in the context of microbiome data due to the excessive number of zero values in the data. Zero-valued sequencing reads, commonly observed in microbiome studies, arise for technical and/or biological reasons. Mediation analysis approaches for analyzing zero-inflated mediators are still lacking largely because of challenges raised by the zero-inflated data structure: (a) disentangling the mediation effect induced by the point mass at zero; and (b) identifying the observed zero-valued data points that are actually not zero (i.e., false zeros). We develop a novel mediation analysis method under the potential-outcomes framework to fill this gap. We show that the mediation effect of the microbiome can be decomposed into two components that are inherent to the two-part nature of zero-inflated distributions. The first component corresponds to the mediation effect attributable to a unit-change over the positive relative abundance and the second component corresponds to the mediation effect attributable to discrete binary change of the mediator from zero to a non-zero state. With probabilistic models to account for observing zeros, we also address the challenge with false zeros. A comprehensive simulation study and the applications in two real microbiome studies demonstrate that our approach outperforms existing mediation analysis approaches.Comment: Corresponding: Zhigang L

Accelerated Metastasis after Short-Term Treatment with a Potent Inhibitor of Tumor Angiogenesis

SummaryHerein we report that the VEGFR/PDGFR kinase inhibitor sunitinib/SU11248 can accelerate metastatic tumor growth and decrease overall survival in mice receiving short-term therapy in various metastasis assays, including after intravenous injection of tumor cells or after removal of primary orthotopically grown tumors. Acceleration of metastasis was also observed in mice receiving sunitinib prior to intravenous implantation of tumor cells, suggesting possible “metastatic conditioning” in multiple organs. Similar findings with additional VEGF receptor tyrosine kinase inhibitors implicate a class-specific effect for such agents. Importantly, these observations of metastatic acceleration were in contrast to the demonstrable antitumor benefits obtained when the same human breast cancer cells, as well as mouse or human melanoma cells, were grown orthotopically as primary tumors and subjected to identical sunitinib treatments

Ethical and methodological issues in engaging young people living in poverty with participatory research methods

This paper discusses the methodological and ethical issues arising from a project that focused on conducting a qualitative study using participatory techniques with children and young people living in disadvantage. The main aim of the study was to explore the impact of poverty on children and young people's access to public and private services. The paper is based on the author's perspective of the first stage of the fieldwork from the project. It discusses the ethical implications of involving children and young people in the research process, in particular issues relating to access and recruitment, the role of young people's advisory groups, use of visual data and collection of data in young people's homes. The paper also identifies some strategies for addressing the difficulties encountered in relation to each of these aspects and it considers the benefits of adopting participatory methods when conducting research with children and young people

Regulation of virulence gene expression resulting from Streptococcus pneumoniae and nontypeable Haemophilus influenzae interactions in chronic disease

Chronic rhinosinusitis (CRS) is a common inflammatory disease of the sinonasal cavity mediated, in part, by polymicrobial communities of bacteria. Recent molecular studies have confirmed the importance of Streptococcus pneumoniae and nontypeable Haemophilus influenzae (NTHi) in CRS. Here, we hypothesize that interaction between S. pneumoniae and NTHi mixed-species communities cause a change in bacterial virulence gene expression. We examined CRS as a model human disease to validate these polymicrobial interactions. Clinical strains of S. pneumoniae and NTHi were grown in mono- and coculture in a standard biofilm assay. Reverse transcriptase real-time PCR (RTqPCR) was used to measure gene expression of key virulence factors. To validate these results, we investigated the presence of the bacterial RNA transcripts in excised human tissue from patients with CRS. Consequences of physical or chemical interactions between microbes were also investigated. Transcription of NTHi type IV pili was only expressed in co-culture in vitro, and expression could be detected ex vivo in diseased tissue. S. pneumoniae pyruvate oxidase was up-regulated in co-culture, while pneumolysin and pneumococcal adherence factor A were down-regulated. These results were confirmed in excised human CRS tissue. Gene expression was differentially regulated by physical contact and secreted factors. Overall, these data suggest that interactions between H. influenzae and S. pneumoniae involve physical and chemical mechanisms that influence virulence gene expression of mixed-species biofilm communities present in chronically diseased human tissue. These results extend previous studies of population-level virulence and provide novel insight into the importance of S. pneumoniae and NTHi in CRS

Understanding children’s constructions of meanings about other children: implications for inclusiveeducation

This paper explores the factors that influence the way children construct meanings about other children, and especially those who seem to experience marginalisation, within school contexts. The research involved an ethnographic study in a primary school in Cyprus over a period of 5 months. Qualitative methods were used, particularly participant observations and interviews with children. Interpretation of the data suggests that children's perceptions about other children, and especially those who come to experience marginalisation, are influenced by the following factors: other children and the interactions between them; adults’ way of behaving in the school; the existing structures within the school; and the cultures of the school and the wider educational context. Even though the most powerful factor was viewed to be the adults’ influence, it was rather the interweaving between different factors that seemed to lead to the creation of particular meanings for other children. In the end, it is argued that children's voices should be seen as an essential element within the process of developing inclusive practices.<br/