The intrinsic value of HFO features as a biomarker of epileptic activity

High frequency oscillations (HFOs) are a promising biomarker of epileptic brain tissue and activity. HFOs additionally serve as a prototypical example of challenges in the analysis of discrete events in high-temporal resolution, intracranial EEG data. Two primary challenges are 1) dimensionality reduction, and 2) assessing feasibility of classification. Dimensionality reduction assumes that the data lie on a manifold with dimension less than that of the feature space. However, previous HFO analyses have assumed a linear manifold, global across time, space (i.e. recording electrode/channel), and individual patients. Instead, we assess both a) whether linear methods are appropriate and b) the consistency of the manifold across time, space, and patients. We also estimate bounds on the Bayes classification error to quantify the distinction between two classes of HFOs (those occurring during seizures and those occurring due to other processes). This analysis provides the foundation for future clinical use of HFO features and buides the analysis for other discrete events, such as individual action potentials or multi-unit activity.Comment: 5 pages, 5 figure

Intraspinal stem cell transplantation for amyotrophic lateral sclerosis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134502/1/ana24584_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134502/2/ana24584.pd

A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification

Introduction: Metabolomics is increasingly being used in the clinical setting for disease diagnosis, prognosis and risk prediction. Machine learning algorithms are particularly important in the construction of multivariate metabolite prediction. Historically, partial least squares (PLS) regression has been the gold standard for binary classification. Nonlinear machine learning methods such as random forests (RF), kernel support vector machines (SVM) and artificial neural networks (ANN) may be more suited to modelling possible nonlinear metabolite covariance, and thus provide better predictive models. Objectives: We hypothesise that for binary classification using metabolomics data, non-linear machine learning methods will provide superior generalised predictive ability when compared to linear alternatives, in particular when compared with the current gold standard PLS discriminant analysis. Methods: We compared the general predictive performance of eight archetypal machine learning algorithms across ten publicly available clinical metabolomics data sets. The algorithms were implemented in the Python programming language. All code and results have been made publicly available as Jupyter notebooks. Results: There was only marginal improvement in predictive ability for SVM and ANN over PLS across all data sets. RF performance was comparatively poor. The use of out-of-bag bootstrap confidence intervals provided a measure of uncertainty of model prediction such that the quality of metabolomics data was observed to be a bigger influence on generalised performance than model choice. Conclusion: The size of the data set, and choice of performance metric, had a greater influence on generalised predictive performance than the choice of machine learning algorithm

The Use of Networking in Developing and Marketing the Irish Ecclesiastical Product

This project seeks to explore the development potential of trails and networks, focusing on ecclesiastical sites in the Republic of Ireland. Two concurrent strands were undertaken: Investigation of visitor markets and their requirements The ecclesiastical / tourist resource and the experience it has to offer to the visitor. The following considerations were taken into account; Richness and range of the ecclesiastical product inIreland Issues of access, structure, interpretation and management Advocation of a market oriented approach using factors and requirements as parameters to segment the markets The approach to the project included the following: Development of a series of geographical clusters. Development of a number of national themed clusters. Identification of the key constructs to ensure effective networking within a cluster. Development of a series of maps to illustrate clusters. Identification of a suggested process through which local communities / destinations can advance should they wish to develop a cluster or destination A key output of this project is this document which could act as a resource for local players to employ as a catalyst for discussion around the development of a tourism cluster, focused on ecclesiastical sites

Knowledge, Attitudes, and Behaviours of New Zealand Youth in Surf Beach Environments

New Zealand youth are over-represented in drowning statistics yet little is known about their understanding of water safety, especially in surf beach context. This study aimed to ascertain current youth surf safety knowledge, specifically rip current awareness, explore self-reported competencies and confidence when surf swimming, and examine youth behaviour when at the beach. A cross-sectional survey was conducted among senior high school students (n = 599) in Auckland, New Zealand. Over half (58%) reported they were unable to swim \u3e 100 m in a pool. Males and students of European-New Zealand and Maori (New Zealand’s indigenous population) heritage were most likely to report risky behaviors such as swimming alone, outside of the patrol flags, or at a beach without lifeguards. Females reported lower swimming competency and confidence. Students of non-European-New Zealand heritage consistently reported lower surf safety knowledge. The results suggest that, in spite of frequent surf beach use and confidence in their ability to cope with risk, the surf safety knowledge, attitudes, and behaviors of most New Zealand youth leaves them at higher risk of drowning

Migrating from partial least squares discriminant analysis to artificial neural networks: A comparison of functionally equivalent visualisation and feature contribution tools using Jupyter Notebooks

Introduction: Metabolomics data is commonly modelled multivariately using partial least squares discriminant analysis (PLS-DA). Its success is primarily due to ease of interpretation, through projection to latent structures, and transparent assessment of feature importance using regression coefficients and Variable Importance in Projection scores. In recent years several non-linear machine learning (ML) methods have grown in popularity but with limited uptake essentially due to convoluted optimisation and interpretation. Artificial neural networks (ANNs) are a non-linear projection-based ML method that share a structural equivalence with PLS, and as such should be amenable to equivalent optimisation and interpretation methods. Objectives: We hypothesise that standardised optimisation, visualisation, evaluation and statistical inference techniques commonly used by metabolomics researchers for PLS-DA can be migrated to a non-linear, single hidden layer, ANN. Methods: We compared a standardised optimisation, visualisation, evaluation and statistical inference techniques workflow for PLS with the proposed ANN workflow. Both workflows were implemented in the Python programming language. All code and results have been made publicly available as Jupyter notebooks on GitHub. Results: The migration of the PLS workflow to a non-linear, single hidden layer, ANN was successful. There was a similarity in significant metabolites determined using PLS model coefficients and ANN Connection Weight Approach. Conclusion: We have shown that it is possible to migrate the standardised PLS-DA workflow to simple non-linear ANNs. This result opens the door for more widespread use and to the investigation of transparent interpretation of more complex ANN architectures

Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing

Background A lack of transparency and reporting standards in the scientific community has led to increasing and widespread concerns relating to reproduction and integrity of results. As an omics science, which generates vast amounts of data and relies heavily on data science for deriving biological meaning, metabolomics is highly vulnerable to irreproducibility. The metabolomics community has made substantial efforts to align with FAIR data standards by promoting open data formats, data repositories, online spectral libraries, and metabolite databases. Open data analysis platforms also exist; however, they tend to be inflexible and rely on the user to adequately report their methods and results. To enable FAIR data science in metabolomics, methods and results need to be transparently disseminated in a manner that is rapid, reusable, and fully integrated with the published work. To ensure broad use within the community such a framework also needs to be inclusive and intuitive for both computational novices and experts alike. Aim of Review To encourage metabolomics researchers from all backgrounds to take control of their own data science, mould it to their personal requirements, and enthusiastically share resources through open science. Key Scientific Concepts of Review This tutorial introduces the concept of interactive web-based computational laboratory notebooks. The reader is guided through a set of experiential tutorials specifically targeted at metabolomics researchers, based around the Jupyter Notebook web application, GitHub data repository, and Binder cloud computing platform

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