Deriving statistical inference from the application of artificial neural networks to clinical metabolomics data

Metabolomics data are complex with a high degree of multicollinearity. As such, multivariate linear projection methods, such as partial least squares discriminant analysis (PLS-DA) have become standard. Non-linear projections methods, typified by Artificial Neural Networks (ANNs) may be more appropriate to model potential nonlinear latent covariance; however, they are not widely used due to difficulty in deriving statistical inference, and thus biological interpretation. Herein, we illustrate the utility of ANNs for clinical metabolomics using publicly available data sets and develop an open framework for deriving and visualising statistical inference from ANNs equivalent to standard PLS-DA methods

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

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

Underwater noise abatement panel and resonator structure

A system for reducing noise emissions in underwater environments is presented. The system can be extended to applications in any two-fluid environments where one fluid (gas) is contained in an enclosed resonator volume connected to the outside environment at an open end of the resonator body. The resonators act as gas-containing (e.g., air) Helmholtz resonators constructed into solid panels that are submerged in the fluid medium (e.g., sea water) in the vicinity of a noise generating source. The oscillations of the trapped air volume in the resonators causes reduction of certain noise energy and a general reduction in the transmitted noise in the environment of the system.Board of Regents, University of Texas Syste

IFN-γ-producing CD4+ T cells promote experimental cerebral malaria by modulating CD8+ T cell accumulation within the brain.

It is well established that IFN-γ is required for the development of experimental cerebral malaria (ECM) during Plasmodium berghei ANKA infection of C57BL/6 mice. However, the temporal and tissue-specific cellular sources of IFN-γ during P. berghei ANKA infection have not been investigated, and it is not known whether IFN-γ production by a single cell type in isolation can induce cerebral pathology. In this study, using IFN-γ reporter mice, we show that NK cells dominate the IFN-γ response during the early stages of infection in the brain, but not in the spleen, before being replaced by CD4(+) and CD8(+) T cells. Importantly, we demonstrate that IFN-γ-producing CD4(+) T cells, but not innate or CD8(+) T cells, can promote the development of ECM in normally resistant IFN-γ(-/-) mice infected with P. berghei ANKA. Adoptively transferred wild-type CD4(+) T cells accumulate within the spleen, lung, and brain of IFN-γ(-/-) mice and induce ECM through active IFN-γ secretion, which increases the accumulation of endogenous IFN-γ(-/-) CD8(+) T cells within the brain. Depletion of endogenous IFN-γ(-/-) CD8(+) T cells abrogates the ability of wild-type CD4(+) T cells to promote ECM. Finally, we show that IFN-γ production, specifically by CD4(+) T cells, is sufficient to induce expression of CXCL9 and CXCL10 within the brain, providing a mechanistic basis for the enhanced CD8(+) T cell accumulation. To our knowledge, these observations demonstrate, for the first time, the importance of and pathways by which IFN-γ-producing CD4(+) T cells promote the development of ECM during P. berghei ANKA infection

Apoptosis, mastocytosis, and diminished adipocytokine gene expression accompany reduced epididymal fat mass in long-standing diet-induced obese mice

<p>Abstract</p> <p>Background</p> <p>Obesity is characterized by increased cell death and inflammatory reactions in the adipose tissue. Here, we explored pathophysiological alterations taking place in the adipose tissue in long-standing obesity. In the epididymal fat of C57BL/6 mice fed a high-fat diet for 20 weeks, the prevalence and distribution of dead adipocytes (crown-like structures), mast cells (toluidine blue, mMCP6), macrophages (F4/80), and apoptotic cells (cleaved caspase-3) were measured. Moreover, gene and/or protein expression of several adipocytokines (leptin, adiponectin, TNF-α, IL-10, IL-6, MCP-1), F4/80, mMCP6, cleaved caspase-3 were determined.</p> <p>Results</p> <p>We observed that the epididymal fat mass was lower in obese than in lean mice. In obese mice, the epididymal fat mass correlated inversely with body weight and liver mass. Dead adipocytes, mast cells, macrophages, and apoptotic cells were abundant in the epididymal fat of obese mice, especially in the rostral vs. caudal zone. Accordingly, mMCP6, F4/80, and cleaved caspase-3 gene and/or protein expression was increased. Conversely, adiponectin, leptin, IL-6, and MCP-1 gene expression levels were lower in the epididymal fat of obese than lean mice. Although TNF-α and IL-10 gene expression was higher in the epididymal fat of obese mice, their expression relative to F4/80 and mMCP6 expression were lower in the heavily infiltrated rostral than caudal zone.</p> <p>Conclusions</p> <p>This study demonstrates that in mice with long-standing obesity diminished gene expression of several adipocytokines accompany apoptosis and reduced mass of the epididymal fat. Our findings suggest that this is due to both increased prevalence of dead adipocytes and altered immune cell activity. Differential distribution of metabolically challenged adipocytes is indicative of the presence of biologically diverse zones within the epididymal fat.</p

Exhausted CD4+ T Cells during Malaria Exhibit Reduced mTORc1 Activity Correlated with Loss of T-bet Expression

CD4&lt;sup&gt;+&lt;/sup&gt; T cell functional inhibition (exhaustion) is a hallmark of malaria and correlates with impaired parasite control and infection chronicity. However, the mechanisms of CD4&lt;sup&gt;+&lt;/sup&gt; T cell exhaustion are still poorly understood. In this study, we show that Ag-experienced (&lt;i&gt;Ag-exp&lt;/i&gt;) CD4&lt;sup&gt;+&lt;/sup&gt; T cell exhaustion during &lt;i&gt;Plasmodium yoelii&lt;/i&gt; nonlethal infection occurs alongside the reduction in mammalian target of rapamycin (mTOR) activity and restriction in CD4&lt;sup&gt;+&lt;/sup&gt; T cell glycolytic capacity. We demonstrate that the loss of glycolytic metabolism and mTOR activity within the exhausted &lt;i&gt;Ag-exp&lt;/i&gt;CD4&lt;sup&gt;+&lt;/sup&gt; T cell population during infection coincides with reduction in T-bet expression. T-bet was found to directly bind to and control the transcription of various mTOR and metabolism-related genes within effector CD4&lt;sup&gt;+&lt;/sup&gt; T cells. Consistent with this, &lt;i&gt;Ag-exp&lt;/i&gt;Th1 cells exhibited significantly higher and sustained mTOR activity than effector T-bet- (non-Th1) &lt;i&gt;Ag-exp&lt;/i&gt;T cells throughout the course of malaria. We identified mTOR to be redundant for sustaining T-bet expression in activated Th1 cells, whereas mTOR was necessary but not sufficient for maintaining IFN-&#x3B3; production by Th1 cells. Immunotherapy targeting PD-1, CTLA-4, and IL-27 blocked CD4&lt;sup&gt;+&lt;/sup&gt; T cell exhaustion during malaria infection and was associated with elevated T-bet expression and a concomitant increased CD4&lt;sup&gt;+&lt;/sup&gt; T cell glycolytic metabolism. Collectively, our data suggest that mTOR activity is linked to T-bet in &lt;i&gt;Ag-exp&lt;/i&gt;CD4&lt;sup&gt;+&lt;/sup&gt; T cells but that reduction in mTOR activity may not directly underpin &lt;i&gt;Ag-exp&lt;/i&gt;Th1 cell loss and exhaustion during malaria infection. These data have implications for therapeutic reactivation of exhausted CD4&lt;sup&gt;+&lt;/sup&gt; T cells during malaria infection and other chronic conditions