Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction

Deep learning for regression tasks on medical imaging data has shown promising results. However, compared to other approaches, their power is strongly linked to the dataset size. In this study, we evaluate 3D-convolutional neural networks (CNNs) and classical regression methods with hand-crafted features for survival time regression of patients with high grade brain tumors. The tested CNNs for regression showed promising but unstable results. The best performing deep learning approach reached an accuracy of 51.5% on held-out samples of the training set. All tested deep learning experiments were outperformed by a Support Vector Classifier (SVC) using 30 radiomic features. The investigated features included intensity, shape, location and deep features. The submitted method to the BraTS 2018 survival prediction challenge is an ensemble of SVCs, which reached a cross-validated accuracy of 72.2% on the BraTS 2018 training set, 57.1% on the validation set, and 42.9% on the testing set. The results suggest that more training data is necessary for a stable performance of a CNN model for direct regression from magnetic resonance images, and that non-imaging clinical patient information is crucial along with imaging information.Comment: Contribution to The International Multimodal Brain Tumor Segmentation (BraTS) Challenge 2018, survival prediction tas

Representing complex data using localized principal components with application to astronomical data

Often the relation between the variables constituting a multivariate data space might be characterized by one or more of the terms: ``nonlinear'', ``branched'', ``disconnected'', ``bended'', ``curved'', ``heterogeneous'', or, more general, ``complex''. In these cases, simple principal component analysis (PCA) as a tool for dimension reduction can fail badly. Of the many alternative approaches proposed so far, local approximations of PCA are among the most promising. This paper will give a short review of localized versions of PCA, focusing on local principal curves and local partitioning algorithms. Furthermore we discuss projections other than the local principal components. When performing local dimension reduction for regression or classification problems it is important to focus not only on the manifold structure of the covariates, but also on the response variable(s). Local principal components only achieve the former, whereas localized regression approaches concentrate on the latter. Local projection directions derived from the partial least squares (PLS) algorithm offer an interesting trade-off between these two objectives. We apply these methods to several real data sets. In particular, we consider simulated astrophysical data from the future Galactic survey mission Gaia.Comment: 25 pages. In "Principal Manifolds for Data Visualization and Dimension Reduction", A. Gorban, B. Kegl, D. Wunsch, and A. Zinovyev (eds), Lecture Notes in Computational Science and Engineering, Springer, 2007, pp. 180--204, http://www.springer.com/dal/home/generic/search/results?SGWID=1-40109-22-173750210-

Outcome-related metabolomic patterns from 1H/31P NMR after mild hypothermia treatments of oxygen–glucose deprivation in a neonatal brain slice model of asphyxia

Human clinical trials using 72 hours of mild hypothermia (32°C–34°C) after neonatal asphyxia have found substantially improved neurologic outcomes. As temperature changes differently modulate numerous metabolite fluxes and concentrations, we hypothesized that 1H/31P nuclear magnetic resonance (NMR) spectroscopy of intracellular metabolites can distinguish different insults, treatments, and recovery stages. Three groups of superfused neonatal rat brain slices underwent 45 minutes oxygen–glucose deprivation (OGD) and then were: treated for 3 hours with mild hypothermia (32°C) that began with OGD, or similarly treated with hypothermia after a 15-minute delay, or not treated (normothermic control group, 37°C). Hypothermia was followed by 3 hours of normothermic recovery. Slices collected at different predetermined times were processed, respectively, for 14.1 Tesla NMR analysis, enzyme-linked immunosorbent assay (ELISA) cell-death quantification, and superoxide production. Forty-nine NMR-observable metabolites underwent a multivariate analysis. Separated clustering in scores plots was found for treatment and outcome groups. Final ATP (adenosine triphosphate) levels, severely decreased at normothermia, were restored equally by immediate and delayed hypothermia. Cell death was decreased by immediate hypothermia, but was equally substantially greater with normothermia and delayed hypothermia. Potentially important biomarkers in the 1H spectra included PCr-1H (phosphocreatine in the 1H spectrum), ATP-1H (adenosine triphosphate in the 1H spectrum), and ADP-1H (adenosine diphosphate in the 1H spectrum). The findings suggest a potential role for metabolomic monitoring during therapeutic hypothermia

Platinum-group element geochemistry of the Paraná flood basalts – modelling metallogenesis in rifting continental plume environments

This is the author accepted manuscript. The final version is available on open access from Elsevier via the DOI in this recordThe 135 Ma Paraná-Etendeka Large Igneous Province (PELIP) is one of the largest areas of continental flood basalt (CFB) volcanism in the world and is widely agreed to be a product of intracontinental melts related to thermal anomalies from the Tristan mantle plume. The province rifted during the break-up of Gondwana, as the plume transitioned into an oceanic geodynamic environment. This study reports analyses of plume-derived basalts from the Brazilian side of the PELIP (the Serra Geral Group) to investigate major, trace and platinum-group element (PGE) abundances in an evolving plume-rift metallogenic setting, with the aim of contextualising metallogenic controls alongside existing magmatic interpretations of the region. The chalcophile geochemistry of these basalts defines three distinct metallogenic groupings that fit with three modern multi-element magma classifications for Serra Geral lavas. In this scheme, Type 4 lavas have a distinctive PGE-poor signature, Type 1 (Central-Northern) lavas are enriched in Pd, Au and Cu, and Type 1 (Southern) lavas are enriched in Ru and Rh. Our trace element melt modelling indicates that the compositional variations result from changes in the melting regime between the garnet and spinel stability fields, in response to the thinning and ‘unlidding’ of the rifting continent above. This process imposes progressively shallower melting depths and higher degrees of partial melting. Accordingly, Type 4 magmas formed from small degree melts, reducing the likelihood of sulfide exhaustion/chalcophile acquisition at source. Type 1 (Central-Northern) magmas incorporated components of the sub-continental lithospheric mantle (SCLM)-derived in higher-degree partial melts; the SCLM was heterogeneously enriched via metasomatism prior to plume melting, and this produced enrichment in volatile metals (Pd, Cu, and Au) in these magmas. In contrast, the Ru-Rh enrichment in Type 1 (Southern) lavas is attributed to increased spinel-group mineral and sulphide incorporation from the mantle into higher degree partial melts close to the continental rift zone. Our models confirm the importance of contributions from SCLM melts in precious metal mineral systems within CFB provinces, and reinforce the role of heterogeneous metasomatic enrichment underneath cratons in boosting intracontinental prospectivity with respect to ore deposits.University of Exete

Alterations in vasodilator-stimulated phosphoprotein (VASP) phosphorylation: associations with asthmatic phenotype, airway inflammation and β(2)-agonist use

BACKGROUND: Vasodilator-stimulated phosphoprotein (VASP) mediates focal adhesion, actin filament binding and polymerization in a variety of cells, thereby inhibiting cell movement. Phosphorylation of VASP via cAMP and cGMP dependent protein kinases releases this "brake" on cell motility. Thus, phosphorylation of VASP may be necessary for epithelial cell repair of damage from allergen-induced inflammation. Two hypotheses were examined: (1) injury from segmental allergen challenge increases VASP phosphorylation in airway epithelium in asthmatic but not nonasthmatic normal subjects, (2) regular in vivo β(2)-agonist use increases VASP phosphorylation in asthmatic epithelium, altering cell adhesion. METHODS: Bronchial epithelium was obtained from asthmatic and non-asthmatic normal subjects before and after segmental allergen challenge, and after regularly inhaled albuterol, in three separate protocols. VASP phosphorylation was examined in Western blots of epithelial samples. DNA was obtained for β(2)-adrenergic receptor haplotype determination. RESULTS: Although VASP phosphorylation increased, it was not significantly greater after allergen challenge in asthmatics or normals. However, VASP phosphorylation in epithelium of nonasthmatic normal subjects was double that observed in asthmatic subjects, both at baseline and after challenge. Regularly inhaled albuterol significantly increased VASP phosphorylation in asthmatic subjects in both unchallenged and antigen challenged lung segment epithelium. There was also a significant increase in epithelial cells in the bronchoalveolar lavage of the unchallenged lung segment after regular inhalation of albuterol but not of placebo. The haplotypes of the β(2)-adrenergic receptor did not appear to associate with increased or decreased phosphorylation of VASP. CONCLUSION: Decreased VASP phosphorylation was observed in epithelial cells of asthmatics compared to nonasthmatic normals, despite response to β-agonist. The decreased phosphorylation does not appear to be associated with a particular β(2)-adrenergic receptor haplotype. The observed decrease in VASP phosphorylation suggests greater inhibition of actin reorganization which is necessary for altering attachment and migration required during epithelial repair

Prediction intervals for future BMI values of individual children - a non-parametric approach by quantile boosting

Background: The construction of prediction intervals (PIs) for future body mass index (BMI) values of individual children based on a recent German birth cohort study with n = 2007 children is problematic for standard parametric approaches, as the BMI distribution in childhood is typically skewed depending on age. Methods: We avoid distributional assumptions by directly modelling the borders of PIs by additive quantile regression, estimated by boosting. We point out the concept of conditional coverage to prove the accuracy of PIs. As conditional coverage can hardly be evaluated in practical applications, we conduct a simulation study before fitting child- and covariate-specific PIs for future BMI values and BMI patterns for the present data. Results: The results of our simulation study suggest that PIs fitted by quantile boosting cover future observations with the predefined coverage probability and outperform the benchmark approach. For the prediction of future BMI values, quantile boosting automatically selects informative covariates and adapts to the age-specific skewness of the BMI distribution. The lengths of the estimated PIs are child-specific and increase, as expected, with the age of the child. Conclusions: Quantile boosting is a promising approach to construct PIs with correct conditional coverage in a non-parametric way. It is in particular suitable for the prediction of BMI patterns depending on covariates, since it provides an interpretable predictor structure, inherent variable selection properties and can even account for longitudinal data structures

Genome-wide DNA methylation analysis for diabetic nephropathy in type 1 diabetes mellitus

BACKGROUND: Diabetic nephropathy is a serious complication of diabetes mellitus and is associated with considerable morbidity and high mortality. There is increasing evidence to suggest that dysregulation of the epigenome is involved in diabetic nephropathy. We assessed whether epigenetic modification of DNA methylation is associated with diabetic nephropathy in a case-control study of 192 Irish patients with type 1 diabetes mellitus (T1D). Cases had T1D and nephropathy whereas controls had T1D but no evidence of renal disease. METHODS: We performed DNA methylation profiling in bisulphite converted DNA from cases and controls using the recently developed Illumina Infinium(R) HumanMethylation27 BeadChip, that enables the direct investigation of 27,578 individual cytosines at CpG loci throughout the genome, which are focused on the promoter regions of 14,495 genes. RESULTS: Singular Value Decomposition (SVD) analysis indicated that significant components of DNA methylation variation correlated with patient age, time to onset of diabetic nephropathy, and sex. Adjusting for confounding factors using multivariate Cox-regression analyses, and with a false discovery rate (FDR) of 0.05, we observed 19 CpG sites that demonstrated correlations with time to development of diabetic nephropathy. Of note, this included one CpG site located 18 bp upstream of the transcription start site of UNC13B, a gene in which the first intronic SNP rs13293564 has recently been reported to be associated with diabetic nephropathy. CONCLUSION: This high throughput platform was able to successfully interrogate the methylation state of individual cytosines and identified 19 prospective CpG sites associated with risk of diabetic nephropathy. These differences in DNA methylation are worthy of further follow-up in replication studies using larger cohorts of diabetic patients with and without nephropathy

Building ensembles of adaptive nested dichotomies with random-pair selection

A system of nested dichotomies is a method of decomposing a multi-class problem into a collection of binary problems. Such a system recursively applies binary splits to divide the set of classes into two subsets, and trains a binary classifier for each split. Although ensembles of nested dichotomies with random structure have been shown to perform well in practice, using a more sophisticated class subset selection method can be used to improve classification accuracy. We investigate an approach to this problem called random-pair selection, and evaluate its effectiveness compared to other published methods of subset selection. We show that our method outperforms other methods in many cases when forming ensembles of nested dichotomies, and is at least on par in all other cases. The software related to this paper is available at https://svn.cms.waikato.ac.nz/svn/weka/trunk/packages/ internal/ensemblesOfNestedDichotomies/

Quantification of Cell Signaling Networks Using Kinase Activity Chemosensors

The ability to directly determine endogenous kinase activity in tissue homogenates provides valuable insights into signaling aberrations that underlie disease phenotypes. When activity data is collected across a panel of kinases, a unique “signaling fingerprint” is generated that allows for discrimination between diseased and normal tissue. Here we describe the use of peptide-based kinase activity sensors to fingerprint the signaling changes associated with disease states. This approach leverages the phosphorylation-sensitive sulfonamido-oxine (Sox) fluorophore to provide a direct readout of kinase enzymatic activity in unfractionated tissue homogenates from animal models or clinical samples. To demonstrate the application of this technology, we focus on a rat model of nonalcoholic fatty liver disease (NAFLD). Sox-based activity probes allow for the rapid and straightforward analysis of changes in kinase enzymatic activity associated with disease states, providing leads for further investigation using traditional biochemical approaches

Evaluation of alternative respiratory syndromes for specific syndromic surveillance of influenza and respiratory syncytial virus: a time series analysis

<p>Abstract</p> <p>Background</p> <p>Syndromic surveillance is increasingly being evaluated for its potential for early warning of increased disease activity in the population. However, interpretation is hampered by the difficulty of attributing a causative pathogen. We described the temporal relationship between laboratory counts of influenza and respiratory syncytial virus (RSV) detection and alternative groupings of Emergency Department (ED) respiratory diagnoses.</p> <p>Methods</p> <p>ED and laboratory data were obtained for the south-eastern area of Sydney, NSW for the period 1 June 2001 - 1 December 2006. Counts of ED visits and laboratory confirmed positive RSV and influenza cases were aggregated by week. Semi-parametric generalized additive models (GAM) were used to determine the association between the incidence of RSV and influenza and the incidence of respiratory syndrome ED presentations while controlling for temporal confounders.</p> <p>Results</p> <p>For every additional RSV laboratory count, ED diagnoses of bronchiolitis increased by 3.1% (95%CI: 2.7%-3.5%) in the same week. For every additional influenza laboratory count, ED diagnoses of influenza-like illness increased by 4.7% (95%CI: 4.2%-5.2%) one week earlier.</p> <p>Conclusion</p> <p>In this study, large increases in ED diagnoses of bronchiolitis and influenza-like illness were independent and proxy indicators for RSV and influenza activity, respectively.</p