Construction of embedded fMRI resting state functional connectivity networks using manifold learning

We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global graph-theoretical properties of the embedded FCN, we compare their classification potential using machine learning techniques. We also assess the performance of two metrics that are widely used for the construction of FCN from fMRI, namely the Euclidean distance and the lagged cross-correlation metric. We show that the FCN constructed with Diffusion Maps and the lagged cross-correlation metric outperform the other combinations

Fluorescence-based analyser as a rapid tool for determining soluble protein content in dairy ingredients and infant milk formula

peer-reviewedAbstract: Milk protein, in particular native whey protein, is of interest to dairy manufacturers as a measure of functional and nutritional quality. However, quantification of soluble whey protein (SP) is time consuming; giving rise to the need to develop rapid, accurate, and portable at-line process analytical technology. In this study, the performance of a fluorescence-based analyser(F) (Amaltheys II, Spectralys Innovations, France) was evaluated for quantification of SPF and whey protein nitrogen index (WPNI)F in skim milk, whey protein concentrate and infant formula powders. Rehydration of powders prior to analysis was a key factor for ensuring repeatability and reproducibility. A comparison of the analyser with reference methods for SPF and WPNIF resulted in coefficient of determination (R2) > 0.993 for both SPKjeldahl method and WPNIGEA. The results show the fluorescence-based analyser to be rapid, compact, and accurate device, suited for providing reliable support to dairy ingredient and infant formula manufacturers. Industrial relevance: The fluorescence based analysis investigated in this article is suitable for application in the dairy industry where it can be used as a rapid, at-line PAT tool for both liquid and powder samples. The technology has the potential to replace well-established methods for measurement of soluble protein. The main benefit to industry is the ability to respond more rapidly to variations in soluble protein without compromising on the accuracy associated with more time consuming methods

Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification

There is no consensus on how to construct structural brain networks from diffusion MRI. How variations in pre-processing steps affect network reliability and its ability to distinguish subjects remains opaque. In this work, we address this issue by comparing 35 structural connectome-building pipelines. We vary diffusion reconstruction models, tractography algorithms and parcellations. Next, we classify structural connectome pairs as either belonging to the same individual or not. Connectome weights and eight topological derivative measures form our feature set. For experiments, we use three test-retest datasets from the Consortium for Reliability and Reproducibility (CoRR) comprised of a total of 105 individuals. We also compare pairwise classification results to a commonly used parametric test-retest measure, Intraclass Correlation Coefficient (ICC).Comment: Accepted for MICCAI 2017, 8 pages, 3 figure

The LONI QC System: A Semi-Automated, Web-Based and Freely-Available Environment for the Comprehensive Quality Control of Neuroimaging Data.

Quantifying, controlling, and monitoring image quality is an essential prerequisite for ensuring the validity and reproducibility of many types of neuroimaging data analyses. Implementation of quality control (QC) procedures is the key to ensuring that neuroimaging data are of high-quality and their validity in the subsequent analyses. We introduce the QC system of the Laboratory of Neuro Imaging (LONI): a web-based system featuring a workflow for the assessment of various modality and contrast brain imaging data. The design allows users to anonymously upload imaging data to the LONI-QC system. It then computes an exhaustive set of QC metrics which aids users to perform a standardized QC by generating a range of scalar and vector statistics. These procedures are performed in parallel using a large compute cluster. Finally, the system offers an automated QC procedure for structural MRI, which can flag each QC metric as being 'good' or 'bad.' Validation using various sets of data acquired from a single scanner and from multiple sites demonstrated the reproducibility of our QC metrics, and the sensitivity and specificity of the proposed Auto QC to 'bad' quality images in comparison to visual inspection. To the best of our knowledge, LONI-QC is the first online QC system that uniquely supports the variety of functionality where we compute numerous QC metrics and perform visual/automated image QC of multi-contrast and multi-modal brain imaging data. The LONI-QC system has been used to assess the quality of large neuroimaging datasets acquired as part of various multi-site studies such as the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study and the Alzheimer's Disease Neuroimaging Initiative (ADNI). LONI-QC's functionality is freely available to users worldwide and its adoption by imaging researchers is likely to contribute substantially to upholding high standards of brain image data quality and to implementing these standards across the neuroimaging community

South African coal and its abrasiveness index determination : an account of challenges

Abstract: Industry end users of coal like electricity generating stations have specifications on coal required in terms of reactive, chemical and physical properties; this includes the ash content, moisture, composition, hardgrove grindability index and abrasiveness index amongst many other properties. These properties affect each other including the overall coal properties and performance required during its specified usage. Some South African coals are known to be very abrasive, this causes operational challenges during the electricity generation combustion process as the coal abrades the plant equipment at a faster rate. Various South African coal samples were tested for abrasiveness index using the Yancey, Geer and Price (YGP) method. Results from these tests showed a lack of repeatability and reproducibility on the abrasiveness index values of coal samples. This lack of repeatability and reproducibility was observed in all coal samples tested. The same was found when either the same sample was tested in different laboratories or even when a mother sample was divided and tested repeatedly in one laboratory. Proximate and Ultimate analysis were conducted on the same South African coal samples for coal characterisation and classification. The size of the analysed sample; the size and shape, the degree of liberation of the abrasive coal component, and the interface between the abrasive component of coal and the blade surface are additional contributing factors. This study gives an account of challenges experienced and observed during the abrasiveness index determination of different South African coal samples. An attempt to holistically integrate the impact of main coal components contributing to the abrasiveness of coal will be presented

GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization

Bioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists' capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter gene sets association is rarely taken into consideration. Deep learning, an emerging machine learning technique in computational biology, can be used to generate an unbiased combination of gene set, and to determine the biological relevance and analysis consistency of these combining gene sets by leveraging large genomic data sets. In this study, we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model with the incorporation of a priori defined gene sets that retain the crucial biological features in the latent layer. We introduced the concept of the gene superset, an unbiased combination of gene sets with weights trained by the autoencoder, where each node in the latent layer is a superset. Trained with genomic data from TCGA and evaluated with their accompanying clinical parameters, we showed gene supersets' ability of discriminating tumor subtypes and their prognostic capability. We further demonstrated the biological relevance of the top component gene sets in the significant supersets. Using autoencoder model and gene superset at its latent layer, we demonstrated that gene supersets retain sufficient biological information with respect to tumor subtypes and clinical prognostic significance. Superset also provides high reproducibility on survival analysis and accurate prediction for cancer subtypes.Comment: Presented in the International Conference on Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA and published in BMC Systems Biology 2018, 12(Suppl 8):14

Ultrasound IMT measurement on a multi-ethnic and multi-institutional database: Our review and experience using four fully automated and one semi-automated methods

Automated and high performance carotid intima-media thickness (IMT) measurement is gaining increasing importance in clinical practice to assess the cardiovascular risk of patients. In this paper, we compare four fully automated IMT measurement techniques (CALEX, CAMES, CARES and CAUDLES) and one semi-automated technique (FOAM). We present our experience using these algorithms, whose lumen-intima and media-adventitia border estimation use different methods that can be: (a) edge-based; (b) training-based; (c) feature-based; or (d) directional Edge-Flow based. Our database (DB) consisted of 665 images that represented a multi-ethnic group and was acquired using four OEM scanners. The performance evaluation protocol adopted error measures, reproducibility measures, and Figure of Merit (FoM). FOAM showed the best performance, with an IMT bias equal to 0.025 ± 0.225 mm, and a FoM equal to 96.6%. Among the four automated methods, CARES showed the best results with a bias of 0.032 ± 0.279 mm, and a FoM to 95.6%, which was statistically comparable to that of FOAM performance in terms of accuracy and reproducibility. This is the first time that completely automated and user-driven techniques have been compared on a multi-ethnic dataset, acquired using multiple original equipment manufacturer (OEM) machines with different gain settings, representing normal and pathologic case

A lexicon based approach to classification of ICD10 codes. IMS unipd at CLEF eHealth task 1

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