Cloud Computing for Detecting High-Order Genome-Wide Epistatic Interaction via Dynamic Clustering

Backgroud: Taking the advan tage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locus-based methods are insufficient to detect interactions consisting of multiple-locus, which are broadly existing in complex traits. In addition, statistic tests for high order epistatic interactions with more than 2 SNPs propose computational and analytical challenges because the computation increases exponentially as the cardinality of SNPs combinations gets larger. Results: In this paper, we provide a simple, fast and powerful method using dynamic clustering and cloud computing to detect genome-wide multi-locus epistatic interactions. We have constructed systematic experiments to compare powers performance against some recently proposed algorithms, including TEAM, SNPRuler, EDCF and BOOST. Furthermore, we have applied our method on two real GWAS datasets, Age-related macular degeneration (AMD) and Rheumatoid arthritis (RA) datasets, where we find some novel potential disease-related genetic factors which are not shown up in detections of 2-loci epistatic interactions. Conclusions: Experimental results on simulated data demonstrate that our method is more powerful than some recently proposed methods on both two- and three-locus disease models. Our method has discovered many novel high-order associations that are significantly enriched in cases from two real GWAS datasets. Moreover, the running time of the cloud implementation for our method on AMD dataset and RA dataset are roughly 2 hours and 50 hours on a cluster with forty small virtual machines for detecting two-locus interactions, respectively. Therefore, we believe that our method is suitable and effective for the full-scale analysis of multiple-locus epistatic interactions in GWAS

Rapid and label-free identification of single leukemia cells from blood in a high-density microfluidic trapping array by fluorescence lifetime imaging microscopy.

The rapid screening and isolation of single leukemia cells from blood has become critical for early leukemia detection and tumor heterogeneity interrogation. However, due to the size overlap between leukemia cells and the more abundant white blood cells (WBCs), the isolation and identification of leukemia cells individually from peripheral blood is extremely challenging and often requires immunolabeling or cytogenetic assays. Here we present a rapid and label-free single leukemia cell identification platform that combines: (1) high-throughput size-based separation of hemocytes via a single-cell trapping array, and (2) leukemia cell identification through phasor approach and fluorescence lifetime imaging microscopy (phasor-FLIM), to quantify changes between free/bound nicotinamide adenine dinucleotide (NADH) as an indirect measurement of metabolic alteration in living cells. The microfluidic trapping array designed with 1600 highly-packed addressable single-cell traps can simultaneously filter out red blood cells (RBCs) and trap WBCs/leukemia cells, and is compatible with low-magnification imaging and fast-speed fluorescence screening. The trapped single leukemia cells, e.g., THP-1, Jurkat and K562 cells, are distinguished from WBCs in the phasor-FLIM lifetime map, as they exhibit significant shift towards shorter fluorescence lifetime and a higher ratio of free/bound NADH compared to WBCs, because of their glycolysis-dominant metabolism for rapid proliferation. Based on a multiparametric scheme comparing the eight parameter-spectra of the phasor-FLIM signatures, spiked leukemia cells are quantitatively distinguished from normal WBCs with an area-under-the-curve (AUC) value of 1.00. Different leukemia cell lines are also quantitatively distinguished from each other with AUC values higher than 0.95, demonstrating high sensitivity and specificity for single cell analysis. The presented platform is the first to enable high-density size-based single-cell trapping simultaneously with RBC filtering and rapid label-free individual-leukemia-cell screening through non-invasive metabolic imaging. Compared to conventional biomolecular diagnostics techniques, phasor-FLIM based single-cell screening is label-free, cell-friendly, robust, and has the potential to screen blood in clinical volumes through parallelization

The identification of optimal pathways in Synechocystis sp. PCC 6803 by flux balance analysis

Cyanobacteria are microorganisms considered advantageous for producing valuable compounds because of their high growth rates compared to plants. They also can be grown at large scale in photobioreactors. This research aims to use metabolic engineering strategies to maximize the phenylalanine yield in Synechocystis sp. PCC 6803. Our hypothesis is flux balance analysis will give different flux distributions with different objective functions. The scope of the project is modeling photoautotrophic metabolism of cyanobacteria with a genome scale stoichiometric model, testing several alternative objective functions. We also examined the tradeoff between growth and L-phenylalanine production with flux balance analysis. A linear programming problem is constructed to solve for the fluxes. Using an available genome-scale model and the COnstraint Based Reconstruction and Analysis (COBRA) toolbox available in MATLAB, we solved for the flux value for each reaction in the wild type strain with different objective functions such as maximizing biomass, maximizing carbon dioxide uptake and minimizing total flux. Of particular interest to metabolic engineers is the production of L-phenylalanine, an essential amino acid. In plants, Phe is the precursor to phenylpropanoids, a family of thousands of compounds with wide ranging applications from pharmaceuticals to cosmetics. Using FBA, we quantitatively defined the tradeoff between directing the carbon flux towards phenylalanine instead of biomass. Future work will involve validating the model’s predictions and making improvements to it, as well as exploring the tradeoff in the production of other molecules in cyanobacteria

Experimental investigation, stochastic modelling and reliability analysis of fatigue behavior of tapered composite laminates

The present thesis contains the results of testing, stochastic process modeling and reliability analysis of symmetric tapered laminates with pre-set delaminations. Two laminate configurations, called lay-up A and lay-up B are considered. Lay-up A is a [0/±45/0/(±45) 3 /±45/0 7 ] s laminate that is reduced to a [0/±45/0/±45/0 7 ] s laminate and lay-up B is a [0 7 /±45/0/(±45) 3 /±45/0] s laminate that is reduced to a [0 7 /±45/0/±45/0] s laminate. Two locations of pre-set delaminations, at the center of the core layer and in between belt and core layers in the thin side of lay-up A tapered laminate, are considered. Two loading conditions, (a) cyclic tension-compression loading and (b) cyclic tension-compression loading with 85% over tension load, are applied for the fatigue tests in the present thesis. A stochastic approach to model the fatigue damage development based on the test data which has been developed and presented in an existing work is used in the present thesis. The Markov Chain is used to represent the fatigue damage accumulation in this approach, and the differences between the true probability distribution and the unconditional probability distribution (or predicted unconditional probability distribution) of the fatigue response parameter are determined by using different methodologies, that are, the Maximum Entropy Method (MEM) and, Gaussian (single and bivariate) probability distribution and joint probability density function. The test data on the fatigue response parameter are analyzed based on the reliability function, hazard rate and failure density function

FractalAD: A simple industrial anomaly detection method using fractal anomaly generation and backbone knowledge distillation

Although industrial anomaly detection (AD) technology has made significant progress in recent years, generating realistic anomalies and learning priors of normal remain challenging tasks. In this study, we propose an end-to-end industrial anomaly detection method called FractalAD. Training samples are obtained by synthesizing fractal images and patches from normal samples. This fractal anomaly generation method is designed to sample the full morphology of anomalies. Moreover, we designed a backbone knowledge distillation structure to extract prior knowledge contained in normal samples. The differences between a teacher and a student model are converted into anomaly attention using a cosine similarity attention module. The proposed method enables an end-to-end semantic segmentation network to be used for anomaly detection without adding any trainable parameters to the backbone and segmentation head, and has obvious advantages over other methods in training and inference speed.. The results of ablation studies confirmed the effectiveness of fractal anomaly generation and backbone knowledge distillation. The results of performance experiments showed that FractalAD achieved competitive results on the MVTec AD dataset and MVTec 3D-AD dataset compared with other state-of-the-art anomaly detection methods.Comment: 12 pages, 5 figure