Gene expression profiling of breast cancer survivability by pooled cDNA microarray analysis using logistic regression, artificial neural networks and decision trees

BACKGROUND: Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse. After data compilation, 757 subjects, 5 clinical variables and 13,452 genetic variables were aggregated. The bootstrap method, Mann–Whitney U test and 20-fold cross-validation were performed to investigate candidate genes with 100 most-significant p-values. The predictive powers of DT, LR and ANN models were assessed using accuracy and the area under ROC curve. The associated genes were evaluated using Cox regression. RESULTS: The DT models exhibited the lowest predictive power and the poorest extrapolation when applied to the test samples. The ANN models displayed the best predictive power and showed the best extrapolation. The 21 most-associated genes, as determined by integration of each model, were analyzed using Cox regression with a 3.53-fold (95% CI: 2.24-5.58) increased risk of breast cancer five-year recurrence… CONCLUSIONS: The 21 selected genes can predict breast cancer recurrence. Among these genes, CCNB1, PLK1 and TOP2A are in the cell cycle G2/M DNA damage checkpoint pathway. Oncologists can offer the genetic information for patients when understanding the gene expression profiles on breast cancer recurrence

CTX: A Clock-Gating-Based Test Relaxation and X-Filling Scheme for Reducing Yield Loss Risk in At-Speed Scan Testing

International audienceAt-speed scan testing is susceptible to yield loss risk due to power supply noise caused by excessive launch switching activity. This paper proposes a novel two-stage scheme, namely CTX (Clock-Gating-Based Test Relaxation and X-Filling), for reducing switching activity when test stimulus is launched. Test relaxation and X-filling are conducted (1) to make as many FFs inactive as possible by disabling corresponding clock-control signals of clock-gating circuitry in Stage-1 (Clock-Disabling), and (2) to make as many remaining active FFs as possible to have equal input and output values in Stage-2 (FF-Silencing). CTX effectively reduces launch switching activity, thus yield loss risk, even with a small number of donpsilat care (X) bits as in test compression, without any impact on test data volume, fault coverage, performance, and circuit design

Red blood cell phenotype fidelity following glycerol cryopreservation optimized for research purposes.

Intact red blood cells (RBCs) are required for phenotypic analyses. In order to allow separation (time and location) between subject encounter and sample analysis, we developed a research-specific RBC cryopreservation protocol and assessed its impact on data fidelity for key biochemical and physiological assays. RBCs drawn from healthy volunteers were aliquotted for immediate analysis or following glycerol-based cryopreservation, thawing, and deglycerolization. RBC phenotype was assessed by (1) scanning electron microscopy (SEM) imaging and standard morphometric RBC indices, (2) osmotic fragility, (3) deformability, (4) endothelial adhesion, (5) oxygen (O2) affinity, (6) ability to regulate hypoxic vasodilation, (7) nitric oxide (NO) content, (8) metabolomic phenotyping (at steady state, tracing with [1,2,3-13C3]glucose ± oxidative challenge with superoxide thermal source; SOTS-1), as well as in vivo quantification (following human to mouse RBC xenotransfusion) of (9) blood oxygenation content mapping and flow dynamics (velocity and adhesion). Our revised glycerolization protocol (40% v/v final) resulted in >98.5% RBC recovery following freezing (-80°C) and thawing (37°C), with no difference compared to the standard reported method (40% w/v final). Full deglycerolization (>99.9% glycerol removal) of 40% v/v final samples resulted in total cumulative lysis of ~8%, compared to ~12-15% with the standard method. The post cryopreservation/deglycerolization RBC phenotype was indistinguishable from that for fresh RBCs with regard to physical RBC parameters (morphology, volume, and density), osmotic fragility, deformability, endothelial adhesivity, O2 affinity, vasoregulation, metabolomics, and flow dynamics. These results indicate that RBC cryopreservation/deglycerolization in 40% v/v glycerol final does not significantly impact RBC phenotype (compared to fresh cells)