Transcriptome analysis of CD26+ prostate cancer cells demonstrated that it was possible to isolate and characterize gene expression in cancer cells from solid tumors weighing 0.5 g or more utilizing our cell sorting methodology.
With this powerful asymmetric methodology in hand, we planned to utilize our Mo-AAA methodology as a key asymmetric step to construct one of the two quaternary stereocenters in -perophoramidine (1).
Motivated by the capability of SHaSM to detect single BTO dimers in vitro, we utilized our concepts and methodology to quantify mRNA corresponding to the human epidermal growth factor receptor 2 (Her2) at the single-cell level.
To demonstrate the utility of our methodology, we utilized three datasets: (i) GSE6313, containing C57/B6 adult mouse retina cDNA profiles (Liu et al., 2007), (ii) GSE7785, containing PANC-1 derived cDNA profiles (Tan et al., 2003) and (iii) GSE4854, containing mouse cortex expression profiles (Kuo et al., 2006).
Our methodology utilizes a hybrid genetic algorithm neural network strategy (GA ANN).
Our methodology utilizes traditional hardware/software co-design approaches to generate a heterogeneous architecture for an FPGA-based simulator.
Our methodology utilizes a cyclic-order solution encoding, which maps a permutation of the customer set to a collection of many possible VRP solutions.
Our methodology utilizes traditional hardware/software co-design approaches to generate a heterogeneous architecture for an FPGA-based simulator by porting the computationally complex regions to hardware.
Our methodology utilizes cell sorting, high-throughput sequencing and statistical data analyses to enable parallel measurements of the activities of hundreds of thousands of sequences from RNA device libraries in the absence and presence of ligands.
To partially overcome such problems, our methodology utilizes a percentage based thresholds as cut-off for network interactions instead of coefficient values based on correlation.
