Addressing Manufacturing Challenges for Commercialization of iPSC-Based Therapies.

The development of reprogramming technology to generate human induced pluripotent stem cells (iPSCs) has tremendously influenced the field of regenerative medicine and clinical therapeutics where curative cell replacement therapies can be used in the treatment of devastating diseases such as Parkinson's disease (PD) and diabetes. In order to commercialize these therapies to treat a large number of individuals, it is important to demonstrate the safety and efficacy of these therapies and ensure that the manufacturing process for iPSC-derived functional cells can be industrialized at an affordable cost. However, there are a number of manufacturing obstacles that need to be addressed in order to meet this vision.

Transfer of plasmid into the pentose-fermenting yeast Pachysolen tannophilus.

The pentose-fermenting yeast Pachysolen tannophilus can convert glucose and xylose in lignocellulosic hydrolysates to ethanol. However, it performs poorly in industrially relevant lignocellulosic hydrolysates containing mixed sugars and inhibitors. Efforts have been directed at improving the performance of this yeast to enable efficient lignocellulosic biomass conversion.

Gene selection for tumor classification using a novel bio-inspired multi-objective approach.

Identifying the informative genes has always been a major step in microarray data analysis. The complexity of various cancer datasets makes this issue still challenging. In this paper, a novel Bio-inspired Multi-objective algorithm is proposed for gene selection in microarray data classification specifically in the binary domain of feature selection.

Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts.

Gene selection is a demanding task for microarray data analysis. The diverse complexity of different cancers makes this issue still challenging. In this study, a novel evolutionary method based on genetic algorithms and artificial intelligence is proposed to identify predictive genes for cancer classification.