Human induced pluripotent stem cells (hiPSCs1–3) are useful in disease modeling and drug discovery, and they promise to provide a new generation of cell-based therapeutics. To date there has been no systematic evaluation of the most widely used techniques for generating integration-free hiPSCs. Here we compare Sendai-viral (SeV)4, episomal (Epi)5 and mRNA transfection mRNA6 methods using a number of criteria. All methods generated high-quality hiPSCs, but significant differences existed in aneuploidy rates, reprogramming efficiency, reliability and workload. We discuss the advantages and shortcomings of each approach, and present and review the results of a survey of a large number of human reprogramming laboratories on their independent experiences and preferences. Our analysis provides a valuable resource to inform the use of specific reprogramming methods for different laboratories and different applications, including clinical translation.

Link to this article: http://journals.cambridge.org/abstract_S1351324905003955How to cite this article: XIN LI and DAN ROTH (2006). Learning question classiers: the role of semantic information. AbstractTo respond correctly to a free form factual question given a large collection of text data, one needs to understand the question to a level that allows determining some of the constraints the question imposes on a possible answer. These constraints may include a semantic classification of the sought after answer and may even suggest using different strategies when looking for and verifying a candidate answer. This work presents a machine learning approach to question classification. Guided by a layered semantic hierarchy of answer types, we develop a hierarchical classifier that classifies questions into fine-grained classes. This work also performs a systematic study of the use of semantic information sources in natural language classification tasks. It is shown that, in the context of question classification, augmenting the input of the classifier with appropriate semantic category information results in significant improvements to classification accuracy. We show accurate results on a large collection of free-form questions used in TREC 10 and 11.

Cerebral ischemia is a major cause of death and long-term disability worldwide. Ischemic penumbra, the electrically silent but metabolically viable perifocal brain tissue, is the target for the much elusive stroke therapy. To characterize the molecular events of the dynamic penumbra, we applied an iTRAQ-based shotgun proteomic approach in an in vitro neuronal model, using the rat B104 neuroblastoma cell line. Various functional and cytometric assays were performed to establish the relevant time-point and conditions for ischemia to recapitulate the pathology of the penumbra. Two replicate iTRAQ experiments identified 1796 and 1566 proteins, respectively (