The Pax6 gene plays several roles in retinal development, including control of cell proliferation, maintenance of the retinogenic potential of progenitor cells, and cell fate specification. Emerging evidence suggests that these different aspects of Pax6 gene function are mediated by different isoforms of the Pax6 protein; however, relatively little is known about the spatiotemporal expression of Pax6 isoforms in the vertebrate retina. Using bacterial artificial chromosome (BAC) technology, we modified a zebrafish Pax6a BAC such that we could distinguish paired-containing Pax6a transcripts from paired-less Pax6a transcripts. In the zebrafish, the spatial and temporal onset of expression of these transcripts suggests that the paired-less isoform is involved in the cell fate decision leading to the generation of amacrine cells; however, because of limitations associated with transient transgenic analysis, it was not feasible to establish whether this promoter was active in all amacrine cells or in a specific population of amacrine cells. By making mice transgenic for the zebrafish Pax6a BAC reporter transgene, we were able to show that paired-containing and paired-less Pax6a transcripts were differentially expressed in amacrine subpopulations. Our study also directly demonstrates the functional conservation of the regulatory mechanisms governing Pax6 transcription in teleosts and mammals.

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The emergent self-organization of a neuronal network in a developing nervous system is the result of a remarkably orchestrated process involving a multitude of chemical, mechanical and electrical signals. Little is known about the dynamic behavior of a developing network (especially in a human model) primarily due to a lack of practical and non-invasive methods to measure and quantify the process. Here we demonstrate that by using a novel optical interferometric technique, we can non-invasively measure several fundamental properties of neural networks from the sub-cellular to the cell population level. We applied this method to quantify network formation in human stem cell derived neurons and show for the first time, correlations between trends in the growth, transport, and spatial organization of such a system. Quantifying the fundamental behavior of such cell lines without compromising their viability may provide an important new tool in future longitudinal studies.

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Abstract. We present the first deterministic sub-linear space algorithms for a number of fundamental problems over update data streams, such as, (a) point queries, (b) range-sum queries, (c) finding approximate frequent items, (d) finding approximate quantiles, (e) finding approximate hierarchical heavy hitters, (f) estimating inner-products, (g) constructing near-optimal B-bucket histograms, (h) estimating entropy of data streams, etc.. We also present new lower bound results for several problems over update data streams.