Main Menu (Mobile)- Block

Main Menu - Block

janelia7_blocks-janelia7_fake_breadcrumb | block
Flyem / Publications
custom | custom

Filter

facetapi-Q2b17qCsTdECvJIqZJgYMaGsr8vANl1n | block
facetapi-W9JlIB1X0bjs93n1Alu3wHJQTTgDCBGe | block

Associated Project Team

facetapi-PV5lg7xuz68EAY8eakJzrcmwtdGEnxR0 | block
facetapi-021SKYQnqXW6ODq5W5dPAFEDBaEJubhN | block
general_search_page-panel_pane_1 | views_panes

5 Publications

Showing 1-5 of 5 results
Your Criteria:
    10/01/10 | Refinement of tools for targeted gene expression in Drosophila.
    Pfeiffer BD, Ngo TB, Hibbard KL, Murphy C, Jenett A, Truman JW, Rubin GM
    Genetics. 2010 Oct;186(2):735-55. doi: 10.1534/genetics.110.119917

    A wide variety of biological experiments rely on the ability to express an exogenous gene in a transgenic animal at a defined level and in a spatially and temporally controlled pattern. We describe major improvements of the methods available for achieving this objective in Drosophila melanogaster. We have systematically varied core promoters, UTRs, operator sequences, and transcriptional activating domains used to direct gene expression with the GAL4, LexA, and Split GAL4 transcription factors and the GAL80 transcriptional repressor. The use of site-specific integration allowed us to make quantitative comparisons between different constructs inserted at the same genomic location. We also characterized a set of PhiC31 integration sites for their ability to support transgene expression of both drivers and responders in the nervous system. The increased strength and reliability of these optimized reagents overcome many of the previous limitations of these methods and will facilitate genetic manipulations of greater complexity and sophistication.

    View Publication Page
    10/01/10 | Semi-automated reconstruction of neural circuits using electron microscopy.
    Chklovskii DB, Vitaladevuni S, Scheffer LK
    Current Opinion in Neurobiology. 2010 Oct;20:667-75. doi: 10.1371/journal.pcbi.1001066

    Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience, and the focus of the nascent field of connectomics. Previously used to reconstruct the C. elegans wiring diagram, serial-section transmission electron microscopy (ssTEM) is a proven technique for the task. However, to reconstruct more complex circuits, ssTEM will require the automation of image processing. We review progress in the processing of electron microscopy images and, in particular, a semi-automated reconstruction pipeline deployed at Janelia. Drosophila circuits underlying identified behaviors are being reconstructed in the pipeline with the goal of generating a complete Drosophila connectome.

    View Publication Page
    01/01/10 | Anatomic analysis of Gal4 expression patterns of the Rubin line collection: the central complex.
    Jenett A, Wolff T, Nern A, Pfeiffer BD, Ngo T, Murphy C, Long F, Peng H, Rubin GM
    Journal of Neurogenetics. 2010;24:71-2
    01/01/10 | Co-clustering of image segments using convex optimization applied to EM neuronal reconstruction.
    Vitaladevuni S, Basri R
    Computer Vision and Pattern Recognition. 2010:2203-10

    This paper addresses the problem of jointly clustering two segmentations of closely correlated images. We focus in particular on the application of reconstructing neuronal structures in over-segmented electron microscopy images. We formulate the problem of co-clustering as a quadratic semi-assignment problem and investigate convex relaxations using semidefinite and linear programming. We further introduce a linear programming method with manageable number of constraints and present an approach for learning the cost function. Our method increases computational efficiency by orders of magnitude while maintaining accuracy, automatically finds the optimal number of clusters, and empirically tends to produce binary assignment solutions. We illustrate our approach in simulations and in experiments with real EM data.

    View Publication Page
    01/01/10 | Increasing depth resolution of electron microscopy of neural circuits using sparse tomographic reconstruction.
    Veeraraghavan A, Genkin AV, Vitaladevuni S, Scheffer L, Xu C, Hess H, Fetter R, Cantoni M, Knott G, Chklovskii DB
    Computer Vision and Pattern Recognition (CVPR). 2010:1767-74. doi: 10.1109/CVPR.2010.5539846