@article {48366,
	title = {Facilitating neuron-specific genetic manipulations in Drosophila using a split GAL4 repressor.},
	journal = {Genetics},
	volume = {206},
	year = {2017},
	month = {2017 Mar 31},
	pages = {775-84},
	abstract = {<p>Efforts to map neural circuits have been galvanized by the development of genetic technologies that permit the manipulation of targeted sets of neurons in the brains of freely behaving animals. The success of these efforts relies on the experimenter{\textquoteright}s ability to target arbitrarily small subsets of neurons for manipulation, but such specificity of targeting cannot routinely be achieved using existing methods. In Drosophila melanogaster, a widely used technique for refined cell-type specific manipulation is the Split GAL4 system, which augments the targeting specificity of the binary GAL4-UAS system by making GAL4 transcriptional activity contingent upon two enhancers, rather than one. To permit more refined targeting, we introduce here the "Killer Zipper" (KZip(+)), a suppressor that makes Split GAL4 targeting contingent upon a third enhancer. KZip(+) acts by disrupting both the formation and activity of Split GAL4 heterodimers, and we show how this added layer of control can be used to selectively remove unwanted cells from a Split GAL4 expression pattern or to subtract neurons of interest from a pattern to determine their requirement in generating a given phenotype. To facilitate application of the KZip(+) technology, we have developed a versatile set of LexAop-KZip(+) fly lines that can be used directly with the large number of LexA driver lines with known expression patterns. The Killer Zipper significantly sharpens the precision of neuronal genetic control available in Drosophila and may be extended to other organisms where Split GAL4-like systems are used.</p>
},
	issn = {1943-2631},
	doi = {10.1534/genetics.116.199687},
	author = {Dolan, Michael-John and Luan, Haojiang and Shropshire, William C and Sutcliffe, Ben and Cocanougher, Benjamin and Scott, Robert L and Frechter, Shahar and Zlatic, Marta and Jefferis, Gregory S X E and White, Benjamin H}
}
@article {48495,
	title = {Semiparametric spectral modeling of the Drosophila connectome.},
	journal = {arXiv},
	year = {2017},
	month = {2017 May 9},
	pages = {1705.03297},
	abstract = {<p>We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome. Motivated by a thorough exploratory data analysis of the network via Gaussian mixture modeling (GMM) in the adjacency spectral embedding (ASE) representation space, we introduce the latent structure model (LSM) for network modeling and inference. LSM is a generalization of the stochastic block model (SBM) and a special case of the random dot product graph (RDPG) latent position model, and is amenable to semiparametric GMM in the ASE representation space. The resulting connectome code derived via semiparametric GMM composed with ASE captures latent connectome structure and elucidates biologically relevant neuronal properties.</p>
},
	url = {https://arxiv.org/abs/1705.03297},
	author = {Priebe, Carey E. and Park, Youngser and Tang, Minh and Athreya, Avanti and Lyzinski, Vince and Vogelstein, Joshua T and Qin, Yichen and Cocanougher, Benjamin and Eichler, Katharina and Zlatic, Marta and Cardona, Albert}
}
