Filter
Associated Lab
- Betzig Lab (2) Apply Betzig Lab filter
- Bock Lab (1) Apply Bock Lab filter
- Cardona Lab (1) Apply Cardona Lab filter
- Dickson Lab (3) Apply Dickson Lab filter
- Druckmann Lab (1) Apply Druckmann Lab filter
- Dudman Lab (3) Apply Dudman Lab filter
- Eddy/Rivas Lab (2) Apply Eddy/Rivas Lab filter
- Egnor Lab (1) Apply Egnor Lab filter
- Fetter Lab (2) Apply Fetter Lab filter
- Fitzgerald Lab (2) Apply Fitzgerald Lab filter
- Gonen Lab (2) Apply Gonen Lab filter
- Heberlein Lab (3) Apply Heberlein Lab filter
- Hess Lab (1) Apply Hess Lab filter
- Jayaraman Lab (1) Apply Jayaraman Lab filter
- Kainmueller Lab (1) Apply Kainmueller Lab filter
- Karpova Lab (1) Apply Karpova Lab filter
- Keleman Lab (2) Apply Keleman Lab filter
- Keller Lab (1) Apply Keller Lab filter
- Koay Lab (1) Apply Koay Lab filter
- Lavis Lab (3) Apply Lavis Lab filter
- Lippincott-Schwartz Lab (1) Apply Lippincott-Schwartz Lab filter
- Magee Lab (1) Apply Magee Lab filter
- Pavlopoulos Lab (2) Apply Pavlopoulos Lab filter
- Reiser Lab (2) Apply Reiser Lab filter
- Riddiford Lab (2) Apply Riddiford Lab filter
- Romani Lab (1) Apply Romani Lab filter
- Rubin Lab (2) Apply Rubin Lab filter
- Schreiter Lab (3) Apply Schreiter Lab filter
- Sgro Lab (1) Apply Sgro Lab filter
- Shroff Lab (1) Apply Shroff Lab filter
- Simpson Lab (1) Apply Simpson Lab filter
- Spruston Lab (4) Apply Spruston Lab filter
- Stern Lab (9) Apply Stern Lab filter
- Svoboda Lab (3) Apply Svoboda Lab filter
- Tervo Lab (1) Apply Tervo Lab filter
- Tjian Lab (5) Apply Tjian Lab filter
- Truman Lab (1) Apply Truman Lab filter
- Turaga Lab (1) Apply Turaga Lab filter
Publication Date
- December 2007 (12) Apply December 2007 filter
- November 2007 (9) Apply November 2007 filter
- October 2007 (12) Apply October 2007 filter
- September 2007 (8) Apply September 2007 filter
- August 2007 (7) Apply August 2007 filter
- July 2007 (16) Apply July 2007 filter
- June 2007 (5) Apply June 2007 filter
- May 2007 (7) Apply May 2007 filter
- April 2007 (8) Apply April 2007 filter
- March 2007 (7) Apply March 2007 filter
- February 2007 (3) Apply February 2007 filter
- January 2007 (12) Apply January 2007 filter
- Remove 2007 filter 2007
Type of Publication
106 Publications
Showing 21-30 of 106 resultsPharmacological and genetic studies have implicated the mu opioid receptor (MOR) in the regulation of ethanol intake in animal models and humans. Non-specific antagonists of opioid receptors have been shown to affect ethanol consumption when infused directly into the ventral tegmental area (VTA) of rats. However, administration of MOR-selective antagonists into the VTA has yielded mixed results. We used RNA interference (RNAi) to specifically decrease levels of MOR messenger RNA in the VTA of mice and examined the effect on ethanol consumption in a two-bottle choice paradigm. Mice were injected in the VTA with lentivirus expressing either a small hairpin RNA (shRNA) targeting MOR or a control shRNA. One week after virus injection, mice were examined for ethanol consumption in a two-bottle choice experiment with increasing concentrations of ethanol over the course of 1 month. Expression of an shRNA targeting MOR in the VTA led to a significant reduction in ethanol consumption. These results strengthen the hypothesis that MOR in the VTA is one of the key brain substrates mediating alcohol consumption. The RNAi combined with lentiviral delivery can be used successfully in brain to effect a sustained reduction in expression of specific genes for behavioral analysis.
Transposons are powerful tools for conducting genetic manipulation and functional studies in organisms that are of scientific, economic, or medical interest. Minos, a member of the Tc1/mariner family of DNA transposons, exhibits a low insertional bias and transposes with high frequency in vertebrates and invertebrates. Its use as a tool for transgenesis and genome analysis of rather different animal species is described.
The hippocampus is essential for episodic memory, which requires single-trial learning. Although long-term potentiation (LTP) of synaptic strength is a candidate mechanism for learning, it is typically induced by using repeated synaptic activation to produce precisely timed, high-frequency, or rhythmic firing. Here we show that hippocampal synapses potentiate robustly in response to strong activation by a single burst. The induction mechanism of this single-burst LTP requires activation of NMDA receptors, L-type voltage-gated calcium channels, and dendritic spikes. Thus, dendritic spikes are a critical trigger for a form of LTP that is consistent with the function of the hippocampus in episodic memory.
Spinocerebellar ataxia type-3 (SCA3) is among the most common dominantly inherited ataxias, and is one of nine devastating human neurodegenerative diseases caused by the expansion of a CAG repeat encoding glutamine within the gene. The polyglutamine domain confers toxicity on the protein Ataxin-3 leading to neuronal dysfunction and loss. Although modifiers of polyglutamine toxicity have been identified, little is known concerning how the modifiers function mechanistically to affect toxicity. To reveal insight into spinocerebellar ataxia type-3, we performed a genetic screen in Drosophila with pathogenic Ataxin-3-induced neurodegeneration and identified 25 modifiers defining 18 genes. Despite a variety of predicted molecular activities, biological analysis indicated that the modifiers affected protein misfolding. Detailed mechanistic studies revealed that some modifiers affected protein accumulation in a manner dependent on the proteasome, whereas others affected autophagy. Select modifiers of Ataxin-3 also affected tau, revealing common pathways between degeneration due to distinct human neurotoxic proteins. These findings provide new insight into molecular pathways of polyQ toxicity, defining novel targets for promoting neuronal survival in human neurodegenerative disease.
The first coupling of atmospheric pressure ionization methods, electrospray ionization (ESI) and desorption electrospray ionization (DESI), to a miniature hand-held mass spectrometer is reported. The instrument employs a rectilinear ion trap (RIT) mass analyzer and is battery-operated, hand-portable, and rugged (total system: 10 kg, 0.014 m(3), 75 W power consumption). The mass spectrometer was fitted with an atmospheric inlet, consisting of a 10 cm x 127 microm inner diameter stainless steel capillary tube which was used to introduce gas into the vacuum chamber at 13 mL/min. The operating pressure was 15 mTorr. Ions, generated by the atmospheric pressure ion source, were directed by the inlet along the axis of the ion trap, entering through an aperture in the dc-biased end plate, which was also operated as an ion gate. ESI and DESI sources were used to generate ions; ESI-MS analysis of an aqueous mixture of drugs yielded detection limits in the low parts-per-billion range. Signal response was linear over more than 3 orders of magnitude. Tandem mass spectrometry experiments were used to identify components of this mixture. ESI was also applied to the analysis of peptides and in this case multiply charged species were observed for compounds of molecular weight up to 1200 Da. Cocaine samples deposited or already present on different surfaces, including currency, were rapidly analyzed in situ by DESI. A geometry-independent version of the DESI ion source was also coupled to the miniature mass spectrometer. These results demonstrate that atmospheric pressure ionization can be implemented on simple portable mass spectrometry systems.
Convolutional networks have achieved a great deal of success in high-level vision problems such as object recognition. Here we show that they can also be used as a general method for low-level image processing. As an example of our approach, convolutional networks are trained using gradient learning to solve the problem of restoring noisy or degraded images. For our training data, we have used electron microscopic images of neural circuitry with ground truth restorations provided by human experts. On this dataset, Markov random field (MRF), conditional random field (CRF), and anisotropic diffusion algorithms perform about the same as simple thresholding, but superior performance is obtained with a convolutional network containing over 34,000 adjustable parameters. When restored by this convolutional network, the images are clean enough to be used for segmentation, whereas the other approaches fail in this respect. We do not believe that convolutional networks are fundamentally superior to MRFs as a representation for image processing algorithms. On the contrary, the two approaches are closely related. But in practice, it is possible to train complex convolutional networks, while even simple MRF models are hindered by problems with Bayesian learning and inference procedures. Our results suggest that high model complexity is the single most important factor for good performance, and this is possible with convolutional networks.
An efficient two-step strategy has been developed to access diversely functionalized benzylic sulfonamides. Execution of this strategy required the development of two reaction methods: the palladium-catalyzed cross-coupling of aryl halides with CH-acidic methanesulfonamides and a metathesis reaction between the resulting alpha-arylated sulfonamides and diverse amines. The broad scope of the cross-coupling process combined with a versatile sulfonamide metathesis constitutes an efficient strategy for the synthesis of various benzylic sulfonamides.
Physical traces underlying simple memories can be confined to a single group of cells in the brain. In the fly Drosophila melanogaster, the Kenyon cells of the mushroom bodies house traces for both appetitive and aversive odor memories. The adenylate cyclase protein, Rutabaga, has been shown to mediate both traces. Here, we show that, for appetitive learning, another group of cells can additionally accommodate a Rutabaga-dependent memory trace. Localized expression of rutabaga in either projection neurons, the first-order olfactory interneurons, or in Kenyon cells, the second-order interneurons, is sufficient for rescuing the mutant defect in appetitive short-term memory. Thus, appetitive learning may induce multiple memory traces in the first- and second-order olfactory interneurons using the same plasticity mechanism. In contrast, aversive odor memory of rutabaga is rescued selectively in the Kenyon cells, but not in the projection neurons. This difference in the organization of memory traces is consistent with the internal representation of reward and punishment.
We developed a series of statistical potentials to recognize the native protein from decoys, particularly when using only a reduced representation in which each side chain is treated as a single C(beta) atom. Beginning with a highly successful all-atom statistical potential, the Discrete Optimized Protein Energy function (DOPE), we considered the implications of including additional information in the all-atom statistical potential and subsequently reducing to the C(beta) representation. One of the potentials includes interaction energies conditional on backbone geometries. A second potential separates sequence local from sequence nonlocal interactions and introduces a novel reference state for the sequence local interactions. The resultant potentials perform better than the original DOPE statistical potential in decoy identification. Moreover, even upon passing to a reduced C(beta) representation, these statistical potentials outscore the original (all-atom) DOPE potential in identifying native states for sets of decoys. Interestingly, the backbone-dependent statistical potential is shown to retain nearly all of the information content of the all-atom representation in the C(beta) representation. In addition, these new statistical potentials are combined with existing potentials to model hydrogen bonding, torsion energies, and solvation energies to produce even better performing potentials. The ability of the C(beta) statistical potentials to accurately represent protein interactions bodes well for computational efficiency in protein folding calculations using reduced backbone representations, while the extensions to DOPE illustrate general principles for improving knowledge-based potentials.