Filter
Associated Lab
- Aguilera Castrejon Lab (15) Apply Aguilera Castrejon Lab filter
- Ahrens Lab (56) Apply Ahrens Lab filter
- Aso Lab (39) Apply Aso Lab filter
- Baker Lab (38) Apply Baker Lab filter
- Betzig Lab (110) Apply Betzig Lab filter
- Beyene Lab (10) Apply Beyene Lab filter
- Bock Lab (17) Apply Bock Lab filter
- Branson Lab (48) Apply Branson Lab filter
- Card Lab (40) Apply Card Lab filter
- Cardona Lab (63) Apply Cardona Lab filter
- Chklovskii Lab (13) Apply Chklovskii Lab filter
- Clapham Lab (12) Apply Clapham Lab filter
- Cui Lab (19) Apply Cui Lab filter
- Darshan Lab (12) Apply Darshan Lab filter
- Dennis Lab (1) Apply Dennis Lab filter
- Dickson Lab (46) Apply Dickson Lab filter
- Druckmann Lab (25) Apply Druckmann Lab filter
- Dudman Lab (46) Apply Dudman Lab filter
- Eddy/Rivas Lab (30) Apply Eddy/Rivas Lab filter
- Egnor Lab (11) Apply Egnor Lab filter
- Espinosa Medina Lab (16) Apply Espinosa Medina Lab filter
- Feliciano Lab (6) Apply Feliciano Lab filter
- Fetter Lab (41) Apply Fetter Lab filter
- Fitzgerald Lab (28) Apply Fitzgerald Lab filter
- Freeman Lab (15) Apply Freeman Lab filter
- Funke Lab (34) Apply Funke Lab filter
- Gonen Lab (91) Apply Gonen Lab filter
- Grigorieff Lab (62) Apply Grigorieff Lab filter
- Harris Lab (58) Apply Harris Lab filter
- Heberlein Lab (94) Apply Heberlein Lab filter
- Hermundstad Lab (22) Apply Hermundstad Lab filter
- Hess Lab (71) Apply Hess Lab filter
- Ilanges Lab (1) Apply Ilanges Lab filter
- Jayaraman Lab (44) Apply Jayaraman Lab filter
- Ji Lab (33) Apply Ji Lab filter
- Johnson Lab (6) Apply Johnson Lab filter
- Kainmueller Lab (19) Apply Kainmueller Lab filter
- Karpova Lab (14) Apply Karpova Lab filter
- Keleman Lab (13) Apply Keleman Lab filter
- Keller Lab (75) Apply Keller Lab filter
- Koay Lab (16) Apply Koay Lab filter
- Lavis Lab (136) Apply Lavis Lab filter
- Lee (Albert) Lab (34) Apply Lee (Albert) Lab filter
- Leonardo Lab (23) Apply Leonardo Lab filter
- Li Lab (25) Apply Li Lab filter
- Lippincott-Schwartz Lab (161) Apply Lippincott-Schwartz Lab filter
- Liu (Yin) Lab (5) Apply Liu (Yin) Lab filter
- Liu (Zhe) Lab (58) Apply Liu (Zhe) Lab filter
- Looger Lab (137) Apply Looger Lab filter
- Magee Lab (49) Apply Magee Lab filter
- Menon Lab (18) Apply Menon Lab filter
- Murphy Lab (13) Apply Murphy Lab filter
- O'Shea Lab (4) Apply O'Shea Lab filter
- Otopalik Lab (13) Apply Otopalik Lab filter
- Pachitariu Lab (41) Apply Pachitariu Lab filter
- Pastalkova Lab (18) Apply Pastalkova Lab filter
- Pavlopoulos Lab (19) Apply Pavlopoulos Lab filter
- Pedram Lab (14) Apply Pedram Lab filter
- Podgorski Lab (16) Apply Podgorski Lab filter
- Reiser Lab (49) Apply Reiser Lab filter
- Riddiford Lab (44) Apply Riddiford Lab filter
- Romani Lab (40) Apply Romani Lab filter
- Rubin Lab (139) Apply Rubin Lab filter
- Saalfeld Lab (60) Apply Saalfeld Lab filter
- Satou Lab (16) Apply Satou Lab filter
- Scheffer Lab (36) Apply Scheffer Lab filter
- Schreiter Lab (62) Apply Schreiter Lab filter
- Sgro Lab (20) Apply Sgro Lab filter
- Shroff Lab (23) Apply Shroff Lab filter
- Simpson Lab (23) Apply Simpson Lab filter
- Singer Lab (80) Apply Singer Lab filter
- Spruston Lab (91) Apply Spruston Lab filter
- Stern Lab (152) Apply Stern Lab filter
- Sternson Lab (54) Apply Sternson Lab filter
- Stringer Lab (29) Apply Stringer Lab filter
- Svoboda Lab (135) Apply Svoboda Lab filter
- Tebo Lab (31) Apply Tebo Lab filter
- Tervo Lab (9) Apply Tervo Lab filter
- Tillberg Lab (17) Apply Tillberg Lab filter
- Tjian Lab (64) Apply Tjian Lab filter
- Truman Lab (88) Apply Truman Lab filter
- Turaga Lab (46) Apply Turaga Lab filter
- Turner Lab (35) Apply Turner Lab filter
- Vale Lab (6) Apply Vale Lab filter
- Voigts Lab (2) Apply Voigts Lab filter
- Wang (Meng) Lab (9) Apply Wang (Meng) Lab filter
- Wang (Shaohe) Lab (24) Apply Wang (Shaohe) Lab filter
- Wu Lab (9) Apply Wu Lab filter
- Zlatic Lab (28) Apply Zlatic Lab filter
- Zuker Lab (25) Apply Zuker Lab filter
Associated Project Team
- CellMap (5) Apply CellMap filter
- COSEM (3) Apply COSEM filter
- Fly Descending Interneuron (10) Apply Fly Descending Interneuron filter
- Fly Functional Connectome (14) Apply Fly Functional Connectome filter
- Fly Olympiad (5) Apply Fly Olympiad filter
- FlyEM (51) Apply FlyEM filter
- FlyLight (46) Apply FlyLight filter
- GENIE (40) Apply GENIE filter
- Integrative Imaging (1) Apply Integrative Imaging filter
- Larval Olympiad (2) Apply Larval Olympiad filter
- MouseLight (16) Apply MouseLight filter
- NeuroSeq (1) Apply NeuroSeq filter
- ThalamoSeq (1) Apply ThalamoSeq filter
- Tool Translation Team (T3) (24) Apply Tool Translation Team (T3) filter
- Transcription Imaging (49) Apply Transcription Imaging filter
Publication Date
- 2024 (141) Apply 2024 filter
- 2023 (175) Apply 2023 filter
- 2022 (192) Apply 2022 filter
- 2021 (193) Apply 2021 filter
- 2020 (196) Apply 2020 filter
- 2019 (202) Apply 2019 filter
- 2018 (232) Apply 2018 filter
- 2017 (217) Apply 2017 filter
- 2016 (209) Apply 2016 filter
- 2015 (252) Apply 2015 filter
- 2014 (236) Apply 2014 filter
- 2013 (194) Apply 2013 filter
- 2012 (190) Apply 2012 filter
- 2011 (190) Apply 2011 filter
- 2010 (161) Apply 2010 filter
- 2009 (158) Apply 2009 filter
- 2008 (140) Apply 2008 filter
- 2007 (106) Apply 2007 filter
- 2006 (92) Apply 2006 filter
- 2005 (67) Apply 2005 filter
- 2004 (57) Apply 2004 filter
- 2003 (58) Apply 2003 filter
- 2002 (39) Apply 2002 filter
- 2001 (28) Apply 2001 filter
- 2000 (29) Apply 2000 filter
- 1999 (14) Apply 1999 filter
- 1998 (18) Apply 1998 filter
- 1997 (16) Apply 1997 filter
- 1996 (10) Apply 1996 filter
- 1995 (18) Apply 1995 filter
- 1994 (12) Apply 1994 filter
- 1993 (10) Apply 1993 filter
- 1992 (6) Apply 1992 filter
- 1991 (11) Apply 1991 filter
- 1990 (11) Apply 1990 filter
- 1989 (6) Apply 1989 filter
- 1988 (1) Apply 1988 filter
- 1987 (7) Apply 1987 filter
- 1986 (4) Apply 1986 filter
- 1985 (5) Apply 1985 filter
- 1984 (2) Apply 1984 filter
- 1983 (2) Apply 1983 filter
- 1982 (3) Apply 1982 filter
- 1981 (3) Apply 1981 filter
- 1980 (1) Apply 1980 filter
- 1979 (1) Apply 1979 filter
- 1976 (2) Apply 1976 filter
- 1973 (1) Apply 1973 filter
- 1970 (1) Apply 1970 filter
- 1967 (1) Apply 1967 filter
Type of Publication
3920 Publications
Showing 2221-2230 of 3920 resultsHealthy mitochondria are critical for reproduction. During aging, both reproductive fitness and mitochondrial homeostasis decline. Mitochondrial metabolism and dynamics are key factors in supporting mitochondrial homeostasis. However, how they are coupled to control reproductive health remains unclear. We report that mitochondrial GTP metabolism acts through mitochondrial dynamics factors to regulate reproductive aging. We discovered that germline-only inactivation of GTP- but not ATP-specific succinyl-CoA synthetase (SCS), promotes reproductive longevity in Caenorhabditis elegans. We further revealed an age-associated increase in mitochondrial clustering surrounding oocyte nuclei, which is attenuated by the GTP-specific SCS inactivation. Germline-only induction of mitochondrial fission factors sufficiently promotes mitochondrial dispersion and reproductive longevity. Moreover, we discovered that bacterial inputs affect mitochondrial GTP and dynamics factors to modulate reproductive aging. These results demonstrate the significance of mitochondrial GTP metabolism in regulating oocyte mitochondrial homeostasis and reproductive longevity and reveal mitochondrial fission induction as an effective strategy to improve reproductive health.
Mitochondrial DNA (mtDNA) is maintained within nucleoprotein complexes known as nucleoids. These structures are highly condensed by the DNA packaging protein, mitochondrial Transcription Factor A (TFAM). Nucleoids also include RNA, RNA:DNA hybrids, and are associated with proteins involved with RNA processing and mitochondrial ribosome biogenesis. Here we characterize the ability of TFAM to bind various RNA containing substrates in order to determine their role in TFAM distribution and function within the nucleoid. We find that TFAM binds to RNA-containing 4-way junctions but does not bind appreciably to RNA hairpins, internal loops, or linear RNA:DNA hybrids. Therefore the RNA within nucleoids largely excludes TFAM, and its distribution is not grossly altered with removal of RNA. Within the cell, TFAM binds to mitochondrial tRNAs, consistent with our RNA 4-way junction data. Kinetic binding assays and RNase-insensitive TFAM distribution indicate that DNA remains the preferred substrate within the nucleoid. However, TFAM binds to tRNA with nanomolar affinity and these complexes are not rare. TFAM-immunoprecipitated tRNAs have processed ends, suggesting that binding is not specific to RNA precursors. The amount of each immunoprecipitated tRNA is not well correlated with tRNA celluar abundance, indicating unequal TFAM binding preferences. TFAM-mt-tRNA interaction suggests potentially new functions for this protein.
The regulation of mitochondrial DNA (mtDNA) expression is crucial for mitochondrial biogenesis during development and differentiation. We have disrupted the mouse gene for mitochondrial transcription factor A (Tfam; formerly known as m-mtTFA) by gene targetting of loxP-sites followed by cre-mediated excision in vivo. Heterozygous knockout mice exhibit reduced mtDNA copy number and respiratory chain deficiency in heart. Homozygous knockout embryos exhibit a severe mtDNA depletion with abolished oxidative phosphorylation. Mutant embryos proceed through implantation and gastrulation, but die prior to embryonic day (E)10.5. Thus, Tfam is the first mammalian protein demonstrated to regulate mtDNA copy number in vivo and is essential for mitochondrial biogenesis and embryonic development.
First identified in dividing cells as revolving clusters of actin filaments, these are now understood as mitochondrially-associated actin waves that are active throughout the cell cycle. These waves are formed from the polymerization of actin onto a subset of mitochondria. Within minutes, this F-actin depolymerizes while newly formed actin filaments assemble onto neighboring mitochondria. In interphase, actin waves locally fragment the mitochondrial network, enhancing mitochondrial content mixing to maintain organelle homeostasis. In dividing cells actin waves spatially mix mitochondria in the mother cell to ensure equitable partitioning of these organelles between daughter cells. Progress has been made in understanding the consequences of actin cycling as well as the underlying molecular mechanisms, but many questions remain, and here we review these elements. Also, we draw parallels between mitochondrially-associated actin cycling and cortical actin waves. These dynamic systems highlight the remarkable plasticity of the actin cytoskeleton.
Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. In this paper we present MLlib, Spark’s open-source distributed machine learning library. MLlib provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives. Shipped with Spark, MLlib supports several languages and provides a high-level API that leverages Spark’s rich ecosystem to simplify the development of end-to-end machine learning pipelines. MLlib has experienced a rapid growth due to its vibrant open-source community of over 140 contributors, and includes extensive documentation to support further growth and to let users quickly get up to speed.
A formalism is given in which the optical field generated by a near-field optical aperture is described as an analytic expansion over a complete set of optical modes. This vectoral solution preserves the divergent behavior of the near field and the dipolar nature of the far field. Numerical calculation of the fields requires only evaluation of a well behaved, one-dimensional integral. The formalism is directly applicable to experiments in near-field scanning optical microscopy when relatively flat samples are evaluated.
We present a method for fully automatic segmentation of the bones and cartilages of the human knee from MRI data. Based on statistical shape models and graph-based optimization, first the femoral and tibial bone surfaces are reconstructed. Starting from the bone sur- faces the cartilages are segmented simultaneously with a multi object technique using prior knowledge on the variation of cartilage thickness. We validate our method on 40 clinical MRI datasets acquired before knee replacement.
The Honeybee Brain Atlas serves as 3D database and communicative platform to accumulate structural data, i.e. reconstructed neurons, derived from confocal scans (Brandt et al., 2005) (www.neurobiologie.fu-berlin.de/beebrain/) (1). Transforming neurons into the atlas requires manual segmentation of neuropils within confocal images, a time-consuming task requiring expertise in identifying biological structures which can result in different outcomes from various segmenters.
Population activity measurement by calcium imaging can be combined with cellular resolution optogenetic activity perturbations to enable the mapping of neural connectivity in vivo. This requires accurate inference of perturbed and unperturbed neural activity from calcium imaging measurements, which are noisy and indirect, and can also be contaminated by photostimulation artifacts. We have developed a new fully Bayesian approach to jointly inferring spiking activity and neural connectivity from in vivo all-optical perturbation experiments. In contrast to standard approaches that perform spike inference and analysis in two separate maximum-likelihood phases, our joint model is able to propagate uncertainty in spike inference to the inference of connectivity and vice versa. We use the framework of variational autoencoders to model spiking activity using discrete latent variables, low-dimensional latent common input, and sparse spike-and-slab generalized linear coupling between neurons. Additionally, we model two properties of the optogenetic perturbation: off-target photostimulation and photostimulation transients. Using this model, we were able to fit models on 30 minutes of data in just 10 minutes. We performed an all-optical circuit mapping experiment in primary visual cortex of the awake mouse, and use our approach to predict neural connectivity between excitatory neurons in layer 2/3. Predicted connectivity is sparse and consistent with known correlations with stimulus tuning, spontaneous correlation and distance.
Population activity measurement by calcium imaging can be combined with cellular resolution optogenetic activity perturbations to enable the mapping of neural connectivity in vivo. This requires accurate inference of perturbed and unperturbed neural activity from calcium imaging measurements, which are noisy and indirect, and can also be contaminated by photostimulation artifacts. We have developed a new fully Bayesian approach to jointly inferring spiking activity and neural connectivity from in vivo all-optical perturbation experiments. In contrast to standard approaches that perform spike inference and analysis in two separate maximum-likelihood phases, our joint model is able to propagate uncertainty in spike inference to the inference of connectivity and vice versa. We use the framework of variational autoencoders to model spiking activity using discrete latent variables, low-dimensional latent common input, and sparse spike-and-slab generalized linear coupling between neurons. Additionally, we model two properties of the optogenetic perturbation: off-target photostimulation and photostimulation transients. Our joint model includes at least two sets of discrete random variables; to avoid the dramatic slowdown typically caused by being unable to differentiate such variables, we introduce two strategies that have not, to our knowledge, been used with variational autoencoders. Using this model, we were able to fit models on 30 minutes of data in just 10 minutes. We performed an all-optical circuit mapping experiment in primary visual cortex of the awake mouse, and use our approach to predict neural connectivity between excitatory neurons in layer 2/3. Predicted connectivity is sparse and consistent with known correlations with stimulus tuning, spontaneous correlation and distance.