citations everywhere but not a drop to drink

more papers, not otherwised discussed on this blog. Brandeis authors in boldface.

  • Strahler J, Kirschbaum C, Rohleder N. Association of blood pressure and antihypertensive drugs with diurnal alpha-amylase activity. Int J Psychophysiol. 2011;81(1):31-7.
  • Carr M, Devadoss SL, Forcey S. Pseudograph associahedra. J Comb Theory A. 2011;118(7):2035-55.
  • Naculich SG, Schnitzer HJ. Eikonal methods applied to gravitational scattering amplitudes. J High Energy Phys. 2011(5).
  • Vizcarra CL, Kreutz B, Rodal AA, Toms AV, Lu J, Zheng W, Quinlan ME, Eck MJ. Structure and function of the interacting domains of Spire and Fmn-family formins. Proc Natl Acad Sci U S A. 2011;108(29):11884-9.
  • Aaltonen et al. (CDF Collaboration). Search for a Very Light CP-Odd Higgs Boson in Top Quark Decays from p(p)over-bar Collisions at root s=1.96 TeV. Physical Review Letters. 2011;107(3).
  • Wu Y, Singh RP, Deng L. Asymmetric Olefin Isomerization of Butenolides via Proton Transfer Catalysis by an Organic Molecule. J Am Chem Soc. 2011.
  • Kim YS, Ryu YB, Curtis-Long MJ, Yuk HJ, Cho JK, Kim JY, Kim KD, Lee WS, Park KH. Flavanones and rotenoids from the roots of Amorpha fruticosa L. that inhibit bacterial neuraminidase. Food Chem Toxicol. 2011;49(8):1849-56.
  • Broderick R, Fishman L, Kleinbock D. Schmidt’s game, fractals, and orbits of toral endomorphisms. Ergod Theor Dyn Syst. 2011;31:1095-107.
  • Lisman, J., Grace, A.A., and Duzel, E. (2011). A neoHebbian framework for episodic memory; role of dopamine-dependent late LTP. Trends Neurosci. (online)
  • Reeves, D., Cheveralls, K., and Kondev, J. (2011). Regulation of biochemical reaction rates by flexible tethers. Phys Rev E 84.
  • Bell, M.R., Roberts, D.H., and Wardle, J.F.C. (2011). Structure and Magnetic Fields in the Precessing Jet System Ss 433. III. Evolution of the Intrinsic Brightness of the Jets from a Deep Multi-Epoch Very Large Array Campaign. Astrophys J 736.
  • Krogman, J.P., Foxman, B.M., and Thomas, C.M. (2011). Activation of CO(2) by a Heterobimetallic Zr/Co Complex. J Am Chem Soc.
  • Friedman, E.J., Wang, H.X., Jiang, K., Perovic, I., Deshpande, A., Pochapsky, T.C., Temple, B.R., Hicks, S.N., Harden, T.K., and Jones, A.M. (2011). Acireductone Dioxygenase 1 (ARD1) Is an Effector of the Heterotrimeric G Protein β Subunit in Arabidopsis. J Biol Chem 286, 30107-30118.
  • Lahiri, S., Shen, K., Klein, M., Tang, A., Kane, E., Gershow, M., Garrity, P., and Samuel, A.D. (2011). Two alternating motor programs drive navigation in Drosophila larva. PLoS One 6, e23180.
  • Fritz-Laylin, L.K., Ginger, M.L., Walsh, C., Dawson, S.C., and Fulton, C. (2011). The Naegleria genome: a free-living microbial eukaryote lends unique insights into core eukaryotic cell biology. Research in microbiology 162, 607-618.
  • Yan, Q., Xin, Y., Zhou, R., Yin, Y., and Yuan, J. (2011). Light-controlled smart nanotubes based on the orthogonal assembly of two homopolymers. Chem Commun (Camb) 47, 9594-9596.
  • Peelle, J.E., Troiani, V., Grossman, M., and Wingfield, A. (2011). Hearing loss in older adults affects neural systems supporting speech comprehension. J Neurosci 31, 12638-12643.
  • Graziano, B.R., Dupage, A.G., Michelot, A., Breitsprecher, D., Moseley, J.B., Sagot, I., Blanchoin, L., and Goode, B.L. (2011). Mechanism and cellular function of Bud6 as an actin nucleation-promoting factor. Mol Biol Cell.
  • Park, D., Hadzic, T., Yin, P., Rusch, J., Abruzzi, K., Rosbash, M., Skeath, J.B., Panda, S., Sweedler, J.V., and Taghert, P.H. (2011). Molecular Organization of Drosophila Neuroendocrine Cells by Dimmed. Curr Biol.
  • Helminck, A.G., and Schwarz, G.W. (2011). On generalized Cartan subspaces. Transform Groups 16, 783-805.

New Computational Neuroscience Training Program

The National Institute on Drug Abuse has recently awarded Brandeis a pair of linked training grants to support student training in computational neuroscience. The program is unusual for NIH training grants in supporting both undergraduate and graduate student research. Funding for the program is approximately $1.8 million over the next five years.

Modeling a biconditional discrimination task, see Bourjaily & Miller, 2011

The program, directed by Professor Eve Marder, will support six Ph.D. students and six undergraduates (juniors or seniors) each year. Students must be working to fulfill an appropriate degree in the Division of Science at Brandeis, and must engaged in research in computational neuroscience. Said Marder,

We are extremely pleased to have received this grant, as it continues a long Brandeis tradition of integrating theory and experimental work in the neurosciences.  We are especially pleased to have the undergraduate component, as we know there are students who are interested in learning how to employ rigorous quantitative methods to study the brain.

Eligibility and program requirements to participate in the program will soon be available at the training grant website.

Some recent publications:

Bourjaily, M.A., and Miller, P. (2011). Synaptic plasticity and connectivity requirements to produce stimulus-pair specific responses in recurrent networks of spiking neurons. Plos Comput Biol 7, e1001091.

Piquado, T., Cousins, K.A., Wingfield, A., and Miller, P. (2010). Effects of degraded sensory input on memory for speech: Behavioral data and a test of biologically constrained computational models. Brain Res 1365, 48-65.

Berkes, P., Orban, G., Lengyel, M., and Fiser, J. (2011). Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science 331, 83-87.

Grashow, R., Brookings, T., and Marder, E. (2010). Compensation for variable intrinsic neuronal excitability by circuit-synaptic interactions. J Neurosci 30, 9145-9156.

Geometry and Dynamics IGERT Awarded

Brandeis has just been awarded an NSF Integrative Graduate Education and Research Traineeship (IGERT) grant in the mathematical sciences.  The grant, titled Geometry and Dynamics: integrated education in the mathematical sciences, is designed to foster interdisciplinary research and education by and for graduate students across the mathematical and theoretical sciences, including chemistry, economics, mathematics, neuroscience, and physics.  It is structured around a number of themes common to these disciplines: complex dynamical systems, stochastic processes, quantum and statistical field theory; and geometry and topology. We believe that it is the first IGERT awarded for the theoretical (as opposed to laboratory) sciences, and are very excited about what we believe to be a highly novel program which will cement existing interdepartmental relationships and encourage exciting new collaborations in the mathematical sciences, including collaborations between the natural sciences and the International Business School (IBS).

The resolution of a singularity that develops along Ricci flow, understood mathematically by Grigori Perelman.  If the red manifold represents the target space of a string, it is conjectured that the corresponding two-dimensonal field theory describing the string undergoes confinement and develops a mass gap for the degrees of freedom corresponding to the singular regime.

The award, for $2,867,668 spread out over five years, provides funds for graduate student stipends, travel, seminar speakers, and interdisciplinary course development.  It contains activities and research opportunities in partnership with the New England Complex Systems Institute (NECSI) in Cambridge, MA.  It also provides opportunities for research internships at the International Center for the Theoretical Sciences in Bangalore.

The PIs on the grant are: Bulbul Chakraborty (Physics); Albion Lawrence (Physics: lead PI); Blake LeBaron (IBS); Paul Miller (Neuroscience); and Daniel Ruberman (Mathematics).  There are 11 additional affiliated Brandeis faculty across biology, chemistry, mathematics, neuroscience, physics, and psychology.  Contact Albion Lawrence ( for more information about the program.

Arrays of repulsively coupled Kuramoto oscillators on a triangular lattice organize into domains with opposite helicities in which phases of any three neighboring oscillators either increase or decrease in a given direction. Fig. (a) illustrates these two helicities in which cyan, ma- genta and blue vary in opposite directions. In Fig. (b), white and green regions represent domains of opposite helicities. The red regions indicate the frequency entrained oscillators, which are predominantly seen in the interior of the domains.

Admission to the program is handled through the Ph.D programs in the various disciplines:

Who pulls the strings in actin cable assembly?

When large structures are built inside of cells, how are their dimensions determined? Are cues received that tell the structure to keep growing, or to slow down, or to stop growing altogether? A recent study published in Developmental Cell by a team led by Molecular and Cell Biology PhD student Melissa Chesarone-Cataldo and Professor of Biology Bruce Goode begins to address these questions by focusing on cytoskeletal structures called yeast actin cables.

Actin cables serve as essential railways for myosin-dependent transport of vesicles, organelles and other cargo, required for yeast cells to grow asymmetrically and produce a daughter cell. Cables are assembled at one end of the mother cell and run the length of the entire cell, but no longer, or else they would hit the back of the cell, buckle and misdirect transport. So how does an actin cable know how long to grow? How are other properties of the cable, such as its thickness and mechanical rigidity determined, and how important are these properties for cable function in vivo?

Actin cables are assembled at the bud neck by the formin protein Bnr1, and rapidly extend into the mother cell at a rate of 0.5-1 µm/s. At this speed, the tip of the actin cable reaches the back end of the cell in about 5-10 seconds. Each cable consists of many shorter overlapping pieces (individual actin filaments) that are stitched or cross-linked together to form a single cable, and cables continuously stream out of the bud neck due to the robust actin assembly activity of Bnr1. Chesarone-Cataldo et al. asked the question, “what mechanism prevents the cables from colliding with the back of the cell and overgrowing?” In doing so, they identified a novel actin cable ‘length sensing’ feedback loop, dependent on the myosin-passenger protein Smy1.

Using live-cell imaging, they showed that Smy1 molecules are transported by myosin from the mother cell to the bud neck, where they pause to interact with the formin Bnr1. Purified Smy1 attenuated Bnr1 activity by slowing down the rate of actin filament elongation. When the SMY1 gene was deleted, cables grew too long, hit the rear of the cell and buckled (see image, right). In addition, the mutant cables abnormally fluctuated in thickness and were kinked, impairing transport of myosin and its cargoes.

The authors propose that a negative feedback loop controls actin cable length. In their model, the cargo (Smy1 in this case) communicates with the machinery that is making the cable (the formin Bnr1), as a means of sensing ‘railway’ length. The longer the railway grows, the more passengers it picks up, and the more transient inhibitory pulses the formin receives. As such, longer cables are selectively attenuated, while shorter cables are allowed to grow rapidly. This negative feedback loop allows yeast cells to tailor actin cable length to the dimensions of the cell and to the needs of its myosin-based transport system.

Current work in the Goode lab is aimed at testing many of the mechanistic predictions of the model above and understanding how Smy1 functions in coordination with other known regulators of Bnr1, all simultaneously present in a cell, to produce actin cables with proper architecture and function. In addition, experiments are underway to find out whether related mechanisms are used to control formins in mammalian cells and to understand the physiological consequences of disrupting those mechanisms.

Chesarone-Cataldo M, Guérin C, Yu JH, Wedlich-Söldner R, Blanchoin L, Goode BL. The Myosin passenger protein Smy1 controls actin cable structure and dynamics by acting as a formin damper. Dev Cell. 2011 Aug 16;21(2):217-30.

Turrigiano Receives HFSP 2012 Nakasone Award

The Human Frontier Science Program Organization (HFSPO) has announced that the 2012 HFSP Nakasone Award has been conferred upon Professor of Biology Gina Turrigiano for introducing the concept of “synaptic scaling”.

Gina is the third recipient of the HFSP Nakasone Award. This award, first given in 2010, honours the vision of former Prime Minister of Japan Yasuhiro Nakasone for his efforts to launch a program of support for international collaboration and to foster early career scientists in a global context. The HFSP Nakasone Award is designed to honour scientists who have undertaken frontier-moving research in biology, encompassing conceptual, experimental or technological breakthroughs. Awardees receive an unrestricted research grant of USD 10,000, a medal and a personalised certificate. The award ceremony will be held at the annual meeting of HFSP awardees to be held in the Republic of Korea in July 2012, where Gina will give the HFSP Nakasone Lecture at the annual meeting of HFSP awardees to be held in the Republic of Korea in July 2012.

From the press release:

The concept of “synaptic scaling” was introduced to resolve an apparent paradox: how can neurons and neural circuits maintain both stability and flexibility? The number and strength of synapses shows major changes during development and in learning and memory. Such changes could potentially lead to massive changes in neuronal output that could have deleterious effects on the stability of neuronal networks and memory storage. Homeostatic mechanisms are therefore required to control neuronal output within certain limits while still maintaining the relative weights of synaptic inputs that underlie information storage. The work of Gina Turrigiano’s laboratory has shown that neurons can “tune” themselves by responding to an increase in firing rate by scaling down all excitatory synaptic strengths and vice versa. Such global changes in synaptic input limits the rate of firing (output) while maintaining changes in the relative strengths of individual synapses (input). She continues to explore the mechanisms that underlie such scaling phenomena and their function in vivo using a variety of molecular, electrophysiological, imaging and computational approaches.

A large multistory atrium curates movement

Seen on the web, an architectural appreciation of the Shapiro Science Center:

A large multistory atrium curates movement through the building. Conceived of as a river, the atrium exists as a linear element that allows for quick transit through and into the building while remaining isolated from the sensitive lab spaces within the structure.

The web also reveals a panoramic view of the atrium.

How many neurons does it take to stay cool?

The worm (nematode) C. elegans is a nice model system for studying neuroscience, combining

  • genetic tools that allow genes to be turned on or off, often on a per cell basis, in the whole organism,
  • tools like laser or genetic ablation that allow individual, identified cells to be selectively eliminated,
  • robust behaviors that can be readily measured, and
  • a well defined nervous system consisting of 302 neurons, each of which can be identified, and whoseanatomical connectivity has been established.

In a paper appearing this month in Journal of Neuroscience, Molecular and Cell Biology grad student Matthew Beverly, undergrad Sriram Anbil, and Professor of Biology Piali Sengupta examined the contribution of sensory neurons to controlling thermotaxis in C. elegans. Worms develop a memory of the temperature at which they have been cultivated, and display a robust behavior in which worms placed on a temperature gradient at temperatures higher than their cultivation temperature will crawl back towards colder temperature (negative thermotaxis – see movie). The behavior depends on TAX-4, a channel protein expressed in a subset of the sensory neurons. In this study, the Brandeis researchers asked the question “how many and which of the sensory neurons are required for the worms to perform negative thermotaxis, and are the required sensory neurons the same regardless of the temperature range examined?” (or, in my paraphrase, “how many and which neurons does it take to stay cool?”)

Worm head, showing expression of the calcim indicator GCaMP in ASI and AWA neurons (used in calcium imaging experiments)

As it turns out, the answer is complicated (and readers are encouraged to read the paper). The researchers found that in addition to the previously known thermosensory neurons AFD and AWC, the ASI neurons previously known to be involved in chemosensation play a significant role in regulating negative thermotaxis. Interestingly, the circuits used seem to be degenerate; under one condition, for example, a particular combination of AFD, AWC or ASI is necessary to generate the behavior, although at other conditions, a different combination is required to generate the same behavior.. And only a couple of degrees Celsius makes a difference — the circuit required for negative thermotaxis on a gradient centered at 8oC above the cultivation temperature is different from a gradient centered at 6oC above.

These and other results taken together suggest that even in the worm, a complex circuit has evolved to control crawling behaviors to cope with temperature changes, and that having degeneracy in the underlying circuits may be a common feature that ensures that behaviors crucial to survival are maintained in a variety of environmental conditions..

Beverly M, Anbil S, Sengupta P. Degeneracy and Neuromodulation among Thermosensory Neurons Contribute to Robust Thermosensory Behaviors in Caenorhabditis elegans. J Neurosci. 2011;31(32):11718-27.

Pay attention!

“Pay attention!” That is often very good advice, but sometimes the advice is hard to obey.   The brain’s limited attentional resources can be overwhelmed when attention has to be distributed among multiple objects.  And the challenge is even greater when the objects are moving.  For example, imagine that you’re driving on Route 128 at rush hour.  You must attend not only to your own car’s path, but also to the whims and surprising behaviors of the cars all around you.   Working in my lab, Heather Sternshein and Yigal Agam, two PhD students in the Neuroscience Program, developed a novel electroencephalographic (EEG) technique to study how selective attention is apportioned in a task that can be described as “Route 128-on Sterioids.”   We were especially interested in the neural correlates of failures of attention, the kind of failure that, on the road, might have serious consequences.

Subjects in our experiment watched as ten identical black discs moved about randomly on a computer display for ten seconds.  The hard part was to keep track the entire time of particular, pre-designated target discs –either three, four or five.  Because all ten moving discs were identical, there were no physical features to distinguish target from non-target discs.  At the end of eight seconds, all discs came to standstill, and a subject tried to identify the discs that he or she had been tracking.  The task required attentive tracking of a subset of identical multiple moving objects, something even more challenging than navigating Route 128 at rush hour.

Every once in a while during the eight-second tracking period, one of the ten discs flashed brightly for 100 msec.  Sometimes, the flashed disc was a target disc, that is, one the subject was trying to track; sometimes the flashed disc was a non-target disc, that is, one that the subject could be ignoring.   The flash evoked a response in the subject’s brain, and our EEG system picked up that response from the subject’s scalp.  Knowing that the evoked response would be larger if the flash were delivered to an object that was being attended,  we used responses to target and non-targets as an index of how attention was distributed among the multiple moving objects.  We focused our analysis on electrodes located over occipital and parietal lobes, toward the brain’s posterior.

As expected, the relative sizes of responses to the two kinds of stimuli differed: on average, flashes on target discs evoked larger responses than flashes on non-target discs.  This difference confirmed that on average subjects were paying more attention to targets than to non-targets. But as the number of discs that had to be tracked increased —from three to four to five– subjects found the task increasingly harder, and made more errors when they had to identify the discs that they had been trying to track.  The EEG revealed the neural correlate of these failures of attention.  The difference between evoked responses to flashed targets and flashed non-targets decreased as the number of targets increased.  This shrinking difference between the two sets of neural responses could explain the systematic increase in errors as the the number of targets increased.  As additional items have to be kept track of, it becomes harder for subjects to apportion attentional resources in a way that preserves a sufficient advantage for targets over non-targets.  As a result, subjects make more errors –mistaking non-targets for targets.

We plan to adapt this basic experimental strategy to study the neural basis of attention in various groups whose performance on our task is likely to abnormal:  older adults (who show impaired behavioral performance) and habitual video-game players (who show far-better-than normal performance).  Yigal Agam is now at MGH’s Martinos Center; Heather Sternshein is in the Department of Neurobiology, Harvard University.

Sternshein H, Agam Y, Sekuler R. EEG Correlates of Attentional Load during Multiple Object Tracking. PLoS One. 2011;6(7):e22660..

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