SEED Grant Winners 2013
Robert S. Haltiwanger, Department of Biochemistry and Cell Biology, SBU
Huilin Li (co-PI), Department of Biology, BNL
"Determination of Structures for Glycosyltransferases that Modify Notch"
The Notch receptors play essential roles in all stages of development, and defects in Notch signaling pathways lead to a wide variety of human diseases, some the result of excessive Notch activity and others the result of too little Notch activity. My laboratory has identified a number of carbohydrate modifications on the extracellular domain of Notch, and we have demonstrated that Notch activity can be modulated by altering the structure of these glycans. We have recently identified all of the enzymes (glycosyltransferases) responsible for addition of an O-glucose trisaccharide (Xylose-Xylose-Glucose-O-Notch) to Notch. Genetic studies reveal that elimination of the enzyme adding glucose reduces Notch activity, while elimination of the enzymes adding xylose enhances Notch activity. Thus, inhibitors to these enzymes could be used to increase or decrease Notch activity and may serve as therapeutics to treat Notchrelated diseases. These studies are designed to determine the three dimensional structure of these enzymes using X-ray crystallography in collaboration with Dr. Huilin Li at BNL. Determination of the structures will provide essential preliminary data for an NIH grant proposal to fund future studies on identification and evaluation of inhibitors.
Arnout van de Rijt, Department of Sociology, SBU
Robert James Harrison (co-PI), Computational Science Center, BNL
"Computational Modeling of Success-breeds-success Dynamics in Big Data on Crowd Funding"
While there are some physical processes, people, ventures, and campaigns that accumulate long strings of positive outcomes or successes, most others fail repeatedly, never achieving major breakthroughs. One explanation is that variation along broad dimensions of fitness – such as innate talent, privilege, and industriousness – equip individuals with unequal a priori chances that are gradually revealed through differential achievement. A competing hypothesis states that ‘success breeds success’. The ultimate success of select persons and projects may thus be born out of small, random initial advantages that grow ever larger through positive feedback. Such ‘cumulative advantage’ has been argued to produce significant, and arbitrary, inequality in many domains of human achievement.
We constructed an experimental design in which we explicitly control the allocation of success. In this setup we bestow early successes upon randomly selected members of a population, thereby ensuring that recipients and non-recipients have equal success expectations prior to intervention. In order to allow a general test of cumulative advantage in multiple contexts, we deployed this design in three naturally occurring systems, representing distinct forms of success – financial gain, social status, and social support.
While confirming the ‘success breeds success’ thesis, the experiments show that when the number of past successes is further increased from just one donor to two donors, this does not further increase the probability of post-treatment funding. Similarly, when the funding amount is increased from 1% to 10% no significant increase in subsequent funding is found. These findings suggest that cumulative advantage processes exhibit strong concavity whereby the difference between zero and minimal past success has much more discriminating power than the difference between varying levels of past success. That is, “success breeds less and less success”.
This finding poses a problem for classic models of positive feedback which assume a linear relationship and give rise to heavy-tailed frequency distributions of success. It also raises the question what happens to the emergent success distributions when a sub-linear relationship that is consistent with the experimental data is assumed. Will they still exhibit power-law behavior?
Neelima Sehgal, Department of Physics and Astronomy, SBU
Anze Slosar (co-PI), Cosmology and Astrophysics Group, BNL
Erin Sheldon, Cosmology and Astrophysics Group, BNL
"Development of Robust Shear Estimators to Realize the Promise of Weak Lensing for Cosmology with the Large Synoptic Survey Telescope"
The nature of Dark Matter and Dark Energy are two of the most pressing mysteries in the field of Cosmology. One promising technique to shed light on these mysteries is to measure the weak gravitational lensing signal to map out the growth of structure in the Universe. Weak gravitational lensing is the bending of light from background galaxies by intervening matter between the sources and us. This effect causes a correlated distortion in the shapes of the background galaxies, which can be measured to reconstruct the amount and distribution of intervening matter (both dark and luminous). The Large Synoptic Survey Telescope (LSST), a half-billion-dollar facility, is being built this decade to measure this weak-lensing signal. Brookhaven National Laboratory (BNL) is co-lead of the LSST camera development. However, while weak lensing is extremely promising, there are still some theoretical and technical challenges in extracting the signal with an unprecedented instrument such as LSST. These challenges include 1.) blurring of galaxy images by the LSST instrument optics and the Earth’s atmosphere, 2.) blending of galaxy images due to the unprecedented depth of LSST, and 3.) developing fast and robust algorithms for processing the unprecedented quantity of data LSST will collect. The goal of this proposal is to solve these challenges by developing robust, efficient algorithms to accurately extract the weak-lensing signal. The SEED grant will be used to fund a Stony Brook University (SBU) graduate student working on this project jointly with Prof. Neelima Sehgal (SBU) and Dr. Anže Slosar (BNL). This proposal will thus join the efforts of Cosmologists at SBU and BNL, and will further enhance their collaboration in this shared research mission.
Dmitri Tsybychev, Department of Physics and Astronomy, SBU
Andrei Nomerotski (co-PI), Department of Physics, BNL
"Dark Energy Investigations with LSST: Instrumentation Aspects of Weak Lensing Sensitivity"
TWe propose an ambitious research project aimed at bringing together groups in BNL and SBU to investigate instrumentation aspects of sensitivity of Large Synoptic Survey Telescope (LSST) to properties of Dark Energy, which is arguably the most intriguing puzzle of modern physics.
LSST is the next generation survey telescope, which will lead the exploration of the Dark Energy in the next decade. LSST has been designed to provide a deep six-band astronomical imaging survey of more than 18000 square degrees of the southern sky. Using active optics, the 8.4-meter aperture and 9.6 square degree field-of-view of the telescope will allow approximately 1000 visits to each patch of sky in ten years with a final depth reaching magnitude of r ∼ 27.5. The LSST project will deliver fully calibrated science quality images and catalogs to the US public with no proprietary period and will be issuing real time alerts for fast transient processes every night.
One of the most critical probes of Dark Energy that the LSST will revolutionize is weak gravitational lensing. The key observable in weak gravitational lensing is the shape of distant galaxies weakly distorted by foreground masses. Because each galaxy is sheared only by a small amount, the lensing signal must be extracted from an average over a population of galaxies. The LSST high statistics observations will also enable other important techniques to study Dark Energy and Dark Matter, such as supernovae and galaxy clusters.
Through testing of the LSST CCD sensors and simulation studies we expect to evaluate contribution of various sensor effects to the LSST sensitivity to the weak lensing signal. In particular, we will investigate the instrument astrometric precision and systematic distortions of the galaxy shapes due to charge transport effects in the sensor; and will develop software tools to describe these effects in simulations. We believe that this will be the first detailed investigation of the subject.
Lei Zuo, Department of Mechanical Engineering, SBU
Thomas Butcher (co-PI), Energy Resources Division, BNL
Xin Wang (co-PI), Department of Electrical and Computer Engineering, BNL
Aristotelis Babajimopoulos, Department of Mechanical Engineering, SBU
Malcolm Bowman, School of Marine and Atmospheric Sciences, SBU
"Ocean Wave Energy Harvesting"
The potential for electricity generation from ocean wave energy in the US is estimated to be 64% of the total electricity generated from all sources in 2010. Over 53% of the US population lives within 50 miles of the coast (NOAA), so ocean waves offer ready opportunity for harvesting power. However, wave energy harvesting is still in its infancy worldwide. The objective is to develop an innovative technology of ocean wave energy harvesting with advantage of high efficiency and reliability. We hope to create a solution to addressing the fundamental challenge of wave energy harvesting through converting the irregular up-and-down motion of the ocean waves into unidirectional rotation of the electrical generator. Starting from this effort of this Seed Grant, we plan to bring in external funding from DOE, NSF, and Navy Research Office.