Publications

Understanding the origin and diversity of emission processes responsible for\nGamma-ray Bursts (GRBs) remains a pressing challenge. While prompt and\ncontemporaneous panchromatic observations have the potential to test\npredictions of the internal-external shock model, extensive multiband imaging\nhas been conducted for only a few GRBs. We present rich, early-time, multiband\ndatasets for two \\swift\\ events, GRB 110205A and GRB 110213A. The former shows\noptical emission since the early stages of the prompt phase, followed by the\nsteep rising in flux up to ~1000s after the burst ($t^{-\\alpha}$ with\n$\\alpha=-6.13 \\pm 0.75$). We discuss this feature in the context of the\nreverse-shock scenario and interpret the following single power-law decay as\nbeing forward-shock dominated. Polarization measurements, obtained with the\nRINGO2 instrument mounted on the Liverpool Telescope, also provide hints on the\nnature of the emitting ejecta. The latter event, instead, displays a very\npeculiar optical to near-infrared lightcurve, with two achromatic peaks. In\nthis case, while the first peak is probably due to the onset of the afterglow,\nwe interpret the second peak to be produced by newly injected material,\nsignifying a late-time activity of the central engine.\n

Misery is not miserly: Sadness increases the amount of money that decision makers give up to acquire a commodity. The present research investigated when and why the misery-is-not-miserly effect occurs. Drawing on William James's concept of the material self, we tested a model specifying relationships among sadness, self-focus, and the amount of money that decision makers spend. Consistent with our Jamesian hypothesis, results demonstrated that the misery-is-not-miserly effect occurs only when self-focus is high. That is, self-focus moderates the effect of sadness on spending. Moreover, mediational analyses revealed that, at sufficiently high levels, self-focus mediates (explains) the relationship between sadness and spending. Because the study used real commodities and real money, the results hold implications for everyday decisions, as well as implications for the development of theory. For example, economic theories of spending may benefit from incorporating psychological theories—specifically, theories of emotion and the self—into their models.

The novel polydentate ligand 1,4-bis(di(N-methylimidazol-2-yl)methyl)phthalazine, bimptz, has been synthesized and its coordination chemistry was investigated. Bimptz is neutral and contains a central phthalazine unit, to which two di-(N-methylimidazol-2-yl)methyl groups are attached in the 1,4-positions. This ligand therefore provides up to 6 donor sites for coordination to metal ions. A series of metal complexes of bimptz was prepared and their molecular structures were determined by X-ray diffraction. Upon reaction of bimptz with two equivalents of MnCl(2)·4H(2)O, CoCl(2)·6H(2)O and [Ru(dmso)(4)Cl(2)], the dinuclear complexes [Mn(2)(bimptz)(µ-Cl)(2)Cl(2)] (1), [Co(2)(bimptz)(CH(3)OH)(2)(µ-Cl)(2)](PF(6))(2) (3) and [Ru(2)(bimptz)(dmso)(2)(µ-Cl)(2)](PF(6))(2) (4), respectively, were isolated. The latter were found to have similar solid state structures with octahedrally coordinated metal centers bridged by the phthalazine unit and two chloro ligands. The cobalt and ruthenium complexes 3 and 4 were isolated as PF(6)(-) salts and contain neutral methanol and dmso ligands, respectively, at the terminal coordination sites of the metal centres. The mononuclear ruthenium complex [Ru(Hbimptz)(2)](PF(6))(4) (6) was obtained from the reaction of two equivalents bimptz with [Ru(dmso)(4)Cl(2)]. In complex 6, three donor sites per ligand molecule are used for coordination of the Ru(ii) center. In each bimptz ligand, one of the remaining, dangling N-methylimidazole rings is protonated and forms a hydrogen bond with the unprotonated N-methylimidazole ring of the other bimptz ligand.

We have recently completed a 64-night spectroscopic monitoring campaign at the Lick Observatory 3-m Shane telescope with the aim of measuring the masses of the black holes in 13 nearby (z < 0.05) Seyfert 1 galaxies with expected masses in the range ~10^6-10^7 M_sun. We present here the first results from this project -- the mass of the central black hole in Arp 151. Strong variability throughout the campaign led to an exceptionally clean Hbeta lag measurement in this object of 4.25(+0.68/-0.66) days in the observed frame. Coupled with the width of the Hbeta emission line in the variable spectrum, we determine a black hole mass of (7.1 +/- 1.2)x10^6 M_sun, assuming the Onken et al. normalization for reverberation-based virial masses. We also find velocity-resolved lag information within the Hbeta emission line which clearly shows infalling gas in the Hbeta-emitting region. Further detailed analysis may lead to a full model of the geometry and kinematics of broad line region gas around the central black hole in Arp 151.

No abstract is provided for this article.

A number of large-scale distributed Internet applications could potentially benefit from some level of knowledge about the relative proximity between its participating host nodes. For example, the performance of large overlay networks could be improved if the application-level connectivity between the nodes in these networks is congruent with the underlying IP-level topology. Similarly, in the case of replicated Web content, client nodes could use topological information in selecting one of multiple available servers. For such applications, one need not find the optimal solution in order to achieve significant practical benefits. Thus, these applications, and presumably others like them, do not require exact topological information and can instead use sufficiently informative hints about the relative positions of Internet hosts. In this paper, we present a binning scheme whereby nodes partition themselves into bins such that nodes that fall within a given bin are relatively close to one another in terms of network latency. Our binning strategy is simple (requiring minimal support from any measurement infrastructure), scalable (requiring no form of global knowledge, each node only needs knowledge of a small number of well-known landmark nodes) and completely distributed (requiring no communication or cooperation between the nodes being binned). We apply this binning strategy to the two applications mentioned above: overlay network construction and server selection. We test our binning strategy and its application using simulation and Internet measurement traces. Our results indicate that the performance of these applications can be significantly improved by even the rather coarse-grained knowledge of topology offered by our binning scheme.

A significant bottleneck in synthetic biology involves screening large genetically encoded libraries for desirable phenotypes such as chemical production. However, transcription factor-based biosensors can be leveraged to screen thousands of genetic designs for optimal chemical production in engineered microbes. In this study we characterize two glutarate sensing transcription factors (CsiR and GcdR) from <i>Pseudomonas putida</i>. The genomic contexts of <i>csiR</i> homologues were analyzed, and their DNA binding sites were bioinformatically predicted. Both CsiR and GcdR were purified and shown to bind upstream of their coding sequencing <i>in vitro</i>. CsiR was shown to dissociate from DNA <i>in vitro</i> when exogenous glutarate was added, confirming that it acts as a genetic repressor. Both transcription factors and cognate promoters were then cloned into broad host range vectors to create two glutarate biosensors. Their respective sensing performance features were characterized, and more sensitive derivatives of the GcdR biosensor were created by manipulating the expression of the transcription factor. Sensor vectors were then reintroduced into <i>P. putida</i> and evaluated for their ability to respond to glutarate and various lysine metabolites. Additionally, we developed a novel mathematical approach to describe the usable range of detection for genetically encoded biosensors, which may be broadly useful in future efforts to better characterize biosensor performance.

Shark is a new data analysis system that marries query processing with complex analytics on large clusters. It leverages a novel distributed memory abstraction to provide a unified engine that can run SQL queries and sophisticated analytics functions (e.g. iterative machine learning) at scale, and efficiently recovers from failures mid-query. This allows Shark to run SQL queries up to 100X faster than Apache Hive, and machine learning programs more than 100X faster than Hadoop. Unlike previous systems, Shark shows that it is possible to achieve these speedups while retaining a MapReduce-like execution engine, and the fine-grained fault tolerance properties that such engine provides. It extends such an engine in several ways, including column-oriented in-memory storage and dynamic mid-query replanning, to effectively execute SQL. The result is a system that matches the speedups reported for MPP analytic databases over MapReduce, while offering fault tolerance properties and complex analytics capabilities that they lack.