A Multidimensional Approach to Understand Chemotaxis and Motility in \u3ci\u3eAzospirillum brasilense\u3c/i\u3e

Bacterial chemotaxis is a key survival strategy in diverse environments. It is also an important behavior that allows motile bacteria to colonize new niches. Azospirillum brasilense are motile diazotrophic bacteria of agricultural interests due to the ability of several strains to promote growth of a variety of plants upon inoculation. The genome of A. brasilense is predicted to encode four chemotaxis pathways, two of which (Che2 and Che3) do not control the chemotaxis response. The chemotaxis system, named Che1, was shown in previous work to regulate transient changes in swimming velocity that occur during chemotaxis. However, Che1 had a minor role in controlling changes in the probability of reversals in the direction of swimming which are also hallmark of the chemotaxis response of motile A. brasilense cells. In this dissertation, using genetic and behavioral assays, we demonstrate that the Che4 chemotaxis system regulates the probability of swimming reversals and is the major signaling pathway for chemotaxis and wheat root surface colonization in A. brasilense. We also showed that Che1 and Che4 function together to coordinate changes in the swimming motility pattern and that the effect of Che1 on swimming speed functions to enhance the chemotactic response. In the latter half of this dissertation, we focused on the motility and the role of different CheY homologs in chemotaxis and motility of A. brasilense. We used high throughput single cell tracking to analyze the swimming pattern of motile A. brasilense and identified three different swimming patterns: run-reverse, run-pause and run-reverse-flick like pattern. We also showed that different CheY homologs differently affect the probability of transient pauses during swimming and obtain evidence that the transient pauses are controlled by chemotaxis signaling. These diverse swimming patterns may be advantageous to navigate the heterogeneous and porous environment of the soil. Collectively, our findings illustrate novel mechanisms by which motile bacteria utilize two chemotaxis systems to regulate speed and reversal frequency, and transient pauses during swimming to enhance chemotaxis

Radion stabilization in higher curvature warped spacetime

We consider a five dimensional AdS spacetime in presence of higher curvature term like $F(R) = R + \alpha R^2$ in the bulk. In this model, we examine the possibility of modulus stabilization from the scalar degrees of freedom of higher curvature gravity free of ghosts. Our result reveals that the model stabilizes itself and the mechanism of modulus stabilization can be argued from a geometric point of view. We determine the region of the parametric space for which the modulus (or radion) can to be stabilized. We also show how the mass and coupling parameters of radion field are modified due to higher curvature term leading to modifications of its phenomenological implications on the visible 3-brane.Comment: 11 pages, 1 figur

On the Permanence of Vertices in Network Communities

Despite the prevalence of community detection algorithms, relatively less work has been done on understanding whether a network is indeed modular and how resilient the community structure is under perturbations. To address this issue, we propose a new vertex-based metric called "permanence", that can quantitatively give an estimate of the community-like structure of the network. The central idea of permanence is based on the observation that the strength of membership of a vertex to a community depends upon the following two factors: (i) the distribution of external connectivity of the vertex to individual communities and not the total external connectivity, and (ii) the strength of its internal connectivity and not just the total internal edges. In this paper, we demonstrate that compared to other metrics, permanence provides (i) a more accurate estimate of a derived community structure to the ground-truth community and (ii) is more sensitive to perturbations in the network. As a by-product of this study, we have also developed a community detection algorithm based on maximizing permanence. For a modular network structure, the results of our algorithm match well with ground-truth communities.Comment: 10 pages, 5 figures, 8 tables, Accepted in 20th ACM SIGKDD Conference on Knowledge Discovery and Data Minin

Multiview Learning with Sparse and Unannotated data.

PhD ThesisObtaining annotated training data for supervised learning, is a bottleneck in many contemporary machine learning applications. The increasing prevalence of multi-modal and multi-view data creates both new opportunities for circumventing this issue, and new application challenges. In this thesis we explore several approaches to alleviating annotation issues in multi-view scenarios. We start by studying the problem of zero-shot learning (ZSL) for image recognition, where class-level annotations for image recognition are eliminated by transferring information from text modality instead. We next look at cross-modal matching, where paired instances across views provide the supervised label information for learning. We develop methodology for unsupervised and semi-supervised learning of pairing, thus eliminating the need for annotation requirements. We rst apply these ideas to unsupervised multi-view matching in the context of bilingual dictionary induction (BLI), where instances are words in two languages and nding a correspondence between the words produces a cross-lingual word translation model. We then return to vision and language and look at learning unsupervised pairing between images and text. We will see that this can be seen as a limiting case of ZSL where text-image pairing annotation requirements are completely eliminated. Overall these contributions in multi-view learning provide a suite of methods for reducing annotation requirements: both in conventional classi cation and cross-view matching settings