Characterization of a High Affinity Glutathione Transporter and Its Impact on Glutathione Depleted \u3ci\u3eSaccharomyces Cerevisiae\u3c/i\u3e

Thiol-disulfide redox homeostasis is integral for maintaining the redox status of proteins and other thiol-containing molecules within the cell. Among the many antioxidants and detoxifying enzymes and small peptides, glutathione (GSH) has proven to be critical for the preservation of function and structural integrity of the cell due to its functionality in areas like oxidative protein folding, thiol redox control, reactive oxygen species (ROS) and xenobiotics removal as well as iron regulation and Fe-S cluster biogenesis. Therefore, our studies are aimed at elucidating the factors that control GSH trafficking and metabolism in the model eukaryote Saccharomyces cerevisiae (baker’s yeast). Using genetic modifications combined with targeted redox-sensitive and pHsensitive green fluorescent protein probes, we have monitored GSH mediated pH changes and subsequently, changes in the redox potential, in the cytosol and mitochondrial matrix in response to fluctuating GSH levels in live yeast cells. These studies reveal that increased uptake of GSH or GSSG via overexpression of HGT1, which encodes a high affinity glutathione transporter, leads to significant changes in the intracellular pH in the cytosol and mitochondrial matrix, which, in turn, strongly impacts the redox state of GSH:GSSG pools. High intracellular GSH is known to be toxic for the cells, but it has not been shown how cells depleted of GSH (gsh1Δ) respond to increased GSH uptake. By expressing HGT1 in GSH-depleted gsh1Δ cells, we show that HGT1 overexpression partially rescues the inviability of GSH-deficient cells. Analysis of iron regulation and Fe-S cluster enzyme activity demonstrates that HGT1 overexpression reverses some of the Fe-S cluster biogenesis and iron homeostasis related defects in gsh1Δ strains. We also demonstrate that cysteine is a key amino acid for this rescue, suggesting that cysteine may partially substitute for GSH in gsh1Δ cells

Motion Strategies for Visibility based Target Tracking in Unknown Environments

Ph.DDOCTOR OF PHILOSOPH

Global Motion Planning under Uncertain Motion, Sensing, and Environment Map

Motion planning that takes into account uncertainty in motion, sensing, and environment map, is critical for autonomous robots to operate reliably in our living spaces. Partially Observable Markov Decision Processes (POMDPs) is a principled and general framework for planning under uncertainty. Although recent development of point-based POMDPs have drastically increased the speed of POMDP planning, even the best POMDP planner today, fails to generate reasonable motion strategies when the environment map is not known exactly. This paper presents Guided Cluster Sampling (GCS), a new point-based POMDP planner for motion planning under uncertain motion, sensing, and environment map, when the robot has active sensing capability. It uses our observations that in this problem, the belief space B can be partitioned into a collection of much smaller subspaces, and an optimal policy can often be generated by sufficient sampling of a small subset of the collection. GCS samples B using two-stage cluster sampling, a subspace is sampled from the collection and then a belief is sampled from the subspace. It uses information from the set of sampled sub-spaces and sampled beliefs to guide subsequent sampling. Preliminary results suggest that GCS generates reasonable policies for motion planning problems with uncertain motion, sensing, and environment map, that are unsolvable by the best point-based POMDP planner today, within reasonable time. Furthermore, GCS handles POMDPs with continuous state, action, and observation spaces. We show that for a class of POMDPs that often occur in robot motion planning, GCS converges to the optimal policy, given enough time. To the best of our knowledge, this is the first convergence result for point-based POMDPs with continuous action space

Learning to Simulate Tree-Branch Dynamics for Manipulation

We propose to use a simulation driven inverse inference approach to model the joint dynamics of tree branches under manipulation. Learning branch dynamics and gaining the ability to manipulate deformable vegetation can help with occlusion-prone tasks, such as fruit picking in dense foliage, as well as moving overhanging vines and branches for navigation in dense vegetation. The underlying deformable tree geometry is encapsulated as coarse spring abstractions executed on parallel, non-differentiable simulators. The implicit statistical model defined by the simulator, reference trajectories obtained by actively probing the ground truth, and the Bayesian formalism, together guide the spring parameter posterior density estimation. Our non-parametric inference algorithm, based on Stein Variational Gradient Descent, incorporates biologically motivated assumptions into the inference process as neural network driven learnt joint priors; moreover, it leverages the finite difference scheme for gradient approximations. Real and simulated experiments confirm that our model can predict deformation trajectories, quantify the estimation uncertainty, and it can perform better when base-lined against other inference algorithms, particularly from the Monte Carlo family. The model displays strong robustness properties in the presence of heteroscedastic sensor noise; furthermore, it can generalise to unseen grasp locations.Comment: 8 pages, 9 figure

Linearization in Motion Planning under Uncertainty

Motion planning under uncertainty is essential to autonomous robots. Over the past decade, the scalability of such planners have advanced substantially. Despite these advances, the problem remains difficult for systems with non-linear dynamics. Most successful methods for planning perform forward search that relies heavily on a large number of simulation runs. Each simulation run generally requires more costly integration for systems with non-linear dynamics. Therefore, for such problems, the entire planning process remains relatively slow. Not surprisingly, linearization-based methods for planning under uncertainty have been proposed. However, it is not clear how linearization affects the quality of the generated motion strategy, and more importantly where to and where not to use such a simplification. This paper presents our preliminary work towards answering such questions. In particular, we propose a measure, called Statistical-distance-based Non-linearity Measure (SNM), to identify where linearization can and where it should not be performed. The measure is based on the distance between the distributions that represent the original motion-sensing models and their linearized version. We show that when the planning problem is framed as the Partially Observable Markov Decision Process (POMDP), the difference between the value of the optimal strategy generated if we plan using the original model and if we plan using the linearized model, can be upper bounded by a function linear in SNM. We test the applicability of this measure in simulation via two venues. First, we compare SNM with a negentropy-based Measure of Non-Gaussianity (MoNG) —a measure that has recently been shown to be a suitable measure of non-linearity for stochastic systems [1]. We compare their performance in measuring the difference between a general POMDP solver [2] that computes motion strategies using the original model and a solver that uses the linearized model (adapted from [3]) on various scenarios. Our results indicate that SNM is more suitable in taking into account the effect that obstacles have on the effectiveness of linearization. In the second set of tests, we use a local estimate of SNM to develop a simple on-line planner that switches between using the original and the linearized model. Simulation results on a car-like robot with second order dynamics and a 4-DOFs and 6-DOFs manipulator with torque control indicate that our simple planner appropriately decides if and when linearization should be use