Neural Network and Bioinformatic Methods for Predicting HIV-1 Protease Inhibitor Resistance

This article presents a new method for predicting viral resistance to seven protease inhibitors from the HIV-1 genotype, and for identifying the positions in the protease gene at which the specific nature of the mutation affects resistance. The neural network Analog ARTMAP predicts protease inhibitor resistance from viral genotypes. A feature selection method detects genetic positions that contribute to resistance both alone and through interactions with other positions. This method has identified positions 35, 37, 62, and 77, where traditional feature selection methods have not detected a contribution to resistance. At several positions in the protease gene, mutations confer differing degress of resistance, depending on the specific amino acid to which the sequence has mutated. To find these positions, an Amino Acid Space is introduced to represent genes in a vector space that captures the functional similarity between amino acid pairs. Feature selection identifies several new positions, including 36, 37, and 43, with amino acid-specific contributions to resistance. Analog ARTMAP networks applied to inputs that represent specific amino acids at these positions perform better than networks that use only mutation locations.Air Force Office of Scientific Research (F49620-01-1-0423); National Geospatial-Intelligence Agency (NMA 201-01-1-2016); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Nutritional labelling in restaurants : whose responsibility is it anyway?

To explore consumer attitudes towards the potential implementation of compulsory nutritional labelling on commercial restaurant menus in the UK. This research was approached from the perspective of the consumer with the intention of gaining an insight into personal attitudes towards nutritional labelling on commercial restaurant menus and three focus groups consisting of participants with distinctly differing approaches to eating outside the home were conducted. The research suggests that while some consumers might welcome the introduction of nutritional labelling it is context dependent and without an appropriate education the information provided may not be understood anyway. The issue of responsibility for public health is unresolved although some effort could be made to provide greater nutritional balance in menus. Following this research up with a quantitative investigation, the ideas presented could be verified with the opinions of a larger sample. For example, a study into the reactions to nutritionally labelled menus in various restaurant environments. Consumers would react differently to the information being presented in a fine-dining restaurant than they would in popular catering or fast food. The obstacles faced by restaurants to provide not only nutritional information, but attractive, nutritious food are significant. Prior to this research there were few, if any, studies into the effects of food labelling on consumer choice behaviour in the context of hospitality management

Molecular Characterization of HERC5: A Novel Multifunctional Antiviral Protein

Interferon (IFN)-induced proteins serve as one of the first lines of defense against viral pathogens such as Human Immunodeficiency Virus (HIV)-1. IFN treatment has been shown to restrict multiple stages of HIV-1 replication. The identities and functions of IFN-induced proteins involved in the inhibition of HIV-1 and other viruses are not fully understood. Homologous to the E6-AP C-terminus (HECT) and regulator of chromosome condensation 1 (RCC1)-like domain (RLD) containing protein 5 (HERC5) is strongly upregulated by the Type I IFN response. HERC5, a member of the small HERC family, is composed of an N-terminus RLD domain and a C-terminus HECT domain separated by a spacer region. With the discovery that HERC5 is the main E3 ligase for ISG15, and given the well-known antiviral properties of ISG15, I hypothesized that HERC5 is an evolutionarily conserved restriction factor that interferes with HIV-1 particle production. Using an HIV-1 particle release assay, I showed HERC5 inhibits HIV-1 assembly and budding using an ISG15 E3 ligase-dependent mechanism which modifies HIV-1 Gag with ISG15. Next, I determined that HERC5 restricts HIV-1 replication by inhibiting unspliced HIV-1 mRNA export, which is dependent on its RLD domain. Using a similar approach, I characterized the entire small HERC family and demonstrated that the other HERC members exhibit differential abilities in inhibiting HIV-1 replication. My research characterizes HERC5 as an antiviral protein that has the ability to potently restrict HIV-1 replication by two novel mechanisms. I provided new insight into how the innate immune system combats viral pathogens such as retroviruses. My research also lays the groundwork for future research into exploiting the antiviral activity of HERC5 by developing drugs that mimic or enhance the antiviral activity of HERC5

A Concrete View of Rule 110 Computation

Rule 110 is a cellular automaton that performs repeated simultaneous updates of an infinite row of binary values. The values are updated in the following way: 0s are changed to 1s at all positions where the value to the right is a 1, while 1s are changed to 0s at all positions where the values to the left and right are both 1. Though trivial to define, the behavior exhibited by Rule 110 is surprisingly intricate, and in (Cook, 2004) we showed that it is capable of emulating the activity of a Turing machine by encoding the Turing machine and its tape into a repeating left pattern, a central pattern, and a repeating right pattern, which Rule 110 then acts on. In this paper we provide an explicit compiler for converting a Turing machine into a Rule 110 initial state, and we present a general approach for proving that such constructions will work as intended. The simulation was originally assumed to require exponential time, but surprising results of Neary and Woods (2006) have shown that in fact, only polynomial time is required. We use the methods of Neary and Woods to exhibit a direct simulation of a Turing machine by a tag system in polynomial time

Prospectus, March 23, 1992 -First Annual Honors\u27 Day Convocation Edition

https://spark.parkland.edu/prospectus_1992/1009/thumbnail.jp

Development of a comprehensive energy model to simulate the energy efficiency of a battery electric vehicle to allow for prototype design optimisation and validation.

Masters Degree in mechanical Engineering. University of KwaZulu-Natal, Durban.This dissertation describes the development of an energy model of a battery electric vehicle (BEV) to assist designers in evaluating the impact of overall energy efficiency on vehicle performance. Energy efficiency is a crucial metric for BEVs as it defines the driving range of the vehicle and optimises the limited amount of energy available from the on-board battery pack, typically the most expensive component of the vehicle. Energy modelling also provides other useful information to the designer, such as the range of the vehicle according to legislative drive cycles and the maximum torque required from the motor. An accurate, fast and efficient model is therefore required to simulate BEVs in the early stages of design and for prototype validation. An extensive investigation into BEV modelling and the mechanisms of energy losses within BEVs was conducted. Existing literature was studied to characterise the effect of operating conditions on the efficiency of each mechanism, as well as investigating existing modelling techniques used to simulate each energy loss. A complete vehicle model was built by considering multiple domain modelling methods and the flow of energy between components in both mechanical and electrical domains. Simscape™, a MathWorks MATLAB™ tool, was used to build a physics based, forward facing model comprising a combination of custom coded blocks representing the flow of energy from the battery pack to the wheels. The acceleration and speed response of the vehicle was determined over a selected drive cycle, based on vehicle parameters. The model is applicable to normal driving conditions where the power of the motor does not exceed its continuous rating. The model relies on datasheet or non-proprietary parameters. These parameters can be changed depending on the architecture of the BEV and the exact components used, providing model flexibility. The primary model input is a drive cycle and the primary model output is range as well as the dynamic response of other metrics such as battery voltage and motor torque. The energy loss mechanisms are then assessed qualitatively and quantitatively to allow vehicle designers to determine effective strategies to increase the overall energy efficiency of the vehicle. The Mamba BEV, a small, high-power, commercially viable electric vehicle with a 21 kWh lithium-ion battery was simulated using the developed model. As the author was involved in the design and development of the vehicle, required vehicle parameters were easily obtained from manufacturers. The range of the vehicle was determined using the World-Harmonised Light Duty Vehicles Test Procedure and provided an estimated range of 285.3 km for the standard cycle and 420.8 km for the city cycle

Small Tile Sets That Compute While Solving Mazes

We ask the question of how small a self-assembling set of tiles can be yet have interesting computational behaviour. We study this question in a model where supporting walls are provided as an input structure for tiles to grow along: we call it the Maze-Walking Tile Assembly Model. The model has a number of implementation prospects, one being DNA strands that attach to a DNA origami substrate. Intuitively, the model suggests a separation of signal routing and computation: The input structure (maze) supplies a routing diagram, and the programmer's tile set provides the computational ability. We ask how simple the computational part can be. We give two tiny tile sets that are computationally universal in the Maze-Walking Tile Assembly Model. The first has four tiles and simulates Boolean circuits by directly implementing NAND, NXOR and NOT gates. Our second tile set has 6 tiles and is called the Collatz tile set as it produces patterns found in binary/ternary representations of iterations of the Collatz function. Using computer search we find that the Collatz tile set is expressive enough to encode Boolean circuits using blocks of these patterns. These two tile sets give two different methods to find simple universal tile sets, and provide motivation for using pre-assembled maze structures as circuit wiring diagrams in molecular self-assembly based computing.ISSN:1868-896

Sparse Bayesian information filters for localization and mapping

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2008This thesis formulates an estimation framework for Simultaneous Localization and Mapping (SLAM) that addresses the problem of scalability in large environments. We describe an estimation-theoretic algorithm that achieves significant gains in computational efficiency while maintaining consistent estimates for the vehicle pose and the map of the environment. We specifically address the feature-based SLAM problem in which the robot represents the environment as a collection of landmarks. The thesis takes a Bayesian approach whereby we maintain a joint posterior over the vehicle pose and feature states, conditioned upon measurement data. We model the distribution as Gaussian and parametrize the posterior in the canonical form, in terms of the information (inverse covariance) matrix. When sparse, this representation is amenable to computationally efficient Bayesian SLAM filtering. However, while a large majority of the elements within the normalized information matrix are very small in magnitude, it is fully populated nonetheless. Recent feature-based SLAM filters achieve the scalability benefits of a sparse parametrization by explicitly pruning these weak links in an effort to enforce sparsity. We analyze one such algorithm, the Sparse Extended Information Filter (SEIF), which has laid much of the groundwork concerning the computational benefits of the sparse canonical form. The thesis performs a detailed analysis of the process by which the SEIF approximates the sparsity of the information matrix and reveals key insights into the consequences of different sparsification strategies. We demonstrate that the SEIF yields a sparse approximation to the posterior that is inconsistent, suffering from exaggerated confidence estimates. This overconfidence has detrimental effects on important aspects of the SLAM process and affects the higher level goal of producing accurate maps for subsequent localization and path planning. This thesis proposes an alternative scalable filter that maintains sparsity while preserving the consistency of the distribution. We leverage insights into the natural structure of the feature-based canonical parametrization and derive a method that actively maintains an exactly sparse posterior. Our algorithm exploits the structure of the parametrization to achieve gains in efficiency, with a computational cost that scales linearly with the size of the map. Unlike similar techniques that sacrifice consistency for improved scalability, our algorithm performs inference over a posterior that is conservative relative to the nominal Gaussian distribution. Consequently, we preserve the consistency of the pose and map estimates and avoid the effects of an overconfident posterior. We demonstrate our filter alongside the SEIF and the standard EKF both in simulation as well as on two real-world datasets. While we maintain the computational advantages of an exactly sparse representation, the results show convincingly that our method yields conservative estimates for the robot pose and map that are nearly identical to those of the original Gaussian distribution as produced by the EKF, but at much less computational expense. The thesis concludes with an extension of our SLAM filter to a complex underwater environment. We describe a systems-level framework for localization and mapping relative to a ship hull with an Autonomous Underwater Vehicle (AUV) equipped with a forward-looking sonar. The approach utilizes our filter to fuse measurements of vehicle attitude and motion from onboard sensors with data from sonar images of the hull. We employ the system to perform three-dimensional, 6-DOF SLAM on a ship hull

Intrinsic Universality in Self-Assembly

We show that the Tile Assembly Model exhibits a strong notion of universality where the goal is to give a single tile assembly system that simulates the behavior of any other tile assembly system. We give a tile assembly system that is capable of simulating a very wide class of tile systems, including itself. Specifically, we give a tile set that simulates the assembly of any tile assembly system in a class of systems that we call \emph{locally consistent}: each tile binds with exactly the strength needed to stay attached, and that there are no glue mismatches between tiles in any produced assembly. Our construction is reminiscent of the studies of \emph{intrinsic universality} of cellular automata by Ollinger and others, in the sense that our simulation of a tile system $T$ by a tile system $U$ represents each tile in an assembly produced by $T$ by a $c \times c$ block of tiles in $U$, where $c$ is a constant depending on $T$ but not on the size of the assembly $T$ produces (which may in fact be infinite). Also, our construction improves on earlier simulations of tile assembly systems by other tile assembly systems (in particular, those of Soloveichik and Winfree, and of Demaine et al.) in that we simulate the actual process of self-assembly, not just the end result, as in Soloveichik and Winfree's construction, and we do not discriminate against infinite structures. Both previous results simulate only temperature 1 systems, whereas our construction simulates tile assembly systems operating at temperature 2