A Constraint-Based Approach to Fast and Exact Structure Prediction in Three-Dimensional Protein Models

Simplified protein models are used for investigating general properties of proteins and principles of protein folding. Furthermore, they are suited for hierarchical approaches to protein structure prediction. A well known protein model is the HP-model of Lau and Dill [Lau, K. F., & Dill, K. A. (1989)]. A lattice statistical mechanics model of the conformational and sequence spaces of proteins. Macromolecules, 22, 3986–3997) which models the important aspect of hydrophobicity. One can define the HP-model for various lattices, among them two-dimensional and three-dimensional ones. Here, we investigate the three-dimensional case. The main motivation for studying simplified protein models is to be able to predict model structures much more quickly and more accurately than is possible for real proteins. However, up to now there was a dilemma: the algorithmically tractable, simple protein models can not model real protein structures with good quality and introduce strong artifacts. We present a constraint-based method that largely improves this situation. It outperforms all existing approaches for lattice protein folding in HP-models. This approach is the first one that can be applied to two three-dimensional lattices, namely the cubic lattice and the face-centered-cubic (FCC) lattice. Moreover, it is the only exact method for the FCC lattice. The ability to use the FCC lattice is a significant improvement over the cubic lattice. The key to our approach is the ability to compute maximally compact sets of points (used as hydrophobic cores), which we accomplish for the first time for the FCC lattice.     

Adaptive modelling for adaptive water quality management in the Great Barrier Reef region,Australia

The development and use of a Bayesian Belief Network (BBN) model, within an adaptive management process for the management of water quality in the Mackay Whitsunday region of Queensland, Australia is described. The management goal is firstly to set achievable targets for water quality entering the Great Barrier Reef lagoon from the Mackay Whitsunday natural resource management region and then secondly to define and implement a strategy to achieve these targets. The BBN serves as an adaptive framework that managers and scientists may use to articulate what they know about the managed system. It then provides a tool to guide where, when and what interventions (including research) are most likely to achieve management outcomes. Importantly the BBN provides a platform for collective learning.BBN estimates of total suspended sediment (TSS) loads and event mean concentrations (EMCs) were compared to observed data and results from current best practice models. The BBN estimates were reasonable relative to empirical observations. Example results from the BBN are thereafter used to illustrate the use of the model in estimating the likelihood of exceeding water quality targets with and without proposed actions to improve water quality. Example results are also used to illustrate what spatial or land use elements might contribute most to exceeding water quality targets. Finally key limitations of the tool are discussed and important learnings from the process are highlighted.     

Learning traversability models for autonomous mobile vehicles

Autonomous mobile robots need to adapt their behavior to the terrain over which they drive, and to predict the traversability of the terrain so that they can effectively plan their paths. Such robots usually make use of a set of sensors to investigate the terrain around them and build up an internal representation that enables them to navigate. This paper addresses the question of how to use sensor data to learn properties of the environment and use this knowledge to predict which regions of the environment are traversable. The approach makes use of sensed information from range sensors (stereo or ladar), color cameras, and the vehicle’s navigation sensors. Models of terrain regions are learned from subsets of pixels that are selected by projection into a local occupancy grid. The models include color and texture as well as traversability information obtained from an analysis of the range data associated with the pixels. The models are learned without supervision, deriving their properties from the geometry and the appearance of the scene. The models are used to classify color images and assign traversability costs to regions. The classification does not use the range or position information, but only color images. Traversability determined during the model-building phase is stored in the models. This enables classification of regions beyond the range of stereo or ladar using the information in the color images. The paper describes how the models are constructed and maintained, how they are used to classify image regions, and how the system adapts to changing environments. Examples are shown from the implementation of this algorithm in the DARPA Learning Applied to Ground Robots (LAGR) program, and an evaluation of the algorithm against human-provided ground truth is presented.

In situ slip estimation for mobile robots in outdoor environments

Accounting for wheel–terrain interaction is crucial for navigation and traction control of mobile robots in outdoor environments and rough terrains. Wheel slip is one of the surface hazards that needs to be detected to mitigate against the risk of losing the robot's controllability or mission failure occurring. The open problems in the Terramechanics field addressed are (1) the need for in situ wheel-slippage estimation in harsh environments using low-cost/power and easy to integrate sensors, and (2) removing the need for prior information of the soil, which is not always available. This paper presents a novel slip estimation method that utilizes only two proprioceptive sensors (IMU and wheel encoder) to estimate the wheel slip using deep learning methods. It is experimentally shown to be real-world feasible in outdoor, uneven terrains without prior soil information assumptions. Comparison with previously used machine learning algorithms for continuous and discrete slip estimation problems show more than 9% and 14% improvement in estimation performance, respectively.     

Emerging AI & Law approaches to automating analysis and retrieval of electronically stored information in discovery proceedings

This article provides an overview of, and thematic justification for, the special issue of the journal of Artificial Intelligence and Law entitled “E-Discovery”. In attempting to define a characteristic “AI & Law” approach to e-discovery, and since a central theme of AI & Law involves computationally modeling legal knowledge, reasoning and decision making, we focus on the theme of representing and reasoning with litigators’ theories or hypotheses about document relevance through a variety of techniques including machine learning. We also identify two emerging techniques for enabling users’ document queries to better express the theories of relevance and connect them to documents: social network analysis and a hypothesis ontology.     

Thermophysical Properties of Fluids from Dynamic Light Scattering

In a continuous development for more than three decades, dynamic light scattering (DLS) has evolved into a versatile and powerful technique for the determination of transport and other thermophysical properties of fluids. The success is founded on its application in a macroscopic thermodynamic equilibrium, i.e., without the need for employing external gradients, to determine a large variety of properties, which can be derived, in some instances even simultaneously, by an appropriate experimental approach and a corresponding signal analysis. The principles, essential features, and variants of the technique are reviewed, and a survey on the determination of various desired transport and thermophysical properties is given, both for the application of light scattering from bulk fluids and for the variant of surface light scattering (SLS). The stage of development and the corresponding uncertainties are discussed for the measurement of the individual quantities, and the performance of the method is demonstrated by representative experimental results for relevant fluids.     

Bruchmechanische Prüfung von Keramiken mit R-Kurvenverhalten

Fracture Testing of Ceramics with R-Curve Behaviour Significant improvements of the fracture toughness are striven for ceramic materials using micromechanical mechanisms of crack tip shielding. The application of Fine Ceramics to structures and engines is planed increasingly in expectancy of higher strength. The aim of the article is the verification of common brittle fracture tests employed for advanced ceramics. Theoretical aspects of the R-curve behaviour are analysed to feature reliably damage of materials. Stable crack growth and failure of standard specimens are assessed using theoretical simulations of experiments. The results are compared with real data. Suggestions are made to interpret properly measurements and to select appropriate specimen geometries for materials which show a distinctive R-curve behaviour.     

Temporally Stable Supramolecular Polymeric Salts Enabling High-Performance 3D All-Aromatic Polyimide Lattices

Vat photopolymerization (VP) Additive Manufacturing (AM), in which UV light is selectively applied to cure photo-active polymers into complex geometries with micron-scale resolution, has a limited selection of aliphatic thermoset materials that exhibit relatively poor thermal performance. Ring-opening dianhydrides with acrylate-containing nucleophiles yielded diacrylate ester-dicarboxylic acids that enabled photo-active polyimide (PI) precursors, termed polysalts, upon neutralization with an aromatic diamine in solution. In situ FTIR spectroscopy coupled with a solution and photo-rheological measurements revealed a previously unknown time-dependent instability of 4,4′-oxydianiline (ODA) polysalts due to an aza-Michael addition. Replacement of the electron-donating ether-containing diamine with an electron withdrawing sulfone-containing monomer, e.g., 4,4′-diaminodiphenyl sulfone (DDS), prohibited the aza-Michael addition of the aromatic amine to the activated acrylate double bond. Novel DDS polysalt photocurable solutions are similarly analyzed and validated long-term stability, which enabled reproducible printing of polyimide organogel intermediates. Subsequent VP AM afforded 3-dimensional (3D) structures of intricate complexity and excellent surface finish, as demonstrated with scanning electron microscopy. In addition, the novel PMDA-HEA/DDS solution enabled the production of the first beam latticed architecture comprised of all-aromatic polyimide. The versatility of a polysalt platform for multi-material printing is further demonstrated by printing parts with alternating polysalt compositions.