Significant Enhancement of Proton Transport in Bioinspired Peptide Fibrils by Single Acidic or Basic Amino Acid Mutation

Bioinspired materials are extremely suitable for the development of biocompatible and environmentally friendly functional materials. Peptide‐based assemblies are remarkably attractive for such tasks, since they provide a simple way to fuse together functional and structural protein motifs in artificial materials. Motivated by this idea, it is shown here that the introduction of a single acidic, or basic, amino acid into the side chain of a heptameric self‐assembling peptide increases proton conduction in the resulting fibers by two orders of magnitude. This self‐doping effect is much more pronounced than the effect induced by the peptide's acidic and basic termini groups. Furthermore, the self‐doping process is found to be significantly more effective for acidic side chains than for basic ones due to both much more effective self‐doping process, resulting in an order of magnitude larger concentration of charge carriers for the acidic assemblies, and higher mobility of the formed charge carriers – almost threefolds in this case. This work facilitates the realization of unique bioinspired self‐assembled proton conducting materials that may find uses in the emerging bioprotonic technology. The presented design flexibility and, in particular, the ability to introduce both proton and proton holes further extend the usefulness of these materials.     

Effects of dilution on fine particle mass and partitioning of semivolatile organics in diesel exhaust and wood smoke

Experiments were conducted to examine the effects of dilution on fine particle mass emissions from a diesel engine and wood stove. Filter measurements were made simultaneously using three dilution sampling systems operating at dilution ratios ranging from 20:1 to 510:1. Denuders and backup filters were used to quantify organic sampling artifacts. For the diesel engine operating at low load and wood combustion, large decreases in fine particle mass emissions were observed with increases in dilution. For example, the PM2.5 mass emission rate from a diesel engine operating at low load decreased by 50% when the dilution ratio was increased from 20:1 to 350:1. Measurements of organic and elemental carbon indicate that the changes in fine particle mass with dilution are caused by changes in partitioning of semivolatile organic compounds. At low levels of dilution semivolatile species largely occur in the particle phase, but increasing dilution reduces the concentration of semivolatile species, shifting this material to the gas phase in order to maintain phase equilibrium. Emissions of elemental carbon do not vary with dilution. Organic sampling artifacts are shown to vary with dilution because of the combination of changes in partitioning coupled with adsorption of gas-phase organics by quartz filters. The fine particle mass emissions from the diesel engine operating at medium load did not vary with dilution because of the lower emissions of semivolatile material and higher emissions of elemental carbon. To measure partitioning of semivolatile materials under atmospheric conditions, partitioning theory indicates that dilution samplers need to be operated such that the diluted exhaust achieves atmospheric levels of dilution. Too little dilution can potentially overestimate the fine particle mass emissions, and too much dilution (with clean air) can underestimate them.     

Perceived density of road maps

Maps should be designed so that users can comprehend and use the information. Display decisions, such as choosing the scale at which an area is shown, depend on properties of the displayed information such as the perceived density (PD) of the information. Taking a psychophysical approach we suggest that the PD of information in a road map is related to the scale and properties of the mapped area. 54 participants rated the PD of 60 maps from different regions. We provide a simple model that predicts the PD of electronic road map displays, using the logarithm of the number of roads, the logarithm of the number of junctions and the length of the shown roads. The PD model was cross-validated using a different set of 60 maps (n = 44). The model can be used for automatically adjusting display scales and for evaluating map designs, considering the required PD to perform a map-related task.     

Development of a sweet pepper harvesting robot

This paper presents the development, testing and validation of SWEEPER, a robot for harvesting sweet pepper fruit in greenhouses. The robotic system includes a six degrees of freedom industrial arm equipped with a specially designed end effector, RGB‐D camera, high‐end computer with graphics processing unit, programmable logic controllers, other electronic equipment, and a small container to store harvested fruit. All is mounted on a cart that autonomously drives on pipe rails and concrete floor in the end‐user environment. The overall operation of the harvesting robot is described along with details of the algorithms for fruit detection and localization, grasp pose estimation, and motion control. The main contributions of this paper are the integrated system design and its validation and extensive field testing in a commercial greenhouse for different varieties and growing conditions. A total of 262 fruits were involved in a 4‐week long testing period. The average cycle time to harvest a fruit was 24 s. Logistics took approximately 50% of this time (7.8 s for discharge of fruit and 4.7 s for platform movements). Laboratory experiments have proven that the cycle time can be reduced to 15 s by running the robot manipulator at a higher speed. The harvest success rates were 61% for the best fit crop conditions and 18% in current crop conditions. This reveals the importance of finding the best fit crop conditions and crop varieties for successful robotic harvesting. The SWEEPER robot is the first sweet pepper harvesting robot to demonstrate this kind of performance in a commercial greenhouse.     

Invariants for homology classes with application to optimal search and planning problem in robotics

We consider planning problems on Euclidean spaces of the form ?, where $\widetilde{\mathcal{O}}$ is viewed as a collection of obstacles. Such spaces are of frequent occurrence as configuration spaces of robots, where $\widetilde{\mathcal{O}}$ represent either physical obstacles that the robots need to avoid (e.g., walls, other robots, etc.) or illegal states (e.g., all legs off-the-ground). As state-planning is translated to path-planning on a configuration space, we collate equivalent plannings via topologically-equivalent paths. This prompts finding or exploring the different homology classes in such environments and finding representative optimal trajectories in each such class. In this paper we start by considering the general problem of finding a complete set of easily computable homology class invariants for (N???1)-cycles in (?. We achieve this by finding explicit generators of the (N???1) ^st de Rham cohomology group of this punctured Euclidean space, and using their integrals to define cocycles. The action of those dual cocycles on (N???1)-cycles gives the desired complete set of invariants. We illustrate the computation through examples. We then show, for the case when N?=?2, due to the integral approach in our formulation, this complete set of invariants is well-suited for efficient search-based planning of optimal robot trajectories with topological constraints. In particular, we show how to construct an ‘augmented graph’, $\widehat{\mathcal{G}}$ , from an arbitrary graph $\mathcal{G}$ in the configuration space. A graph construction and search algorithm can hence be used to find optimal trajectories in different topological classes. Finally, we extend this approach to computation of invariants in spaces derived from (?by collapsing a subspace, thereby permitting application to a wider class of non-Euclidean ambient spaces.     

Effects of Dilution Sampling on Fine Particle Emissions from Pulverized Coal Combustion

A dilution sampler was used to examine the effects of dilution ratio and residence time on fine-particle emissions from a pilot-scale pulverized coal combustor. Measurements include the particle size distribution from 0.003 to 2.5 μm, PM_2.5 mass, and PM_2.5 composition (OC/EC, major ions, and elemental). Heated filter samples were also collected simultaneously at stack temperatures in order to compare the dilution sampler measurements with standard stack sampling methodologies. Measurements were made both before and after the bag house, the particle control device used on the coal combustor, and while firing three different coal types and one coal–biomass blend. The PM_2.5 mass emission rates measured using the dilution sampler agreed to within experimental uncertainty with those measured with the hot-filter sampler. Relative to the heated filter sample, dilution did increase the PM_2.5 mass fraction of selenium for all fuels tested, as well as ammonium and sulfate for selected fuels. However, the additional particulate mass created by gas-to-particle conversion of these species is within the uncertainty of the gravimetric analysis used to determine the overall mass emission rate. The enrichment of PM_2.5 selenium caused by dilution did not vary with dilution ratio and residence time. The enrichment of PM_2.5 sulfate and ammonium varied with fuel composition and dilution ratio but not residence time. For example, ammonium was only enriched in diluted acidic aerosol samples. A comparison of the PM_2.5 emission profiles for each of the fuels tested underscores how differences in PM_2.5 composition are related to the fuel ash composition. When sampling after the bag house, the particle size distribution and total particle number emission rate did not depend on residence time and dilution ratio because of the much lower particle number concentrations in diluted sample and the absence of nucleation. These results provide new insight into the effects of dilution sampling on measurements of fine particle emissions, providing important data for the ongoing effort of the EPA and ASTM to define a standardized dilution sampling methodology for characterizing emissions from stationary combustion sources.     

Graphene-Based Nanomaterials for Neuroengineering: Recent Advances and Future Prospective

Graphene unique physicochemical properties made it prominent among other allotropic forms of carbon, in many areas of research and technological applications. Interestingly, in recent years, many studies exploited the use of graphene family nanomaterials (GNMs) for biomedical applications such as drug delivery, diagnostics, bioimaging, and tissue engineering research. GNMs are successfully used for the design of scaffolds for controlled induction of cell differentiation and tissue regeneration. Critically, it is important to identify the more appropriate nano/bio material interface sustaining cells differentiation and tissue regeneration enhancement. Specifically, this review is focussed on graphene-based scaffolds that endow physiochemical and biological properties suitable for a specific tissue, the nervous system, that links tightly morphological and electrical properties. Different strategies are reviewed to exploit GNMs for neuronal engineering and regeneration, material toxicity, and biocompatibility. Specifically, the potentiality for neuronal stem cells differentiation and subsequent neuronal network growth as well as the impact of electrical stimulation through GNM on cells is presented. The use of field effect transistor (FET) based on graphene for neuronal regeneration is described. This review concludes the important aspects to be controlled to make graphene a promising candidate for further advanced application in neuronal tissue engineering and biomedical use.     

Learning to classify with missing and corrupted features

A common assumption in supervised machine learning is that the training examples provided to the learning algorithm are statistically identical to the instances encountered later on, during the classification phase. This assumption is unrealistic in many real-world situations where machine learning techniques are used. We focus on the case where features of a binary classification problem, which were available during the training phase, are either deleted or become corrupted during the classification phase. We prepare for the worst by assuming that the subset of deleted and corrupted features is controlled by an adversary, and may vary from instance to instance. We design and analyze two novel learning algorithms that anticipate the actions of the adversary and account for them when training a classifier. Our first technique formulates the learning problem as a linear program. We discuss how the particular structure of this program can be exploited for computational efficiency and we prove statistical bounds on the risk of the resulting classifier. Our second technique addresses the robust learning problem by combining a modified version of the Perceptron algorithm with an online-to-batch conversion technique, and also comes with statistical generalization guarantees. We demonstrate the effectiveness of our approach with a set of experiments.     

Design and Evaluation of a Portable Dilution Sampling System for Measuring Fine Particle Emissions

The size and complexity of current dilution samplers is a mrajor barrier to more wide-spread application of these systems for source characterization. A new, more portable dilution sampler has been designed to provide masurements consistent with the widely cited Caltech di1ution sampler. Intercomparison experiments were performed using a diesel engine and wood stove to evaluate the comparability of the new design with a sampler based on the Caltech design. These experiments involved simultaneouos operation of mutiple dilution sampies from the same source. Filter based measurements included PM_2.5 mass, organic Carbon and elemeanta1 carbon emissions. Particle size distributions in the range from 10–480 nm were measured using a scanning mobility particle sizer. The filter based and integrated-total volume measurements made with the two designs are in good agreement. FOr example, the average relative bias between the two sampler of PM_2.5 mass emission rate measured with Teflon filters is 1%. Nucleation. was intermittently observed in the sampler based on the Caltech design, but rarely observed in the new design. Significant discrepancies in total number emissions between the two samplers occurred during periods of nucleation. Ewperiments were also conducted to examine the effects of residence time on the diluted emissions. No changes in the filter based or integrated colame measurements were observed with an additional 40-s residence time, indicating that phase equilibrium is established in the 2.5 s of residence time provided by the dilution tunnel, This conclusion is consistent with theoretical analysis. These results provide new insight into the effects of dilution sampling on measurements of fine particle emissions, providing important data for the ongoing effort of the EPA and ASTM to define a standardized dilution sampling methodology for characterizing emissions from stationary combustion sources.