The influence of the HLA-DRB1*13 allele on measles vaccine response

BACKGROUND: Measles remains a public health threat in the United States with over 50,000 cases being reported from 1989 through 1991 with continued smaller outbreaks. Measles vaccine failure is in part to blame for these large-scale outbreaks. The human leukocyte antigen (HLA) genes are important determinants of immune response to measles virus and vaccine. To examine the influence that HLA polymorphisms may have on measles vaccine antibody response, we compared the distribution of HLA-DRB1 alleles between measles vaccine nonresponders and hyper-responders. METHODS: We determined the seroprevalence of measles antibody in 881 school children immunized with measles-mumps-rubella-II at age 15 months using a whole virus IgG EIA. We performed class II HLA-DR typing by PCR with sequence specific primers (PCR-SSP) on 81 nonresponders (IgG seronegative) and 65 hyper-responders (from the upper 10th percentile of IgG levels of all subjects). We then compared the distribution of alleles between nonresponders and hyper-responders. RESULTS: The distribution of HLA-DRB1 alleles among nonresponders compared to hyper-responders was significantly different (p = 0.014). Nonresponders were significantly less likely to carry the HLA-DRB1*13 alleles than were hyper-responders (7.4% vs 16.2%;p = 0.02). Nonresponders also had an excess of HLA-DRB1*07 alleles (15.4% vs 6.2%; p = 0.015). CONCLUSIONS: The absence of HLA-DRB1*13 alleles is associated with measles vaccine nonresponse. The absence of this allele has also been associated with susceptibility to other infectious diseases. The role of this gene in the immunogenetic response to infectious diseases requires further study.     

Watermarks encrypted in a concealogram and deciphered by a modified joint-transform correlator

We describe an electro-optical method of deciphering a watermark from a recently invented encoded image termed a concealogram. The watermark is revealed as a result of spatial correlation between two concealograms, one containing the watermark and the other containing the deciphering key. The two are placed side by side on the input plane of a modified joint-transform correlator. When the input plane is illuminated by a plane wave, the watermark image is reconstructed on part of the correlator's output plane. The key function deciphers the concealed watermark from the visible picture only when the two specific concealograms are matched. To illustrate the system's performance, both simulation and experimental results are presented.     

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.     

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.     

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.     

Computational Analysis of Particle Nucleation in Dilution Tunnels: Effects of Flow Configuration and Tunnel Geometry

Measurement of fine particle emission from combustion sources is important to understand their health effects, and to develop emissions regulations. Dilution sampling is the most commonly used technique to measure particle number distribution because it simulates the mixing and cooling of combustion exhaust with atmospheric air, which drives nucleation and condensation of semi volatile material. Experiments suggest that the measured size distribution is dependent on the dilution ratio used and the tunnel design. In the present work, computational analysis using a large-eddy-simulation (LES) based model is performed to investigate the effect of tunnel flow and geometric parameters on H₂SO₄-H₂O binary nucleation inside dilution tunnels. Model predictions suggest that the experimental trends are likely due to differences in the intensity of turbulent mixing inside the tunnels. It is found that the interaction of dilution air and combustion exhaust in the mixing layer greatly impacts the extent of nucleation. In general, a cross-flow interaction with enhanced turbulent mixing leads to greater number of nucleation-mode particles than an axial-flow interaction of combustion sample and dilution air.

Copyright 2014 American Association for Aerosol Research     

Fabrication of coaxial metal nanocables using a self-assembled peptide nanotube scaffold

The design and fabrication of complex nanostructures with specific geometry and composition is one of the main challenges of nanotechnology. Here we demonstrate the devise of metal-insulator-metal, trilayered, coaxial nanocables. Such coaxial geometry may give rise to useful and unique electromagnetic properties. We have fabricated these nanostructures using a scaffold of self-assembled peptide nanotubes. Gold nanoparticles were bound to the surface of peptide nanotubes via a common molecular recognition element that was included in various linker peptides. This enabled us to promote site-specific metal reduction and to create the coaxial nanostructure. Using electron microscopy, 1H NMR spectra, and energy-dispersive X-ray analysis, we monitored the different steps within the process, gaining further understanding of its mechanism.