Proteomics integrated with Escherichia coli vector-based vaccines and antigen microarrays reveals the immunogenicity of a surface sialidase-like protein of Propionibacterium acnes

Proteomics is a powerful tool for the identification of proteins, which provides a basis for rational vaccine design. However, it is still a highly technical and time‐consuming task to examine a protein's immunogenicity utilizing traditional approaches. Here, we present a platform for effectively evaluating protein immunogenicity and antibody detection. A tetanus toxin C fragment (Tet‐c) was used as a representative antigen to establish this platform. A cell wall‐anchoring sialidase‐like protein (SLP) of Propionibacterium acnes was utilized to assess the efficacy of this platform. We constructed an Escherichia coli vector‐based vaccine by overexpressing Tet‐c or SLP in E. coli and utilized an intact particle of E. coli itself as a vaccine (E. coli Tet‐c or SLP vector). After ultraviolet (UV) irradiation, the E. coli vector‐based vaccines were administered intranasally into imprinting control region mice without adding exogenous adjuvants. For antibody detection, we fabricated antigen microarrays by printing with purified recombinant proteins including Tet‐c and SLP. Our results demonstrated that detectable antibodies were elicited in mice 6 weeks after intranasal administration of UV‐irradiated E. coli vector‐based vaccines. The antibody production of Tet‐c and SLP was significantly elevated after boosting. Notably, the platform with main benefits of using E. coli itself as a vaccine carrier provides a critical template for applied proteomics aimed at screening novel vaccine targets. In addition, the novel immunogenic SLP potentially serves as an antigen candidate for the development of vaccines targeting P. acnes‐associated diseases.     

Experimental technique using FTIR to estimate IR optical properties at variable temperatures: application to PMDA-ODA polyimide thin films from 100 to 380 degrees C

An experimental technique is presented to measure reflectance at high sample temperature with respect to room temperature in the infrared using Fourier transform infrared fitted with a reflectometer. Sample temperature artifacts are accounted for by sequential measurements taken with the lamp source on and with the lamp source off. The sequential measurements are shown mathematically to correct for the modulation of sample and detector thermal emissions. Further, the technique is applied to a polyimide (PMDA-ODA) film on a layer of gold deposited on a thermally oxidized Si wafer. It is shown that the optical properties (index of refraction and extinction coefficient) remain relatively constant with temperature (from room temperature to 380 degrees C) in the 4000-6000 cm(-1) spectral region. The significant changes that occur with temperature are the change in thickness of the film and also the spectral properties in the 2000-4000 cm(-1) region. Also, by using a Lorentz oscillator model, it is shown that this method is able to discern that spectral features corresponding to the OH stretching bands at 3630 and 3470 cm(-1) show significant variation with increasing temperature.     

A unified access bound on comparison-based dynamic dictionaries

We present a dynamic comparison-based search structure that supports insertions, deletions, and searches within the unified bound. The unified bound specifies that it is quick to access an element that is near a recently accessed element. More precisely, if w(y)$w\left(y\right)$ distinct elements have been accessed since the last access to element y$y$, and d(x,y)$d(x,y)$ denotes the rank distance between x$x$ and y$y$ among the current set of elements, then the amortized cost to access element x$x$ is O(_minylog[w(y)+d(x,y)+2])$O({min}_{y}log[w\left(y\right)+d(x,y)+2])$. This property generalizes the working-set and dynamic-finger properties of splay trees.     

Bounded Model Checking Using Satisfiability Solving

The phrase model checking refers to algorithms for exploring the state space of a transition system to determine if it obeys a specification of its intended behavior. These algorithms can perform exhaustive verification in a highly automatic manner, and, thus, have attracted much interest in industry. Model checking programs are now being commercially marketed. However, model checking has been held back by the state explosion problem, which is the problem that the number of states in a system grows exponentially in the number of system components. Much research has been devoted to ameliorating this problem.In this tutorial, we first give a brief overview of the history of model checking to date, and then focus on recent techniques that combine model checking with satisfiability solving. These techniques, known as bounded model checking, do a very fast exploration of the state space, and for some types of problems seem to offer large performance improvements over previous approaches. We review experiments with bounded model checking on both public domain and industrial designs, and propose a methodology for applying the technique in industry for invariance checking. We then summarize the pros and cons of this new technology and discuss future research efforts to extend its capabilities.     

Shape Morphing-Based Control of Robotic Visual Servoing

We present an approach for controlling robotic interactions with objects, using synthetic images generated by morphing shapes. In particular, we attempt the problem of positioning an eye-in-hand robotic system with respect to objects in the workspace for grasping and manipulation. In our formulation, the grasp position (and consequently the approach trajectory of the manipulator), varies with each object. The proposed solution to the problem consists of two parts. First, based on a model-based object recognition framework, images of the objects taken at the desired grasp pose are stored in a database. The recognition and identification of the grasp position for an unknown input object (selected from the family of recognizable objects) occurs by morphing its contour to the templates in the database and using the virtual energy spent during the morph as a dissimilarity measure. In the second step, the images synthesized during the morph are used to guide the eye-in-hand system and execute the grasp. The proposed method requires minimal calibration of the system. Furthermore, it conjoins techniques from shape recognition, computer graphics, and vision-based robot control in a unified engineering amework. Potential applications range from recognition and positioning with respect to partially-occluded or deformable objects to planning robotic grasping based on human demonstration.     

Toward a theoretical approach to medical error reporting system research and design

The release of the Institute of Medicine (Kohn et al., 2000) report "To Err is Human", brought attention to the problem of medical errors, which led to a concerted effort to study and design medical error reporting systems for the purpose of capturing and analyzing error data so that safety interventions could be designed. However, to make real gains in the efficacy of medical error or event reporting systems, it is necessary to begin developing a theory of reporting systems adoption and use and to understand how existing theories may play a role in explaining adoption and use. This paper presents the results of a 9-month study exploring the barriers and facilitators for the design of a statewide medical error reporting system and discusses how several existing theories of technology acceptance, adoption and implementation fit with many of the results. In addition we present an integrated theoretical model of medical error reporting system design and implementation.     

Memory capacity for sequences in a recurrent network with biological constraints

The CA3 region of the hippocampus is a recurrent neural network that is essential for the storage and replay of sequences of patterns that represent behavioral events. Here we present a theoretical framework to calculate a sparsely connected network's capacity to store such sequences. As in CA3, only a limited subset of neurons in the network is active at any one time, pattern retrieval is subject to error, and the resources for plasticity are limited. Our analysis combines an analytical mean field approach, stochastic dynamics, and cellular simulations of a time-discrete McCulloch-Pitts network with binary synapses. To maximize the number of sequences that can be stored in the network, we concurrently optimize the number of active neurons, that is, pattern size, and the firing threshold. We find that for one-step associations (i.e., minimal sequences), the optimal pattern size is inversely proportional to the mean connectivity c, whereas the optimal firing threshold is independent of the connectivity. If the number of synapses per neuron is fixed, the maximum number P of stored sequences in a sufficiently large, nonmodular network is independent of its number N of cells. On the other hand, if the number of synapses scales as the network size to the power of 3/2, the number of sequences P is proportional to N. In other words, sequential memory is scalable. Furthermore, we find that there is an optimal ratio r between silent and nonsilent synapses at which the storage capacity alpha = P//[c(1 + r)N] assumes a maximum. For long sequences, the capacity of sequential memory is about one order of magnitude below the capacity for minimal sequences, but otherwise behaves similar to the case of minimal sequences. In a biologically inspired scenario, the information content per synapse is far below theoretical optimality, suggesting that the brain trades off error tolerance against information content in encoding sequential memories.