In search of a combined brucellosis and tuberculosis vaccine for cattle

Bovine brucellosis is caused by Brucella abortus. The bacterial pathogen causes economic losses because it induces abortion in cattle. Vaccination of calves with live B. abortus strain 19 induces a certain level of protection but induces persistent antibodies against cell envelope lipopolysaccharide that make it difficult to Distinguish Infected from Vaccinated Animals (DIVA). Live vaccine B. abortus strain RB51 was developed to eliminate such interfering antibodies and therefore, facilitate the differentiation of infected from vaccinated animals and help in the eradication of the disease. Vaccination with strain RB51 induces levels of protection similar to strain 19 but neither of the two vaccines give complete protection. We have been working to enhance protection induced by strain RB51 vaccine. Protective Brucella antigens can be over-expressed in strain RB51 by introducing a plasmid containing the leuB gene and the genes encoding such antigens. To avoid the expression of antibiotic resistance genes, we produced a leuB deficient strain RB51 and introduced a plasmid containing the leuB gene and the genes to be over-expressed. This new strain maintains the plasmid and has induced significantly high protection levels in mice. In addition, it allowed the construction of an RB51 vaccine strain able to express Mycobacterium bovis protective antigens so that the vaccine could protect against brucellosis and tuberculosis simultaneously

Simulated dynamics of optically pumped dilute nitride 1300 nm spin vertical-cavity surface-emitting lasers

The authors report a theoretical analysis of optically pumped 1300 nm dilute nitride spin-polarised vertical-cavity surface-emitting lasers (VCSELs) using the spin-flip model to determine the regions of stability and instability. The dependence of the output polarisation ellipticity on that of the pump is investigated, and the results are presented in twodimensional contour maps of the pump polarisation against the magnitude of the optical pump. Rich dynamics and various forms of oscillatory behaviour causing self-sustained oscillations in the polarisation of the spin-VCSEL subject to continuouswave pumping have been found because of the competition of the spin-flip processes and birefringence. The authors also reveal the importance of considering both the birefringence rate and the linewidth enhancement factor when engineering a device for high-frequency applications. A very good agreement is found with the experimental results reported by the authors' group. © The Institution of Engineering and Technology 2014

Optically-pumped dilute nitride spin-VCSEL

We report the first room temperature optical spin-injection of a dilute nitride 1300 nm vertical-cavity surface-emitting laser (VCSEL) under continuous-wave optical pumping. We also present a novel experimental protocol for the investigation of optical spin-injection with a fiber setup. The experimental results indicate that the VCSEL polarization can be controlled by the pump polarization, and the measured behavior is in excellent agreement with theoretical predictions using the spin flip model. The ability to control the polarization of a long-wavelength VCSEL at room temperature emitting at the wavelength of 1.3 μm opens up a new exciting research avenue for novel uses in disparate fields of technology ranging from spintronics to optical telecommunication networks. © 2012 Optical Society of America

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

CellExcite: an efficient simulation environment for excitable cells

Background Brain, heart and skeletal muscle share similar properties of excitable tissue, featuring both discrete behavior (all-or-nothing response to electrical activation) and continuous behavior (recovery to rest follows a temporal path, determined by multiple competing ion flows). Classical mathematical models of excitable cells involve complex systems of nonlinear differential equations. Such models not only impair formal analysis but also impose high computational demands on simulations, especially in large-scale 2-D and 3-D cell networks. In this paper, we show that by choosing Hybrid Automata as the modeling formalism, it is possible to construct a more abstract model of excitable cells that preserves the properties of interest while reducing the computational effort, thereby admitting the possibility of formal analysis and efficient simulation. Results We have developed CellExcite, a sophisticated simulation environment for excitable-cell networks. CellExcite allows the user to sketch a tissue of excitable cells, plan the stimuli to be applied during simulation, and customize the diffusion model. CellExcite adopts Hybrid Automata (HA) as the computational model in order to efficiently capture both discrete and continuous excitable-cell behavior. Conclusions The CellExcite simulation framework for multicellular HA arrays exhibits significantly improved computational efficiency in large-scale simulations, thus opening the possibility for formal analysis based on HA theory. A demo of CellExcite is available at http://www.cs.sunysb.edu/~eha/ webcite

Finding Your Mate at a Cocktail Party: Frequency Separation Promotes Auditory Stream Segregation of Concurrent Voices in Multi-Species Frog Choruses

Vocal communication in crowded social environments is a difficult problem for both humans and nonhuman animals. Yet many important social behaviors require listeners to detect, recognize, and discriminate among signals in a complex acoustic milieu comprising the overlapping signals of multiple individuals, often of multiple species. Humans exploit a relatively small number of acoustic cues to segregate overlapping voices (as well as other mixtures of concurrent sounds, like polyphonic music). By comparison, we know little about how nonhuman animals are adapted to solve similar communication problems. One important cue enabling source segregation in human speech communication is that of frequency separation between concurrent voices: differences in frequency promote perceptual segregation of overlapping voices into separate “auditory streams” that can be followed through time. In this study, we show that frequency separation (ΔF) also enables frogs to segregate concurrent vocalizations, such as those routinely encountered in mixed-species breeding choruses. We presented female gray treefrogs (Hyla chrysoscelis) with a pulsed target signal (simulating an attractive conspecific call) in the presence of a continuous stream of distractor pulses (simulating an overlapping, unattractive heterospecific call). When the ΔF between target and distractor was small (e.g., ≤3 semitones), females exhibited low levels of responsiveness, indicating a failure to recognize the target as an attractive signal when the distractor had a similar frequency. Subjects became increasingly more responsive to the target, as indicated by shorter latencies for phonotaxis, as the ΔF between target and distractor increased (e.g., ΔF = 6–12 semitones). These results support the conclusion that gray treefrogs, like humans, can exploit frequency separation as a perceptual cue to segregate concurrent voices in noisy social environments. The ability of these frogs to segregate concurrent voices based on frequency separation may involve ancient hearing mechanisms for source segregation shared with humans and other vertebrates

Detecting a stochastic gravitational wave background with the Laser Interferometer Space Antenna

The random superposition of many weak sources will produce a stochastic background of gravitational waves that may dominate the response of the LISA (Laser Interferometer Space Antenna) gravitational wave observatory. Unless something can be done to distinguish between a stochastic background and detector noise, the two will combine to form an effective noise floor for the detector. Two methods have been proposed to solve this problem. The first is to cross-correlate the output of two independent interferometers. The second is an ingenious scheme for monitoring the instrument noise by operating LISA as a Sagnac interferometer. Here we derive the optimal orbital alignment for cross-correlating a pair of LISA detectors, and provide the first analytic derivation of the Sagnac sensitivity curve.Comment: 9 pages, 11 figures. Significant changes to the noise estimate

Rules extraction from neural networks applied to the prediction and recognition of prokaryotic promoters

Promoters are DNA sequences located upstream of the gene region and play a central role in gene expression. Computational techniques show good accuracy in gene prediction but are less successful in predicting promoters, primarily because of the high number of false positives that reflect characteristics of the promoter sequences. Many machine learning methods have been used to address this issue. Neural Networks (NN) have been successfully used in this field because of their ability to recognize imprecise and incomplete patterns characteristic of promoter sequences. In this paper, NN was used to predict and recognize promoter sequences in two data sets: (i) one based on nucleotide sequence information and (ii) another based on stability sequence information. The accuracy was approximately 80% for simulation (i) and 68% for simulation (ii). In the rules extracted, biological consensus motifs were important parts of the NN learning process in both simulations

Tumor Angiogenesis and Vascular Patterning: A Mathematical Model

Understanding tumor induced angiogenesis is a challenging problem with important consequences for diagnosis and treatment of cancer. Recently, strong evidences suggest the dual role of endothelial cells on the migrating tips and on the proliferating body of blood vessels, in consonance with further events behind lumen formation and vascular patterning. In this paper we present a multi-scale phase-field model that combines the benefits of continuum physics description and the capability of tracking individual cells. The model allows us to discuss the role of the endothelial cells' chemotactic response and proliferation rate as key factors that tailor the neovascular network. Importantly, we also test the predictions of our theoretical model against relevant experimental approaches in mice that displayed distinctive vascular patterns. The model reproduces the in vivo patterns of newly formed vascular networks, providing quantitative and qualitative results for branch density and vessel diameter on the order of the ones measured experimentally in mouse retinas. Our results highlight the ability of mathematical models to suggest relevant hypotheses with respect to the role of different parameters in this process, hence underlining the necessary collaboration between mathematical modeling, in vivo imaging and molecular biology techniques to improve current diagnostic and therapeutic tools