Response of the mosquito protein interaction network to dengue infection

<p>Abstract</p> <p>Background</p> <p>Two fifths of the world's population is at risk from dengue. The absence of effective drugs and vaccines leaves vector control as the primary intervention tool. Understanding dengue virus (DENV) host interactions is essential for the development of novel control strategies. The availability of genome sequences for both human and mosquito host greatly facilitates genome-wide studies of DENV-host interactions.</p> <p>Results</p> <p>We developed the first draft of the mosquito protein interaction network using a computational approach. The weighted network includes 4,214 <it>Aedes aegypti </it>proteins with 10,209 interactions, among which 3,500 proteins are connected into an interconnected scale-free network. We demonstrated the application of this network for the further annotation of mosquito proteins and dissection of pathway crosstalk. Using three datasets based on physical interaction assays, genome-wide RNA interference (RNAi) screens and microarray assays, we identified 714 putative DENV-associated mosquito proteins. An integrated analysis of these proteins in the network highlighted four regions consisting of highly interconnected proteins with closely related functions in each of replication/transcription/translation (RTT), immunity, transport and metabolism. Putative DENV-associated proteins were further selected for validation by RNAi-mediated gene silencing, and dengue viral titer in mosquito midguts was significantly reduced for five out of ten (50.0%) randomly selected genes.</p> <p>Conclusions</p> <p>Our results indicate the presence of common host requirements for DENV in mosquitoes and humans. We discuss the significance of our findings for pharmacological intervention and genetic modification of mosquitoes for blocking dengue transmission.</p

Reciprocal polarization imaging of complex media

The vectorial evolution of polarized light interaction with a medium can reveal its microstructure and anisotropy beyond what can be obtained from scalar light interaction. Anisotropic properties (diattenuation, retardance, and depolarization) of a complex medium can be quantified by polarization imaging by measuring the Mueller matrix. However, polarization imaging in the reflection geometry, ubiquitous and often preferred in diverse applications, has suffered a poor recovery of the medium's anisotropic properties due to the lack of suitable decomposition of the Mueller matrices measured inside a backward geometry. Here, we present reciprocal polarization imaging of complex media after introducing reciprocal polar decomposition for backscattering Mueller matrices. Based on the reciprocity of the optical wave in its forward and backward scattering paths, the anisotropic diattenuation, retardance, and depolarization of a complex medium are determined by measuring the backscattering Mueller matrix. We demonstrate reciprocal polarization imaging in various applications for quantifying complex non-chiral and chiral media (birefringence resolution target, tissue sections, and glucose suspension), uncovering their anisotropic microstructures with remarkable clarity and accuracy. We also highlight types of complex media that Lu-Chipman and differential decompositions of backscattering Mueller matrices lead to erroneous medium polarization properties, whereas reciprocal polar decomposition recovers properly. Reciprocal polarization imaging will be instrumental in imaging complex media from remote sensing to biomedicine and will open new applications of polarization optics in reflection geometry

The Endosymbiotic Bacterium Wolbachia Induces Resistance to Dengue Virus in Aedes aegypti

Genetic strategies that reduce or block pathogen transmission by mosquitoes have been proposed as a means of augmenting current control measures to reduce the growing burden of vector-borne diseases. The endosymbiotic bacterium Wolbachia has long been promoted as a potential vehicle for introducing disease-resistance genes into mosquitoes, thereby making them refractory to the human pathogens they transmit. Given the large overlap in tissue distribution and intracellular localization between Wolbachia and dengue virus in mosquitoes, we conducted experiments to characterize their interactions. Our results show that Wolbachia inhibits viral replication and dissemination in the main dengue vector, Aedes aegypti. Moreover, the virus transmission potential of Wolbachia-infected Ae. aegypti was significantly diminished when compared to wild-type mosquitoes that did not harbor Wolbachia. At 14 days post-infection, Wolbachia completely blocked dengue transmission in at least 37.5% of Ae. aegypti mosquitoes. We also observed that this Wolbachia-mediated viral interference was associated with an elevated basal immunity and increased longevity in the mosquitoes. These results underscore the potential usefulness of Wolbachia-based control strategies for population replacement

Reverse Engineering User Behaviors From Network Traffic

In today's world, more and more people are managing every aspect of their lives over the Internet. As a result, the study of Internet traffic, which is undergoing constant evolution as new technologies emerge, has attracted much attention from the research community. In this dissertation, we present a three-pronged approach to help ISPs and network administrators: a) gain insight about the applications that generate traffic in their networks, b) understand the Web browsing behaviors of their users, and c) detect in a timely fashion when external malicious entities seek to compromise their websites.The first component of our approach is SubFlow, a Machine Learning-based tool that classifies traffic flows into classes of applications that generate them, for example P2P or Web. The key novelty of SubFlow is its ability to learn the characteristics of the traffic from each application class in isolation while traditional approaches simply try to assign flows to predefined categories. This allows SubFlow to exhibit very high classification accuracy even when new applications emerge.The second component is ReSurf, a tool to reconstruct users' web-surfing activities from Web traffic. ReSurf enables the separation of users' intentional web-browsing (such as the click user makes) from the traffic automatically generated when the website is rendered. ReSurf, then, can be an effective method to study the browsing behaviors of users and gain insights into the evolution of modern Web traffic, which accounts for about 80% of Internet traffic.The last component of our approach is Scanner Hunter, an algorithm to detect HTTP Scanners, external entities that selectively probe websites for vulnerabilities that may be exploited in subsequent intrusion attempts. Our algorithm is developed in response to the fact that HTTP scanners have not received much attention despite the high risk and danger they pose. Scanner Hunter utilizes a novel combination of graph-mining approaches to expose the community structure of scanners. Using Scanner Hunter, we conduct the first extensive study of scanners in the wild during a half-year period, which we also provide novel insight on this little-studied emerging phenomenon

Compositional reasoning for hardware/software co-verification

Abstract. In this paper, we present and illustrate an approach to compositional reasoning for hardware/software co-verification of embedded systems. The major challenges in compositional reasoning for co-verification include: (1) the hardware/software semantic gaps, (2) lack of common property specification languages for hardware and software, and (3) lack of compositional reasoning rules that are applicable across the hardware/software boundaries. Our approach addresses these challenges by (1) filling the hardware/software semantic gaps via translation of hardware and software into a common formal language, (2) defining a unified property specification language for hardware, software, and entire systems, and (3) enabling application of existing compositional reasoning rules across the hardware/software boundaries based on translation, developing a new rule for compositional reasoning with components that share sub-components, and extending the applicability of these rules via dependency refinement. Our approach has been applied to co-verification of networked sensors. The case studies have shown that our approach is very effective in enabling application of compositional reasoning to co-verification of non-trivial embedded systems.

A Heuristic Evolutionary Algorithm of UAV Path Planning

With the rapid development of the network and the informatization of society, how to improve the accuracy of information is an urgent problem to be solved. The existing method is to use an intelligent robot to carry sensors to collect data and transmit the data to the server in real time. Many intelligent robots have emerged in life; the UAV (unmanned aerial vehicle) is one of them. With the popularization of UAV applications, the security of UAV has also been exposed. In addition to some human factors, there is a major factor in the UAV’s endurance. UAVs will face a problem of short battery life when performing flying missions. In order to solve this problem, the existing method is to plan the path of UAV flight. In order to find the optimal path for a UAV flight, we propose three cost functions: path security cost, length cost, and smoothness cost. The path security cost is used to determine whether the path is feasible; the length cost and smoothness cost of the path directly affect the cost of the energy consumption of the UAV flight. We proposed a heuristic evolutionary algorithm that designed several evolutionary operations: substitution operations, crossover operations, mutation operations, length operations, and smoothness operations. Through these operations to enhance our build path effect. Under the analysis of experimental results, we proved that our solution is feasible

Component-Based Hardware/Software Co-Verification for Building Trustworthy Embedded Systems ∗

We present a novel component-based approach to hardware/software co-verification of embedded systems using model checking. Embedded systems are pervasive and often mission-critical, therefore, they must be highly trustworthy. Trustworthy embedded systems require extensive verification. The close interactions between hardware and software of embedded systems demand co-verification. Due to their diverse applications and often strict physical constraints, embedded systems are increasingly component-based and include only the necessary components for their missions. In our approach, a component model for embedded systems which unifies the concepts of hardware IPs (i.e., hardware components) and software components is defined. Hardware and software components are verified as they are developed bottom-up. Whole systems are co-verified as they are developed top-down. Interactions of bottom-up and top-down verification are exploited to reduce verification complexity by facilitating compositional reasoning and verification reuse. Case studies on a suite of networked sensors have shown that our approach facilitates major verification reuse and leads to order-of-magnitude reduction on verification complexity. 1