Phenoloxidase activity acts as a mosquito innate immune response against infection with semliki forest virus

Several components of the mosquito immune system including the RNA interference (RNAi), JAK/STAT, Toll and IMD pathways have previously been implicated in controlling arbovirus infections. In contrast, the role of the phenoloxidase (PO) cascade in mosquito antiviral immunity is unknown. Here we show that conditioned medium from the Aedes albopictus-derived U4.4 cell line contains a functional PO cascade, which is activated by the bacterium Escherichia coli and the arbovirus Semliki Forest virus (SFV) (Togaviridae; Alphavirus). Production of recombinant SFV expressing the PO cascade inhibitor Egf1.0 blocked PO activity in U4.4 cell- conditioned medium, which resulted in enhanced spread of SFV. Infection of adult female Aedes aegypti by feeding mosquitoes a bloodmeal containing Egf1.0-expressing SFV increased virus replication and mosquito mortality. Collectively, these results suggest the PO cascade of mosquitoes plays an important role in immune defence against arboviruses

Gene silencing in tick cell lines using small interfering or long double-stranded RNA

Gene silencing by RNA interference (RNAi) is an important research tool in many areas of biology. To effectively harness the power of this technique in order to explore tick functional genomics and tick-microorganism interactions, optimised parameters for RNAi-mediated gene silencing in tick cells need to be established. Ten cell lines from four economically important ixodid tick genera (Amblyomma, Hyalomma, Ixodes and Rhipicephalus including the sub-species Boophilus) were used to examine key parameters including small interfering RNA (siRNA), double stranded RNA (dsRNA), transfection reagent and incubation time for silencing virus reporter and endogenous tick genes. Transfection reagents were essential for the uptake of siRNA whereas long dsRNA alone was taken up by most tick cell lines. Significant virus reporter protein knockdown was achieved using either siRNA or dsRNA in all the cell lines tested. Optimum conditions varied according to the cell line. Consistency between replicates and duration of incubation with dsRNA were addressed for two Ixodes scapularis cell lines; IDE8 supported more consistent and effective silencing of the endogenous gene subolesin than ISE6, and highly significant knockdown of the endogenous gene 2I1F6 in IDE8 cells was achieved within 48 h incubation with dsRNA. In summary, this study shows that gene silencing by RNAi in tick cell lines is generally more efficient with dsRNA than with siRNA but results vary between cell lines and optimal parameters need to be determined for each experimental system

Sampling Rate Prediction of Biosensors in Wireless Body Area Networks using Deep-Learning Methods

In this paper, we propose a scheme which aims at determining and forecasting sampling rate of active biosensors in Wireless Body Area Networks (WBANs). In this regard, from the first round until a certain round, the sampling rate of biosensors would be determined. Accordingly, we introduce our modified Fisher test, develop Spline interpolation method, introduce three main parameters namely information of patient's activity, patient's risk and pivot biosensor's value. Then, by employing these parameters plus introduced statistical and mathematical based strategies, the sampling rate of the active biosensors in the next round would be determined at the end of each entire round. After reaching a pre-denoted round the sampling rate of biosensors would be predicted through forecasting methods. In this regard, we develop two machine learning based techniques namely Adaptive Neuro Fuzzy Inference System (ANFIS) and Long Short Term Memory (LSTM) and compare them with four famous similar techniques. In addition to using forecasted sampling frequencies of the biosensors for controlling their energy expenditure, these forecasted values would also be used to forecast patient's status in the future. This is the first work in this domain that uses current information of the patient to determine adaptive sampling frequency and then employs the time series of determined sampling frequencies to forecast the patient's status and biosensors energy expenditure in the future. For estimating our schemes, we simulated them in MATLAB R2018b software and compared the results with a number of similar schemes. Based on the simulation results, the proposed schemes are capable to reduce data traffic by 81, decrease energy consumption of the network by 73 while having the capability of predicting sampling rate of biosensors with 97 accuracy. © 2020 Elsevier B.V

A composable and predictable MPSoC design flow for multiple real-time applications

Design of real-time MPSoC systems including multiple applications is challenging because temporal requirements of each application must be respected throughout the entire design flow. Currently the design of different applications is often interdependent, making converge to a solution for each application difficult. This chapter proposes a compositional method to design applications independently, and then to execute them without interference. We define a formal modeling framework as a suitable entry point for application design. The models are executable, which enables early detection of specification errors, and include the formal properties of the applications based on well-defined models of computation. We combine this with a predictable MPSoC platform template that has a supporting design flow but lacks a simulation front-end. The structure and behavior of the application models are exported to an intermediate format via introspection which is iteratively transformed for the backend flow. We identify the problems arising in this transformation and provide appropriate solutions. The design flow is demonstrated by a system consisting of two streaming applications where less than half of the design time is dedicated to operating on the integrated system model

A PSO-based model to increase the accuracy of software development effort estimation

Development effort is one of the most important metrics that must be estimated in order to design the plan of a project. The uncertainty and complexity of software projects make the process of effort estimation dif?cult and ambiguous. Analogy-based estimation (ABE) is the most common method in this area because it is quite straightforward and practical, relying on comparison between new projects and completed projects to estimate the development effort. Despite many advantages, ABE is unable to produce accurate estimates when the importance level of project features is not the same or the relationship among features is dif?cult to determine. In such situations, ef?cient feature weighting can be a solution to improve the performance of ABE. This paper proposes a hybrid estimation model based on a combination of a particle swarm optimization (PSO) algorithm and ABE to increase the accuracy of software development effort estimation. This combination leads to accurate identi?cation of projects that are similar, based on optimizing the performance of the similarity function in ABE. A framework is presented in which the appropriate weights are allocated to project features so that the most accurate estimates are achieved. The suggested model is ?exible enough to be used in different datasets including categorical and non-categorical project features. Three real data sets are employed to evaluate the proposed model, and the results are compared with other estimation models. The promising results show that a combination of PSO and ABE could signi?cantly improve the performance of existing estimation models

Treatment of seborrheic dermatitis: a comprehensive review

Seborrheic dermatitis (SD) is a chronic, recurring inflammatory skin disorder that manifests as erythematous macules or plaques with varying levels of scaling associated with pruritus. The condition typically occurs as an inflammatory response to Malassezia species and tends to occur on seborrheic areas, such as the scalp, face, chest, back, axilla, and groin areas. SD treatment focuses on clearing signs of the disease; ameliorating associated symptoms, such as pruritus; and maintaining remission with long-term therapy. Since the primary underlying pathogenic mechanisms comprise Malassezia proliferation and inflammation, the most commonly used treatment is topical antifungal and anti-inflammatory agents. Other broadly used therapies include lithium gluconate/succinate, coal tar, salicylic acid, selenium sulfide, sodium sulfacetamide, glycerin, benzoyl peroxide, aloe vera, mud treatment, phototherapy, among others. Alternative therapies have also been reported, such as tea tree oil, Quassia amara, and Solanum chrysotrichum. Systemic therapy is reserved only for widespread lesions or in cases that are refractory to topical treatment. Thus, in this comprehensive review, we summarize the current knowledge on SD treatment and attempt to provide appropriate directions for future cases that dermatologists may face