Expression Analysis and Knockdown of Two Antennal Odorant-Binding Protein Genes in Aedes aegypti

The presence and expression of odorant-binding proteins (OBPs) in the olfactory organs suggest that they play an important role in mosquito olfaction. However, no direct evidence has been found for their involvement in the host-seeking behavior of mosquitoes. It is important to establish a method in which a loss-of-function test can be performed to determine the possible role of these genes in olfaction. In this study, a double subgenomic Sindbis virus expression system was used to reduce the expression of two Obp genes in Aedes aegypti L (Diptera: Culicidae), AaegObp1 and AaegObp2. Quantitative real-time PCR analysis showed predominant expression of both genes in the female antennae, the primary olfactory tissue of mosquitoes. Moreover, at 11 days post virus-inoculation, the mRNA levels of AaegObp1 and AaegObp2 were significantly reduced in olfactory tissues of recombinant virus-inoculated female mosquitoes compared to that of controls by approximately 8 and 100-fold, respectively. These data suggest that the double subgenomic Sindbis virus expression system can be efficiently used to knockdown Obp gene expression in olfactory tissues of mosquitoes. We discuss the potential for a systematic analysis of the molecular players involved in mosquito olfaction using this newly developed technique. Such analysis will provide an important step to interfere with the host-seeking behavior of mosquitoes to prevent the transmission of diseases

Phytochemical and antioxidant characteristics of medlar fruits (Mespilus germanica L.)

Eleven medlar (Mespilus germanica L.) genotypes sampled from Turkey were analyzed for their fruit weight, fruit dimensions, fruit firmness, ostiole diameter, shape index, skin color, moisture (%), ash (%), reducing sugar (%), crude protein (%), pH, soluble solid content (%), vitamin C (mg/100 g), minerals (P, K, Ca, Mg, Fe, Zn, Mn), total phenolic content and total antioxidant capacity. A wide variation among genotypes on most of the searched parameters was evident. Fruit weight varied from 11.21 g to 33.24 g indicating high variability among genotypes. Determination of antioxidant activities by β-carotene–linoleic acid and 2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging assays resulted in average 80.8%, and 46.6 μg/ml FW DPPH, respectively. The total phenolic contents of eleven medlar genotypes varied from 114 to 293 mg gallic acid equivalent in 100 g fresh weight basis. The medlar fruits were found to be rich in terms of potassium, calcium, phosphorus, magnesium and iron

Self-optimization of coverage and capacity based on a fuzzy neural network with cooperative reinforcement learning

Self-organization is a key concept in long-term evolution (LTE) systems to reduce capital and operational expenditures (CAPEX and OPEX). Self-optimization of coverage and capacity, which allows the system to periodically and automatically adjust the key radio frequency (RF) parameters through intelligent algorithms, is one of the most important tasks in the context of self-organizing networks (SON). In this paper, we propose self-optimization of antenna tilt and power using a fuzzy neural network optimization based on reinforcement learning (RL-FNN). In our approach, a central control mechanism enables cooperation-based learning by allowing distributed SON entities to share their optimization experience, represented as the parameters of learning method. Specifically, SON entities use cooperative Q-learning and reinforced back-propagation method to acquire and adjust their optimization experience. To evaluate the coverage and capacity performance of RL-FNN, we analyze cell-edge performance and cell-center performance indicators jointly across neighboring cells and specifically consider the difference in load distribution in a given region. The simulation results show that RL-FNN performs significantly better than the best fixed configuration proposed in the literature. Furthermore, this is achieved with significantly lower energy consumption. Finally, since each self-optimization round completes in less than a minute, RL-FNN can meet the need of practical applications of self-optimization in a dynamic environment

Self-optimized heterogeneous networks for energy efficiency

© 2015, Fan et al.; licensee Springer. Explosive increase in mobile data traffic driven by the demand for higher data rates and ever-increasing number of wireless users results in a significant increase in power consumption and operating cost of communication networks. Heterogeneous networks (HetNets) provide a variety of coverage and capacity options through the use of cells of different sizes. In these networks, an active/sleep scheduling strategy for base stations (BSs) becomes an effective way to match capacity to demand and also improve energy efficiency. At the same time, environmental awareness and self-organizing features are expected to play important roles in improving the network performance. In this paper, we propose a new active/sleep scheduling scheme based on the user activity sensing of small cell BSs. To this end, coverage probability, network capacity, and energy consumption of the proposed scheme in K-tier heterogeneous networks are analyzed using stochastic geometry, accounting for cell association uncertainties due to random positioning of users and BSs, channel conditions, and interference. Based on the analysis, we propose a sensing probability optimization (SPO) approach based on reinforcement learning to acquire the experience of optimizing the user activity sensing probability of each small cell tier. Simulation results show that SPO adapts well to user activity fluctuations and improves energy efficiency while maintaining network capacity and coverage probability guarantees.National Natural Science Foundation of China (No. 61471060); National Major Science and Technology Special Project of China (No. 2013ZX03003016); Funds for Creative Research Groups of China (No. 61421061)

Participatory Co-Design of Wearable Solutions for Monitoring Infants in Remote Environments in Sierra Leone

Corresponding author: Steven Sam, [email protected]: This project explores the potential of digital health technologies, including wearable Internet of Things (IoT) solutions, to remotely monitor and capture parameters and data for detecting and improving communications in accessing health care. The main aim is to facilitate the prevention of various health conditions (e.g., preterm, tetanus, fevers, measles and diarrhoea) in infants in Sierra Leone that lead to high infant mortality rates in Sierra Leone. Sierra Leone has one of the highest infant mortality rates, estimated at 39 per 1000 live births – three times higher than the Sustainable Development Goal (SDG) target of 12 infant deaths per 1000 live births by 2030. Lack of access to and low utilisation of effective and efficient health systems, aggravated by a range of factors, such as inequity in coverage, inadequate human resources and weak infrastructure, referral information and community health systems, are major contributing factors for high infant deaths. The solutions will assist in addressing these challenges. Objectives: The project aims to assist efforts to reduce infant deaths through developing culturally appropriate, people-led design of digital health technologies, including mobile and wearable IoT solutions to: (i) facilitate remote monitoring of infant health (vital signs such as temperature, pulse rates, respiration blood pressure, etc.) and alerting of potential critical illness in non-clinical settings, and (ii) improve healthcare access and ethical and FAIR data sharing between parents and health workers in Sierra Leone. Methods: The overall research approach is informed by a participatory design thinking framework, with the emphasis on four fundamental principles critical for developing appropriate and contextualised digital technologies in resource-poor settings: availability, accessibility, affordability and usability. It adopts a mix of socio-technical research using literature review and interviews and a co-design workshop with healthcare providers and caregivers. The socio-technical research provides in-depth insights into the contextual issues around the current healthcare system, challenges and opportunities for new technology innovations. The design workshop embodied inputs from the socio-technical research, participants’ experiences, design scenarios, image-based reflections, journey maps and group discussions. Results: Timing is everything to reduce child deaths. Both healthcare workers and caregivers believe that acting as soon as the first symptoms are detected can make a significant difference in reducing the risk of infant deaths. Therefore, the remote monitoring solutions should provide early alerting for symptoms of common child illnesses such as malaria, pneumonia and diarrhoea, to facilitate informed decision making, timely response and treatment. Results further show a layered deployment of remote monitoring technology ecosystem including a paired wearable design used both on infants and primary carers, a mobile application and community/mobile internet kiosk to facilitate communication among different stakeholders, which, at times, may not even share a common spoken language. This layered approach is also expected to facilitate technology adoption in phases. Conclusion: Our research confirms that digital technologies solutions designed together with the actual users can address contextually relevant healthcare solutions that reflect their actual needs. The layered deployment ecosystem emerged as a potential solution to strengthen information sharing and timely decision making and provide effective routes for remote monitoring and early alerting and access to the treatment of leading causes of infants’ deaths in Sierra Leone.Engineering and Physical Sciences Research Council ref: EP/S032045/1 (SMILE - System for Monitoring Infants in Low-Resource Environments)

Egr1 regulates the coordinated expression of numerous EGF receptor target genes as identified by ChIP-on-chip

UV stimulation of prostate cells causes an apoptotic response that is dependent on the zinc finger transcription factor Egr1; downstream targets of Egr1 in this response were identified