The Effect of Superparamagnetic Iron Oxide Nanoparticle Surface Charge on Antigen Cross-Presentation.

Magnetic nanoparticles (NPs) of superparamagnetic iron oxide (SPIO) have been explored for different kinds of applications in biomedicine, mechanics, and information. Here, we explored the synthetic SPIO NPs as an adjuvant on antigen cross-presentation ability by enhancing the intracellular delivery of antigens into antigen presenting cells (APCs). Particles with different chemical modifications and surface charges were used to study the mechanism of action of antigen delivery. Specifically, two types of magnetic NPs, γF

Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs

Skin lesion segmentation is a fundamental task in dermoscopic image analysis. The complex features of pixels in the lesion region impede the lesion segmentation accuracy, and existing deep learning-based methods often lack interpretability to this problem. In this work, we propose a novel unsupervised Skin Lesion sEgmentation framework based on structural entropy and isolation forest outlier Detection, namely SLED. Specifically, skin lesions are segmented by minimizing the structural entropy of a superpixel graph constructed from the dermoscopic image. Then, we characterize the consistency of healthy skin features and devise a novel multi-scale segmentation mechanism by outlier detection, which enhances the segmentation accuracy by leveraging the superpixel features from multiple scales. We conduct experiments on four skin lesion benchmarks and compare SLED with nine representative unsupervised segmentation methods. Experimental results demonstrate the superiority of the proposed framework. Additionally, some case studies are analyzed to demonstrate the effectiveness of SLED.Comment: 10 pages, 8 figures, conference. Accepted by IEEE ICDM 202

On Enabling Mobile Crowd Sensing for Data Collection in Smart Agriculture

Smart agriculture enables the efficiency and intelligence of production in physical farm management. Though promising, due to the limitation of the existing data collection methods, it still encounters few challenges that are required to be considered. Mobile Crowd Sensing (MCS) embeds three beneficial characteristics: a) cost-effectiveness, b) scalability, and c) mobility and robustness. With the Internet of Things (IoT) becoming a reality, the smart phones are widely becoming available even in remote areas. Hence, both the MCSs characteristics and the plug and play widely available infrastructure provides huge opportunities for the MCS-enabled smart agriculture.opening up several new opportunities at the application level. In this paper, we extensively evaluate the Agriculture Mobile Crowd Sensing (AMCS) and provide insights for agricultural data collection schemes. In addition, we provide a comparative study with the existing agriculture data collection solutions and conclude that AMCS has significant benefits in terms of flexibility, collecting implicit data, and low cost requirements. However, we note that AMCSs may still posses limitations in regard to data integrity and quality to be considered as a future work. To this end, we perform a detailed analysis of the challenges and opportunities that concerns the MCS-enabled agriculture by putting forward six potential applications of AMCS-enabled agriculture. Finally, we propose future research and focus on agricultural characteristics, e.g., seasonality and regionality

ieee access special section editorial recent advances on radio access and security methods in 5g networks

Serviceability is the ability of a network to serve user equipments (UEs) within desired requirements (e.g., throughput, delay, and packet loss). High serviceability is considered as one of the key foundational criteria towards a successful fog radio access infrastructure satisfying the Internet of Things paradigm in the 5G era. In the article by Dao et al. , "Adaptive resource balancing for serviceability maximization in fog radio access networks," the authors propose an adaptive resource balancing (ARB) scheme for serviceability maximization in fog radio access networks wherein the resource block (RB) utilization among remote radio heads (RRHs) is balanced using the backpressure algorithm with respect to a time-varying network topology issued by potential RRH motilities. The optimal UE selection for service migration from a high-RB-utilization RRH to its neighboring low RB-utilization RRHs is determined by the Hungarian method to minimize RB occupation after moving the service. Analytical results reveal that the proposed ARB scheme provides substantial gains compared to the standalone capacity-aware, max-rate, and cache-aware UE association approaches in terms of serviceability, availability, and throughput

Effects of habitat usage on hypoxia avoidance behavior and exposure in reef-dependent marine coastal species

Reef habitat in coastal ecosystems is increasingly being augmented with artificial reefs (ARs) and is simultaneously experiencing increasing hypoxia due to eutrophication and climate change. Relatively little is known about the effects of hypoxia on organisms that use complex habitat arrangements and how the presence of highly preferred AR habitat can affect the exposure of organisms to low dissolved oxygen (DO). We performed two laboratory experiments that used video recording of behavioral movement to explore 1) habitat usage and staying duration of individuals continuously exposed to 3, 5, and 7 mg/L dissolved oxygen (DO) in a complex of multiple preferred and avoided habitat types, and 2) the impact of ARs on exposure to different DO concentrations under a series of two-way replicated choice experiments with or without AR placement on the low-oxygen side. Six common reef-dependent species found in the northeastern sea areas of China were used (i.e., rockfish Sebastes schlegelii and Hexagrammos otakii, filefish Thamnaconus modestus, flatfish Pseudopleuronectes yokohamae, sea cucumber Stichopus japonicus, and crab Charybdis japonica). Results showed that lower DO levels decreased the usage of preferred habitats of the sea cucumber and the habitat-generalist filefish but increased the habitat affinity to preferred habitat types for the two habitat-specific rockfishes. Low DO had no effect on the crab’s habitat usage. In the choice experiment, all three fish species avoided 1 mg/L, and the rockfish S. schlegelii continued to avoid the lower DO when given choices involving pairs of 3, 5, and 7 mg/L, while H. otakii and the flatfish showed less avoidance. The availability of ARs affected exposure to low DO for the habitat-preferring rockfishes but was not significant for the flatfish. This study provides information for assessing the ecological effects and potential for adaptation through behavioral movement for key reef-dependent species under the increasing overlap of ARs and hypoxia anticipated in the future

Who should be prioritized for COVID-19 vaccination in China? A descriptive study.

BACKGROUND: All countries are facing decisions about which population groups to prioritize for access to COVID-19 vaccination after the first vaccine products have been licensed, at which time supply shortages are inevitable. Our objective is to define the key target populations, their size, and priority for a COVID-19 vaccination program in the context of China. METHODS: On the basis of utilitarian and egalitarian principles, we define and estimate the size of tiered target population groups for a phased introduction of COVID-19 vaccination, considering evolving goals as vaccine supplies increase, detailed information on the risk of illness and transmission, and past experience with vaccination during the 2009 influenza pandemic. Using publicly available data, we estimated the size of target population groups, and the number of days needed to vaccinate 70% of the target population. Sensitivity analyses considered higher vaccine coverages and scaled up vaccine delivery relative to the 2009 pandemic. RESULTS: Essential workers, including staff in the healthcare, law enforcement, security, nursing homes, social welfare institutes, community services, energy, food and transportation sectors, and overseas workers/students (49.7 million) could be prioritized for vaccination to maintain essential services in the early phase of a vaccination program. Subsequently, older adults, individuals with underlying health conditions and pregnant women (563.6 million) could be targeted for vaccination to reduce the number of individuals with severe COVID-19 outcomes, including hospitalizations, critical care admissions, and deaths. In later stages, the vaccination program could be further extended to target adults without underlying health conditions and children (784.8 million), in order to reduce symptomatic infections and/or to stop virus transmission. Given 10 million doses administered per day, and a two-dose vaccination schedule, it would take 1 week to vaccinate essential workers but likely up to 7 months to vaccinate 70% of the overall population. CONCLUSIONS: The proposed framework is general but could assist Chinese policy-makers in the design of a vaccination program. Additionally, this exercise could be generalized to inform other national and regional strategies for use of COVID-19 vaccines, especially in low- and middle-income countries

Study on optimization of rake head density of suction hopper dredger based on bat algorithm and extreme learning machine

The dredging output of suction dredger mainly comes from the suction density of the rake head. Accurate prediction of suction density is of great significance to improve the dredging output of suction dredger. In order to overcome the shortcomings of low accuracy and poor real-time performance of the current inhalation density prediction methods, a bat algorithm is proposed to optimize the inhalation density prediction method of extreme learning machine. The bat algorithms for optimizing extreme learning machines prediction model is constructed based on the measured construction data of “Xinhaifeng” Yangtze Estuary, and compared with other prediction models. Finally, the bat algorithms for optimizing extreme learning machines model is used to build the output simulator of inhalation density. Compared with the actual construction, the selection of control parameters is analyzed when the output of inhalation density is the best. Experients show that bat algorithms for optimizing extreme learning machines prediction has high accuracy and good stability, and can provide scientific and effective reference for yield prediction and construction guidance

Study on optimization of rake head density of suction hopper dredger based on bat algorithm and extreme learning machine

The dredging output of suction dredger mainly comes from the suction density of the rake head. Accurate prediction of suction density is of great significance to improve the dredging output of suction dredger. In order to overcome the shortcomings of low accuracy and poor real-time performance of the current inhalation density prediction methods, a bat algorithm is proposed to optimize the inhalation density prediction method of extreme learning machine. The bat algorithms for optimizing extreme learning machines prediction model is constructed based on the measured construction data of “Xinhaifeng” Yangtze Estuary, and compared with other prediction models. Finally, the bat algorithms for optimizing extreme learning machines model is used to build the output simulator of inhalation density. Compared with the actual construction, the selection of control parameters is analyzed when the output of inhalation density is the best. Experients show that bat algorithms for optimizing extreme learning machines prediction has high accuracy and good stability, and can provide scientific and effective reference for yield prediction and construction guidance