Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal ...Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.展开更多
Mild cognitive impairment(MCI)is a prodrome of Alzheimer’s disease pathology.Cognitive impairment patients often have a delayed diagnosis because there are no early symptoms or conventional diagnostic methods.Exosome...Mild cognitive impairment(MCI)is a prodrome of Alzheimer’s disease pathology.Cognitive impairment patients often have a delayed diagnosis because there are no early symptoms or conventional diagnostic methods.Exosomes play a vital role in cell-to-cell communications and can act as promising biomarkers in diagnosing diseases.This study was designed to identify serum exosomal candidate proteins that may play roles in diagnosing MCI.Mass spectrometry coupled with tandem mass tag approach-based non-targeted proteomics was used to show the differentially expressed proteins in exosomes between MCI patients and healthy controls,and these differential proteins were validated using immunoblot and enzyme-linked immunosorbent assays.Correlation of cognitive performance with the serum exosomal protein level was determined.Nanoparticle tracking analysis suggested that there was a higher serum exosome concentration and smaller exosome diameter in individuals with MCI compared with healthy controls.We identified 69 exosomal proteins that were differentially expressed between MCI patients and healthy controls using mass spectrometry analysis.Thirty-nine exosomal proteins were upregulated in MCI patients compared with those in control patients.Exosomal fibulin-1,with an area under the curve value of 0.81,may be a biomarker for an MCI diagnosis.The exosomal protein signature from MCI patients reflected the cell adhesion molecule category.In particular,higher exosomal fibulin-1 levels correlated with lower cognitive performance.Thus,this study revealed that exosomal fibulin-1 is a promising biomarker for diagnosing MCI.展开更多
基金supported by National Key Research and Development Program of China(2018YFC1504502).
文摘Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.
基金supported by the National Natural Science Foundation of China,No.81801071(to YJL)Top-notch Postgraduate Talent Cultivation Program of Chongqing Medical University,No.BJRC202106(to BC).
文摘Mild cognitive impairment(MCI)is a prodrome of Alzheimer’s disease pathology.Cognitive impairment patients often have a delayed diagnosis because there are no early symptoms or conventional diagnostic methods.Exosomes play a vital role in cell-to-cell communications and can act as promising biomarkers in diagnosing diseases.This study was designed to identify serum exosomal candidate proteins that may play roles in diagnosing MCI.Mass spectrometry coupled with tandem mass tag approach-based non-targeted proteomics was used to show the differentially expressed proteins in exosomes between MCI patients and healthy controls,and these differential proteins were validated using immunoblot and enzyme-linked immunosorbent assays.Correlation of cognitive performance with the serum exosomal protein level was determined.Nanoparticle tracking analysis suggested that there was a higher serum exosome concentration and smaller exosome diameter in individuals with MCI compared with healthy controls.We identified 69 exosomal proteins that were differentially expressed between MCI patients and healthy controls using mass spectrometry analysis.Thirty-nine exosomal proteins were upregulated in MCI patients compared with those in control patients.Exosomal fibulin-1,with an area under the curve value of 0.81,may be a biomarker for an MCI diagnosis.The exosomal protein signature from MCI patients reflected the cell adhesion molecule category.In particular,higher exosomal fibulin-1 levels correlated with lower cognitive performance.Thus,this study revealed that exosomal fibulin-1 is a promising biomarker for diagnosing MCI.
