In this paper, the problem of enhancing the virtual reality (VR) experience for wireless users is investigated by minimizing the occurrence of breaks in presence (BIP) that can detach the users from ...their virtual world. To measure the BIP for wireless VR users, a novel model that jointly considers the VR application type, transmission delay, VR video quality, and users' awareness of the virtual environment is proposed. In the developed model, base stations (BSs) transmit VR videos to the wireless VR users using directional transmission links so as to provide high data rates for the VR users, thus, reducing the number of BIP for each user. Since the body movements of a VR user may result in a blockage of its wireless link, the location and orientation of VR users must also be considered when minimizing BIP. The BIP minimization problem is formulated as an optimization problem which jointly considers the predictions of users' locations, orientations, and their BS association. To predict the orientation and locations of VR users, a distributed learning algorithm based on the machine learning framework of deep echo state networks (ESNs) is proposed. The proposed algorithm uses federated learning to enable multiple BSs to locally train their deep ESNs using their collected data and cooperatively build a learning model to predict the entire users' locations and orientations. Using these predictions, the user association policy that minimizes BIP is derived. Simulation results demonstrate that the developed algorithm reduces the users' BIP by up to 16% and 26%, respectively, compared to centralized ESN and deep learning algorithms.
In this paper, the problem of trajectory design for energy harvesting unmanned aerial vehicles (UAVs) is studied. In the considered model, the UAV acts as a moving base station to serve the ground ...users, while collecting energy from the charging stations located at the center of a user group. For this purpose, the UAV must be examined and repaired regularly. In consequence, it is necessary to optimize the trajectory design of the UAV while jointly considering the maintenance costs, the reward of serving users, the energy management, and the user service time. To capture the relationship among these factors, we first model the completion of service and the harvested energy as the reward, and the energy consumption during the deployment as the cost. Then, the
is defined as the ratio of the reward to the cost of the UAV trajectory. Based on this definition, the trajectory design problem is formulated as an optimization problem whose goal is to maximize the deployment profitability of the UAV. To solve this problem, a foraging-based algorithm is proposed to find the optimal trajectory so as to maximize the deployment profitability and minimize the average user service time. The proposed algorithm can find the optimal trajectory for the UAV with low time complexity at the level of polynomial. Fundamental analysis shows that the proposed algorithm achieves the maximal deployment profitability. Simulation results show that, compared to Q-learning algorithm, the proposed algorithm effectively reduces the operation time and the average user service time while achieving the maximal deployment profitability.
Network function virtualisation (NFV), software defined networks (SDNs), and mobile edge computing (MEC) are emerging as core technologies to satisfy increasing number of users' demands in 5G and ...beyond wireless networks. SDN provides clean separation of the control plane from the data plane while NFV enables the flexible and on‐the‐fly creation and placement of virtual network functions (VNFs) and are able to be executed within the various locations of a distributed system. In this paper, VNF placement and resource allocation (VNFPRA) problem is considered which involves placing VNFs optimally in distributed NFV‐enabled MEC nodes and assigning MEC resources efficiently to these VNFs to satisfy users' requests in the network. Current solutions to this problem are slow and cannot handle real‐time requests. To this end, an SDN‐NFV infrastructure is proposed to tackle the VNFPRA problem in wireless MEC networks. Our aim is to minimise the overall placement and resource cost and also to minimise the total number of VNF migrations. A genetic based heuristic algorithm is proposed. The superior performance of the proposed solution is confirmed in comparison with four existing algorithms, i.e. resource utilisation‐single objective evolutionary algorithm (RU‐SOEA), genetic non‐bandwidth link allocation algorithm (GA‐NBA), random‐fit placement algorithm (RFPA), and first‐fit placement algorithm (FFPA). The results demonstrate that a coordinated placement of VNFs in SDN/NFV enabled MEC networks can satisfy the objective of overall reduced cost. Simulation results also reveal that the proposed scheme approximates well with the optimal solution returned by Gurobi and also achieves reduction on overall cost compared to other methods.
In this article, a joint task, spectrum, and transmit power allocation problem is investigated for a wireless network in which the base stations (BSs) are equipped with mobile-edge computing (MEC) ...servers to jointly provide computational and communication services to users. Each user can request one computational task from three types of computational tasks. Since the data size of each computational task is different, as the requested computational task varies, the BSs must adjust their resource (subcarrier and transmit power) and task allocation schemes to effectively serve the users. This problem is formulated as an optimization problem whose goal is to minimize the maximal computational and transmission delay among all users. A multistack reinforcement learning (RL) algorithm is developed to solve this problem. Using the proposed algorithm, each BS can record the historical resource allocation schemes and users' information in its multiple stacks to avoid learning the same resource allocation scheme and users' states, thus improving the convergence speed and learning efficiency. The simulation results illustrate that the proposed algorithm can reduce the number of iterations needed for convergence and the maximal delay among all users by up to 18% and 11.1% compared to the standard Q-learning algorithm.
Asthma is a common chronic inflammatory disease of the airways with no known cure. Lipid mediators (LMs) are a kind of inflammatory signaling molecules which are believed to be involved in the ...development of asthma.
Boriss. is a traditional Uyghur medicine, which is widely used in the treatment of asthma and other respiratory diseases. Extraction of
Boriss. was reported to neutralize asthma symptoms. The purpose of the study was to investigate both the anti-inflammatory and immunoregulation properties of the
Boriss. extract (SXCF) and its main active constituent, rosmarinic acid (RosA), in vivo. The effect of RosA, a major constituent of SXCF, was evaluated on an asthmatic model, with both anti-inflammatory and immunoregulation properties.
Anti-inflammatory effect of SXCF and RosA was assessed using OVA-induced asthma model mice by UPLC-MS/MS method.
Overall, RosA played a critical role in anti-asthma treatment. In total, 90% of LMs species that were significantly regulated by SXCF were covered. On the most important LMs associated with asthma, RosA equivalent induced similar effects as SXCF did. It is believed that some constituents in SXCF could neutralize RosA excessive impacts on LMs.
The importance of discriminating acute from non-acute thrombus is highlighted. The study aims to investigate the feasibility of readout-segmented diffusion weighted (DW) cardiovascular magnetic ...resonance (CMR) for discrimination of acute from non-acute deep venous thrombus (DVT).
For this prospective study from December 2015 to December 2017, 85 participants (mean age = 53 years, age range = 34~74) with DVT of lower extremities underwent readout-segmented DW CMR. DVT of ≤14 days were defined as acute (n = 55) and > 14 days as non-acute (n = 30). DVT visualization on b = 0, b = 800, and apparent diffusion coefficient (ADC) images were assessed using a 4-point scale (0~3, poor~excellent). DW CMR parameters were measured using region of interest (ROI). Relative signal intensity (rSI) and ADC were compared between acute and non-acute DVT using a Mann Whitney test. Sensitivity and specificity for ADC and rSI were calculated.
ADC maps had higher visualization scores than b = 0 and b = 800 images (2.7 ± 0.5, 2.5 ± 0.6, and 2.4 ± 0.6 respectively, P<0.05). The mean ADC was higher in acute DVT than non-acute DVT (0.56 ± 0.17 × 10
vs. 0.22 ± 0.12 × 10
mm
/s, P<0.001). Using 0.32 × 10
mm
/s as the cutoff, sensitivity and specificity for ADC to discriminate acute from non-acute DVT were 93 and 90% respectively. Sensitivity and specificity were 73 and 60% for rSI on b = 0, and 75 and 63% for rSI on b = 800.
Readout segmented diffusion-weighted CMR derived ADC distinguishes acute from non-acute DVT.
This study is retrospectively registered.
HUST-TJH-2015-146 .
In Feb 2020, we developed a physiologically-based pharmacokinetic (PBPK) model of hydroxychloroquine (HCQ) and integrated
anti-viral effect to support dosing design of HCQ in the treatment of ...COVID-19 patients in China. This, along with emerging research and clinical findings, supported broader uptake of HCQ as a potential treatment for COVID-19 globally at the beginning of the pandemics. Therefore, many COVID-19 patients have been or will be exposed to HCQ, including specific populations with underlying intrinsic and/or extrinsic characteristics that may affect the disposition and drug actions of HCQ. It is critical to update our PBPK model of HCQ with adequate drug absorption and disposition mechanisms to support optimal dosing of HCQ in these specific populations. We conducted relevant
and
experiments to support HCQ PBPK model update. Different aspects of this model are validated using PK study from 11 published references. With parameterization informed by results from monkeys, a permeability-limited lung model is employed to describe HCQ distribution in the lung tissues. The updated model is applied to optimize HCQ dosing regimens for specific populations, including those taking concomitant medications. In order to meet predefined HCQ exposure target, HCQ dose may need to be reduced in young children, elderly subjects with organ impairment and/or coadministration with a strong CYP2C8/CYP2D6/CYP3A4 inhibitor, and be increased in pregnant women. The updated HCQ PBPK model informed by new metabolism and distribution data can be used to effectively support dosing recommendations for clinical trials in specific COVID-19 patients and treatment of patients with malaria or autoimmune diseases.
Background:
Physiologically based pharmacokinetic (PBPK) modeling and simulating may be a powerful tool in predicting drug behaviors in specific populations. It is a mathematical model that relates ...the pharmacokinetic (PK) profile of a compound with human anatomical characteristics, physiological characteristics, and biochemical parameters. Predictions using PBPK models offer a promising way to guide drug development and can be used to optimize clinical dosing regimens. However, PK data of new drugs in the pediatric population are too limited to guide clinical therapy, which may lead to frequent adverse events or insufficient efficacy for pediatric patients, particularly in neonates and infants.
Objective:
The objective of this study was to establish a virtual Chinese pediatric population based on the physiological parameters of Chinese children that could be utilized in PBPK models.
Methods:
A Chinese pediatric PBPK model was developed in Simcyp Simulator by collecting published Chinese pediatric physiological and anthropometric data to use as system parameters. This pediatric population model was then evaluated in the Chinese pediatric population by predicting the pharmacokinetic characteristics of four probe drugs: theophylline (major CYP1A2 substrate), fentanyl (major CYP3A4 substrate), vancomycin, and ceftazidime (renal-eliminated).
Results:
The predicted maximum concentration (C
max
), area under the curve of concentration-time (AUC), and clearance (CL) for theophylline (CYP1A2 metabolism pathway) and fentanyl (CYP3A4 metabolism pathway) were within two folds of the observed data. For drugs mainly eliminated by renal clearance (vancomycin and ceftazidime) in the Chinese pediatric population, the ratio of prediction to observation for major PK parameters was within a 2-fold error range.
Conclusion:
The model is a supplement to the previous Chinese population PBPK model. We anticipate the model to be a better representative of the pediatric Chinese population for drugs PK, offering greater clinical precision for medication given to the pediatric population, ultimately advancing clinical development of pediatric drugs. We can refine this model further by collecting more physiological parameters of Chinese children.
Granulocyte-macrophage colony-stimulating factor has been widely used as an adjuvant therapy for cancer patients exhibiting myelosuppression induced by chemotherapy or radiotherapy. However, the ...effects of granulocyte-macrophage colony-stimulating factor on tumor growth, as well as its precise mechanism, are still controversial due to inconsistent evidence. This study investigated the effect of exogenous granulocyte-macrophage colony-stimulating factor on the growth of B16 melanoma, S180 sarcoma, and U14 cervical carcinoma in mice. The angiogenesis and recruitment of bone-marrow-derived cells were analyzed in tumor tissues. Interactions among granulocyte-macrophage colony-stimulating factor, bone-marrow-derived cells, and B16 tumor cells were investigated in vitro. Proangiogenic types of bone-marrow-derived cells in blood were assessed both in vivo and in vitro. The results showed that granulocyte-macrophage colony-stimulating factor markedly facilitated the growth of B16 and S180 tumors, but not U14 tumors. Granulocyte-macrophage colony-stimulating factor increased the densities of blood vessels and the number of bone-marrow-derived cells in B16 tumor tissues. The granulocyte-macrophage colony-stimulating factor–induced enhancement of tumor cell proliferation was mediated by bone-marrow-derived cells in vitro. Meanwhile, a distinct synergistic effect on endothelial cell function between granulocyte-macrophage colony-stimulating factor and bone-marrow-derived cells was observed. After separating two types of bone-marrow-derived cells, granulocyte-macrophage colony-stimulating factor–induced enhancement of tumor growth and angiogenesis in vivo was mediated by proangiogenic cells in granulocytes, but not monocytes, with CD11b+, vascular endothelial growth factor receptor 2, and C-X-C chemokine receptor 4 granulocytes possibly involved. These data suggest that granulocyte-macrophage colony-stimulating factor contributes to the growth and angiogenesis of certain types of tumor, and these mechanisms are probably mediated by proangiogenic cells in granulocytes. Applying granulocyte-macrophage colony-stimulating factor may attenuate the antitumor effects of chemotherapy and radiotherapy in certain types of tumor.
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