119 research outputs found
Enhanced C-V2X Mode 4 to Optimize Age of Information and Reliability for IoV
Internet of vehicles (IoV) has emerged as a key technology to realize
real-time vehicular application. For IoV, vehicles adopt cellular
vehicle-to-everything (C-V2X) standard to support direct communication among
them. C-V2X mode 4 controls resource allocation without the assistance of
cellular network, hence it is widely used for IoV. However, C-V2X mode 4 has
two drawbacks. First is that vehicles cannot communicate with each other for a
period in some case which will cause an increase in age of information (AoI);
second is that vehicles may select resource already occupied by others which
will deteriorate the reliability. To address the two drawbacks, we propose an
enhanced C-V2X mode 4 to optimize AoI and reliability. In addition, we consider
the fact that for most vehicular applications, each vehicle periodically
requires fresh information of vehicles within a certain distance and propose a
new performance metric to evaluate the system AoI for IoV. Furthermore, we
construct a platform through integrating SUMO and NS3. We demonstrate the
superiority of the enhanced C-V2X mode 4 base on this simulation platform.Comment: This paper has been accpeted by ICCT 2023. The source code can be
found at https://github.com/qiongwu86/ns3 sumo cv2x mode4.gi
Transcriptome-based network analysis related to M2-like tumor-associated macrophage infiltration identified VARS1 as a potential target for improving melanoma immunotherapy efficacy
Rationale
The M2-like tumor-associated macrophages (TAMs) are independent prognostic factors in melanoma.
Methods
We performed weighted gene co-expression network analysis (WGCNA) to identify the module most correlated with M2-like TAMs. The Cancer Genome Atlas (TCGA) patients were classified into two clusters that differed based on prognosis and biological function, with consensus clustering. A prognostic model was established based on the differentially expressed genes (DEGs) of the two clusters. We investigated the difference in immune cell infiltration and immune response-related gene expression between the high and low risk score groups.
Results
The risk score was defined as an independent prognostic value in melanoma. VARS1 was a hub gene in the M2-like macrophage-associated WGCNA module that the DepMap portal demonstrated was necessary for melanoma growth. Overexpressing VARS1 in vitro increased melanoma cell migration and invasion, while downregulating VARS1 had the opposite result. VARS1 overexpression promoted M2 macrophage polarization and increased TGF-β1 concentrations in tumor cell supernatant in vitro. VARS1 expression was inversely correlated with immune-related signaling pathways and the expression of several immune checkpoint genes. In addition, the VARS1 expression level helped predict the response to anti-PD-1 immunotherapy. Pan-cancer analysis demonstrated that VARS1 expression negatively correlated with CD8 T cell infiltration and the immune response-related pathways in most cancers.
Conclusion
We established an M2-like TAM-related prognostic model for melanoma and explored the role of VARS1 in melanoma progression, M2 macrophage polarization, and the development of immunotherapy resistance
Asynchronous Federated Learning for Edge-assisted Vehicular Networks
Vehicular networks enable vehicles support real-time vehicular applications
through training data. Due to the limited computing capability, vehicles
usually transmit data to a road side unit (RSU) at the network edge to process
data. However, vehicles are usually reluctant to share data with each other due
to the privacy issue. For the traditional federated learning (FL), vehicles
train the data locally to obtain a local model and then upload the local model
to the RSU to update the global model, thus the data privacy can be protected
through sharing model parameters instead of data. The traditional FL updates
the global model synchronously, i.e., the RSU needs to wait for all vehicles to
upload their models for the global model updating. However, vehicles may
usually drive out of the coverage of the RSU before they obtain their local
models through training, which reduces the accuracy of the global model. It is
necessary to propose an asynchronous federated learning (AFL) to solve this
problem, where the RSU updates the global model once it receives a local model
from a vehicle. However, the amount of data, computing capability and vehicle
mobility may affect the accuracy of the global model. In this paper, we jointly
consider the amount of data, computing capability and vehicle mobility to
design an AFL scheme to improve the accuracy of the global model. Extensive
simulation experiments have demonstrated that our scheme outperforms the FL
schemeComment: This paper has been submitted to WCS
Asynchronous Federated Learning Based Mobility-aware Caching in Vehicular Edge Computing
Vehicular edge computing (VEC) is a promising technology to support real-time
applications through caching the contents in the roadside units (RSUs), thus
vehicles can fetch the contents requested by vehicular users (VUs) from the RSU
within short time. The capacity of the RSU is limited and the contents
requested by VUs change frequently due to the high-mobility characteristics of
vehicles, thus it is essential to predict the most popular contents and cache
them in the RSU in advance. The RSU can train model based on the VUs' data to
effectively predict the popular contents. However, VUs are often reluctant to
share their data with others due to the personal privacy. Federated learning
(FL) allows each vehicle to train the local model based on VUs' data, and
upload the local model to the RSU instead of data to update the global model,
and thus VUs' privacy information can be protected. The traditional synchronous
FL must wait all vehicles to complete training and upload their local models
for global model updating, which would cause a long time to train global model.
The asynchronous FL updates the global model in time once a vehicle's local
model is received. However, the vehicles with different staying time have
different impacts to achieve the accurate global model. In this paper, we
consider the vehicle mobility and propose an Asynchronous FL based
Mobility-aware Edge Caching (AFMC) scheme to obtain an accurate global model,
and then propose an algorithm to predict the popular contents based on the
global model. Experimental results show that AFMC outperforms other baseline
caching schemes.Comment: This paper has been submitted to The 14th International Conference on
Wireless Communications and Signal Processing (WCSP 2022
High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networks
Federated edge learning (FEEL) technology for vehicular networks is
considered as a promising technology to reduce the computation workload while
keep the privacy of users. In the FEEL system, vehicles upload data to the edge
servers, which train the vehicles' data to update local models and then return
the result to vehicles to avoid sharing the original data. However, the cache
queue in the edge is limited and the channel between edge server and each
vehicle is a time varying wireless channel, which makes a challenge to select a
suitable number of vehicles to upload data to keep a stable cache queue in edge
server and maximize the learning accuracy. Moreover, selecting vehicles with
different resource statuses to update data will affect the total amount of data
involved in training, which further affects the model accuracy. In this paper,
we propose a vehicle selection scheme, which maximizes the learning accuracy
while ensuring the stability of the cache queue, where the statuses of all the
vehicles in the coverage of edge server are taken into account. The performance
of this scheme is evaluated through simulation experiments, which indicates
that our proposed scheme can perform better than the known benchmark scheme.Comment: This paper has been submitted to China Communication
A transcriptomic map of EGFR-induced epithelial-to-mesenchymal transition identifies prognostic and therapeutic targets for head and neck cancer
Background
Epidermal growth factor receptor (EGFR) is both a driver oncogene and a therapeutic target in advanced head and neck squamous cell carcinoma (HNSCC). However, response to EGFR treatment is inconsistent and lacks markers for treatment prediction. This study investigated EGFR-induced epithelial-to-mesenchymal transition (EMT) as a central parameter in tumor progression and identified novel prognostic and therapeutic targets, and a candidate predictive marker for EGFR therapy response.
Methods
Transcriptomic profiles were analyzed by RNA sequencing (RNA-seq) following EGFR-mediated EMT in responsive human HNSCC cell lines. Exclusive genes were extracted via differentially expressed genes (DEGs) and a risk score was determined through forward feature selection and Cox regression models in HNSCC cohorts. Functional characterization of selected prognostic genes was conducted in 2D and 3D cellular models, and findings were validated by immunohistochemistry in primary HNSCC.
Results
An EGFR-mediated EMT gene signature composed of n = 171 genes was identified in responsive cell lines and transferred to the TCGA-HNSCC cohort. A 5-gene risk score comprising DDIT4, FADD, ITGB4, NCEH1, and TIMP1 prognosticated overall survival (OS) in TCGA and was confirmed in independent HNSCC cohorts. The EGFR-mediated EMT signature was distinct from EMT hallmark and partial EMT (pEMT) meta-programs with a differing enrichment pattern in single malignant cells. Molecular characterization showed that ITGB4 was upregulated in primary tumors and metastases compared to normal mucosa and correlated with EGFR/MAPK activity in tumor bulk and single malignant cells. Preferential localization of ITGB4 together with its ligand laminin 5 at tumor-stroma interfaces correlated with increased tumor budding in primary HNSCC tissue sections. In vitro, ITGB4 knock-down reduced EGFR-mediated migration and invasion and ITGB4-antagonizing antibody ASC8 impaired 2D and 3D invasion. Furthermore, a logistic regression model defined ITGB4 as a predictive marker of progression-free survival in response to Cetuximab in recurrent metastatic HNSCC patients.
Conclusions
EGFR-mediated EMT conveyed through MAPK activation contributes to HNSCC progression upon induction of migration and invasion. A 5-gene risk score based on a novel EGFR-mediated EMT signature prognosticated survival of HNSCC patients and determined ITGB4 as potential therapeutic and predictive target in patients with strong EGFR-mediated EMT
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