Vehicular Visible Light Communications

Vehicular communications are foreseen to play a key role to increase road safety and realize autonomous driving. In addition to the radio frequency (RF)-based dedicated short range communication (DSRC) and long-term evolution (LTE) communication technologies, vehicular visible light communication (V2LC) is proposed as a complementary solution, utilizing readily deployed vehicle light emitting diode (LED) lights as transmitter with image sensors such as photodetector (PD) and camera as the receivers. V2LC fundamentals including transmitter and receiver characteristics with dimming capabilities are reviewed in this chapter. Depending on the field measurements using off-the-shelf automotive LED light, communication constraints are demonstrated. Moreover, considering the line-of-sight (LoS) characteristics, security aspects of V2LC is compared with the DSRC for a practical vehicle-to-vehicle (V2V) communication scenario. Finally, superiority of V2LC in terms of communication security with the proposed SecVLC method is demonstrated through simulation results

Poster: Fault-tolerant Consensus for Connected Vehicles: A Case Study

Connected vehicles are already being deployed for increased safety and convenience, more fuel-efficient routing and real-time navigation. One major technical challenge is achieving coordination among vehicles in the presence of failures in communication, sensor, object detection, etc. This poster focuses on using distributed fault-tolerant consensus algorithms to achieve coordination among nearby vehicles. Vehicles are in a one-hop network using vehicle-to-vehicle communication. We discuss fundamental limitations and present a practical solution that tolerates both Byzantine fault and lossy communication. Simulation results demonstrate that our algorithm can reach consensus efficiently

Enabling Pervasive Federated Learning using Vehicular Virtual Edge Servers

Recent works have proposed various distributed federated learning (FL) systems for the edge computing paradigm. These FL algorithms can assist pervasive applications in various aspects, e.g., decision making, pattern recognition, and behavior prediction. Existing solutions do not efficiently support the training based on the real-time location-specific data, because fundamentally, the \u27data collection\u27 problem is rarely studied in the context of FL systems. To address this problem, we present a novel system, VC-SGD (Vehicular Clouds-Stochastic Gradient Descent), which seamlessly integrates the emerging concept of vehicular clouds with an edge-based FL. We show that by using vehicular clouds as virtual edge servers, VC-SGD is able to effectively support FL algorithms that use real-time location-specific data. We develop a general simulator that uses SUMO to simulate vehicle mobility and MXNet to perform real training. We use our simulator to verify the efficacy of VC-SGD. The experimental results demonstrate that VC-SGD improves over existing solutions

Enterotoxins A and B produced by Staphylococcus aureus increase cell proliferation, invasion and cytarabine resistance in acute myeloid leukemia cell lines

As in the case of cancer, the risk of infection increases when the host's immune system is not working properly. It has been shown that toxins produced by the bacteria responsible for bacterial infections can alter the properties of cancer cells as well as their sensitivity to chemotherapy agents. Staphylococcus aureus (S. aureus) is one of the most prevalent pathogens in acute myeloid leukemia (AML) patients and it produces several virulence factors, including Staphylococcal enterotoxin A (SEA) and Staphylococcal enterotoxin B (SEB). Cytotoxicity, transwell migration, invasion assays, and various transcriptomic and gene set enrichment (GSE) analyses were used to determine how SEA and SEB alter cell proliferation, migration, invasion, and Cytarabine (Cyt) resistance in AML cell lines. The treatment of AML cell lines with SEA/SEB caused an increase in cell proliferation and Cyt resistance. Toxins enhanced the proclivity of cells to migrate and invade, with around 50% of cells in the presence of SEA and SEB. Transcriptomic and gene set enrichment analyses, and subsequent PCR validations showed dysregulation of immune related genes and genesets. Apparently, this allows AML cells to escape and survive the undesirable environment created by toxins, possibly via the ER stress signaling pathway. Therefore, SEA and SEB can significantly alter the characteristics of AML cancer cells and evaluation of alterations in responsible immune genes and pathways may be crucial for controlling the progression of cancer. In addition, our results suggest that there may be a strong interaction between the immune related pathways and the ER signaling pathway

Renin angiotensin system genes are biomarkers for personalized treatment of acute myeloid leukemia with Doxorubicin as well as etoposide.

Despite the availability of various treatment protocols, response to therapy in patients with Acute Myeloid Leukemia (AML) remains largely unpredictable. Transcriptomic profiling studies have thus far revealed the presence of molecular subtypes of AML that are not accounted for by standard clinical parameters or by routinely used biomarkers. Such molecular subtypes of AML are predicted to vary in response to chemotherapy or targeted therapy. The Renin-Angiotensin System (RAS) is an important group of proteins that play a critical role in regulating blood pressure, vascular resistance and fluid/electrolyte balance. RAS pathway genes are also known to be present locally in tissues such as the bone marrow, where they play an important role in leukemic hematopoiesis. In this study, we asked if the RAS genes could be utilized to predict drug responses in patients with AML. We show that the combined in silico analysis of up to five RAS genes can reliably predict sensitivity to Doxorubicin as well as Etoposide in AML. The same genes could also predict sensitivity to Doxorubicin when tested in vitro. Additionally, gene set enrichment analysis revealed enrichment of TNF-alpha and type-I IFN response genes among sensitive, and TGF-beta and fibronectin related genes in resistant cancer cells. However, this does not seem to reflect an epithelial to mesenchymal transition per se. We also identified that RAS genes can stratify patients with AML into subtypes with distinct prognosis. Together, our results demonstrate that genes present in RAS are biomarkers for drug sensitivity and the prognostication of AML