DSRC versus 4G-LTE for Connected Vehicle Applications: A Study on Field Experiments of Vehicular Communication Performance

Dedicated short-range communication (DSRC) and 4G-LTE are two widely used candidate schemes for Connected Vehicle (CV) applications. It is thus of great necessity to compare these two most viable communication standards and clarify which one can meet the requirements of most V2X scenarios with respect to road safety, traffic efficiency, and infotainment. To the best of our knowledge, almost all the existing studies on comparing the feasibility of DRSC or LTE in V2X applications use software-based simulations, which may not represent realistic constraints. In this paper, a Connected Vehicle test-bed is established, which integrates the DSRC roadside units, 4G-LTE cellular communication stations, and vehicular on-board terminals. Three Connected Vehicle application scenarios are set as Collision Avoidance, Traffic Text Message Broadcast, and Multimedia File Download, respectively. A software tool is developed to record GPS positions/velocities of the test vehicles and record certain wireless communication performance indicators. The experiments have been carried out under different conditions. According to our results, 4G-LTE is more preferred for the nonsafety applications, such as traffic information transmission, file download, or Internet accessing, which does not necessarily require the high-speed real-time communication, while for the safety applications, such as Collision Avoidance or electronic traffic sign, DSRC outperforms the 4G-LTE. Document type: Articl

Visual High-Throughput Screening for Developing a Fatty Acid Amide Hydrolase Natural Inhibitor Based on an Enzyme-Activated Fluorescent Probe

Fatty acid amide hydrolase (FAAH) is an important drug target for the treatment of many disease related conditions such as pain, inflammation, and mood disorders due to its vital role in the metabolism of endocannabinoid. In our present work, a FAAH-activated fluorescent probe named THPO was developed, which possessed high selectivity and excellent sensitivity for FAAH in complex systems. Critically, its metabolite 7-amino-3H-phenoxazin-3-one (AHPO) has long excitation and emission wavelengths and high fluorescence quantum yield, which are necessary for monitoring the activity of FAAH in living systems. In addition, a visual high-throughput screening method for FAAH inhibitors was established using THPO, which resulted in the discovery of an efficient natural inhibitor Neobavaisoflavone that was identified from 68 traditional herbal medicines. These results indicated that THPO can be used as a molecular tool for the rapid evaluation of FAAH activity in complex systems as well as providing an effective approach to screen FAAH inhibitors and providing a boost for the discovery of therapeutic agents toward FAAH related diseases. </p

Endoplasmic Reticulum Targeting Ratiometric Fluorescent Probe for Carboxylesterase 2 Detection in Drug-Induced Acute Liver Injury

Carboxylesterase 2 (CES2), an endoplasmic reticulum (ER) located phase I enzyme, plays a vital role in the metabolism of various endogenous and exogenous substances, and is regarded as an important target for the design of prodrugs. Unfortunately, superior highly selective ER targeting fluorescent probes for monitoring of CES2 are not currently available. Herein, we report an ER targeting CES2 selective and sensitive ratiometric fluorescent probe ERNB based on the ER localizing group p-toluenesulfonamide. ERNB possessed high specificity, sensitivity, and exhibited excellent subcellular localization when compared to commercial ER tracker, and was used to image CES2 in the ER of living cells. Additionally, using ERNB we evaluated the CES2 regulation under d,l-dithiothreitol and tunicamycin-induced ER stress. Furthermore, we determined the down regulation of CES2 activity and expression in the acetaminophen-induced acute liver injury model. On the basis of these results, we conclude that ERNB is a promising tool for highlighting the role of CES2 in the ER and in exploring the role of CES2 in the development of diseases associated with ER stress.</p

Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs

Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding. In recent years, Large Language Models (LLMs) have been proven to possess extensive common knowledge and powerful semantic comprehension abilities that have revolutionized existing workflows to handle text data. In this paper, we aim to explore the potential of LLMs in graph machine learning, especially the node classification task, and investigate two possible pipelines: LLMs-as-Enhancers and LLMs-as-Predictors. The former leverages LLMs to enhance nodes' text attributes with their massive knowledge and then generate predictions through GNNs. The latter attempts to directly employ LLMs as standalone predictors. We conduct comprehensive and systematical studies on these two pipelines under various settings. From comprehensive empirical results, we make original observations and find new insights that open new possibilities and suggest promising directions to leverage LLMs for learning on graphs.Comment: fix some minor typos and error

ROS-dependent catalytic mechanism of melatonin metabolism and its application in the measurement of reactive oxygen

Melatonin (Mel) is an endogenous active molecule whose metabolism progress significantly influences its bioactivity. However, the detailed metabolic pathway of Mel in the pathological state has not yet been fully illustrated. In this study, 16 metabolites of Mel in cancer cells and human liver microsomes were identified, of which seven novel metabolites were newly discovered. Among them, 2-hydroxymelatonin (2-O-Mel), as the major metabolite in cancer cells, was revealed for the first time, which was different from the metabolite found in the human liver. Furthermore, CYP1A1/1A2- and reactive oxygen species (ROS)-mediated 2-hydroxylation reactions of Mel were verified to be the two metabolic pathways in the liver and cancer cells, respectively. ROS-dependent formation of 2-O-Mel was the major pathway in cancer cells. Furthermore, the underlying catalytic mechanism of Mel to 2-O-Mel in the presence of ROS was fully elucidated using computational chemistry analysis. Therefore, the generation of 2-O-Mel from Mel could serve as another index for the endogenous reactive oxygen level. Finally, based on the ROS-dependent production of 2-O-Mel, Mel was successfully used for detecting the oxygen-carrying capacity of hemoglobin in human blood. Our investigation further enriched the metabolic pathway of Mel, especially for the ROS-dependent formation of 2-O-Mel that serves as a diagnostic and therapeutic target for the rational use of Mel in clinics