Cache-Enabled in Cooperative Cognitive Radio Networks for Transmission Performance

The proliferation of mobile devices that support the acceleration of data services (especially smartphones) has resulted in a dramatic increase in mobile traffic. Mobile data also increased exponentially, already exceeding the throughput of the backhaul. To improve spectrum utilization and increase mobile network traffic, in combination with content caching, we study the cooperation between primary and secondary networks via content caching. We consider that the secondary base station assists the primary user by pre-caching some popular primary contents. Thus, the secondary base station can obtain more licensed bandwidth to serve its own user. We mainly focus on the time delay from the backhaul link to the secondary base station. First, in terms of the content caching and the transmission strategies, we provide a cooperation scheme to maximize the secondary user’s effective data transmission rates under the constraint of the primary users target rate. Then, we investigate the impact of the caching allocation and prove that the formulated problem is a concave problem with regard to the caching capacity allocation for any given power allocation. Furthermore, we obtain the joint caching and power allocation by an effective bisection search algorithm. Finally, our results show that the content caching cooperation scheme can achieve significant performance gain for the primary and secondary systems over the traditional two-hop relay cooperation without caching

Identification of SARS-CoV-2 Main Protease Inhibitors from a Library of Minor Cannabinoids by Biochemical Inhibition Assay and Surface Plasmon Resonance Characterized Binding Affinity

The replication of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is mediated by its main protease (Mpro), which is a plausible therapeutic target for coronavirus disease 2019 (COVID-19). Although numerous in silico studies reported the potential inhibitory effects of natural products including cannabis and cannabinoids on SARS-CoV-2 Mpro, their anti-Mpro activities are not well validated by biological experimental data. Herein, a library of minor cannabinoids belonging to several chemotypes including tetrahydrocannabinols, cannabidiols, cannabigerols, cannabichromenes, cannabinodiols, cannabicyclols, cannabinols, and cannabitriols was evaluated for their anti-Mpro activity using a biochemical assay. Additionally, the binding affinities and molecular interactions between the active cannabinoids and the Mpro protein were studied by a biophysical technique (surface plasmon resonance; SPR) and molecular docking, respectively. Cannabinoids tetrahydrocannabutol and cannabigerolic acid were the most active Mpro inhibitors (IC50 = 3.62 and 14.40 μM, respectively) and cannabigerolic acid had a binding affinity KD=2.16×10−4 role= presentation \u3e=2.16×10−4 M). A preliminary structure and activity relationship study revealed that the anti-Mpro role= presentation \u3eMpro effects of cannabinoids were influenced by the decarboxylation of cannabinoids and the length of cannabinoids’ alkyl side chain. Findings from the biochemical, biophysical, and computational assays support the growing evidence of cannabinoids’ inhibitory effects on SARS-CoV-2 Mpro

MPAI-EEV: Standardization Efforts of Artificial Intelligence based End-to-End Video Coding

The rapid advancement of artificial intelligence (AI) technology has led to the prioritization of standardizing the processing, coding, and transmission of video using neural networks. To address this priority area, the Moving Picture, Audio, and Data Coding by Artificial Intelligence (MPAI) group is developing a suite of standards called MPAI-EEV for "end-to-end optimized neural video coding." The aim of this AI-based video standard project is to compress the number of bits required to represent high-fidelity video data by utilizing data-trained neural coding technologies. This approach is not constrained by how data coding has traditionally been applied in the context of a hybrid framework. This paper presents an overview of recent and ongoing standardization efforts in this area and highlights the key technologies and design philosophy of EEV. It also provides a comparison and report on some primary efforts such as the coding efficiency of the reference model. Additionally, it discusses emerging activities such as learned Unmanned-Aerial-Vehicles (UAVs) video coding which are currently planned, under development, or in the exploration phase. With a focus on UAV video signals, this paper addresses the current status of these preliminary efforts. It also indicates development timelines, summarizes the main technical details, and provides pointers to further points of reference. The exploration experiment shows that the EEV model performs better than the state-of-the-art video coding standard H.266/VVC in terms of perceptual evaluation metric

A physics-constrained machine learning method for mapping gapless land surface temperature

More accurate, spatio-temporally, and physically consistent LST estimation has been a main interest in Earth system research. Developing physics-driven mechanism models and data-driven machine learning (ML) models are two major paradigms for gapless LST estimation, which have their respective advantages and disadvantages. In this paper, a physics-constrained ML model, which combines the strengths in the mechanism model and ML model, is proposed to generate gapless LST with physical meanings and high accuracy. The hybrid model employs ML as the primary architecture, under which the input variable physical constraints are incorporated to enhance the interpretability and extrapolation ability of the model. Specifically, the light gradient-boosting machine (LGBM) model, which uses only remote sensing data as input, serves as the pure ML model. Physical constraints (PCs) are coupled by further incorporating key Community Land Model (CLM) forcing data (cause) and CLM simulation data (effect) as inputs into the LGBM model. This integration forms the PC-LGBM model, which incorporates surface energy balance (SEB) constraints underlying the data in CLM-LST modeling within a biophysical framework. Compared with a pure physical method and pure ML methods, the PC-LGBM model improves the prediction accuracy and physical interpretability of LST. It also demonstrates a good extrapolation ability for the responses to extreme weather cases, suggesting that the PC-LGBM model enables not only empirical learning from data but also rationally derived from theory. The proposed method represents an innovative way to map accurate and physically interpretable gapless LST, and could provide insights to accelerate knowledge discovery in land surface processes and data mining in geographical parameter estimation

Synthesis and biological evaluations of oleanolic acid indole derivatives as hyaluronidase inhibitors with enhanced skin permeability

Oleanolic acid (OA) is a natural cosmeceutical compound with various skin beneficial activities including inhibitory effect on hyaluronidase but the anti-hyaluronidase activity and mechanisms of action of its synthetic analogues remain unclear. Herein, a series of OA derivatives were synthesised and evaluated for their inhibitory effects on hyaluronidase. Compared to OA, an induction of fluorinated (6c) and chlorinated (6g) indole moieties led to enhanced anti-hyaluronidase activity (IC50 = 80.3 vs. 9.97 and 9.57 µg/mL, respectively). Furthermore, spectroscopic and computational studies revealed that 6c and 6g can bind to hyaluronidase protein and alter its secondary structure leading to reduced enzyme activity. In addition, OA indole derivatives showed feasible skin permeability in a slightly acidic environment (pH = 6.5) and 6c exerted skin protective effect by reducing cellular reactive oxygen species in human skin keratinocytes. Findings from the current study support that OA indole derivatives are potential cosmeceuticals with anti-hyaluronidase activity

Inhibitory Effects of Skin Permeable Glucitol-core Containing Gallotannins from Red Maple Leaves on Elastase and their Protective Effects on Human Keratinocytes

Glucitol-core containing gallotannins (GCGs) from the red maple (Acer rubrum) species have been reported to exhibit skin beneficial activities but their inhibitory effects on elastase remain unclear. Herein, we evaluated a series of GCGs for their anti-elastase activity, skin permeability, and cytoprotective effects in human keratinocytes HaCaT cells. GCGs’ anti-elastase effects were enhanced as their number of galloyl groups increased, which may be attributed to the formation of more stable protein–ligand complexes. In addition, GCGs were predicted to have moderate skin permeability and ginnalin A (GA) showed favorable permeability in the PAMPA model and cell uptake assay. Moreover, GA, ginnalin B, and maplexin F (at 50 µM) reduced H2O2-induced reactive oxygen species in HaCaT cells by 70.8, 92.8, and 84.6%, respectively. In conclusion, red maple GCGs are skin permeable elastase inhibitors with antioxidant activity, which may contribute to their overall skin beneficial effects and support their potential for cosmeceutical applications