Selenium Lessens Osteoarthritis by Protecting Articular Chondrocytes from Oxidative Damage through Nrf2 and NF-κB Pathways.

Osteoarthritis (OA) causes joint pain and disability due to the abnormal production of inflammatory cytokines and reactive oxygen species (ROS) in chondrocytes, leading to cell death and cartilage matrix destruction. Selenium (Se) intake can protect cells against oxidative damage. It is still unknown whether Se supplementation is beneficial for OA. This study investigated the effects of Se on sodium iodoacetate (MIA)-imitated OA progress in human chondrocyte cell line (SW1353 cells) and rats. The results showed that 0.3 µM of Se treatment could protect SW1353 cells from MIA-induced damage by the Nrf2 pathway by promoting the gene expression of glutathione-synthesis-related enzymes such as the glutamate-cysteine ligase catalytic subunit, the glutamate-cysteine ligase modifier subunit, and glutathione synthetase. In addition, glutathione, superoxide dismutase, glutathione peroxidase, and glutathione reductase expressions are also elevated to eliminate excessive ROS production. Moreover, Se could downregulate NF-κB, leading to a decrease in cytokines, matrix proteases, and glycosaminoglycans. In the rats, MIA-induced cartilage loss was lessened after 2 weeks of Se supplementation by oral gavage; meanwhile, glutathione synthesis was increased, and the expressions of pro-inflammatory cytokines were decreased. These results suggest that Se intake is beneficial for OA due to its effects of decreasing cartilage loss by enhancing antioxidant capacity and reducing inflammation.

RNA-guided genome engineering: paradigm shift towards transposons.

CRISPR-Cas systems revolutionized the genome engineering field but need to induce double-strand breaks (DSBs) and may be difficult to deliver due to their large protein size. Tn7-like transposons such as CRISPR-associated transposons (CASTs) can be repurposed for RNA-guided DSB-free integration, and obligate mobile element guided activity (OMEGA) proteins of the IS200/IS605 transposon family have been developed as hypercompact RNA-guided genome editing tools. CASTs and OMEGA are exciting, innovative genome engineering tools that can improve the precision and efficiency of editing. This review explores the recent developments and uses of CASTs and OMEGA in genome editing across prokaryotic and eukaryotic cells. The pros and cons of these transposon-based systems are deliberated in comparison to other CRISPR systems.

Applications, challenges, and solutions of unmanned aerial vehicles in smart city using blockchain.

Real-time data gathering, analysis, and reaction are made possible by this information and communication technology system. Data storage is also made possible by it. This is a good move since it enhances the administration and operation services essential to any city's efficient operation. The idea behind "smart cities" is that information and communication technology (ICTs) need to be included in a city's routine activities in order to gather, analyze, and store enormous amounts of data in real-time. This is helpful since it makes managing and governing urban areas easier. The "drone" or "uncrewed aerial vehicle" (UAV), which can carry out activities that ordinarily call for a human driver, serves as an example of this. UAVs could be used to integrate geospatial data, manage traffic, keep an eye on objects, and help in an emergency as part of a smart urban fabric. This study looks at the benefits and drawbacks of deploying UAVs in the conception, development, and management of smart cities. This article describes the importance and advantages of deploying UAVs in designing, developing, and maintaining in smart cities. This article overviews UAV uses types, applications, and challenges. Furthermore, we presented blockchain approaches for addressing the given problems for UAVs in smart research topics and recommendations for improving the security and privacy of UAVs in smart cities. Furthermore, we presented Blockchain approaches for addressing the given problems for UAVs in smart cities. Researcher and graduate students are audience of our article.

Development of validated sandwich ELISA for detecting peanut allergen Ara h 3 in food.

Peanut is an important food that can cause food allergies, often leading to moderate and severe allergic symptoms such as skin rashes, asthma, and even anaphylactic shock.Research indicates that Ara h 3 is one of the major peanut allergen. In order to establish a simple analytical method for detecting Ara h 3, we developed a sandwich enzyme-linked immunosorbent assay (ELISA) with antibodies that were induced from purified Ara h 3. The experimental results showed that the purified Ara h 3 had good purity, and we successfully prepared capture and detection antibodies. The method established in this study exhibited high specificity and did not cross-react with soybeans, cashew nuts, and sesame. For validation, including precision, recovery and sensitivity were in good condition. We also detected the Ara h 3 in peanut related foods. Overall, the ELISA developed in this study is a reliable method for Ara h 3 detection.

Enhanced Calvarial Bone Repair Using ASCs Engineered with RNA-Guided Split dCas12a System that Co-Activates Sox 5, Sox6, and Long Non-Coding RNA H19.

Healing of large calvarial bone defects remains challenging. An RNA-guided Split dCas12a system is previously harnessed to activate long non-coding RNA H19 (lncRNA H19, referred to as H19 thereafter) in bone marrow-derived mesenchymal stem cells (BMSCs). H19 activation in BMSCs induces chondrogenic differentiation, switches bone healing pathways, and improves calvarial bone repair. Since adipose-derived stem cells (ASCs) can be harvested more easily in large quantity, here it is aimed to use ASCs as an alternative cell source. However, H19 activation alone using the Split dCas12a system in ASCs failed to elicit evident chondrogenesis. Therefore, split dCas12a activators are designed more to co-activate other chondroinductive transcription factors (Sox5, Sox6, and Sox9) to synergistically potentiate differentiation. It is found that co-activation of H19/Sox5/Sox6 in ASCs elicited more potent chondrogenic differentiation than activation of Sox5/Sox6/Sox9 or H19 alone. Co-activating H19/Sox5/Sox6 in ASCs significantly augmented in vitro cartilage formation and in vivo calvarial bone healing. These data altogether implicated the potentials of the Split dCas12a system to trigger multiplexed gene activation in ASCs for differentiation pathway reprogramming and tissue regeneration.

Rotablation for Octogenarians in a Modern Cathlab: Short- and Intermediate-Term Results.

Background: There are limited reports on the treatment of complex calcified lesions using rotational atherectomy (RA) in octogenarians, particularly in high-risk patients. Objective: To evaluate procedural and clinical outcomes of RA in octogenarians. Methods: Consecutive RA patients from 2010 to 2018 were selected from our catheterization laboratory database, stratified into two groups (≥ or < 80 years old), and analyzed. Results: A total of 411 patients (269 males and 142 females) with a mean age of 73.8 ± 11.3 years were enrolled, of whom 153 were ≥ 80 years old and 258 were < 80 years old. Most of the patients displayed high-risk features. The baseline Syntax scores were high in both groups, and most lesions were heavily calcified (96.1% vs. 97.3%, p = 0.969, respectively). The use of hemodynamic support intra-aortic balloon pump was more frequent in the octogenarians (21.6% vs. 11.6%, p = 0.007), but the RA completion rate was similarly high (95.9% vs. 99.1%, p = 0.842). There was no difference in acute complications. The total/cardiovascular (CV) death rate within one year was higher in the octogenarians, along with higher major adverse cardiovascular event (MACE)/CV MACE rates in the first month. Cox regression analysis showed that age ≥ 80 years, acute coronary syndrome, ischemic cardiomyopathy/shock, multi-vessel disease and serum creatinine were all predictors of MACE, and that these factors plus peripheral artery disease were predictors of all-cause mortality in these patients. Conclusions: RA is feasible with a very high success rate in high-risk octogenarians with complex anatomies, and with equal safety and no increase in complications. The higher rates of all-cause death and MACE were attributed to an older age and other traditional risk factors.

Split dCas12a activator for lncRNA H19 activation to enhance BMSC differentiation and promote calvarial bone healing.

Healing of large calvarial bone defects in adults is challenging. We previously showed that inducing chondrogenic differentiation of mesenchymal stem cells from bone marrow (BMSC) or adipose tissue (ASC) before implantation can switch the repair pathway and improve calvarial bone healing. Split dCas12a activator is a new CRISPR activation system comprising the amino (N) and carboxyl (C) fragments of dCas12a protein, each being fused with synthetic transcription activators at both termini. The split dCas12a activator was shown to induce programmable gene expression in cell lines. Here we exploited the split dCas12a activator to activate the expression of chondroinductive long non-coding RNA H19. We showed that co-expression of the split N- and C-fragments resulted in spontaneous dimerization, which elicited stronger activation of H19 than full-length dCas12a activator in rat BMSC and ASC. We further packaged the entire split dCas12a activator system (13.2 kb) into a hybrid baculovirus vector, which enhanced and prolonged H19 activation for at least 14 days in BMSC and ASC. The extended H19 activation elicited potent chondrogenic differentiation and inhibited adipogenesis. Consequently, the engineered BMSC promoted in vitro cartilage formation and augmented calvarial bone healing in rats. These data implicated the potentials of the split dCas12a activator for stem cell engineering and regenerative medicine.

Joint Data Transmission and Energy Harvesting for MISO Downlink Transmission Coordination in Wireless IoT Networks.

The advent of simultaneous wireless information and power (SWIPT) has been regarded as a promising technique to provide power supplies for an energy sustainable Internet of Things (IoT), which is of paramount importance due to the proliferation of high data communication demands of low-power network devices. In such networks, a multi-antenna base station (BS) in each cell can be utilized to concurrently transmit messages and energies to its intended IoT user equipment (IoT-UE) with a single antenna under a common broadcast frequency band, resulting in a multi-cell multi-input single-output (MISO) interference channel (IC). In this work, we aim to find the trade-off between the spectrum efficiency (SE) and energy harvesting (EH) in SWIPT-enabled networks with MISO ICs. For this, we derive a multi-objective optimization (MOO) formulation to obtain the optimal beamforming pattern (BP) and power splitting ratio (PR), and we propose a fractional programming (FP) model to find the solution. To tackle the nonconvexity of FP, an evolutionary algorithm (EA)-aided quadratic transform technique is proposed, which recasts the nonconvex problem as a sequence of convex problems to be solved iteratively. To further reduce the communication overhead and computational complexity, a distributed multi-agent learning-based approach is proposed that requires only partial observations of the channel state information (CSI). In this approach, each BS is equipped with a double deep Q network (DDQN) to determine the BP and PR for its UE with lower computational complexity based on the observations through a limited information exchange process. Finally, with the simulation experiments, we verify the trade-off between SE and EH, and we demonstrate that, apart from the FP algorithm introduced to provide superior solutions, the proposed DDQN algorithm also shows its performance gain in terms of utility to be up to 1.23-, 1.87-, and 3.45-times larger than the Advantage Actor Critic (A2C), greedy, and random algorithms, respectively, in comparison in the simulated environment.

Rational genome and metabolic engineering of Candida viswanathii by split CRISPR to produce hundred grams of dodecanedioic acid.

Candida viswanathii is a promising cell factory for producing dodecanedioic acid (DDA) and other long chain dicarboxylic acids. However, metabolic engineering of C. viswanathii is difficult partly due to the lack of synthetic biology toolkits. Here we developed CRISPR-based approaches for rational genome and metabolic engineering of C. viswanathii. We first optimized the CRISPR system and protocol to promote the homozygous gene integration efficiency to >60%. We also designed a split CRISPR system for one-step integration of multiple genes into C. viswanathii. We uncovered that co-expression of CYP52A19, CPRb and FAO2 that catalyze different steps in the biotransformation enhances DDA production and abolishes accumulation of intermediates. We also unveiled that co-expression of additional enzyme POS5 further promotes DDA production and augments cell growth. We harnessed the split CRISPR system to co-integrate these 4 genes (13.6 kb) into C. viswanathii and generated a stable strain that doubles the DDA titer (224 g/L), molar conversion (83%) and productivity (1.87 g/L/h) when compared with the parent strain. This study altogether identifies appropriate enzymes/promoters to augment dodecane conversion to DDA and implicates the potential of split CRISPR system for metabolic engineering of C. viswanathii.

Transplantation of Adipose-Tissue-Engineered Constructs with CRISPR-Mediated UCP1 Activation.

Thermogenic adipocytes have potential utility for the development of approaches to treat type 2 diabetes and obesity-associated diseases. Although several reports have proved the positive effect of beige and brown adipocyte transplantation in obese mice, translation to human cell therapy needs improvement. Here, we describe the application of CRISPR activation (CRISPRa) technology for generating safe and efficient adipose-tissue-engineered constructs with enhanced mitochondrial uncoupling protein 1 (UCP1) expression. We designed the CRISPRa system for the activation of UCP1 gene expression. CRISPRa-UCP1 was delivered into mature adipocytes by a baculovirus vector. Modified adipocytes were transplanted in C57BL/6 mice, followed by analysis of grafts, inflammation and systemic glucose metabolism. Staining of grafts on day 8 after transplantation shows them to contain UCP1-positive adipocytes. Following transplantation, adipocytes remain in grafts and exhibit expression of PGC1α transcription factor and hormone sensitive lipase (HSL). Transplantation of CRISPRa-UCP1-modified adipocytes does not influence glucose metabolism or inflammation in recipient mice. We show the utility and safety of baculovirus vectors for CRISPRa-based thermogenic gene activation. Our findings suggest a means of improving existing cell therapy approaches using baculovirus vectors and CRISPRa for modification and transplantation of non-immunogenic adipocytes.

Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations' COVID-19 Pandemic.

In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R 2 score values.

The Efficacy of HGF/VEGF Gene Therapy for Limb Ischemia in Mice with Impaired Glucose Tolerance: Shift from Angiogenesis to Axonal Growth and Oxidative Potential in Skeletal Muscle.

BACKGROUND: Combined non-viral gene therapy (GT) of ischemia and cardiovascular disease is a promising tool for potential clinical translation. In previous studies our group has developed combined gene therapy by vascular endothelial growth factor 165 (VEGF165) + hepatocyte growth factor (HGF). Our recent works have demonstrated that a bicistronic pDNA that carries both human HGF and VEGF165 coding sequences has a potential for clinical application in peripheral artery disease (PAD). The present study aimed to test HGF/VEGF combined plasmid efficacy in ischemic skeletal muscle comorbid with predominant complications of PAD-impaired glucose tolerance and type 2 diabetes mellitus (T2DM). METHODS: Male C57BL mice were housed on low-fat (LFD) or high-fat diet (HFD) for 10 weeks and metabolic parameters including FBG level, ITT, and GTT were evaluated. Hindlimb ischemia induction and plasmid administration were performed at 10 weeks with 3 weeks for post-surgical follow-up. Limb blood flow was assessed by laser Doppler scanning at 7, 14, and 21 days after ischemia induction. The necrotic area of m.tibialis anterior, macrophage infiltration, angio- and neuritogenesis were evaluated in tissue sections. The mitochondrial status of skeletal muscle (total mitochondria content, ETC proteins content) was assessed by Western blotting of muscle lysates. RESULTS: At 10 weeks, the HFD group demonstrated impaired glucose tolerance in comparison with the LFD group. HGF/VEGF plasmid injection aggravated glucose intolerance in HFD conditions. Blood flow recovery was not changed by HGF/VEGF plasmid injection either in LFD or HFD conditions. GT in LFD, but not in HFD conditions, enlarged the necrotic area and CD68+ cells infiltration. However, HGF/VEGF plasmid enhanced neuritogenesis and enlarged NF200+ area on muscle sections. In HFD conditions, HGF/VEGF plasmid injection significantly increased mitochondria content and ETC proteins content. CONCLUSIONS: The current study demonstrated a significant role of dietary conditions in pre-clinical testing of non-viral GT drugs. HGF/VEGF combined plasmid demonstrated a novel aspect of potential participation in ischemic skeletal muscle regeneration, through regulation of innervation and bioenergetics of muscle. The obtained results made HGF/VEGF combined plasmid a very promising tool for PAD therapy in impaired glucose tolerance conditions.

IoT-Based Smart Mask Protection against the Waves of COVID-19.

In the year 2020, the word "pandemic" has become quite popular. A pandemic is a disease that spreads over a wide geographical region. The massive outbreak of coronavirus popularly known as COVID-19 has halted normal life worldwide. On 11th March 2020, the World Health Organization (WHO) quoted the COVID-19 outbreak as a "Pandemic". The outbreak pattern differs widely across the globe based on the findings discovered so far; however, fever is a common and easily detectable symptom of COVID-19 and the new COVID strain. After the virus outbreak, thermal scanning is done using infrared thermometers in most public places to detect infected persons. It is time-consuming to track the body temperature of each person. Besides, close contact with infected persons can spread the virus from the infected persons to the individual performing the screening or vice-versa. In this research, we propose a device architecture capable of automatically detecting the coronavirus or new COVID strain from thermal images; the proposed architecture comprises a smart mask equipped with a thermal imaging system, which reduces human interactions. The thermal camera technology is integrated with the smart mask powered by the Internet of Things (IoT) to proactively monitor the screening procedure and obtain data based on real-time findings. Besides, the proposed system is fitted with facial recognition technology; therefore, it can also display personal information. It will automatically measure the temperature of each person who came into close contact with the infected humans or humans in public spaces, such as markets or offices. The new design is very useful in healthcare and could offer a solution to preventing the growth of the coronavirus. The presented work hasa key focus on the integration of advanced algorithms for the predictive analytics of parameters required for in-depth evaluations. The proposed work and the results are pretty effectual and performance cognizant for predictive analytics. The manuscript and associated research work integrate the IoT and Internet of Everything (IoE) based analytics with sensor technologies with real-time data so that the overall predictions will be more accurate and integrated with the health sector. Supplementary Information: The online version contains supplementary material available at 10.1007/s12652-022-04395-7.

A bi-directional deep learning architecture for lung nodule semantic segmentation.

Lung nodules are abnormal growths and lesions may exist. Both lungs may have nodules. Most lung nodules are harmless (not cancerous/malignant). Pulmonary nodules are rare in lung cancer. X-rays and CT scans identify the lung nodules. Doctors may term the growth a lung spot, coin lesion, or shadow. It is necessary to obtain properly computed tomography (CT) scans of the lungs to get an accurate diagnosis and a good estimate of the severity of lung cancer. This study aims to design and evaluate a deep learning (DL) algorithm for identifying pulmonary nodules (PNs) using the LUNA-16 dataset and examine the prevalence of PNs using DB-Net. The paper states that a new, resource-efficient deep learning architecture is called for, and it has been given the name of DB-NET. When a physician orders a CT scan, they need to employ an accurate and efficient lung nodule segmentation method because they need to detect lung cancer at an early stage. However, segmentation of lung nodules is a difficult task because of the nodules' characteristics on the CT image as well as the nodules' concealed shape, visual quality, and context. The DB-NET model architecture is presented as a resource-efficient deep learning solution for handling the challenge at hand in this paper. Furthermore, it incorporates the Mish nonlinearity function and the mask class weights to improve segmentation effectiveness. In addition to the LUNA-16 dataset, which contained 1200 lung nodules collected during the LUNA-16 test, the LUNA-16 dataset was extensively used to train and assess the proposed model. The DB-NET architecture surpasses the existing U-NET model by a dice coefficient index of 88.89%, and it also achieves a similar level of accuracy to that of human experts.

Computer-Aided Design and Synthesis of (Functionalized quinazoline)-(α-substituted coumarin)-arylsulfonate Conjugates against Chikungunya Virus.

Chikungunya virus (CHIKV) has repeatedly spread via the bite of an infected mosquito and affected more than 100 countries. The disease poses threats to public health and the economy in the infected locations. Many efforts have been devoted to identifying compounds that could inhibit CHIKV. Unfortunately, successful clinical candidates have not been found yet. Computations through the simulating recognition process were performed on complexation of the nsP3 protein of CHIKV with the structures of triply conjugated drug lead candidates. The outcomes provided the aid on rational design of functionalized quinazoline-(α-substituted coumarin)-arylsulfonate compounds to inhibit CHIKV in Vero cells. The molecular docking studies showed a void space around the ß carbon atom of coumarin when a substituent was attached at the α position. The formed vacancy offered a good chance for a Michael addition to take place owing to steric and electronic effects. The best conjugate containing a quinazolinone moiety exhibited potency with EC50 = 6.46 µM, low toxicity with CC50 = 59.7 µM, and the selective index (SI) = 9.24. Furthermore, the corresponding 4-anilinoquinazoline derivative improved the anti-CHIKV potency to EC50 = 3.84 µM, CC50 = 72.3 µM, and SI = 18.8. The conjugate with 4-anilinoquinazoline exhibited stronger binding affinity towards the macro domain than that with quinazolinone via hydrophobic and hydrogen bond interactions.

Combinatorial CRISPR Interference Library for Enhancing 2,3-BDO Production and Elucidating Key Genes in Cyanobacteria.

Cyanobacteria can convert CO2 to chemicals such as 2,3-butanediol (2,3-BDO), rendering them promising for renewable production and carbon neutralization, but their applications are limited by low titers. To enhance cyanobacterial 2,3-BDO production, we developed a combinatorial CRISPR interference (CRISPRi) library strategy. We integrated the 2,3-BDO pathway genes and a CRISPRi library into the cyanobacterium PCC7942 using the orthogonal CRISPR system to overexpress pathway genes and attenuate genes that inhibit 2,3-BDO formation. The combinatorial CRISPRi library strategy allowed us to inhibit fbp, pdh, ppc, and sps (which catalyzes the synthesis of fructose-6-phosphate, acetyl-coenzyme A, oxaloacetate, and sucrose, respectively) at different levels, thereby allowing for rapid screening of a strain that enhances 2,3-BDO production by almost 2-fold to 1583.8 mg/L. Coupled with a statistical model, we elucidated that differentially inhibiting all the four genes enhances 2,3-BDO synthesis to varying degrees. fbp and pdh suppression exerted more profound effects on 2,3-BDO production than ppc and sps suppression, and these four genes can be repressed simultaneously without mutual interference. The CRISPRi library approach paves a new avenue to combinatorial metabolic engineering of cyanobacteria.

χ2-BidLSTM: A Feature Driven Intrusion Detection System Based on χ2 Statistical Model and Bidirectional LSTM.

In a network architecture, an intrusion detection system (IDS) is one of the most commonly used approaches to secure the integrity and availability of critical assets in protected systems. Many existing network intrusion detection systems (NIDS) utilize stand-alone classifier models to classify network traffic as an attack or as normal. Due to the vast data volume, these stand-alone models struggle to reach higher intrusion detection rates with low false alarm rates( FAR). Additionally, irrelevant features in datasets can also increase the running time required to develop a model. However, data can be reduced effectively to an optimal feature set without information loss by employing a dimensionality reduction method, which a classification model then uses for accurate predictions of the various network intrusions. In this study, we propose a novel feature-driven intrusion detection system, namely χ2-BidLSTM, that integrates a χ2 statistical model and bidirectional long short-term memory (BidLSTM). The NSL-KDD dataset is used to train and evaluate the proposed approach. In the first phase, the χ2-BidLSTM system uses a χ2 model to rank all the features, then searches an optimal subset using a forward best search algorithm. In next phase, the optimal set is fed to the BidLSTM model for classification purposes. The experimental results indicate that our proposed χ2-BidLSTM approach achieves a detection accuracy of 95.62% and an F-score of 95.65%, with a low FAR of 2.11% on NSL-KDDTest+. Furthermore, our model obtains an accuracy of 89.55%, an F-score of 89.77%, and an FAR of 2.71% on NSL-KDDTest-21, indicating the superiority of the proposed approach over the standard LSTM method and other existing feature-selection-based NIDS methods.

Joint Beamforming, Power Allocation, and Splitting Control for SWIPT-Enabled IoT Networks with Deep Reinforcement Learning and Game Theory.

Future wireless networks promise immense increases on data rate and energy efficiency while overcoming the difficulties of charging the wireless stations or devices in the Internet of Things (IoT) with the capability of simultaneous wireless information and power transfer (SWIPT). For such networks, jointly optimizing beamforming, power control, and energy harvesting to enhance the communication performance from the base stations (BSs) (or access points (APs)) to the mobile nodes (MNs) served would be a real challenge. In this work, we formulate the joint optimization as a mixed integer nonlinear programming (MINLP) problem, which can be also realized as a complex multiple resource allocation (MRA) optimization problem subject to different allocation constraints. By means of deep reinforcement learning to estimate future rewards of actions based on the reported information from the users served by the networks, we introduce single-layer MRA algorithms based on deep Q-learning (DQN) and deep deterministic policy gradient (DDPG), respectively, as the basis for the downlink wireless transmissions. Moreover, by incorporating the capability of data-driven DQN technique and the strength of noncooperative game theory model, we propose a two-layer iterative approach to resolve the NP-hard MRA problem, which can further improve the communication performance in terms of data rate, energy harvesting, and power consumption. For the two-layer approach, we also introduce a pricing strategy for BSs or APs to determine their power costs on the basis of social utility maximization to control the transmit power. Finally, with the simulated environment based on realistic wireless networks, our numerical results show that the two-layer MRA algorithm proposed can achieve up to 2.3 times higher value than the single-layer counterparts which represent the data-driven deep reinforcement learning-based algorithms extended to resolve the problem, in terms of the utilities designed to reflect the trade-off among the performance metrics considered.

Synthesis and antiviral activities of quinazolinamine-coumarin conjugates toward chikungunya and hepatitis C viruses.

Development of new drugs with broad-spectrum to combat RNA viruses would be beneficial to mankind but faces a great challenge. We designed and efficiently synthesized a series of quinazolin-4-amine-SCH2-coumarin conjugated compounds. Our data of the virus-cell-based assay show five new conjugates inhibit chikungunya virus with EC50 values as potent as 1.96 µM and two conjugates inhibit hepatitis C virus with EC50 values as low as 16.6 µM. These conjugates possess a xylene substituent at the C-4 amino group of quinazoline and a t-butyl substituent at the C-6' position of coumarin.