Stixis scandens leaf extract-loading ZnO nanoparticles for porcine epidemic diarrhea virus (PEDV) treatment.

Porcine epidemic diarrhea (PED) is one of the diseases that causes great losses for livestock farmers. Because vaccines against the disease are not very effective, there is a great demand for biological products with effective resistance to PED virus (PEDV). One of the most important trends today is the use of active ingredients from nature in animal husbandry. This study aimed to create an effective agent against PEDV from the extract of Stixis scandens, which has been shown to inhibit PEDV. The aqueous (denoted as TCN) and ethanolic extracts (denoted as TCC) of Stixis scandens leaves were first prepared and then qualitatively analyzed for their chemical compositions. The TCN was used to synthesize ZnO nanoparticles (NPs) at various sizes from 20 to 120 nm. Subsequently, TCC was loaded on ZnO NPs to form ZnO-extract nanoformulations with an extract loading content of 5.8-7.6%. Total polyphenols (TP) and total alkaloids (TA) in TCC were 38.51 ± 0.25 µg GAE per mg and 22.37 ± 0.41 µg AtrE per mg, respectively. TP was less loaded but more released from the nanoformulations than TA. The A1T nanoformulation, containing only 7.6% extract, had a minimum PEDV inhibitory concentration of 3.9 µg mL-1, which was comparable to that of TCC. The experiments confirmed that the nanoformulations are promising for PEDV inhibition applications.

Multilayer immobilizing of denitrifying Bacillus sp. and TiO2-AgNPs on floating expanded clay carrier for co-treatment of nitrite and pathogens in aquaculture.

Nitrite contamination and the spread of pathogens can seriously degrade water quality. To simultaneously control these factors, an innovative approach of fabricating a remediation agent that contained denitrifying bacteria and TiO2-AgNPs co-immobilized on floating expanded clay (EC) was proposed in this study. The EC was fabricated from a mixture of clay and rice husk through pyrolysis at a high temperature of 1200 °C, followed by a rapid cooling step to create a porous structure for the material. TiO2NPs were modified with Ag to shift the absorbance threshold of TiO2-AgNPs into the visible region of 700-800 nm. The experimental results showed that the stirring speed of 250 rpm was suitable for immobilizing TiO2-AgNPs on EC and achieved the highest Ti and Ag content of 639.38 ± 3.04 and 200.51 ± 3.71 ppm, respectively. Coating TiO2-Ag/EC with chitosan (0.5%) significantly reduced the detachment level of immobilized TiO2-AgNPs compared to that of the material with no coating. In particular, this functionalized material inhibited 99.93 ± 0.1% of Vibrio parahaemolyticus pathogen but did not adversely affect the denitrifying bacteria after 2 h of visible light irradiation. Based on the electrostatic bond between oppositely charged polymers, the denitrifying bacteria, Bacillus sp., in alginate solution was successfully immobilized on the chitosan-coated TiO2-Ag/EC with a bacteria density of (76.67 ± 9.43) × 107 CFU g-1, retaining its nitrite removal efficiency at 99.0 ± 0.27% through six treatment cycles. These findings provide solid evidence for further investigating the combination of biodegradation and photodegradation in wastewater treatment.

Time Series Electrical Motor Drives Forecasting Based on Simulation Modeling and Bidirectional Long-Short Term Memory.

Accurately forecasting electrical signals from three-phase Direct Torque Control (DTC) induction motors is crucial for achieving optimal motor performance and effective condition monitoring. However, the intricate nature of multiple DTC induction motors and the variability in operational conditions present significant challenges for conventional prediction methodologies. To address these obstacles, we propose an innovative solution that leverages the Fast Fourier Transform (FFT) to preprocess simulation data from electrical motors. A Bidirectional Long Short-Term Memory (Bi-LSTM) network then uses this altered data to forecast processed motor signals. Our proposed approach is thoroughly examined using a comparative examination of cutting-edge forecasting models such as the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). This rigorous comparison underscores the remarkable efficacy of our approach in elevating the precision and reliability of forecasts for induction motor signals. The results unequivocally establish the superiority of our method across stator and rotor current testing data, as evidenced by Mean Absolute Error (MAE) average results of 92.6864 and 93.8802 for stator and rotor current data, respectively. Additionally, compared to alternative forecasting models, the Root Mean Square Error (RMSE) average results of 105.0636 and 85.7820 underscore reduced prediction loss.

High Biocompatibility, MRI Enhancement, and Dual Chemo- and Thermal-Therapy of Curcumin-Encapsulated Alginate/Fe3O4 Nanoparticles.

(1) Background: Magnetite (Fe3O4) nanoparticles have great potential for biomedical applications, including hyperthermia and magnetic resonance imaging. In this study, we aimed to identify the biological activity of nanoconjugates composed of superparamagnetic Fe3O4 nanoparticles coated with alginate and curcumin (Fe3O4/Cur@ALG) in cancer cells. (2) Methods: The nanoparticles were evaluated for the biocompatibility and toxicity on mice. The MRI enhancement and hyperthermia capacities of Fe3O4/Cur@ALG were determined in both in vitro and in vivo sarcoma models. (3) Results: The results show that the magnetite nanoparticles exhibit high biocompatibility and low toxicity in mice at Fe3O4 concentrations up to 120 mg/kg when administered via intravenous injection. The Fe3O4/Cur@ALG nanoparticles enhance the magnetic resonance imaging contrast in cell cultures and tumor-bearing Swiss mice. The autofluorescence of curcumin also allowed us to observe the penetration of the nanoparticles into sarcoma 180 cells. In particular, the nanoconjugates synergistically inhibit the growth of sarcoma 180 tumors via magnetic heating and the anticancer effects of curcumin, both in vitro and in vivo. (4) Conclusions: Our study reveals that Fe3O4/Cur@ALG has a high potential for medicinal applications and should be further developed for cancer diagnosis and treatment.

Flamingo-Optimization-Based Deep Convolutional Neural Network for IoT-Based Arrhythmia Classification.

Cardiac arrhythmia is a deadly disease that threatens the lives of millions of people, which shows the need for earlier detection and classification. An abnormal signal in the heart causing arrhythmia can be detected at an earlier stage when the health data from the patient are monitored using IoT technology. Arrhythmias may suddenly lead to death and the classification of arrhythmias is considered a complicated process. In this research, an effective classification model for the classification of heart disease is developed using flamingo optimization. Initially, the ECG signal from the heart is collected and then it is subjected to the preprocessing stage; to detect and control the electrical activity of the heart, the electrocardiogram (ECG) is used. The input signals collected using IoT nodes are collectively presented in the base station for the classification using flamingo-optimization-based deep convolutional networks, which effectively predict the disease. With the aid of communication technologies and the contribution of IoT, medical professionals can easily monitor the health condition of patients. The performance is analyzed in terms of accuracy, sensitivity, and specificity.

Allium sativum@AgNPs and Phyllanthus urinaria@AgNPs: a comparative analysis for antibacterial application.

Although medicinal herbs contain many biologically active ingredients that can act as antibiotic agents, most of them are difficult to dissolve in lipids and absorb through biofilms in the gastrointestinal tract. Besides, silver nanoparticles (AgNPs) have been widely used as a potential antibacterial agent, however, to achieve a bactericidal effect, high concentrations are required. In this work, AgNPs were combined into plant-based antibiotic nanoemulsions using biocompatible alginate/carboxyl methylcellulose scaffolds. The silver nanoparticles were prepared by a green method with an aqueous extract of Allium sativum or Phyllanthus urinaria extract. The botanical antibiotic components in the alcoholic extract of these plants were encapsulated with emulsifier poloxamer 407 to reduce the particle size, and make the active ingredients both water-soluble and lipid-soluble. Field emission scanning electron microscopy (FESEM) and energy-dispersive X-ray (EDX) analysis showed that the prepared nanosystems were spherical with a size of about 20 nm. Fourier transform infrared spectroscopy (FTIR) confirmed the interaction of the extracts and the alginate/carboxyl methylcellulose carrier. In vitro drug release kinetics of allicin and phyllanthin from the nanosystems exhibited a retarded release under different biological pH conditions. The antimicrobial activity of the synthesized nanoformulations were tested against Escherichia coli. The results showed that the nanosystem based on Allium sativum possesses a significantly higher antimicrobial activity against the tested organisms. Therefore, the combination of AgNPs with active compounds from Allium sativum extract is a good candidate for in vivo infection treatment application.

RealPrice: Blockchain-Powered Real-Time Pricing for Software-Defined Enabled Edge Network.

With the limited Internet bandwidth in a given area, unlimited data plans can create congestion because there is no retribution for transmitting many packets. The real-time pricing mechanism can inform users of their Internet consumption to limit congestion during peak hours. However, implementing real-time pricing is opex-heavy from the network provider side and requires high-integrity operations to gain consumer trust. This paper aims to leverage the software-defined network to solve the opex issues and blockchain technology to solve trust issues. First, the network congestion level in a given area is analyzed. Then, the price is adjusted accordingly. Devices that send a lot of traffic during congestion will be charged more expensive bills than if transmitting traffic during an off-peak period. To prevent over-charging, the consumers can pre-configure a customized Internet profile stating how many data bytes they are willing to send during congestion. The software-defined controller also authenticates consumers and checks whether they have enough token deposits in the blockchain as Internet usage fees. We implement our work using Ethereum and POX controllers. The experiment results show that the proposed real-time pricing can be performed seamlessly, and the network provider can reap up to 72.91% more profits than existing approaches, such as usage-based pricing or time-dependent pricing. The fairness and trustability of real-time pricing is also guaranteed through the proof-of-usage mechanism and the transparency of the blockchain.

Modification of expanded clay carrier for enhancing the immobilization and nitrogen removal capacity of nitrifying and denitrifying bacteria in the aquaculture system.

In aquaculture systems, the treatment of nitrogen pollution has always been a center of attention due to its impact on productiveness. The bioremediation method based on simultaneous nitrification and denitrification was often used to effectively remove ammonium, nitrite, and nitrate compounds. In addition, the attachment and biofilm formation of the nitrogen-converting bacteria on carriers had superior removal efficiency over the suspended bacteria. Thus, this study focused on the fabrication of a porosity floatable expanded clay (EC) carrier that provided the basic structure for the immobilization of the nitrifiers Nitrosomonas sp., Nitrobacter sp., and the denitrifier Bacillus sp. The EC was also coated with alginate and essential nutrient to support the cohesion and growth of bacteria. Especially, the selected Bacillus sp. previously proved was able to reduce nitrite/nitrate in aerobic conditions. The co-immobilization of these three aerobic bacteria on the prepared carrier would simply the treatment process in practical use. Initial results showed that the integration of essential nutrients (N, P, K) on alginate coated EC (EC_Alg_N) increased bacterial density to (57 ± 3) × 107 - (430 ± 30) × 108 CFU/g, which then led to the enhancement of removal efficiency up to 91.62 ± 0.67% in the medium containing initial nitrogen content of 60 mg-N/L. The nitrogen removal efficacy of bacterial immobilized EC_Alg_N remained at 83.95 ± 0.15% after being reused for 6 cycles. In conclusion, the bacterial immobilized EC_Alg_N could be a potential material for nitrogen polluted wastewater treatment in aquaculture systems.

Classification and Explanation for Intrusion Detection System Based on Ensemble Trees and SHAP Method.

In recent years, many methods for intrusion detection systems (IDS) have been designed and developed in the research community, which have achieved a perfect detection rate using IDS datasets. Deep neural networks (DNNs) are representative examples applied widely in IDS. However, DNN models are becoming increasingly complex in model architectures with high resource computing in hardware requirements. In addition, it is difficult for humans to obtain explanations behind the decisions made by these DNN models using large IoT-based IDS datasets. Many proposed IDS methods have not been applied in practical deployments, because of the lack of explanation given to cybersecurity experts, to support them in terms of optimizing their decisions according to the judgments of the IDS models. This paper aims to enhance the attack detection performance of IDS with big IoT-based IDS datasets as well as provide explanations of machine learning (ML) model predictions. The proposed ML-based IDS method is based on the ensemble trees approach, including decision tree (DT) and random forest (RF) classifiers which do not require high computing resources for training models. In addition, two big datasets are used for the experimental evaluation of the proposed method, NF-BoT-IoT-v2, and NF-ToN-IoT-v2 (new versions of the original BoT-IoT and ToN-IoT datasets), through the feature set of the net flow meter. In addition, the IoTDS20 dataset is used for experiments. Furthermore, the SHapley additive exPlanations (SHAP) is applied to the eXplainable AI (XAI) methodology to explain and interpret the classification decisions of DT and RF models; this is not only effective in interpreting the final decision of the ensemble tree approach but also supports cybersecurity experts in quickly optimizing and evaluating the correctness of their judgments based on the explanations of the results.

Dual Loading of Doxorubicin and Magnetic Iron Oxide into PLA-TPGS Nanoparticles: Design, in vitro Drug Release Kinetics, and Biological Effects on Cancer Cells.

The multifunctional nano drug delivery system (MNDDS) has much revolutionized in cancer treatment, aiming to eliminate many disadvantages of conventional formulations. This paper herein proposes and demonstrates MNDDS inspired by poly(lactide)-tocopheryl polyethylene glycol succinate (PLA-TPGS) copolymer co-loaded Doxorubicin and magnetic iron oxide nanoparticles (MIONs) with a 1 : 1 (w/w) optimal ratio. In vitro drug release kinetics of Doxorubicin from this nanosystem fitted best to the Weibull kinetic model and can be described by the classical Fickian diffusion mechanism under acidic pH conditions. The combination of MIONs and Doxorubicin in the PLA-TPGS copolymer has maintained the fluorescence properties of Doxorubicin and good cell penetration, especially inside the nucleus and its vicinity. Moreover, different cell cycle profiles were observed in HeLa cell lines treated with MNDDSs.

Background Load Denoising across Complex Load Based on Generative Adversarial Network to Enhance Load Identification.

Non-Intrusive Load Monitoring (NILM) allows load identification of appliances through a single sensor. By using NILM, users can monitor their electricity consumption, which is beneficial for energy efficiency or energy saving. In advance NILM systems, identification of appliances on/off events should be processed instantly. Thus, it is necessary to use an extremely short period signal of appliances to shorten the time delay for users to acquire event information. However, acquiring event information from a short period signal raises another problem. The problem is target load feature to be easily mixed with background load. The more complex the background load has, the noisier the target load occurs. This issue certainly reduces the appliance identification performance. Therefore, we provide a novel methodology that leverages Generative Adversarial Network (GAN) to generate noise distribution of background load then use it to generate a clear target load. We also built a Convolutional Neural Network (CNN) model to identify load based on single load data. Then we use that CNN model to evaluate the target load generated by GAN. The result shows that GAN is powerful to denoise background load across the complex load. It yields a high accuracy of load identification which could reach 92.04%.

Design, Synthesis and Biological Evaluation of Novel N-hydroxyheptanamides Incorporating 6-hydroxy-2-methylquinazolin-4(3H)-ones as Histone Deacetylase Inhibitors and Cytotoxic Agents.

BACKGROUND: Target-based approach to drug discovery currently attracts a great deal of interest from medicinal chemists in anticancer drug discovery and development worldwide, and Histone Deacetylase (HDAC) inhibitors represent an extensive class of targeted anti-cancer agents. Among the most explored structure moieties, hydroxybenzamides and hydroxypropenamides have been demonstrated to have potential HDAC inhibitory effects. Several compounds of these structural classes have been approved for clinical uses to treat different types of cancer, such as vorinostat and belinostat. AIMS: This study aims at developing novel HDAC inhibitors bearing quinazolinone scaffolds with potential cytotoxicity against different cancer cell lines. METHODS: A series of novel N-hydroxyheptanamides incorporating 6-hydroxy-2 methylquinazolin-4(3H)-ones (14a-m) was designed, synthesized and evaluated for HDAC inhibitory potency as well as cytotoxicity against three human cancer cell lines, including HepG-2 (liver cancer), MCF-7 (breast cancer) and SKLu-1 (lung cancer). Molecular simulations were finally carried out to gain more insight into the structure-activity relationships. ADME-T predictions for selected compounds were also performed to predict some important features contributing to the absorption profile of the present hydroxamic derivatives. RESULTS: It was found that the N-hydroxyheptanamide 14i and 14j were the most potent, both in terms of HDAC inhibition and cytotoxicity. These compounds displayed up to 21-71-fold more potent than SAHA (suberoylanilide hydroxamic acid, vorinostat) in terms of cytotoxicity, and strong inhibition against the whole cell HDAC enzymes with IC50 values of 7.07-9.24µM. Docking experiments on HDAC2 isozyme using Autodock Vina showed all compounds bound to HDAC2 with relatively higher affinities (from -7.02 to -11.23 kcal/mol) compared to SAHA (-7.4 kcal/mol). It was also found in this research that most of the target compounds seemed to be more cytotoxic toward breast cancer cells (MCF-7) than liver (HepG2), and lung (SKLu-1) cancer cells.

Convenient one-pot access to 2H-3-nitrothiochromenes from 2-bromobenzaldehydes, sodium sulfide and ß-nitrostyrenes.

An efficient synthesis of 2H-3-nitrothiochromenes via a cascade reaction was established. Starting from commercially available o-bromobenzaldehydes and ß-nitrostyrenes with sodium sulfide nonahydrate as an inexpensive sulfur source, various substituted thiochromenes were synthesized with high functional group tolerance without any added transition metal catalyst or additive.

Beyond model interpretability using LDA and decision trees for α-amylase and α-glucosidase inhibitor classification studies.

In this report are used two data sets involving the main antidiabetic enzyme targets α-amylase and α-glucosidase. The prediction of α-amylase and α-glucosidase inhibitory activity as antidiabetic is carried out using LDA and classification trees (CT). A large data set of 640 compounds for α-amylase and 1546 compounds in the case of α-glucosidase are selected to develop the tree model. In the case of CT-J48 have the better classification model performances for both targets with values above 80%-90% for the training and prediction sets, correspondingly. The best model shows an accuracy higher than 95% for training set; the model was also validated using 10-fold cross-validation procedure and through a test set achieving accuracy values of 85.32% and 86.80%, correspondingly. Additionally, the obtained model is compared with other approaches previously published in the international literature showing better results. Finally, we can say that the present results provided a double-target approach for increasing the estimation of antidiabetic chemicals identification aimed by double-way workflow in virtual screening pipelines.

In Silico Assessment of ADME Properties: Advances in Caco-2 Cell Monolayer Permeability Modeling.

One of the main goals of in silico Caco-2 cell permeability models is to identify those drug substances with high intestinal absorption in human (HIA). For more than a decade, several in silico Caco-2 models have been made, applying a wide range of modeling techniques; nevertheless, their capacity for intestinal absorption extrapolation is still doubtful. There are three main problems related to the modest capacity of obtained models, including the existence of inter- and/or intra-laboratory variability of recollected data, the influence of the metabolism mechanism, and the inconsistent in vitro-in vivo correlation (IVIVC) of Caco-2 cell permeability. This review paper intends to sum up the recent advances and limitations of current modeling approaches, and revealed some possible solutions to improve the applicability of in silico Caco-2 permeability models for absorption property profiling, taking into account the above-mentioned issues.

Study of Clinical Characteristics and Cytokine Profiles of Asthmatic Children with Rhinovirus Infection during Acute Asthma Exacerbation at National Hospital of Pediatrics.

Background: In children with asthma, the viral infection of airways is usually a main cause of acute asthma exacerbation and hospitalization. However, few studies on clinical and biomolecular characteristics of asthmatic children in this field have been done, especially in emergent countries. Objective: This study described the clinical and biological characteristics of asthmatic children who had acute asthma exacerbation and rhinovirus (RV) infection. Methods: Children under 15 years of age hospitalized for acute asthma exacerbation were included. They underwent clinical examination and peripheral blood analyses for the cytokine profile. The severity of acute asthma exacerbation was evaluated by Pediatric Asthma Score (PAS). Healthy children under 15 years of age were also invited in this study. Results: One hundred fifteen asthmatic children were included in this study. There were 18.2% of mild PAS, 37.4% of moderate PAS, and 44.4% of severe PSA. Among them, 63/115 (54.8%) asthmatic children had positive RV infection (RV+). The percentages of asthmatic children with RV+ had increased polymorphonuclear leucocytes were significantly higher than asthmatic children with RV-. There were no significant differences of the concentrations of non-Th2-related cytokines in asthmatic children with RV- and RV+. The concentration of Th2-related cytokines (IL-5 and IL-13) in asthmatic children with RV+ was significantly higher than those with RV-. However, there was no significant difference for the cytokine profile between mild, moderate, and severe asthma. Conclusion: RV infection is a main cause of acute asthma exacerbation in children with asthma. The increase of Th2-related cytokines, especially IL-5 and IL-13, is a relevant biomarker for RV infection in asthmatic children with severe exacerbation.

Crystal structure of (E)-N'-[1-(4-amino-phen-yl)ethyl-idene]-2-hy-droxy-5-iodo-benzohydrazide methanol monosolvate.

In the title compound, C15H14IN3O2·CH3OH, two aromatic rings are linked by an N-substituted hydrazide function. The dihedral angle between the aromatic rings is 10.53 (8)°. The stereochemistry about the imine function is E. The methanol mol-ecule forms an O-H⋯O hydrogen bond to the hydrazide O atom. In the crystal, chains of mol-ecules running along the c-axis direction are formed by O-H⋯O hydrogen bonds. Adjacent chains are linked through N-H⋯O hydrogen bonds and π-π stacking inter-actions. The inter-molecular inter-actions in the crystal packing were investigated using Hirshfeld surface analysis, which indicated that the most significant contacts are H⋯H (38.2%), followed by C⋯H/H⋯C (20.6%), O⋯H/H⋯O (11.1%) and I⋯H/H⋯I (9.7%).

Corrigendum to "Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature".

[This corrects the article DOI: 10.1155/2017/4216281.].

A desirability-based multi objective approach for the virtual screening discovery of broad-spectrum anti-gastric cancer agents.

Gastric cancer is the third leading cause of cancer-related mortality worldwide and despite advances in prevention, diagnosis and therapy, it is still regarded as a global health concern. The efficacy of the therapies for gastric cancer is limited by a poor response to currently available therapeutic regimens. One of the reasons that may explain these poor clinical outcomes is the highly heterogeneous nature of this disease. In this sense, it is essential to discover new molecular agents capable of targeting various gastric cancer subtypes simultaneously. Here, we present a multi-objective approach for the ligand-based virtual screening discovery of chemical compounds simultaneously active against the gastric cancer cell lines AGS, NCI-N87 and SNU-1. The proposed approach relays in a novel methodology based on the development of ensemble models for the bioactivity prediction against each individual gastric cancer cell line. The methodology includes the aggregation of one ensemble per cell line using a desirability-based algorithm into virtual screening protocols. Our research leads to the proposal of a multi-targeted virtual screening protocol able to achieve high enrichment of known chemicals with anti-gastric cancer activity. Specifically, our results indicate that, using the proposed protocol, it is possible to retrieve almost 20 more times multi-targeted compounds in the first 1% of the ranked list than what is expected from a uniform distribution of the active ones in the virtual screening database. More importantly, the proposed protocol attains an outstanding initial enrichment of known multi-targeted anti-gastric cancer agents.

Learning from Multiple Classifier Systems: Perspectives for Improving Decision Making of QSAR Models in Medicinal Chemistry.

Quantitative Structure - Activity Relationship (QSAR) modeling has been widely used in medicinal chemistry and computational toxicology for many years. Today, as the amount of chemicals is increasing dramatically, QSAR methods have become pivotal for the purpose of handling the data, identifying a decision, and gathering useful information from data processing. The advances in this field have paved a way for numerous alternative approaches that require deep mathematics in order to enhance the learning capability of QSAR models. One of these directions is the use of Multiple Classifier Systems (MCSs) that potentially provide a means to exploit the advantages of manifold learning through decomposition frameworks, while improving generalization and predictive performance. In this paper, we presented MCS as a next generation of QSAR modeling techniques and discuss the chance to mining the vast number of models already published in the literature. We systematically revisited the theoretical frameworks of MCS as well as current advances in MCS application for QSAR practice. Furthermore, we illustrated our idea by describing ensemble approaches on modeling histone deacetylase (HDACs) inhibitors. We expect that our analysis would contribute to a better understanding about MCS application and its future perspectives for improving the decision making of QSAR models.