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In this work, benzene based hypercrosslinked polymer (HCP) as an adsorbent was modified using amine group to enhance CO2 uptake capability and selectivity. Based on BET analysis result, the HCP and the modified HCP provide surface area of 806 (m2 g−1) and micropore volume of 453 (m2 g−1) and 0.19 (cm3 g−1) and 0.14 (cm3 g−1), respectively. The CO2 and N2 gases adsorption were performed in a laboratory scale reactor at a temperature between 298 and 328 K and pressure up to 9 bar. The experimental data were evaluated using isotherm, kinetic and thermodynamic models to identify the absorbent behavior. The maximum CO2 adsorption capacity at 298 K and 9 bar was obtained 301.67 (mg g−1) for HCP and 414.41 (mg g−1) for amine modified HCP. The CO2 adsorption thermodynamic parameters assessment including enthalpy changes, entropy changes, and Gibbs free energy changes at 298 K were resulted − 14.852 (kJ mol−1), − 0.024 (kJ mol−1 K−1), − 7.597 (kJ mol−1) for HCP and − 17.498 (kJ mol−1), − 0.029(kJ mol−1 K−1), − 8.9 (kJ mol−1) for amine functionalized HCP, respectively. Finally, the selectivity of the samples were calculated at a CO2/N2 composition of 15:85 (v/v) and 43% enhancement in adsorption selectivity at 298 K was obtained for amine modified HCP. [ABSTRACT FROM AUTHOR]

In this work, benzene based hypercrosslinked polymer (HCP) as an adsorbent was modified using amine group to enhance CO2 uptake capability and selectivity. Based on BET analysis result, the HCP and the modified HCP provide surface area of 806 (m2 g−1) and micropore volume of 453 (m2 g−1) and 0.19 (cm3 g−1) and 0.14 (cm3 g−1), respectively. The CO2 and N2 gases adsorption were performed in a laboratory scale reactor at a temperature between 298 and 328 K and pressure up to 9 bar. The experimental data were evaluated using isotherm, kinetic and thermodynamic models to identify the absorbent behavior. The maximum CO2 adsorption capacity at 298 K and 9 bar was obtained 301.67 (mg g−1) for HCP and 414.41 (mg g−1) for amine modified HCP. The CO2 adsorption thermodynamic parameters assessment including enthalpy changes, entropy changes, and Gibbs free energy changes at 298 K were resulted − 14.852 (kJ mol−1), − 0.024 (kJ mol−1 K−1), − 7.597 (kJ mol−1) for HCP and − 17.498 (kJ mol−1), − 0.029(kJ mol−1 K−1), − 8.9 (kJ mol−1) for amine functionalized HCP, respectively. Finally, the selectivity of the samples were calculated at a CO2/N2 composition of 15:85 (v/v) and 43% enhancement in adsorption selectivity at 298 K was obtained for amine modified HCP. [ABSTRACT FROM AUTHOR]

The influence of distributed generation sources in the Islanded microgrid has adverse effects on frequency stability because of the absence of inertia. Accordingly, the concept of a virtual synchronous generator (VSG) has been proposed, which follows the behavior of conventional synchronous generators. The virtual inertia control can be implemented on energy storage systems (ESSs) as a part of a virtual synchronous generator. Virtual inertial control does not perform optimally against the uncertainty of islanded microgrid parameters and disturbances. Therefore, virtual inertial control requires a supplementary controller in its structure that is resistant to these effects. In this paper, a new robust output feedback control method was used to control the virtual inertia in a two-area microgrid, which is based on the linear matrix inequality. The proposed control method does not need to measure all modes and only uses the output feedback. We compared the results of the proposed controller for controlling the virtual inertia in multiple different scenarios by considering the uncertainty of the two-area microgrid parameters as well as disturbances applied to the microgrid with the virtual inertia control based on optimized PI controllers (genetic algorithm), no controller (virtual inertia), and without considering virtual inertia (conventional methods). Accordingly, the effectiveness of the proposed method was demonstrated in terms of response speed, reduction of the frequency deviations and tie-line power deviations, robustness against parameters' uncertainty, and disturbances occurring to the two-area microgrid. [ABSTRACT FROM AUTHOR]

Copy number variation (CNV) is one of the main sources of variation between different individuals that has recently attracted much researcher interest as a major source for heritable variation in complex traits. The aim of this study was to identify CNVs in Afghan indigenous sheep consisting of three Arab, Baluchi, and Gadik breeds using genomic arrays containing 53,862 single nucleotide polymorphism (SNP) markers. Data were analyzed using the Hidden Markov Model (HMM) of PennCNV software. In this study, out of 45 sheep studied, 97.8% (44 animals) have shown CNVs. In total, 411 CNVs were observed for autosomal chromosomes and the entire sequence length of around 144 Mb was identified across the genome. The average number of CNVs per each sheep was 9.13. The identified CNVs for Arab, Baluchi, and Gadik breeds were 306, 62, and 43, respectively. After merging overlapped regions, a total of 376 copy number variation regions (CNVR) were identified, which are 286, 50, and 40 for Arab, Baluchi, and Gadik breeds, respectively. Bioinformatics analysis was performed to identify the genes and QTLs reported in these regions and the biochemical pathways involved by these genes. The results showed that many of these CNVRs overlapped with the genes or QTLs that are associated with various pathways such as immune system development, growth, reproduction, and environmental adaptions. Furthermore, to determine a genome-wide pattern of selection signatures in Afghan sheep breeds, the unbiased estimates of FST was calculated and the results indicated that 37 of the 376 CNVRs (~ 10%) have been also under selection signature, most of those overlapped with the genes influencing production, reproduction and immune system. Finally, the statistical methods used in this study was applied in an external dataset including 96 individuals of the Iranian sheep breed. The results indicated that 20 of the 114 CNVRs (18%) identified in Iranian sheep breed were also identified in our study, most of those overlapped with the genes influencing production, reproduction and immune system. Overall, this is the first attempts to develop the genomic map of loss and gain variation in the genome of Afghan indigenous sheep breeds, and may be important to shed some light on the genomic regions associated with some economically important traits in these breeds. [ABSTRACT FROM AUTHOR]

Three-dimensional printing (3DP) is an evolutionary solution for making customize items for all sectors, but it has become more prominent in the healthcare sector. In this field, some solutions have to be adapted to patients. This is especially true for dentistry, where all the patients have their own unique mouth and tooth structure. It is now possible to provide an accurate model of the patient's mouth and teeth with solutions that are perfectly compatible with them, leading to the provision of a dental service with a high success rate. Even if there is a problem, it is enough to change the three-dimensional design. Therefore, it is a time-saving method, too. The purpose of this study is to investigate the role of 3DP in dentistry and to identify the processes and procedures resulting from the use of this technology. To do so, with the help of a case study, a 3DP-based dental clinic that provides implant, orthodontics, restoration and dentures services is simulated in Arena software. The current state of the system is assessed by defining appropriate evaluation criteria including net profit, utilization, waiting time, patients makespan and laboratory makespan. The simulation model is then developed with innovations such as adding an inventory control policy, creating rest time for resources and controlling the policy of sending products from laboratory to the clinic. After an extensive sensitivity analysis, improving the performance of the system is on the agenda of this paper by examining various scenarios. Results show that scenarios such as reducing some resources of the system or considering rest time in exchange for increasing the duration of the work shift can have a significant impact on clinic performance. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 is a corona virus that has been the cause for one of the deadliest pandemics of history, started since 2019. Suppressing the activity of the critical enzymes in the SARS-CoV-2 could potentially inhibit a vital step in viral life cycle. Papain-like protease (PLpro) could be regarded as a critical enzyme in viral replication of SARS-CoV-2. In this research, it was aimed to suppress the activity of PLpro enzyme by using potential plant-derived protease inhibitor peptides. For this purpose, 11 plant derived peptides that could potentially inhibit protease activity were selected from literature. The structures of the PLpro and the peptide ligands were acquired from PDB (protein data bank) and after structural optimization, were docked by using HADDOCK 2.4 program. Analyzing the results indicated that VcTI from Veronica hederifolia provides effective molecular interactions at both liable Zn site and classic active site of PLpro, making it a potential inhibitory ligand for this enzyme that could be used for halting the replication of SARS-CoV-2. Molecular dynamic assay confirmed that the selected receptor and ligand complex was stable. Future in vitro and in vivo investigations are required to verify the efficiency of this compound as a potential therapeutic against SARS-CoV-2 infection. [ABSTRACT FROM AUTHOR]

Designing an effective vaccine against different subtypes of Influenza A virus is a critical issue in the field of medical biotechnology. At the current study, a novel potential multi-epitope vaccine candidate based on the neuraminidase proteins for seven subtypes of Influenza virus was designed, using the in silico approach. Potential linear B-cell and T-cell binding epitopes from each neuraminidase protein (N1, N2, N3, N4, N6, N7, N8) were predicted by in silico tools of epitope prediction. The selected epitopes were joined by three different linkers, and physicochemical properties, toxicity, and allergenecity were investigated. The final multi-epitope construct was modeled using GalaxyWEB server, and the molecular interactions with immune receptors were investigated and the immune response simulation assay was performed. A multi-epitope construct with GPGPGPG linker with the lowest allergenicity and highest stability was selected. The molecular docking assay indicated the interactions with immune system receptors, including HLA1, HLA2, and TLR-3. Immune response simulation detected both humoral and cellular response, including the elevated count of B-cells, T-cell, and Nk-cells. [ABSTRACT FROM AUTHOR]

The time delay between distributed generation sources puts the frequency stability at risk. Moreover, increasing the number of distributed generation sources in islanded microgrids due to the lack of inertia has undesirable effects on frequency stability. In this article, the notion of a virtual synchronous generator which follows the characteristics of a conventional synchronous generator is introduced. Virtual inertia control can be implemented on sources of energy storage. In this article, the problem of designing a Virtual Inertia Control method is based on Robust Model Predictive Controller (RMPC), considering the time delays in microgrids is addressed. To model the uncertainty of the system, employed the polytopic model and the Sedumi solver to solve the Linear Matrix Inequalities (LMI), in a shorter time. Compared the results of the proposed controller under several different scenarios with virtual inertial control schemes based on the optimized-PI-controller (Genetic algorithm), the H-infinity controller, and the control free procedure, considering uncertainties in microgrid parameters, time delays and input disturbances. The efficiency of the RMPC method has been indicated via the simulations performed in MATLAB. [ABSTRACT FROM AUTHOR]

Diabetes mellitus is a metabolic syndrome characterized by elevated blood glucose. The α-glucosidase enzyme is responsible for the hydrolysis of carbohydrates. This in silico study aimed to evaluate the inhibitory effects of the isolated compounds from Allium sativum L. on α-glucosidase. At first, sulfur and phenolic compounds of A. sativum L. were obtained from PubChem database, and α-glucosidase enzyme structure was obtained from Protein Data Bank. Toxicity class of compounds and the Lipinski parameter were predicted by Toxtree and Protox II and the Swiss ADME tools, respectively. Finally, the molecular interaction analysis between α-glucosidase and compounds from A. sativum L. was performed by AutoDock 4.2.6. Molecular interactions were investigated using Discovery Studio Visulizer and Ligplot 2.1 program. All of the selected sulfur and phenolic compounds from A. sativum L. followed the Lipinski's rules, had an acceptable binding energy, and lacked toxicity; therefore, they were appropriate candidates for α-glucosidase inhibition. Among these compounds, methionol and caffeic acid showed the lowest binding energy, and the highest inhibitory effect on α-glucosidase enzyme with − 3.9 and − 4.8 kcal/mol, respectively. These compounds also indicated the lower binding energy than the standard inhibitor (miglitol). Among the sulfur and phenolic compounds in A. sativum L., methionol and caffeic acid were predicted to be the powerful inhibitors, due to having more hydrogen binds and hydrophobic interactions with the active site of α-glucosidase. [ABSTRACT FROM AUTHOR]

Diabetes mellitus is a metabolic syndrome characterized by elevated blood glucose. The α-glucosidase enzyme is responsible for the hydrolysis of carbohydrates. This in silico study aimed to evaluate the inhibitory effects of the isolated compounds from Allium sativum L. on α-glucosidase. At first, sulfur and phenolic compounds of A. sativum L. were obtained from PubChem database, and α-glucosidase enzyme structure was obtained from Protein Data Bank. Toxicity class of compounds and the Lipinski parameter were predicted by Toxtree and Protox II and the Swiss ADME tools, respectively. Finally, the molecular interaction analysis between α-glucosidase and compounds from A. sativum L. was performed by AutoDock 4.2.6. Molecular interactions were investigated using Discovery Studio Visulizer and Ligplot 2.1 program. All of the selected sulfur and phenolic compounds from A. sativum L. followed the Lipinski's rules, had an acceptable binding energy, and lacked toxicity; therefore, they were appropriate candidates for α-glucosidase inhibition. Among these compounds, methionol and caffeic acid showed the lowest binding energy, and the highest inhibitory effect on α-glucosidase enzyme with − 3.9 and − 4.8 kcal/mol, respectively. These compounds also indicated the lower binding energy than the standard inhibitor (miglitol). Among the sulfur and phenolic compounds in A. sativum L., methionol and caffeic acid were predicted to be the powerful inhibitors, due to having more hydrogen binds and hydrophobic interactions with the active site of α-glucosidase. [ABSTRACT FROM AUTHOR]

In recent years, the concept of reading people's minds while performing specific tasks has grown in popularity, especially in the field of brain–computer interface systems. The goal of this research is to offer a new comprehensive framework for visual image classification utilizing EEG signals. Here, LSTM network is chosen to extract the feature of the EEG signal. To improve the classification accuracy in comparison to recent works in this field, a ResNet is used to extract the feature from the images, and a fuzzy regression is used to map the features extracted from the images on the features extracted from the EEG signal. Because medical data usually have uncertainty and ambiguity, and the number of data samples is small, in this research, interval type-2 fuzzy regression is used. This study makes use of data from Stanford University. Classification accuracy, precision, recall, and F1 score are the measures used to evaluate outcomes. In this case, the LSTM network is able to classify images based on EEG signals with 55.55% accuracy and 55.73% precision. The findings of utilizing ResNet and interval type-2 fuzzy regression suggest that regression can significantly enhance classification accuracy. Classification accuracy and precision obtained with interval type-2 fuzzy regression and SVM classifier are 66.67% and 66.66%, respectively, for all participants. These results outperform classification accuracy and precision obtained using type-1 fuzzy, neural network and polynomial regression. In addition, the mean accuracy for all participants obtained in this article is 4.09% greater than the best result reported in a previous article using the same database. This leads to the conclusion that fuzzy regression, especially interval type-2 fuzzy regression, performs better in high uncertainty environments. Therefore, it is concluded that the method proposed in this research can be used for decoding brain response to visual image stimuli and classifying the stimulus images with acceptable accuracy, thereby advancing the goal of mind reading. [ABSTRACT FROM AUTHOR]

The assignment of an individual to the true population of origin using a low-panel of discriminant SNP markers is one of the most important applications of genomic data for practical use. The aim of this study was to evaluate the potential of different Artificial Neural Networks (ANNs) approaches consisting Deep Neural Networks (DNN), Garson and Olden methods for feature selection of informative SNP markers from high-throughput genotyping data, that would be able to trace the true breed of unknown samples. The total of 795 animals from 37 breeds, genotyped by using the Illumina SNP 50k Bead chip were used in the current study and principal component analysis (PCA), log-likelihood ratios (LLR) and Neighbor-Joining (NJ) were applied to assess the performance of different assignment methods. The results revealed that the DNN, Garson, and Olden methods are able to assign individuals to true populations with 4270, 4937, and 7999 SNP markers, respectively. The PCA was used to determine how the animals allocated to the groups using all genotyped markers available on 50k Bead chip and the subset of SNP markers identified with different methods. The results indicated that all SNP panels are able to assign individuals into their true breeds. The success percentage of genetic assignment for different methods assessed by different levels of LLR showed that the success rate of 70% in the analysis was obtained by three methods with the number of markers of 110, 208, and 178 tags for DNN, Garson, and Olden methods, respectively. Also the results showed that DNN performed better than other two approaches by achieving 93% accuracy at the most stringent threshold. Finally, the identified SNPs were successfully used in independent out-group breeds consisting 120 individuals from eight breeds and the results indicated that these markers are able to correctly allocate all unknown samples to true population of origin. Furthermore, the NJ tree of allele-sharing distances on the validation dataset showed that the DNN has a high potential for feature selection. In general, the results of this study indicated that the DNN technique represents an efficient strategy for selecting a reduced pool of highly discriminant markers for assigning individuals to the true population of origin. [ABSTRACT FROM AUTHOR]

The article discusses the risk assessment practice in the field of geotechnical engineering. It mentions the risk analysis method in the rock tunnel boring machines (TBM) at the Zagros long tunnel located at Zahab, Iran, and hazards related to the geotechnical analysis in TBM including groundwater inflow, squeezing zones and falling rocks. Also mentioned is that this study concludes that risk matrix method was utilized in carrying the risk analysis due to geotechnical hazard in TBM.

The article presents a study on the simulation of anisoplanatism of adaptive optical system in inhomogenous turbulent atmosphere. The study involved the use of a numerical model with layers of turbulence software to calculate the optical transfer function and system isoplanatic angle. The results incurred dealt with astronomical observations of very distant targets when incoming waves are plane, and the development of the software assists in the calculation of the effects of beam diffraction.

The aim of this investigation was to study the effect of tool pin geometry and pass number on mechanical and microstructural behaviour of SiC-incorporated dissimilar friction stir welding of 6-mm-thick plates of AA6061-T6 and AA2024-T351. Two tool pin geometries, square and triangle frustum, were used. It was found that the weld made by square frustum pin and two passes with rotation in the same direction produces more homogenized and finer distribution of SiC particles. Also, the grain size of stir zone in the weld made by two passes is finer than that made by one pass. The weld made by triangle frustum pin shows higher thermo-mechanically affected zone hardness, natural aging kinetics and tensile strength than the others. [ABSTRACT FROM AUTHOR]

The Glutathione S-Transferases (GSTs) are detoxification enzymes which exist in variety of living organisms such as bacteria, fungi, plants and animals. These multifunctional enzymes play important roles in the biosynthesis of steroids, prostaglandins, apoptosis regulation, and stress signaling. In this study, we designed a method to independently predict the structures of animal, fungal and plant GSTs using Chou's pseudo-amino acid composition concept. Support vector machine (SVM), Random Forests (RF), Covariance Discrimination (CD) and Optimized Evidence-Theoretic K-nearest Neighbor (OET-KNN) were used as powerful machine learnings algorithms. Based on our results, Random Forests demonstrated the best prediction for animal GSTs with 0.9339 accuracy and SVM showed the best results for fungal and plant GSTs with 0.8982 and 0.9655 accuracy, respectively. Our study provided an effective prediction for GSTs based on the concept of PseAAC and four different machine learning algorithms. [ABSTRACT FROM AUTHOR]

Blocking of atmospheric systems may lead to slowing down and even stopping the movement of western wind waves. This condition prolongs the warm days in catchment areas in front of the wave and induces continuation of cold and frost days in catchment areas behind the wave. The effect of cold and frost on the crops, especially in spring, is undeniable. Therefore, analyzing the blocking systems, named as omega, is important in Iran's spring frost duration in southern middle latitudes. The daily minimum temperature data of 69 meteorological stations were collected from the Iranian Meteorological Organization from March 20, 1968, to June 21, 2014, in order to determine the effects of omega blocking systems on duration of the spring frost waves in Iran (equivalent to spring season in the Iranian calendar). By adjusting the tables of daily temperature distribution and plotting the frequency variation diagrams, 114 synoptic waves lasting 3–25 days were identified. Four durable waves (> 12 days) and ten low-durability waves (< 12 days) were identified by dividing the 114 waves into two groups (47 durable waves and 67 low-durability waves) and designing the atmospheric circulation pattern for the middle eastern region based on the air maps of 500 hPa. The results showed that blocking patterns usually occur in the areas of central Europe to the Black Sea basin and cause advective cold air from Scandinavian and central Russia basins to Iran, thereby resulting in durable frosts during spring. [ABSTRACT FROM AUTHOR]

In the current investigation, the electrochemical oxidation of caffeic acid (CA) in the absence and presence of 1,4-benzenediboronic acid (BDA) was studied. The voltammetric study indicates that CA (1) oxidized to its o-benzoquinone (1ox) within a two-electron process. In basic pHs, the electrochemically generated 1ox is unstable and participates in the dimerization reaction. The results indicate that BDA reacts with CA, and a complex is produced. By the use of cyclic voltammetry, the electrochemical behavior of CA–BDA complex has been studied in aqueous solution. It was found that the electrochemical mechanism of the produced CA–BDA complex is CEC. Also, the electrochemical study of the CA–BDA complex was studied in the presence of glucose. The results show that the tendency of BDA to form the complex with CA decreases due to the addition of glucose, and the anodic peak of CA–BDA complex decreases with the increasing concentration of glucose. [ABSTRACT FROM AUTHOR]

Brain connectivity measures the relationship between brain regions that occur in response to a task (task-related) or in resting state (intrinsic). Although differences in resting-state connectivity are frequently attributed to task-related brain activity, there is currently no valid criterion for directly evaluating task-related connectivity from signal data. This study aims to establish a fuzzy index based on fuzzy Subsethood (FSH) measure for estimating task-related connectivity. First, two sets of simulated time series with linear and nonlinear interactions were used to evaluate the proposed approach. The results of FSH method on simulated data indicate that the fuzzy Subsethood criterion could identify and estimate task-related connectivity. Afterward, an EEG dataset with an SSVEP task in a mobile BCI system was used to test the approach using real-world data. To address the volume conduction effect and the problem of fake connectivity, the signals were first mapped into the domain of independent components using the Fourier-ICA algorithm, and brain connectivity among brain sources was investigated. The FSH approach was then used to estimate task-related connectivity, and a pattern recognition algorithm was used to determine stimulus frequency. The results demonstrated that the FSH approach is robust when modifying the user's speed. Furthermore, at a speed of 2 m/s, the FSH approach significantly outperformed the standard CCA method (79.48% vs. 55%, respectively; p value = 0.0075). Based on the findings, it is possible to conclude that the FSH index is a suitable measure for estimating task-related connectivity. [ABSTRACT FROM AUTHOR]

Older people spend most of their time indoors. Limited evidence demonstrates that exposure to indoor air pollutants might be related to chronic complications. This study aimed to estimate the correlation between household air pollution (HAP)'s long-term exposure and the prevalence of elevated hypertension, diabetes mellitus (DM), obesity, and low-density lipoprotein (LDL) cholesterol. From the Global Burden disease dataset, we extracted HAP, hypertension, DM, body mass index, and LDL cholesterol data from Iran from 1990 to 2019 to males and females in people over 50 years. We present APC and AAPC and their confidence intervals using Joinpoint Software statistical software. R software examined the correlation between HAP and hypertension, DM2, Obesity, and high LDL cholesterol. Our finding showed a significant and positive correlation between HAP exposure and prevalence of high low-density lipoprotein cholesterol (p ≤ 0.001, r = 0.70), high systolic blood pressure (p ≤ 0.001, r = 0.63), and high body mass index (p ≤ 0.001, r = 0.57), and DM2 (p ≤ 0.001, r = 0.38). The analysis results also illustrated a positive correlation between indoor air pollution and smoking (p ≤ 0.001, r = 0.92). HAP exposure might be a risk factor for elevated blood pressure, DM, obesity, and LDL cholesterol and, consequently, more serious health problems. According to our results, smoking is one of the sources of HAP. However, ecological studies cannot fully support causal relationships, and this article deals only with Iran. Our findings should be corroborated in personal exposure and biomonitoring approach studies. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid method is proposed to damp frequency and power oscillations in the power system equipped with unified power flow controller (UPFC) and power system stabilizer (PSS) controllers. The method is robust with respect to operating point’s changes. This hybrid method consists of two stages: offline and online. In the offline stage, the coefficients of PSS and UPFC controllers for different operating points have been found by PSO algorithm; then in the second stage, online new fuzzy controller is proposed to select the best PSS and UPFC coefficients according to operating point. The proposed method is simulated for single machine infinite bus system-associated PSS and UPFC for three different operating points in MATLAB software, and results of proposed method simulation are investigated and compared with conventional PSS (CPSS) + UPFC, CPSS controllers. Simulation results show that the proposed method has a better performance. [ABSTRACT FROM AUTHOR]

Human serum albumin (HSA) is an important carrier protein in plasma with various functions, and it is exposed to glycation. Alkaloids are diverse compounds that have showed different anti-glycation activities. The present study aimed to investigate the anti-glycation activity of thirty alkaloid compounds. First, the compounds were studied via Lipinski's rule; then, among thirty alkaloids, twenty-three were further designated for docking study, using AutoDock program. Three compounds which provided the minimum docking score were selected for further assays. Jatrorrhizine displayed potential activity to inhibit the HSA glycation. The stability of the jatrorrhizine-HSA complex was confirmed by molecular dynamics simulation (MDs). To approve the efficacy of this compound, future in vitro and in vivo studies are required. [ABSTRACT FROM AUTHOR]

Copy number variants (CNVs) are a type of genetic polymorphism which contribute to phenotypic variation in several species, including livestock. In this study, we used genomic data of 192 animals from 3 Iranian sheep breeds including 96 Baluchi sheep and 47 Lori-Bakhtiari sheep as fat-tailed breeds and 47 Zel sheep as thin-tailed sheep breed genotyped with Illumina OvineSNP50K Beadchip arrays. Also, for association test, 70 samples of Valle del Belice sheep were added to the association test as thin-tailed sheep breed. PennCNV and CNVRuler software were, respectively, used to study the copy number variation and genomic association analyses. We detected 573 and 242 CNVs in the fat and thin tailed breeds, respectively. In terms of CNV regions (CNVRs), these represented 328 and 187 CNVRs that were within or overlapping with 790 known Ovine genes. The CNVRs covered approximately 73.85 Mb of the sheep genome with average length 146.88 kb, and corresponded to 2.6% of the autosomal genome sequence. Five CNVRs were randomly chosen for validation, of which 4 were experimentally confirmed using Real time qPCR. Functional enrichment analysis showed that genes harbouring CNVs in thin-tailed sheep were involved in the adaptive immune response, regulation of reactive oxygen species biosynthetic process and response to starvation. In fat-tailed breeds these genes were involved in cellular protein modification process, regulation of heart rate, intestinal absorption, olfactory receptor activity and ATP binding. Association test identified one copy gained CNVR on chromosomes 6 harbouring two protein-coding genes HGFAC and LRPAP1. Our findings provide information about genomic structural changes and their association to the interested traits including fat deposition and environmental compatibility in sheep. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid method is proposed for multi-channel electroencephalograms (EEG) signal compression. This new method takes advantage of two different compression techniques: fractal and wavelet-based coding. First, an effective decorrelation is performed through the principal component analysis of different channels to efficiently compress the multi-channel EEG data. Then, the decorrelated EEG signal is decomposed using wavelet packet transform (WPT). Finally, fractal encoding is applied to the low frequency coefficients of WPT, and a modified wavelet-based coding is used for coding the remaining high frequency coefficients. This new method provides improved compression results as compared to the wavelet and fractal compression methods. [ABSTRACT FROM AUTHOR]

Inflammation is a natural protective response toward various simulators, including tissue damage or pathogens. The cyclooxygenase-2 (COX-2) is a very important protein in triggering pain and inflammation. Previous studies have claimed that Allium sativum offers a wide range of anti-inflammatory therapeutics for human consumption. Drug discovery is a complicated process, though in silico methods can make this procedure simpler and more cost-effective. At the current study, we performed the virtual screening of eight Allium sativum–derived compounds via molecular docking with COX-2 enzyme and confirmed the binding energy by docking score estimate followed by ADMET and drug-likeness investigation. The resulting highest-docking scored compound was exposed to molecular dynamics simulation (MDS) for evaluating stability of the docked enzyme-ligand complex and to gauge the oscillation and conformational alterations for the time of enzyme-ligand interaction. The factors of RMSD, RMSF, hydrogen bond interactions, and Rg after 100 ns of MDS proved the stability of alliin in the active site of COX-2 in comparison with celecoxib (CEL) as the control. Moreover, we investigated the binding affinity analysis of all compounds via MM/PBSA method. The results from this study suggest that alliin (a sulfuric compound) exhibits a higher binding affinity for the COX-2 enzyme compared to the other compounds and CEL. Alliin showed to be a possible anti-inflammatory therapeutic candidate for managing the inflammatory conditions. [ABSTRACT FROM AUTHOR]

In this work, layer by layer (LbL) thin film nanocomposite (TFN) membranes were prepared and used as pervaporation membranes to separate methyl tertiary butyl ether (MTBE)/methanol (MeOH) mixture. TFN membranes, comprising of carbon nanotubes (CNT), were prepared by interfacial polymerization (IP) of m-phenylenediamine (MPD) and trimesoyl chloride (TMC) monomers on the porous polyethersulfone/polyethylene glycol (PES/PEG) sublayer. Carbon nanotubes were functionalized by ethylenediamine (EDA) and incorporated into the polyamide (PA) layer at 0.01–0.04 wt.% loadings. After that, the surface roughness, surface hydrophilicity, and morphology of the resulting membranes were characterized. Also, the effect of the sequence of reactants deposition and concentrations of the nanotubes on the membrane performance were investigated using pervaporation of MTBE/MeOH under feed temperature and feed flow rate of 30 °C and 4 L.h-1, respectively. The pressure of permeate-side was about 100 mbar. Obtained results showed that both separation factor and permeation flux of LbL TFN membranes increased by adding CNT into the PA layer up to 0.02 wt.%. By increasing CNT loading to 0.04 wt.%, the separation factor decreased from 106 to 77.6, while permeation flux increased from 0.56 to 0.81 kg.m-2.h-1. It should be noted that, in the single-layer membrane, the separation factor decreased from 73.2 for TFC membrane to 56.4 for TFN membrane with increasing the CNT loading from the beginning. According to these results, LbL TFC and TFN membranes are expected to be the next generation of high-performance PV membranes. [ABSTRACT FROM AUTHOR]

hybrid high order neural network (HHONN) and a feed forward neural network (FNN) are developed and applied to find an optimized empirical correlation for prediction of dryout (DO) heat transfer. The values predicted by the HHONN and FNN models are compared with each other and also with the previous values of empirical correlation. HONN successfully provides an efficient open-box model of nonlinear input-output mapping which provides easier understanding of data mining. By removing the hidden layers, HONN structures become simpler than FNNs and initialization of learning parameters (weights) will not be catastrophic. The RMS results show that the HHONN model has superior fitting specification for prediction of DO heat transfer problem compared to the other prediction methods. [ABSTRACT FROM AUTHOR]