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Organic acid is an important factor that accelerates the aging of cellulosic insulation materials. In this study, the interactions between cellulose and five acids, representative of what may be found in an aging transformer, were studied using molecular dynamics. The adsorption process of the five acids onto the surface of crystalline cellulose shows that the three low molecular acids are more readily adsorbed onto cellulose than the two high molecular acids. The deformation and adsorption energies of the acids all increase with an increase in molecular weight when they are stably interacting with cellulose. However, the differences between adsorption energies and deformation energies are positive for the three low molecular acids, whereas they are negative for the two high molecular acids. This indicates that the attachments onto cellulose of low molecular acids are considerably more stabilized than those of the high molecular acids. This is consistent with the experimental results. Furthermore, based on the calculated solubility parameters of acids, the experimental result that the three low molecular acids are to a large degree absorbed onto the cellulose, whereas the two high molecular acids remain in the oil, was theoretically elucidated using the theory of similarity and intermiscibility.

We report a fabrication method of the conductive pattern based on in situ reactive silver precursor inks by inkjet printing. The reactive silver precursor inks were prepared with ethylene glycol and deionized water mixture as the solvent, and silver nitrate as silver source. Sodium borohydride solution as the reducing agent was first coated on photographic paper by screen printing process, and then dried at 50 &deg, C for 4 h. Furthermore, the reactive silver precursor inks were printed on a photographic paper coated with sodium borohydride using inkjet printing to form silver nanoparticles in situ due to redox reaction, and thus a conductive pattern was obtained. The effects of the reactive silver precursor ink concentration and printing layer number and treatment temperature on the electrical properties and microstructures of the printed patterns were investigated systematically. The size range of in situ-formed silver nanoparticles was 50&ndash, 90 nm. When the reactive silver precursor ink concentration was 0.13 g/mL, the five-layer printed pattern exhibited a sheet resistance of 4.6 &Omega, /&gamma, after drying at room temperature for 2 h, furthermore, the sheet resistance of the printed pattern decreased to 1.4 &Omega, after drying at 130 &deg, C for 2 h. In addition, the display function circuit was printed on the photographic paper to realize the display of the numbers 0&ndash, 99. It provides new research ideas for the development of environmentally friendly and low-cost flexible paper-based circuits.

Digestate is characterized by high water content, and in the water and wastewater treatment settings, necessitates both large storage capacities and a high cost of disposal. By seeding digestate with four magnetic nanoparticles (MNPs), this study aimed to recover biogas and boost its methane potential anaerobically. This was carried out via biochemical methane potential (BMP) tests with five 1 L bioreactors, with a working volume of 80% and 20% head space. These were operated under anaerobic conditions at a temperature 40 °C for a 30 d incubation period. The SEM/EDX results revealed that the morphological surface area of the digestate with the MNPs increased as compared to its raw state. Comparatively, the degree of degradation of the bioreactors with MNPs resulted in over 75% decontamination (COD, color, and turbidity) as compared to the control system result of 60% without MNPs. The highest biogas production (400 mL/day) and methane yield (100% CH4) was attained with 2 g of Fe2O4-TiO2 MNPs as compared to the control biogas production (350 mL/day) and methane yield (65% CH4). Economically, the highest energy balance achieved was estimated as 320.49 ZAR/kWh, or 22.89 USD/kWh in annual energy savings for this same system. These findings demonstrate that digestate seeded with MNPs has great potential to improve decontamination efficiency, biogas production and circular economy in wastewater management.

A compact Modular Chemical Descriptor Language (MCDL) chemical structure editor (Java applet) is described. The small size (approximately 200 KB) of the applet allows its use to display and edit chemical structures in various Internet applications. The editor supports the MCDL format, in which structures are presented in compact canonical form and is capable of restoring bond orders as well as of managing atom and bond drawing overlap. A small database of cage and large cyclic fragment is used for optimal representation of difficult-to-draw molecules. The improved algorithm of the structure diagram generation can be used for other chemical notations that lack atomic coordinates (SMILES, InChI).

Research on cytokine recognition is of great significance in the medical field due to the fact cytokines benefit the diagnosis and treatment of diseases, but the current methods for cytokine recognition have many shortcomings, such as low sensitivity and low F-score. Therefore, this paper proposes a new method on the basis of feature combination. The features are extracted from compositions of amino acids, physicochemical properties, secondary structures, and evolutionary information. The classifier used in this paper is SVM. Experiments show that our method is better than other methods in terms of accuracy, sensitivity, specificity, F-score and Matthew&rsquo, s correlation coefficient.

Molecular docking is a hard optimization problem that has been tackled in the past with metaheuristics, demonstrating new and challenging results when looking for one objective: the minimum binding energy. However, only a few papers can be found in the literature that deal with this problem by means of a multi-objective approach, and no experimental comparisons have been made in order to clarify which of them has the best overall performance. In this paper, we use and compare, for the first time, a set of representative multi-objective optimization algorithms applied to solve complex molecular docking problems. The approach followed is focused on optimizing the intermolecular and intramolecular energies as two main objectives to minimize. Specifically, these algorithms are: two variants of the non-dominated sorting genetic algorithm II (NSGA-II), speed modulation multi-objective particle swarm optimization (SMPSO), third evolution step of generalized differential evolution (GDE3), multi-objective evolutionary algorithm based on decomposition (MOEA/D) and S-metric evolutionary multi-objective optimization (SMS-EMOA). We assess the performance of the algorithms by applying quality indicators intended to measure convergence and the diversity of the generated Pareto front approximations. We carry out a comparison with another reference mono-objective algorithm in the problem domain (Lamarckian genetic algorithm (LGA) provided by the AutoDock tool). Furthermore, the ligand binding site and molecular interactions of computed solutions are analyzed, showing promising results for the multi-objective approaches. In addition, a case study of application for aeroplysinin-1 is performed, showing the effectiveness of our multi-objective approach in drug discovery.

This paper describes the development of the unified conformational sampling and docking tool called Sampler for Multiple Protein-Ligand Entities (S4MPLE). The main novelty in S4MPLE is the unified dealing with intra- and intermolecular degrees of freedom (DoF). While classically programs are either designed for folding or docking, S4MPLE transcends this artificial specialization. It supports folding, docking of a flexible ligand into a flexible site and simultaneous docking of several ligands. The trick behind it is the formal assimilation of inter-molecular to intra-molecular DoF associated to putative inter-molecular contact axes. This is implemented within the genetic operators powering a Lamarckian Genetic Algorithm (GA). Further novelty includes differentiable interaction fingerprints to control population diversity, and fitting a simple continuum solvent model and favorable contact bonus terms to the AMBER/GAFF force field. Novel applications—docking of fragment-like compounds, simultaneous docking of multiple ligands, including free crystallographic waters—were published elsewhere. This paper discusses: (a) methodology, (b) set-up of the force field energy functions and (c) their validation in classical redocking tests. More than 80% success in redocking was achieved (RMSD of top-ranked pose &lt, 2.0 Å).

Protein–protein interactions play pivotal roles in life, and the protein interaction affinity confers specific protein interaction events in physiology or pathology. Förster resonance energy transfer (FRET) has been widely used in biological and biomedical research to detect molecular interactions in vitro and in vivo. The FRET assay provides very high sensitivity and efficiency. Several attempts have been made to develop the FRET assay into a quantitative measurement for protein–protein interaction affinity in the past. However, the progress has been slow due to complicated procedures or because of challenges in differentiating the FRET signal from other direct emission signals from donor and receptor. This review focuses on recent developments of the quantitative FRET analysis and its application in the determination of protein–protein interaction affinity (KD), either through FRET acceptor emission or donor quenching methods. This paper mainly reviews novel theatrical developments and experimental procedures rather than specific experimental results. The FRET-based approach for protein interaction affinity determination provides several advantages, including high sensitivity, high accuracy, low cost, and high-throughput assay. The FRET-based methodology holds excellent potential for those difficult-to-be expressed proteins and for protein interactions in living cells.

Ultrasonic testing is an emerging non-destructive testing technology with high repeatability and precision. Milk is a very complex liquid and the change of its viscosity is a highly relevant property throughout conversion into other dairy products. In the following paper, we propose a novel method for the monitoring of viscosity during enzymatic milk clotting by ultrasonic spectroscopy. An ultrasonic transducer–receiver couple with a 250 kHz nominal frequency was submerged in the samples and an enveloped sweep (“chirp”) signal was applied in a through-transmission mode. Simultaneously, the change in viscosity was measured with a rotational viscometer at a constant shearing speed. The data were analyzed with an algorithm developed by the authors for spectral ultrasonic testing. Estimations yielded a high adjusted R2 (0.963–0.998) and low cross-validated estimation error (RPD: 4.38–14.22), suggesting that the method is suitable for industrial use given the right instrumentation.

Amyloidosis is a relatively rare human disease caused by the deposition of abnormal protein fibres in the extracellular space of various tissues, impairing their normal function. Proteomic analysis of patients’ biopsies, developed by Dogan and colleagues at the Mayo Clinic, has become crucial for clinical diagnosis and for identifying the amyloid type. Currently, the proteomic approach is routinely used at National Amyloidosis Centre (NAC, London, UK) and Istituto di Tecnologie Biomediche-Consiglio Nazionale delle Ricerche (ITB-CNR, Milan, Italy). Both centres are members of the European Proteomics Amyloid Network (EPAN), which was established with the aim of sharing and discussing best practice in the application of amyloid proteomics. One of the EPAN’s activities was to evaluate the quality and the confidence of the results achieved using different software and algorithms for protein identification. In this paper, we report the comparison of proteomics results obtained by sharing NAC proteomics data with the ITB-CNR centre. Mass spectrometric raw data were analysed using different software platforms including Mascot, Scaffold, Proteome Discoverer, Sequest and bespoke algorithms developed for an accurate and immediate amyloid protein identification. Our study showed a high concordance of the obtained results, suggesting a good accuracy of the different bioinformatics tools used in the respective centres. In conclusion, inter-centre data exchange is a worthwhile approach for testing and validating the performance of software platforms and the accuracy of results, and is particularly important where the proteomics data contribute to a clinical diagnosis.

This article belongs to the Special Issue Advances in Alkali-Activated Materials., This paper reports an Alkali-Activated Materials (AAM) using two different precursors, metakaolin and a metallurgical slag with photocatalytic zinc oxide nanoparticles, as novel photocatalytic composites. The photodegradation performance of the composites using methylene blue (MB) dye as a wastewater model was investigated by ultraviolet radiations (UV-vis) spectroscopy. Adsorption in dark conditions and photodegradation under UV irradiation are the mechanisms for removing MB dye. The pseudo-first-order kinetic and pseudo-second-order kinetic models were employed, and the experimental data agreed with the pseudo-second-order model in both cases with UV and without UV irradiations. As new photocatalytic materials, these composites offer an alternative for environmental applications., This research was partially funded by CONACyT (National Council for Science and Technology) grant number 397245 and by the Spanish Ministry of the Economy, Industry and Competitiveness and ERDF under project BIA2016-76466-R.

Utilizing liquid rubber to toughen epoxy resin is one of the most mature and promising methods. However, the dielectric relaxation characteristics of the epoxy/liquid rubber composites have not been studied systematically, while the relaxation behaviours are a critical factor for both micro and macro properties. In this paper, hydroxyl-terminated liquid nitrile rubber (HTBN) is employed to reinforce a kind of room-temperature-cured epoxy resin. The dielectric spectrum is measured and analysed. Results show that two relaxation processes are introduced in the binary composites. The &alpha, relaxation of HTBN shows a similar temperature dependence with the &beta, relaxation of epoxy resin. The interfacial polarization leads to an increase of complex permittivity, which reaches its maximum at 70 &deg, C. In addition, affected by interfacial polarization, the thermionic polarization is inhibited, and the samples with filler ratios of 15% and 25% show lower DC-conductivity below 150 &deg, C. In addition, the &alpha, relaxation and thermionic polarization of epoxy resin obey the Vogel‒Fulcher‒Tammann law, while the interfacial polarization and DC-conductivity satisfy with the Arrhenius law. Furthermore, the fitting results of the Vogel temperature of &alpha, relaxation, glass transition temperature, apparent activation energy of interfacial polarization and DC-conductivity all decline with HTBN content. These results can provide a reference and theoretical guidance for the assessment of dielectric properties and the improvement of the formulation of liquid-rubber-toughened epoxy resin.

In the present study, the effect of 2H/1H isotopic exchange in hydrogen bonds between nitrogenous base pairs on occurrence and open states zones dynamics is investigated. These processes are studied using mathematical modeling, taking into account the number of open states between base pairs. The calculations of the probability of occurrence of open states in different parts of the gene were done depending on the localization of the deuterium atom. The mathematical modeling study demonstrated significant inequality (dependent on single 2H/1H replacement in DNA) among three parts of the gene similar in length of the frequency of occurrence of the open states. In this paper, the new convenient approach of the analysis of the abnormal frequency of open states in different parts of the gene encoding interferon alpha 17 was presented, which took into account both rising and decreasing of them that allowed to make a prediction of the functional instability of the specific DNA regions. One advantage of the new algorithm is diminishing the number of both false positive and false negative results in data filtered by this approach compared to the pure fractile methods, such as deciles or quartiles.

The last two decades have witnessed an increasing interest in the use of the so-called rapid analytical methods or high throughput techniques. Most of these applications reported the use of vibrational spectroscopy methods (near infrared (NIR), mid infrared (MIR), and Raman) in a wide range of samples (e.g., food ingredients and natural products). In these applications, the analytical method is integrated with a wide range of multivariate data analysis (MVA) techniques (e.g., pattern recognition, modelling techniques, calibration, etc.) to develop the target application. The availability of modern and inexpensive instrumentation together with the access to easy to use software is determining a steady growth in the number of uses of these technologies. This paper underlines and briefly discusses the three critical pillars—the sample (e.g., sampling, variability, etc.), the spectra and the mathematics (e.g., algorithms, pre-processing, data interpretation, etc.)—that support the development and implementation of vibrational spectroscopy applications.

The APEX (aqueous photochemistry of environmentally occurring xenobiotics) software computes the phototransformation kinetics of compounds that occur in sunlit surface waters. It is free software based on Octave, and was originally released in 2014. Since then, APEX has proven to be a remarkably flexible platform, allowing for the addressing of several environmental problems. However, considering APEX as a stand-alone software is not conducive to exploiting its full potentialities. Rather, it is part of a whole ecosystem that encompasses both the software and the laboratory protocols that allow for the measurement of substrate photoreactivity parameters. Coherently with this viewpoint, the present paper shows both how to use APEX, and how to experimentally derive or approximately assess the needed input data. Attention is also given to some issues that might provide obstacles to users, including the extension of APEX beyond the simple systems for which it was initially conceived. In particular, we show how to use APEX to deal with compounds that undergo acid&ndash, base equilibria, and with the photochemistry of systems such as stratified lakes, lakes undergoing evaporation, and rivers. Hopefully, this work will provide a reference for the smooth use of one of the most powerful instruments for the modeling of photochemical processes in freshwater environments.

Density prediction is of great significance for molecular design of energetic materials, since detonation velocity linearly with density and detonation pressure increases with the density squared. However, the accuracy and generalization of former reported prediction models need further improvement, because most of them are derived from small data sets and few molecular descriptors. As shown in this paper, for molecules presenting brick-like shape or containing more hydrogen-bond donors the predicted densities have large negative deviations from experimental values. Thus, a molecular morphology descriptor &eta, and a hydrogen-bond descriptor Hb are introduced as correction items to build 3 new QSPR models. Besides, 3694 nitro compounds are adopted as data set by this work. The accuracies are obviously improved, and the generalizations are verified by an independent test set. At the level of B3PW91/6-31G(d,p), the effective ratios (ERs) of the 3 Equations, for &Delta, &rho, &lt, 5%, are 92.7%, 91.8%, and 93.3%, for &Delta, 2%, the values are 53.5%, 51.3%, and 54.7%. At the level of B3LYP/6-31G**, for &Delta, 5%, the values are 92.3%, 91.4% and 92.9%, 2%, the values are 53.7%, 51.4% and 53.2%.

Drug side-effects have become a major public health concern as they are the underlying cause of over a million serious injuries and deaths each year. Therefore, it is of critical importance to detect side-effects as early as possible. Existing computational methods mainly utilize the drug chemical profile and the drug biological profile to predict the side-effects of a drug. In the utilized drug biological profile information, they only focus on drug&ndash, target interactions and neglect the modes of action of drugs on target proteins. In this paper, we develop a new method for predicting potential side-effects of drugs based on more comprehensive drug information in which the modes of action of drugs on target proteins are integrated. Drug information of multiple types is modeled as a signed heterogeneous information network. We propose a signed heterogeneous information network embedding framework for learning drug embeddings and predicting side-effects of drugs. We use two bias random walk procedures to obtain drug sequences and train a Skip-gram model to learn drug embeddings. We experimentally demonstrate the performance of the proposed method by comparison with state-of-the-art methods. Furthermore, the results of a case study support our hypothesis that modes of action of drugs on target proteins are meaningful in side-effect prediction.

Molecular toxicity prediction is one of the key studies in drug design. In this paper, a deep learning network based on a two-dimension grid of molecules is proposed to predict toxicity. At first, the van der Waals force and hydrogen bond were calculated according to different descriptors of molecules, and multi-channel grids were generated, which could discover more detail and helpful molecular information for toxicity prediction. The generated grids were fed into a convolutional neural network to obtain the result. A Tox21 dataset was used for the evaluation. This dataset contains more than 12,000 molecules. It can be seen from the experiment that the proposed method performs better compared to other traditional deep learning and machine learning methods.

To successfully design and optimize the application of hydrogel matrices one has to effectively combine computational design tools with experimental methods. In this context, one of the most promising techniques is molecular modeling, which requires however accurate molecular models representing the investigated material. Although this method has been successfully used over the years for predicting the properties of polymers, its application to biopolymers, including gelatin, is limited. In this paper we provide a method for creating an atomistic representation of gelatin based on the modified FASTA codes of natural collagen. We show that the model created in this manner reproduces known experimental values of gelatin properties like density, glass-rubber transition temperature, WAXS profile and isobaric thermal expansion coefficient. We also present that molecular dynamics using the INTERFACE force field provides enough accuracy to track changes of density, fractional free volume and Hansen solubility coefficient over a narrow temperature regime (273&ndash, 318 K) with 1 K accuracy. Thus we depict that using molecular dynamics one can predict properties of gelatin biopolymer as an efficient matrix for immobilization of various bioactive compounds, including enzymes.

4,8-Dihydrodifurazano[3,4-b,e]pyrazine (DFP) is one kind of parent compound for the synthesis of various promising difurazanopyrazine derivatives. In this paper, eleven series of energetic salts composed of 4,8-dihydrodifurazano[3,4-b,e]pyrazine-based anions and ammonium-based cations were designed. Their densities, heats of formation, energetic properties, impact sensitivity, and thermodynamics of formation were studied and compared based on density functional theory and volume-based thermodynamics method. Results show that ammonium and hydroxylammonium salts exhibit higher densities and more excellent detonation performance than guanidinium and triaminoguanidinium salts. Therein, the substitution with electron-withdrawing groups (&ndash, NO2, &ndash, CH2NF2, &ndash, CH2ONO2, &ndash, C(NO2)3, &ndash, CH2N3) contributes to enhancing the densities, heats of formation, and detonation properties of the title salts, and the substitution of &ndash, C(NO2)3 features the best performance. Incorporating N&ndash, O oxidation bond to difurazano[3,4-b,e]pyrazine anion gives a rise to the detonation performance of the title salts, while increasing their impact sensitivity meanwhile. Importantly, triaminoguanidinium 4,8-dihydrodifurazano[3,4-b,e]pyrazine (J4) has been successfully synthesized. The experimentally determined density and H50 value of J4 are 1.602 g/cm3 and higher than 112 cm, which are consistent with theoretical values, supporting the reliability of calculation methods. J4 proves to be a thermally stable and energetic explosive with decomposition peak temperature of 216.7 &deg, C, detonation velocity 7732 m/s, and detonation pressure 25.42 GPa, respectively. These results confirm that the derivative work in furazanopyrazine compounds is an effective strategy to design and screen out potential candidates for high-performance energetic salts.

A reaction system is a modeling framework for investigating the functioning of the living cell, focused on capturing cause&ndash, effect relationships in biochemical environments. Biochemical processes in this framework are seen to interact with each other by producing the ingredients enabling and/or inhibiting other reactions. They can also be influenced by the environment seen as a systematic driver of the processes through the ingredients brought into the cellular environment. In this paper, the first attempt is made to implement reaction systems in the hardware. We first show a tight relation between reaction systems and synchronous digital circuits, generally used for digital electronics design. We describe the algorithms allowing us to translate one model to the other one, while keeping the same behavior and similar size. We also develop a compiler translating a reaction systems description into hardware circuit description using field-programming gate arrays (FPGA) technology, leading to high performance, hardware-based simulations of reaction systems. This work also opens a novel interesting perspective of analyzing the behavior of biological systems using established industrial tools from electronic circuits design.

In this paper we review the current status of high-performance computing applications in the general area of drug discovery. We provide an introduction to the methodologies applied at atomic and molecular scales, followed by three specific examples of implementation of these tools. The first example describes in silico modeling of the adsorption of small molecules to organic and inorganic surfaces, which may be applied to drug delivery issues. The second example involves DNA translocation through nanopores with major significance to DNA sequencing efforts. The final example offers an overview of computer-aided drug design, with some illustrative examples of its usefulness.

Significant efforts in wet and dry laboratories are devoted to resolving molecular structures. In particular, computational methods can now compute thousands of tertiary structures that populate the structure space of a protein molecule of interest. These advances are now allowing us to turn our attention to analysis methodologies that are able to organize the computed structures in order to highlight functionally relevant structural states. In this paper, we propose a methodology that leverages community detection methods, designed originally to detect communities in social networks, to organize computationally probed protein structure spaces. We report a principled comparison of such methods along several metrics on proteins of diverse folds and lengths. We present a rigorous evaluation in the context of decoy selection in template-free protein structure prediction. The results make the case that network-based community detection methods warrant further investigation to advance analysis of protein structure spaces for automated selection of functionally relevant structures.

Wavelength selection is an important preprocessing issue in near-infrared (NIR) spectroscopy analysis and modeling. Swarm optimization algorithms (such as genetic algorithm, bat algorithm, etc.) have been successfully applied to select the most effective wavelengths in previous studies. However, these algorithms suffer from the problem of unrobustness, which means that the selected wavelengths of each optimization are different. To solve this problem, this paper proposes a novel wavelength selection method based on the binary dragonfly algorithm (BDA), which includes three typical frameworks: single-BDA, multi-BDA, ensemble learning-based BDA settings. The experimental results for the public gasoline NIR spectroscopy dataset showed that: (1) By using the multi-BDA and ensemble learning-based BDA methods, the stability of wavelength selection can improve, (2) With respect to the generalized performance of the quantitative analysis model, the model established with the wavelengths selected by using the multi-BDA and the ensemble learning-based BDA methods outperformed the single-BDA method. The results also indicated that the proposed method is not limited to the dragonfly algorithm but can also be combined with other swarm optimization algorithms. In addition, the ensemble learning idea can be applied to other feature selection areas to obtain more robust results.

In the past few decades, the number and variety of genomic and proteomic data available have increased dramatically. Molecular or functional interaction networks are usually constructed according to high-throughput data and the topological structure of these interaction networks provide a wealth of information for inferring the function of genes or proteins. It is a widely used way to mine functional information of genes or proteins by analyzing the association networks. However, it remains still an urgent but unresolved challenge how to combine multiple heterogeneous networks to achieve more accurate predictions. In this paper, we present a method named ReprsentConcat to improve function inference by integrating multiple interaction networks. The low-dimensional representation of each node in each network is extracted, then these representations from multiple networks are concatenated and fed to gcForest, which augment feature vectors by cascading and automatically determines the number of cascade levels. We experimentally compare ReprsentConcat with a state-of-the-art method, showing that it achieves competitive results on the datasets of yeast and human. Moreover, it is robust to the hyperparameters including the number of dimensions.

Hyperpolarization is one of the approaches to enhance Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) signal by increasing the population difference between the nuclear spin states. Imaging hyperpolarized solids opens up extensive possibilities, yet is challenging to perform. The highly populated state is normally not replenishable to the initial polarization level by spin-lattice relaxation, which regular MRI sequences rely on. This makes it necessary to carefully &ldquo, budget&rdquo, the polarization to optimize the image quality. In this paper, we present a theoretical framework to address such challenge under the assumption of either variable flip angles or a constant flip angle. In addition, we analyze the gradient arrangement to perform fast imaging to overcome intrinsic short decoherence in solids. Hyperpolarized diamonds imaging is demonstrated as a prototypical platform to test the theory.

Analysis of fluids in porous media is of great importance in many applications. There are many mathematical models that can be used in the analysis. More realistic models should account for the stochastic variations of the model parameters due to the nature of the porous material and/or the properties of the fluid. In this paper, the standard porous media problem with random permeability is considered. Both the deterministic and stochastic problems are analyzed using the finite volume technique. The solution statistics of the stochastic problem are computed using both Polynomial Chaos Expansion (PCE) and the Karhunen-Loeve (KL) decomposition with an exponential correlation function. The results of both techniques are compared with the Monte Carlo sampling to verify the efficiency. Results have shown that PCE with first order polynomials provides higher accuracy for lower (less than 20%) permeability variance. For higher permeability variance, using higher-order PCE considerably improves the accuracy of the solution. The PCE is also combined with KL decomposition and faster convergence is achieved. The KL-PCE combination should carefully choose the number of KL decomposition terms based on the correlation length of the random permeability. The suggested techniques are successfully applied to the quarter-five spot problem.

The main goal of the current work was to study the coupled mechanism of thermophoretic transportation and mixed convection flow around the surface of the sphere. To analyze the characteristics of heat and fluid flow in the presence of thermophoretic transportation, a mathematical model in terms of non-linear coupled partial differential equations obeying the laws of conservation was formulated. Moreover, the mathematical model of the proposed phenomena was approximated by implementing the finite difference scheme and boundary value problem of fourth order code BVP4C built-in scheme. The novelty point of this paper is that the primitive variable formulation is introduced to transform the system of partial differential equations into a primitive form to make the line of the algorithm smooth. Secondly, the term thermophoretic transportation in the mass equation is introduced in the mass equation and thus the effect of thermophoretic transportation can be calculated at different positions of the sphere. Basically, in this study, some favorite positions around the sphere were located, where the velocity field, temperature distribution, mass concentration, skin friction, and rate of heat transfer can be calculated simultaneously without any separation in flow around the surface of the sphere.

We discuss the use of the regularized linear discriminant analysis (LDA) as a model reduction technique combined with particle swarm optimization (PSO) in protein tertiary structure prediction, followed by structure refinement based on singular value decomposition (SVD) and PSO. The algorithm presented in this paper corresponds to the category of template-based modeling. The algorithm performs a preselection of protein templates before constructing a lower dimensional subspace via a regularized LDA. The protein coordinates in the reduced spaced are sampled using a highly explorative optimization algorithm, regressive&ndash, regressive PSO (RR-PSO). The obtained structure is then projected onto a reduced space via singular value decomposition and further optimized via RR-PSO to carry out a structure refinement. The final structures are similar to those predicted by best structure prediction tools, such as Rossetta and Zhang servers. The main advantage of our methodology is that alleviates the ill-posed character of protein structure prediction problems related to high dimensional optimization. It is also capable of sampling a wide range of conformational space due to the application of a regularized linear discriminant analysis, which allows us to expand the differences over a reduced basis set.

This paper discusses specific features of the interactions of small-diameter liposomes with the cytoplasmic membrane of endothelial cells using in silico methods. The movement pattern of the liposomal drug delivery system was modeled in accordance with the conditions of the near-wall layer of blood flow. Our simulation results show that the liposomes can become stuck in the intercellular gaps and even break down when the gap is reduced. Liposomes stuck in the gaps are capable of withstanding a shell deformation of ~15% with an increase in liposome energy by 26%. Critical deformation of the membrane gives an impetus to drug release from the liposome outward. We found that the liposomes moving in the near-wall layer of blood flow inevitably stick to the membrane. Liposome sticking on the membrane is accompanied by its gradual splicing with the membrane bilayer. This leads to a gradual drug release inside the cell.

In the recent decade, deep eutectic solvents (DESs) have occupied a strategic place in green chemistry research. This paper discusses the application of DESs as functionalization agents for multi-walled carbon nanotubes (CNTs) to produce novel adsorbents for the removal of 2,4-dichlorophenol (2,4-DCP) from aqueous solution. Also, it focuses on the application of the feedforward backpropagation neural network (FBPNN) technique to predict the adsorption capacity of DES-functionalized CNTs. The optimum adsorption conditions that are required for the maximum removal of 2,4-DCP were determined by studying the impact of the operational parameters (i.e., the solution pH, adsorbent dosage, and contact time) on the adsorption capacity of the produced adsorbents. Two kinetic models were applied to describe the adsorption rate and mechanism. Based on the correlation coefficient (R2) value, the adsorption kinetic data were well defined by the pseudo second-order model. The precision and efficiency of the FBPNN model was approved by calculating four statistical indicators, with the smallest value of the mean square error being 5.01 &times, 10&minus, 5. Moreover, further accuracy checking was implemented through the sensitivity study of the experimental parameters. The competence of the model for prediction of 2,4-DCP removal was confirmed with an R2 of 0.99.

In recent years, many papers describing ionic liquids (IL) as promising solvents in separation techniques have been published. The conscious choice of appropriate ionic liquid as absorption media in effective extraction of selected types of analytes requires deeper understanding of the analyte&minus, IL interactions. Therefore, intensive research is conducted to determine the values of activity coefficient at infinite dilution, which allows us to characterize the nature of these interactions. Based on the inverse gas chromatography retention data, activity coefficients at infinite dilution &gamma, 13 &infin, of 48 different organic compounds in the ionic liquids N-ethyl-N-methylmorpholinium bis(trifluoromethanesulfonyl)imide [C2C1Mor][TFSI] and N-octyl-N-methylmorpholinium bis(trifluoromethanesulfonyl)imide [C8C1Mor][TFSI] were determined. The measurements covered a broad range of volatile organic compounds, including n-alkanes, n-alkenes, n-alkynes, alcohols, aldehydes, ketones, aromatic compounds and common polar solvents, representing different types of interactions. Activity coefficients at infinite dilution were measured in the temperature range from 313.15 to 363.15 K. The excess partial molar enthalpies and entropies at infinite dilution were determined. Selectivity at infinite dilution was also calculated for exemplary separation processes in the hexane/benzene system. The obtained results were analyzed and compared with literature data for ionic liquids containing the same anion [TFSI]&macr, and different cations. The study results indicate that some potential applications of the investigated ionic liquids in separation problems exist.

Overlapping structures of protein&ndash, protein interaction networks are very prevalent in different biological processes, which reflect the sharing mechanism to common functional components. The overlapping community detection (OCD) algorithm based on central node selection (CNS) is a traditional and acceptable algorithm for OCD in networks. The main content of CNS is the central node selection and the clustering procedure. However, the original CNS does not consider the influence among the nodes and the importance of the division of the edges in networks. In this paper, an OCD algorithm based on a central edge selection (CES) algorithm for detection of overlapping communities of protein&ndash, protein interaction (PPI) networks is proposed. Different from the traditional CNS algorithms for OCD, the proposed algorithm uses community magnetic interference (CMI) to obtain more reasonable central edges in the process of CES, and employs a new distance between the non-central edge and the set of the central edges to divide the non-central edge into the correct cluster during the clustering procedure. In addition, the proposed CES improves the strategy of overlapping nodes pruning (ONP) to make the division more precisely. The experimental results on three benchmark networks and three biological PPI networks of Mus. musculus, Escherichia coli, and Cerevisiae show that the CES algorithm performs well.

Accumulated studies have shown that environmental factors (EFs) can regulate the expression of microRNA (miRNA) which is closely associated with several diseases. Therefore, identifying miRNA-EF associations can facilitate the study of diseases. Recently, several computational methods have been proposed to explore miRNA-EF interactions. In this paper, a novel computational method, MEI-BRWMLL, is proposed to uncover the relationship between miRNA and EF. The similarities of miRNA-miRNA are calculated by using miRNA sequence, miRNA-EF interaction, and the similarities of EF-EF are calculated based on the anatomical therapeutic chemical information, chemical structure and miRNA-EF interaction. The similarity network fusion is used to fuse the similarity between miRNA and the similarity between EF, respectively. Further, the multiple-label learning and bi-random walk are employed to identify the association between miRNA and EF. The experimental results show that our method outperforms the state-of-the-art algorithms.

Mixtures analysis can provide more information than individual components. It is important to detect the different compounds in the real complex samples. However, mixtures are often disturbed by impurities and noise to influence the accuracy. Purification and denoising will cost a lot of algorithm time. In this paper, we propose a model based on convolutional neural network (CNN) which can analyze the chemical peak information in the tandem mass spectrometry (MS/MS) data. Compared with traditional analyzing methods, CNN can reduce steps in data preprocessing. This model can extract features of different compounds and classify multi-label mass spectral data. When dealing with MS data of mixtures based on the Human Metabolome Database (HMDB), the accuracy can reach at 98%. In 600 MS test data, 451 MS data were fully detected (true positive), 142 MS data were partially found (false positive), and 7 MS data were falsely predicted (true negative). In comparison, the number of true positive test data for support vector machine (SVM) with principal component analysis (PCA), deep neural network (DNN), long short-term memory (LSTM), and XGBoost respectively are 282, 293, 270, and 402, the number of false positive test data for four models are 318, 284, 198, and 168, the number of true negative test data for four models are 0, 23, 7, 132, and 30. Compared with the model proposed in other literature, the accuracy and model performance of CNN improved considerably by separating the different compounds independent MS/MS data through three-channel architecture input. By inputting MS data from different instruments, adding more offset MS data will make CNN models have stronger universality in the future.