Gold nanocages in cancer diagnosis,therapy, and theranostics: A brief review

Regarding the increasing number of cancer patients, the global burden of this disease is continuing to grow. Despite a considerable improvement in the diagnosis and treatment of various types of cancer, new diagnosis and treatment strategies are required. Nanotechnology, as an interesting and advanced field in medicine, is aimed to further advance both cancer diagnosis and treatment. Gold nanocages (AuNCs), with hollow interiors and porous walls, have received a great deal of interest in various biomedical applications such as diagnosis, imaging, drug delivery, and hyperthermia therapy due to their special physicochemical characteristics including the porous structure and surface functionalization as well as optical and photothermal properties. This review is focused on recent developments in therapeutic and diagnostic and applications of AuNCs with an emphasis on their theranostic applications in cancer diseases.     

Coordination of gene expression noise with cell size: analytical results for agent-based models of growing cell populations

The chemical master equation and the Gillespie algorithm are widely used to model the reaction kinetics inside living cells. It is thereby assumed that cell growth and division can be modelled through effective dilution reactions and extrinsic noise sources. We here re-examine these paradigms through developing an analytical agent-based framework of growing and dividing cells accompanied by an exact simulation algorithm, which allows us to quantify the dynamics of virtually any intracellular reaction network affected by stochastic cell size control and division noise. We find that the solution of the chemical master equation—including static extrinsic noise—exactly agrees with the agent-based formulation when the network under study exhibits stochastic concentration homeostasis, a novel condition that generalizes concentration homeostasis in deterministic systems to higher order moments and distributions. We illustrate stochastic concentration homeostasis for a range of common gene expression networks. When this condition is not met, we demonstrate by extending the linear noise approximation to agent-based models that the dependence of gene expression noise on cell size can qualitatively deviate from the chemical master equation. Surprisingly, the total noise of the agent-based approach can still be well approximated by extrinsic noise models.     

Aggregation and clogging phenomena of rigid microparticles in microfluidics

We developed a numerical tool to investigate the phenomena of aggregation and clogging of rigid microparticles suspended in a Newtonian fluid transported through a straight microchannel. In a first step, we implement a time-dependent one-way coupling Discrete Element Method (DEM) technique to simulate the movement and effect of adhesion on rigid microparticles in two- and three-dimensional computational domains. The Johnson–Kendall–Roberts (JKR) theory of adhesion is applied to investigate the contact mechanics of particle–particle and particle–wall interactions. Using the one-way coupled solver, the agglomeration, aggregation and deposition behavior of the microparticles is studied by varying the Reynolds number and the particle adhesion. In a second step, we apply a two-way coupling CFD–DEM approach, which solves the equation of motion for each particle, and transfers the force field corresponding to particle–fluid interactions to the CFD toolbox OpenFOAM. Results for the one-way (DEM) and two-way (CFD–DEM) coupling techniques are compared in terms of aggregate size, aggregate percentages, spatial and temporal evaluation of aggregates in 2D and 3D. We conclude that two-way coupling is the more realistic approach, which can accurately capture the particle–fluid dynamics in microfluidic applications.     

RBF Neural Network Sliding Mode Consensus of Multiagent Systems with Unknown Dynamical Model of Leader-follower Agents

International Journal of Control, Automation and Systems - This paper proposed a new methodology to cover the problem of consensus of multiagent systems with sliding mode control based on Radial...     

Linking Hydro-Physical Variables and Landscape Metrics using Advanced Data Mining for Stream-Flow Prediction

Water Resources Management - In Streamflow prediction the most important triggering/controlling variables are related to climate, physiography, and landscape patterns. This study investigated the...     

Cell Selection for Load Balancing in Heterogeneous Networks

The vision of advanced long-term evolution (LTE-A) project is set to ultimate increase of network capacity in heterogeneous networks (HetNets). In HetNets with small cell configuration, a considerable majority of user devices is eventually connected to the macrocell base station (MBS), while small base stations (BSs), such as femtocell access points (FAPs), are still without any user. This results in unbalanced load and reduces the data rate of macrocell user equipment (MUE). In this paper, a method is proposed for load balancing among FAPs, while desired throughput is achieved. The proposed method uses the estimated received signal strength from different BSs and adjusted pilot signals. Under the critical signal to interference plus noise ratio (SINR) condition, a list of candidate FAPs is prepared. The updated candidate list henceforth does not include the least visited FAPs, which in turn leads to lower unnecessary handoffs. Once the BS with the highest number of free RBs and the highest pilot signal power is selected, FAP allocates the RBs with higher SINRs (qualified RBs) to user. In the case of FAP unavailability, the algorithm compels users to connect to the MBS with adequate qualified RBs. The performance of the proposed method was evaluated under a variety of FAPs density, and the number and velocity of users in terms of throughput and Jain’s fairness index. The results evidence affordable improvements in the throughput and Jain’s index in comparison with other methods.     

Oligomeric Composition of Polyols From Fatty Acid Methyl Ester: The Effect of Ring‐Opening Reactants of Epoxide Groups

Commercial availability of fatty acid methyl ester (FAME) from palm oil targeted for biodiesel offers a good feedstock for the production of structurally well‐defined polyols for polyurethane applications. The effect of molecular weight (MW), odd and even carbon numbers, and the linear and branched structure reactants used in the ring‐opening reaction of epoxidized fatty acid methyl ester (E‐FAME) on the properties of polyols was investigated. Conversions of E‐FAME to PolyFAME polyols were confirmed by Fourier transform infrared analysis, oxirane oxygen content, and hydroxyl number. Gel permeation chromatography (GPC) calibrated against polyether polyols as a standard and vapor pressure osmometry were used for MW determination. GPC chromatograms of PolyFAME polyols clearly demonstrated the formation of oligomers during ring‐opening reactions. MW, and odd and even carbon numbers in a structure of linear diols and branched diol used in the syntheses of PolyFAME polyols did not have an effect on crystallinity, glass transition, or melt temperatures measured using Differential scanning calorimetry (DSC). PolyFAME polyols ring‐opened with water, methanol, and 1,2‐propanediol contained secondary hydroxyl groups, whereas PolyFAME polyols ring‐opened with linear diols contained a mixture of primary and secondary hydroxyl groups. It was found that the concentration of primary hydroxyl groups increased significantly by increasing the number of carbons from C2 to C3 in the linear diols. The viscosity of PolyFAME polyols also increased with the MW of linear diols used in the E‐FAME ring‐opening reaction. These findings would be beneficial for formulators in choosing the most cost effective polyols for polyurethane formulations.     

Ultra-high-Q optical filter based on photonic crystal ring resonator

In this study, a photonic crystal ring resonator with a triangular lattice is used to design an optical filter. The proposed structure is able to filter the central wavelength of 1548 nm with a transmission coefficient of over 95%. Moreover, this structure has an ultra-high-quality factor (Q) of about 1290. With altering the features of the structure including the refractive index, the lattice constant and the radius of the rods in the resonator core, their effects on the central wavelength of the filter, transmission coefficient, quality factor and bandwidth are investigated. The plane wave expansion and finite-difference time-domain methods are used to extract photonic band gap and investigate the photonic behavior of the proposed structure, respectively.     

Performance improvement for recommender systems using ontology

Recommender systems have emerged in the e-commerce domain and have been developed to actively recommend appropriate items to online users. The use of recently developed hybrid recommendation systems has helped overcome the main drawbacks of Content-Based Filtering (CBF) and Collaborative Filtering (CF). In hybrid recommendation systems that combine CF and CBF, the CF part uses two methods, including memory- and model-based approaches. Both approaches have some advantages and disadvantages for item recommendation. Sparsity has been one of the main difficulties associated with these approaches, whereas recommendation with high accuracy has been one of the important advantages of the memory-based approach. However, this approach is not scalable for current recommendation systems as their databases include huge numbers of items and users. In contrast, the model-based approach generates recommendations with low accuracy but is scalable for large databases in e-commerce recommender systems. Accordingly, to address this problem and take advantage of both approaches, in this work, we propose a new hybrid recommendation method and evaluate it using a real-world dataset. The aim is to improve efficiency and accuracy by designing a heuristic hybrid recommender method that combines memory-based and model-based approaches. Specifically, we use ontology in the CF part and improve ontology structure by eliminating uniformity of edges of the hierarchical relation between concepts (IS-A relation) in item ontology in the CBF part. Ontology structure is considered for improving accuracy; according to this, a new method for measuring semantic similarity that is more accurate than the traditional methods is presented. This new method can enhance the accuracy of CF and CBF in our method. In addition, the number of searches required to find similar clusters and neighbor users of the target user is decreased significantly using ontology, enhanced clustering and the new proposed algorithm. We evaluate the proposed method using a real-world dataset. The experimental results show that our method is more scalable and accurate than the benchmark k-Nearest Neighbor (k-NN) and model-based recommendation methods.