Double Bonds in Fused Hexacyclic Systems

If a hexacyclic graph, G, represents a benzenoid, a perfect matching corresponds to the location of double bonds (pi-bonds). We present an algorithm for counting the number of configurations of double bonds for various benzenoids.     

On the Distribution of π Bonds in Cyclofusene

We present a type of coronafusene termed cyclofusene, in which each hexacycle shares exactly two nonadjacent edges with other hexacycles. Cyclofusene has exactly four configurations of bonds such that each bond belongs to the inner or outer boundary. In each of these configurations, the outer boundary has six more bonds than the inner boundary. The number of shared bonds in any mixed configuration is even. Let m be the number of shared bonds in a mixed configuration for a cyclofusene with exactly k linear chains. Then m k. Furthermore, there exists a mixed configuration with exactly k shared bonds.     

Detection of zinc ions by differential circularly polarized fluorescence excitation

A new chiroptical spectroscopic approach, differential circularly polarized fluorescence excitation (CPE), can be used to provide a selective method for detecting the presence of zinc ions. The approach utilizes the same instrumentation as fluorescence-detected circular dichroism and provides strong contrast in metal detection due to response to both chelation-enhanced fluorescence and circular dichroism upon metal ion binding. The observed contrast is therefore better than either of the parent spectroscopic detection methods. CPE also provides a strategy to reduce interference from background such as protein-based tryptophan fluorescence.     

Bilayered cyclofusene

Multiply-connected monolayered cyclofusene (MMC) is a fused hexacyclic system with at least two interior empty regions called holes. Multiply-connected bilayered cyclofusene (MBC) is a structure derived from an MMC by replacing each layer of hexacycles by two layers. Various properties of the equitability of these bipartite graphs are examined.     

Fuzzy turnover rate chance constraints portfolio model

One concern of many investors is to own the assets which can be liquidated easily. Thus, in this paper, we incorporate portfolio liquidity in our proposed model. Liquidity is measured by an index called turnover rate. Since the return of an asset is uncertain, we present it as a trapezoidal fuzzy number and its turnover rate is measured by fuzzy credibility theory. The desired portfolio turnover rate is controlled through a fuzzy chance constraint. Furthermore, to manage the portfolios with asymmetric investment return, other than mean and variance, we also utilize the third central moment, the skewness of portfolio return. In fact, we propose a fuzzy portfolio mean–variance–skewness model with cardinality constraint which combines assets limitations with liquidity requirement. To solve the model, we also develop a hybrid algorithm which is the combination of cardinality constraint, genetic algorithm, and fuzzy simulation, called FCTPM.     

A Simple Primal-Dual Feasible Interior-Point Method for Nonlinear Programming with Monotone Descent

We propose and analyze a primal-dual interior point method of the feasible type, with the additional property that the objective function decreases at each iteration. A distinctive feature of the method is the use of different barrier parameter values for each constraint, with the purpose of better steering the constructed sequence away from non-KKT stationary points. Assets of the proposed scheme include relative simplicity of the algorithm and of the convergence analysis, strong global and local convergence properties, and good performance in preliminary tests. In addition, the initial point is allowed to lie on the boundary of the feasible set.     

Signal Amplification Technologies for the Detection of Nucleic Acids: from Cell-Free Analysis to Live-Cell Imaging

Due to their unique properties, such as programmability, ligand-binding capability, and flexibility, nucleic acids can serve as analytes and/or recognition elements for biosensing. To improve the sensitivity of nucleic acid-based biosensing and hence the detection of a few copies of target molecule, different modern amplification methodologies, namely target-and-signal-based amplification strategies, have already been developed. These recent signal amplification technologies, which are capable of amplifying the signal intensity without changing the targets’ copy number, have resulted in fast, reliable, and sensitive methods for nucleic acid detection. Working in cell-free settings, researchers have been able to optimize a variety of complex and quantitative methods suitable for deploying in live-cell conditions. In this study, a comprehensive review of the signal amplification technologies for the detection of nucleic acids is provided. We classify the signal amplification methodologies into enzymatic and non-enzymatic strategies with a primary focus on the methods that enable us to shift away from in vitro detecting to in vivo imaging. Finally, the future challenges and limitations of detection for cellular conditions are discussed.     

Green and Straightforward Modification of Graphite Electrode via In Situ Synthesis of Graphene Nanosheets for Quantifying Prazosin Hydrochloride in Urine Samples and Pharmaceutical Formulations

A sensitive electrochemical sensor based on in situ modification of graphite electrode via graphene nanosheets (GNs) was developed as a green method for prazosin hydrochloride (PRA) analyses. In this study, GNs were electrochemically synthesized on the surface of graphite electrode via in situ approach and used for the analyses. The proposed sensor showed several advantages such as high sensitivity, low LOD, and excellent repeatability. The oxidation peak current at the optimum analytical conditions using a GNs/graphite electrode at pH 6.0 was linearly dependent on PRA concentration in the range of 0.09–100 µM. A LOD of 0.02 µM and RSD of 3.8 % for 10 µM solution of PRA with a great recovery were obtained.     

In Silico Evaluation of Iranian Medicinal Plant Phytoconstituents as Inhibitors against Main Protease and the Receptor-Binding Domain of SARS-CoV-2

The novel coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which initially appeared in Wuhan, China, in December 2019. Elderly individuals and those with comorbid conditions may be more vulnerable to this disease. Consequently, several research laboratories continue to focus on developing drugs to treat this infection because this disease has developed into a global pandemic with an extremely limited number of specific treatments available. Natural herbal remedies have long been used to treat illnesses in a variety of cultures. Modern medicine has achieved success due to the effectiveness of traditional medicines, which are derived from medicinal plants. The objective of this study was to determine whether components of natural origin from Iranian medicinal plants have an antiviral effect that can prevent humans from this coronavirus infection using the most reliable molecular docking method; in our case, we focused on the main protease (M^pro) and a receptor-binding domain (RBD). The results of molecular docking showed that among 169 molecules of natural origin from common Iranian medicinal plants, 20 molecules (chelidimerine, rutin, fumariline, catechin gallate, adlumidine, astragalin, somniferine, etc.) can be proposed as inhibitors against this coronavirus based on the binding free energy and type of interactions between these molecules and the studied proteins. Moreover, a molecular dynamics simulation study revealed that the chelidimerine–M^pro and somniferine–RBD complexes were stable for up to 50 ns below 0.5 nm. Our results provide valuable insights into this mechanism, which sheds light on future structure-based designs of high-potency inhibitors for SARS-CoV-2.