Ashraf Abu-Seida

We report on the chemical analysis of a mud dauber wasp nest-associated fungus, Penicillium sp. CMB-MD22, leading to the discovery and structure elucidation of three known (1-3) and two new (4 and 5) anthrones, and a family of new and known bianthrones, neobulgarones 6-23. Detection and structure elucidation of 1-23 was supported by detailed spectroscopic analysis, as well as chemical (thermal) transformations, and global natural products social (GNPS) molecular networking. An empirical approach using HPLC retention times was effective at differentiating anti from syn bianthrone isomers, while a facile thermal equilibration was shown to favor anti over syn isomers. The neobulgarones 6-23 are natural products, and a crude extract rich in 6-23 exhibits selective antifungal activity against a co-isolated mud dauber wasp nest-associated fungus, suggestive of a possible ecological role as an antifungal chemical defense.

Thorectandra choanoides (CMB-01889) was prioritized as a source of promising new chemistry from a library of 960 southern Australian marine sponge extracts, using a global natural products social (GNPS) molecular networking approach. The sponge was collected at a depth of 45 m. Chemical fractionation followed by detailed spectroscopic analysis led to the discovery of a new tryptophan-derived alkaloid, thorectandrin A (1), with the GNPS cluster revealing a halo of related alkaloids 1a–1n. In considering biosynthetic origins, we propose that Thorectandrachoanoides (CMB-01889) produces four well-known alkaloids, 6-bromo-1′,8-dihydroaplysinopsin (2), 6-bromoaplysinopsin (3), aplysinopsin (4), and 1′,8-dihydroaplysinopsin (10), all of which are susceptible to processing by a putative indoleamine 2,3-dioxygenase-like (IDO) enzyme to 1a–1n. Where the 1′,8-dihydroalkaloids 2 and 10 are fully transformed to stable ring-opened thorectandrins 1 and 1a–1b, and 1h–1j, respectively, the conjugated precursors 3 and 4 are transformed to highly reactive Michael acceptors that during extraction and handling undergo complete transformation to artifacts 1c–1g, and 1k–1n, respectively. Knowledge of the susceptibility of aplysinopsins as substrates for IDOs, and the relative reactivity of Michael acceptor transformation products, informs our understanding of the pharmaceutical potential of this vintage marine pharmacophore. For example, the cancer tissue specificity of IDOs could be exploited for an immunotherapeutic response, with aplysinopsins transforming in situ to Michael acceptor thorectandrins, which covalently bind and inhibit the enzyme.

For the solar photovoltaic (PV) system to operate efficiently, it is necessary to effectively establish an equivalent model of PV cell and extract the relevant unknown model parameters accurately. This paper introduces a new metaheuristic algorithm, i.e., gaining-sharing knowledge based algorithm (GSK) to solve the solar PV model parameter extraction problem. This algorithm simulates the process of knowledge acquisition and sharing in the human life cycle and is with strong competitiveness in solving optimization problems. It includes two significant phases. The first phase is the beginner–intermediate or junior acquisition and sharing stage, and the second phase is the intermediate–expert or senior acquisition and sharing stage. In order to verify the effectiveness of GSK, it is applied to five PV models including the single diode model, double diode model, and three PV modules. The influence of population size on the algorithm performance is empirically investigated. Besides, it is further compared with some other excellent metaheuristic algorithms including basic algorithms and advanced algorithms. Among the five PV models, the root mean square error values between the measured data and the calculated data of GSK are 9.8602E−04 ± 2.18E−17, 9.8280E−04 ± 8.72E−07, 2.4251E−03 ± 1.04E−09, 1.7298E−03 ± 6.25E−18, and 1.6601E−02 ± 1.44E−16, respectively. The results show that GSK has overall better robustness, convergence, and accuracy.

In nuclear theory, there is always a quest for possible extensions of the nuclear landscape and extending our knowledge to the limits of nuclear existence. In this study, we examine the stability and structural properties of a wide range of nuclei in super- and ultra-heavy region in a phenomenological semi-microscopic approach. we calculated the shell correlation energy, residual pairing correction energy, two-nucleon separation energy and two-nucleon energy gap for 3670 even?even nuclei along ?-stability line and two-neutron driplines in the ranges 70 ≤ Z ≤ 274 with 80 ≤ N ≤ 548 and 70 ≤ Z ≤ 212 with 126 ≤ N ≤ 548, respectively. To assure reliability and confidence of the new results in the ultra-heavy region, we extended the search space to include heavy and super-heavy nuclei. We report 83 double magic nuclei and address the predominance of proton and neutron magic numbers. Our calculations reproduced known results on nuclear magicity and present strong evidences on islands of stability and magic numbers in super- and ultra-heavy regions. We also address shifts in nuclear magicity along the nuclear landscape close to the ?-stability line and close to the neutron rich regions.In nuclear theory, there is always a quest for possible extensions of the nuclear landscape and extending our knowledge to the limits of nuclear existence. In this study, we examine the stability and structural properties of a wide range of nuclei in super- and ultra-heavy region in a phenomenological semi-microscopic approach. we calculated the shell correlation energy, residual pairing correction energy, two-nucleon separation energy and two-nucleon energy gap for 3670 even?even nuclei along ?-stability line and two-neutron driplines in the ranges 70 ≤ Z ≤ 274 with 80 ≤ N ≤ 548 and 70 ≤ Z ≤ 212 with 126 ≤ N ≤ 548, respectively. To assure reliability and confidence of the new results in the ultra-heavy region, we extended the search space to include heavy and super-heavy nuclei. We report 83 double magic nuclei and address the predominance of proton and neutron magic numbers. Our calculations reproduced known results on nuclear magicity and present strong evidences on islands of stability and magic numbers in super- and ultra-heavy regions. We also address shifts in nuclear magicity along the nuclear landscape close to the ?-stability line and close to the neutron rich regions.

SARS-CoV-2 is a newly emerged human coronavirus that severely affected human health and the economy. The viral RNA-dependent RNA polymerase (RdRp) is a crucial protein target to stop virus replication. The adenosine derivative, remdesivir, was authorized for emergency use 10 months ago by the United States FDA against COVID-19 despite its doubtful efficacy against SARS-CoV-2.

Background: SARS-CoV-2 is a newly emerged human coronavirus that severely affected human health and the economy. The viral RNA-dependent RNA polymerase (RdRp) is a crucial protein target to stop virus replication. The adenosine derivative, remdesivir, was authorized for emergency use 10 months ago by the United States FDA against COVID-19 despite its doubtful efficacy against SARS-CoV-2. Methods: A dozen modifications based on remdesivir are tested against SARS-CoV-2 RdRp using combined molecular docking and dynamics simulation in this work. Results: The results reveal a better binding affinity of 11 modifications compared to remdesivir. Compounds 8, 9, 10, and 11 show the best binding affinities against SARS-CoV-2 RdRp conformations gathered during 100 ns of the Molecular Dynamics Simulation (MDS) run (− 8.13 ± 0.45 kcal/mol, − 8.09 ± 0.67 kcal/mol, − 8.09 ± 0.64 kcal/mol, and − 8.07 ± 0.73 kcal/mol, respectively). Conclusions: The present study suggests these four compounds as potential SARS-CoV-2 RdRp inhibitors, which need to be validated experimentally. Graphic abstract: [Figure not available: see fulltext.]. © 2021, Maj Institute of Pharmacology Polish Academy of Sciences.

To obtain the optimal set of features in feature selection problems is the most challenging and prominent problem in machine learning. Very few human-related metaheuristic algorithms were developed and solved this type of problem. It motivated us to check the performance of recently developed gaining–sharing knowledge-based optimization algorithm (GSK), which is based on the concept of gaining and sharing knowledge of humans throughout their lifespan. It depends on two stages: beginners–intermediate gaining and sharing stage and intermediate–experts gaining and sharing stage. In this study, two approaches are proposed to solve feature selection problems: FS-BGSK: a novel binary version of GSK algorithm that relies on these two stages with knowledge factor 1 and FS-pBGSK: a population reduction technique that is employed on BGSK algorithm to enhance the exploration and exploitation quality of FS-BGSK. The proposed approaches are checked on twenty two feature selection benchmark datasets from UCI repository that contains small, medium and large dimensions datasets. The obtained results are compared with seven state-of-the-art metaheuristic algorithms; binary differential evolution, binary particle swarm optimization algorithm, binary bat algorithm, binary grey wolf optimizer, binary ant lion optimizer, binary dragonfly algorithm and binary salp swarm algorithm. It concludes that FS-pBGSK and FS-BGSK outperform other algorithms in terms of accuracy, convergence and robustness in most of the datasets.

The various malformations of the aerodigestive tract collectively known as esophageal atresia/tracheoesophageal fistula (EA/TEF) constitute a rare group of birth defects of largely unknown etiology. Previous studies have identified a small number of rare genetic variants causing syndromes associated with EA/TEF. We performed a pilot exome sequencing study of 45 unrelated simplex trios (probands and parents) with EA/TEF. Thirteen had isolated and 32 had nonisolated EA/TEF; none had a family history of EA/TEF. We identified de novo variants in protein-coding regions, including 19 missense variants predicted to be deleterious (D-mis) and 3 likely gene-disrupting (LGD) variants. Consistent with previous studies of structural birth defects, there is a trend of increased burden of de novo D-mis in cases (1.57-fold increase over the background mutation rate), and the burden is greater in constrained genes (2.55-fold, p = 0.003). There is a frameshift de novo variant in EFTUD2, a known EA/TEF risk gene involved in mRNA splicing. Strikingly, 15 out of 19 de novo D-mis variants are located in genes that are putative target genes of EFTUD2 or SOX2 (another known EA/TEF gene), much greater than expected by chance (3.34-fold, p value = 7.20e−5). We estimated that 33% of patients can be attributed to de novo deleterious variants in known and novel genes. We identified APC2, AMER3, PCDH1, GTF3C1, POLR2B, RAB3GAP2, and ITSN1 as plausible candidate genes in the etiology of EA/TEF. We conclude that further genomic analysis to identify de novo variants will likely identify previously undescribed genetic causes of EA/TEF.

Solid contact electrodes are widely used in analytical fields due to their outstanding performance over classical ones. However, they showed formation of a water layer affecting stability of those electrodes’ type. Herein, we develop a solid contact ion selective electrode to overcome this common drawback through application of multi-walled carbon nanotubes as a hydrophobic layer between the ion sensing membrane and a glassy carbon electrode. This fine modification improved stability of the electrode via preventing the formation of this water layer. The obtained potential was steady over 30 days with a drift of ∼0.8 mV h−1. The MWCNTs-modified electrode was used for determination of xipamide with a Nernstian slope of −56.01 over a linearity range of 1.0 × 10−5–1.0 × 10−2 mol l−1 and detection limit of 6.0 × 10−6 mol l−1. The proposed sensor was effectively applied for determination of the cited drug in its marketed pharmaceutical dosage form and spiked human plasma.

The Middle East Respiratory Syndrome Coronavirus (MERS CoV), also termed camel flu, is a new viral infection that first reported in the year 2012 in the Middle East region and further spread during the last seven years. MERS CoV is characterized by its high mortality rate among different human coronaviruses. MERS CoV polymerase shares more than 20% sequence identity with the Hepatitis C Virus (HCV) Non-structural 5b (NS5b) RNA dependent RNA polymerase (RdRp). Despite the low sequence identity, the active site is conserved between the two proteins, with two consecutive aspartates that are crucial in the nucleotide transfer reaction. In this study, seven nucleotide inhibitors have been tested against MERS CoV RdRp using molecular modeling and docking simulations, from which four are novel compounds. Molecular Dynamics Simulation for 260 nanoseconds is performed on the MERS CoV RdRp model to test the effect of protein dynamics on the binding affinities to the tested nucleotide inhibitors. Results support the hypothesis of using the anti-polymerases (Anti-HCV drugs) against MERS CoV RdRp as a potent candidates. Besides four novel compounds are suggested as a seed for high performance inhibitors against MERS CoV RdRp. Communicated by Ramaswamy H. Sarma. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

Gatifloxacin is the drug of choice in the treatment of community-acquired pneumonia in many studies. However, cytotoxicity was reported at its high doses. Therefore, gatifloxacin overdose monitoring is very important. In this sense, there is a need for developing fast and cheap analytical methods for gatifloxacin quantitation in biological fluids. In the present study, a novel detection strategy involving gatifloxacin quantification in urine samples was developed. The approach has been adapted for the use of solid inner contact and rapid disposal screen-printed graphitic carbon electrodes exhibiting high sensitivity toward gatifloxacin without interference from several ions found in urine samples. The developed electrodes showed linear responses in the concentration ranges from 1 × 10–5 to 0.01 and 1 × 10–6 to 0.01 M for a solid contact glassy carbon ion selective electrode (GSC) and a screen printed electrode (GSP), respectively. The analytical applicability of the approach was demonstrated through recovery experiments of gatifloxacin trace concentrations in urine. GSP ion selective electrode (ISE) was found to have superior stability, shorter response time, higher selectivity and sensitivity and longer shelf life compared to GSC ISE. GSP ISE showed the best Nernestian slope as well as the lowest detection limit. Moreover, the inherent advantages of screen-printed electrodes technology (low sample consumption, low cost and point of care testing) make this methodology very attractive in this field. As a result, the developed ISEs can be the best choice for in-line determination of gatifloxacin in urine samples to detect overdose intake and its associated symptoms as well as for quality-control laboratories without pre-treatment or separation steps.

Two novel chemotherapeutic chalcones were synthesized and their structures were confirmed by different spectral tools. Theoretical studies such as molecular modeling were done to detect the mechanism of action of these compounds. In vitro cytotoxicity showed a strong effect against all tested cell lines (MCF7, A459, HepG2, and HCT116), and low toxic effect against normal human melanocytes (HFB4). The lung carcinoma cell line was chosen for further molecular studies. Real-time PCR demonstrated that the two compounds upregulated gene expression of (BAX, p53, casp-3, casp-8, casp-9) genes and decreased the expression of anti-apoptotic genes bcl2, CDK4, and MMP1. Flow-cytometry indicated that cell cycle arrest of A459 was induced at the G2/M phase and the apoptotic percentage increased significantly compared to the control sample. Cytochrome c oxidase and VEGF enzyme activity were detected by ELISA assay. SEM tool was used to follow the morphological changes that occurred on the cell surface, cell granulation, and average roughness of the cell surface. The change in the number and morphology of mitochondria, cell shrinkage, increase in the number of cytoplasmic organelles, membrane blebbing, chromatin condensation, and apoptotic bodies were observed using TEM. The obtained data suggested that new chalcones exerted their pathways on lung carcinoma through induction of two pathways of apoptosis.

This paper shows the ability of Fractional-Order (FO) dynamics in adaptation laws of indirect adaptive fuzzy logic control (AFLC). The parameters of the FO-adaptive laws are optimized using Particle Swarm Optimization algorithm without the presence of the disturbance. The optimal FO-AFLC (OFO-AFLC) is introduced in this paper to overcome the drawbacks of AFLC. The complexity in AFLC is due to using the projection algorithm to keep the adapted parameters bounded. Also the AFLC uses a complex control to guarantee the error convergence. It has proven that the OFO-AFLC can guarantee the error convergence without using neither the projection algorithm nor complex control. The applicability and efficiency of the proposed method is compared to the ordinary AFLC, and demonstrated through simulations made on inverted pendulum model. Furthermore, simulation results show that the control signal in OFO-AFLC is smoother with less oscillations.

Many application problems are formulated as nonlinear binary programming models which are hard to be solved using exact algorithms especially in large dimensions. One of these practical applications is to optimally distribute protective materials for the newly emerged COVID-19. It is defined for a decision-maker who wants to choose a subset of candidate hospitals comprising the maximization of the distributed quantities of protective materials to a set of chosen hospitals within a specific time shift. A nonlinear binary mathematical programming model for the problem is introduced with a real application case study; the case study is solved using a novel discrete binary gaining-sharing knowledge-based optimization algorithm (DBGSK). The solution algorithm proposes a novel binary adaptation of a recently developed gaining-sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge through their life span. Discrete binary version of GSK named novel binary gaining-sharing knowledge-based optimization algorithm (DBGSK) depends mainly on two binary stages: binary junior gaining-sharing stage and binary senior gaining-sharing stage with knowledge factor 1. These two stages enable DBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space.

In this chapter we give a survey for the use of sinc methods in computing eigenvaluesEigenvaluesof various types of boundary value problems. The techniques cover the classical sinc-method, regularized sinc-method, Hermite interpolations and the associated regularized technique, sinc-Gaussian, Hermite-Gauss and generalized sinc-Gaussian methods. The application of these methods covers a very wide class of problems, involving, but not limited to, second order differential operators, λ-type problems in L2(a,b)⊕ℂr$$L^{2}(a,b)\oplus \mathbb {C}^{r}$$spaces, discontinuous problems, multiparameter problems, in self-adjoint and non self-adjoint settings, regular and singular problems. Both horizontal and vertical extensions of the application of the technique are still open and under consideration.