Shahid Chamran University of Ahvaz

Abstract Microbial colonization on material surfaces is ubiquitous. Biofilms derived from surface‐colonized microbes pose serious problems to the society from both an economical perspective and a health concern. Incorporation of antimicrobial nanocompounds within or on the surface of materials, or by coatings, to prevent microbial adhesion or kill the microorganisms after their attachment to biofilms, represents an important strategy in an increasingly challenging field. Over the last decade, many studies have been devoted to preparing meta‐based nanomaterials that possess antibacterial, antiviral, and antifungal activities to combat pathogen‐related diseases. Herein, an overview on the state‐of‐the‐art antimicrobial nanosized metal‐based compounds is provided, including metal and metal oxide nanoparticles as well as transition metal nanosheets. The antimicrobial mechanism of these nanostructures and their biomedical applications such as catheters, implants, medical delivery systems, tissue engineering, and dentistry are discussed. Their properties as well as potential caveats such as cytotoxicity, diminishing efficacy, and induction of antimicrobial resistance of materials incorporating these nanostructures are reviewed to provide a backdrop for future research.

Wireless communication at the terahertz (THz) frequency bands (0.1–10 THz) is viewed as one of the cornerstones of tomorrow’s 6G wireless systems. Owing to the large amount of available bandwidth, if properly deployed, THz frequencies can potentially provide significant wireless capacity performance gains and enable high-resolution environment sensing. However, operating a wireless system at high-frequency bands such as THz is limited by a highly uncertain and dynamic channel. Effectively, these channel limitations lead to unreliable intermittent links as a result of an inherently short communication range, and a high susceptibility to blockage and molecular absorption. Consequently, such impediments could disrupt the THz band’s promise of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">high-rate communications and high-resolution sensing</i> capabilities. In this context, this paper panoramically examines the steps needed to efficiently and reliably deploy and operate next-generation THz wireless systems that will synergistically support a fellowship of communication and sensing services. For this purpose, we first set the stage by describing the fundamentals of the THz frequency band. Based on these fundamentals, we characterize and comprehensively investigate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">seven unique defining features of THz wireless systems</i> : 1) Quasi-opticality of the band, 2) THz-tailored wireless architectures, 3) Synergy with lower frequency bands, 4) Joint sensing and communication systems, 5) PHY-layer procedures, 6) Spectrum access techniques, and 7) Real-time network optimization. These seven defining features allow us to shed light on how to re-engineer wireless systems as we know them today so as to make them ready to support THz bands and their unique environments. On the one hand, THz systems benefit from their quasi-opticality and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">can turn every communication challenge into a sensing opportunity</i> , thus contributing to a new generation of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">versatile wireless systems</i> that can perform multiple functions beyond basic communications. On the other hand, THz systems can capitalize on the role of intelligent surfaces, lower frequency bands, and machine learning (ML) tools to guarantee a robust system performance. We conclude our exposition by presenting the key THz 6G use cases along with their associated major challenges and open problems. Ultimately, the goal of this article is to chart a forward-looking roadmap that exposes the necessary solutions and milestones for enabling THz frequencies to realize their potential as a game changer for next-generation wireless systems.

Long non-coding RNAs (lncRNAs) refer to a group of RNAs that are usually more than 200 nucleotides and are not involved in protein generation. Instead, lncRNAs are involved in different regulatory processes, such as regulation of gene expression. Different lncRNAs exist throughout the genome. LncRNAs are also known for their roles in different human diseases such as cancer. HOTAIR is an lncRNA that plays a role as an oncogenic molecule in different cancer cells, such as breast, gastric, colorectal, and cervical cancer cells. Therefore, HOTAIR expression level is a potential biomarker for diagnostic and therapeutic purposes in several cancers. This RNA takes part in epigenetic regulation of genes and plays an important role in different cellular pathways by interacting with Polycomb Repressive Complex 2 (PRC2). In this review, we describe the molecular function and regulation of HOTAIR and its role in different types of cancers.

Skin infections caused by bacteria, viruses and fungi are difficult to treat by conventional topical administration because of poor drug penetration across the stratum corneum. This results in low bioavailability of drugs to the infection site, as well as the lack of prolonged release. Emerging antimicrobial transdermal and ocular microneedle patches have become promising medical devices for the delivery of various antibacterial, antifungal, and antiviral therapeutics. In the present review, skin anatomy and its barriers along with skin infection are discussed. Potential strategies for designing antimicrobial microneedles and their targeted therapy are outlined. Finally, biosensing microneedle patches associated with personalized drug therapy and selective toxicity toward specific microbial species are discussed.

Developing accurate soft computing methods for groundwater level (GWL) forecasting is essential for enhancing the planning and management of water resources. Over the past two decades, significant progress has been made in GWL prediction using machine learning (ML) models. Several review articles have been published, reporting the advances in this field up to 2018. However, the existing review articles do not cover several aspects of GWL simulations using ML, which are significant for scientists and practitioners working in hydrology and water resource management. The current review article aims to provide a clear understanding of the state-of-the-art ML models implemented for GWL modeling and the milestones achieved in this domain. The review includes all of the types of ML models employed for GWL modeling from 2008 to 2020 (138 articles) and summarizes the details of the reviewed papers, including the types of models, data span, time scale, input and output parameters, performance criteria used, and the best models identified. Furthermore, recommendations for possible future research directions to improve the accuracy of GWL prediction models and enhance the related knowledge are outlined.

Flavonoids are characterized as the low molecular weight polyphenolic compounds universally distributed in planta. They are a chemically varied group of secondary metabolites with a broad range of biological activity. The increasing amount of evidence has demonstrated the various physiological functions of flavonoids in stress response. In this paper, we provide a brief introduction to flavonoids’ biochemistry and biosynthesis. Then, we review the recent findings on the alternation of flavonoid content under different stress conditions to come up with an overall picture of the mechanism of involvement of flavonoids in plants’ response to various abiotic stresses. The participation of flavonoids in antioxidant systems, flavonoid-mediated response to different abiotic stresses, the involvement of flavonoids in stress signaling networks, and the physiological response of plants under stress conditions are discussed in this review. Moreover, molecular and genetic approaches to tailoring flavonoid biosynthesis and regulation under abiotic stress are addressed in this review.

The change in stomatal conductance measured soon after durum wheat (Triticum turgidum ssp. durum Desf.) was exposed to salinity was verified as an indicator of osmotic stress tolerance. It was a reliable and useful screening technique for identifying genotypic variation. The minimum NaCl treatment needed to obtain a significant stomatal response was 50 mM, but 150 mM was needed to obtain significant differences between genotypes. The response to the NaCl was osmotic rather than Na+-specific. Stomatal conductance responded similarly to iso-osmotic concentrations of KCl and NaCl, both in the speed and extent of closure, and in the difference between genotypes. The new reduced rate of stomatal conductance in response to addition of 50 mM NaCl or KCl occurred within 45 min, and was independent of the concentration of Na+ in leaves. The difference between genotypes was long-lasting, translating into differences in shoot biomass and tiller number after a month. These results indicate that the relative size of the change in stomatal conductance when the salinity is introduced could be a means of screening for osmotic stress tolerance in wheat and other cereals.

In this paper, an efficient algorithm for texture recognition of synthetic aperture radar (SAR) images is developed based on wavelet transform as a feature extraction tool and support vector machine (SVM) as a classifier. SAR image segmentation is an important step in texture recognition of SAR images. SAR images cannot be segmented successfully by using traditional methods because of the existence of speckle noise in SAR images. The algorithm, proposed in this paper, extracts the texture feature by using wavelet transform; then, it forms a feature vector composed of kurtosis value of wavelet energy feature of SAR image. In the next step, segmentation of different textures is applied by using feature vector and level set function. At last, an SVM classifier is designed and trained by using normalized feature vectors of each region texture. The testing sets of SAR images are segmented by this trained SVM. Experimental results on both agricultural and urban SAR images show that the proposed algorithm is effective for classification of different textures in SAR images, and it is also insensitive to the intensity.

Abstract Excessive and unwarranted administration of antibiotics has invigorated the evolution of multidrug‐resistant microbes. There is, therefore, an urgent need for advanced active compounds. Ionic liquids with short‐lived ion‐pair structures are highly tunable and have diverse applications. Apart from their unique physicochemical features, the newly discovered biological activities of ionic liquids have fascinated biochemists, microbiologists, and medical scientists. In particular, their antimicrobial properties have opened new vistas in overcoming the current challenges associated with combating antibiotic‐resistant pathogens. Discussions regarding ionic liquid derivatives in monomeric and polymeric forms with antimicrobial activities are presented here. The antimicrobial mechanism of ionic liquids and parameters that affect their antimicrobial activities, such as chain length, cation/anion type, cation density, and polymerization, are considered. The potential applications of ionic liquids in the biomedical arena, including regenerative medicine, biosensing, and drug/biomolecule delivery, are presented to stimulate the scientific community to further improve the antimicrobial efficacy of ionic liquids.

Recently, one-dimensional nanostructures with different morphologies (such as nanowires, nanorods (NRs), and nanotubes) have become the focus of intensive research, because of their unique properties with potential applications. Among them, zinc oxide (ZnO) nanomaterials has been found to be highly attractive, because of the remarkable potential for applications in many different areas such as solar cells, sensors, piezoelectric devices, photodiode devices, sun screens, antireflection coatings, and photocatalysis. Here, we present an innovative approach to create a new modified textile by direct in situ growth of vertically aligned one-dimensional (1D) ZnO NRs onto textile surfaces, which can serve with potential for biosensing, photocatalysis, and antibacterial applications. ZnO NRs were grown by using a simple aqueous chemical growth method. Results from analyses such as X-ray diffraction (XRD) and scanning electron microscopy (SEM) revealed that the ZnO NRs were dispersed over the entire surface of the textile. We have demonstrated the following applications of these multifunctional textiles: (1) as a flexible working electrode for the detection of aldicarb (ALD) pesticide, (2) as a photocatalyst for the degradation of organic molecules (i.e., Methylene Blue and Congo Red), and (3) as antibacterial agents against Escherichia coli. The ZnO-based textile exhibited excellent photocatalytic and antibacterial activities, and it showed a promising sensing response. The combination of sensing, photocatalysis, and antibacterial properties provided by the ZnO NRs brings us closer to the concept of smart textiles for wearable sensing without a deodorant and antibacterial control. Perhaps the best known of the products that is available in markets for such purposes are textiles with silver nanoparticles. Our modified textile is thus providing acceptable antibacterial properties, compared to available commercial modified textiles.

This paper introduces a new fault-tolerant operation method for a symmetrical six-phase induction machine (6PIM) when one or several phases are lost. A general decoupled model of the induction machine with up to three open phases is given. This model illustrates the existence of a pulsating torque when phases are opened. Then, a new control method reducing the pulsating torque and the motor losses is proposed in order to improve the drive performances. The proposed method is compared to two other existing techniques. The simulation and experimental results obtained on a dedicated test-rig confirm the validity and the efficiency of the proposed method for a fault-tolerant symmetrical 6PIM drive.

Currently, there is an increasing interest for setting up medical systems that can screen a large number of people for sight threatening diseases, such as diabetic retinopathy. This paper presents a method for automated identification of exudate pathologies in retinopathy images based on computational intelligence techniques. The color retinal images are segmented using fuzzy c-means clustering following some preprocessing steps, i.e., color normalization and contrast enhancement. The entire segmented images establish a dataset of regions. To classify these segmented regions into exudates and nonexudates, a set of initial features such as color, size, edge strength, and texture are extracted. A genetic-based algorithm is used to rank the features and identify the subset that gives the best classification results. The selected feature vectors are then classified using a multilayer neural network classifier. The algorithm was implemented using a large image dataset consisting of 300 manually labeled retinal images, and could identify affected retinal images with 96.0% sensitivity while it recognized 94.6% of the normal images, i.e., the specificity. Moreover, the proposed scheme illustrated an accuracy including 93.5% sensitivity and 92.1% predictivity for identification of retinal exudates at the pixel level.

Bacterial cellulose nanopaper (BC) is a multifunctional material known for numerous desirable properties: sustainability, biocompatibility, biodegradability, optical transparency, thermal properties, flexibility, high mechanical strength, hydrophilicity, high porosity, broad chemical-modification capabilities and high surface area. Herein, we report various nanopaper-based optical sensing platforms and describe how they can be tuned, using nanomaterials, to exhibit plasmonic or photoluminescent properties that can be exploited for sensing applications. We also describe several nanopaper configurations, including cuvettes, plates and spots that we printed or punched on BC. The platforms include a colorimetric-based sensor based on nanopaper containing embedded silver and gold nanoparticles; a photoluminescent-based sensor, comprising CdSe@ZnS quantum dots conjugated to nanopaper; and a potential up-conversion sensing platform constructed from nanopaper functionalized with NaYF4:Yb(3+)@Er(3+)&SiO2 nanoparticles. We have explored modulation of the plasmonic or photoluminescent properties of these platforms using various model biologically relevant analytes. Moreover, we prove that BC is and advantageous preconcentration platform that facilitates the analysis of small volumes of optically active materials (∼4 μL). We are confident that these platforms will pave the way to optical (bio)sensors or theranostic devices that are simple, transparent, flexible, disposable, lightweight, miniaturized and perhaps wearable.

OBJECTIVES: Ellagic acid (EA) has shown antinociceptive and anti-inflammatory effects. Inducible nitric oxide synthase (iNOS), cyclooxygenase 2 (COX-2) enzymes and also cytokines play a key role in many inflammatory conditions. This study was aimed to investigate the mechanisms involved in the anti-inflammatory effect of EA. MATERIALS AND METHODS: Carrageenan-induced mouse paw edema model was used for induction of inflammation. RESULTS: The results showed that intraplantar injection of carrageenan led to time-dependent development of peripheral inflammation, which resulted in a significant increase in the levels of tumor necrosis factor α (TNF-α) and interleukin 1 (IL-1) β, nitric oxide (NO) and prostaglandin E2 (PGE2) and also iNOS and COX-2 protein expression in inflamed paw. However, systemic administration of EA (1-30 mg/kg, intraperitoneal [i.p.]) could reduce edema in a dose-dependent fashion in inflamed rat paws with ED50 value 8.41 (5.26-14.76) mg/kg. It decreased the serum concentration of NO, PGE2, aspartate aminotransferase and alanine aminotransferase, and suppress the protein expression of iNOS, COX-2 enzymes, and attenuated the formation of PGE2, TNF-α and IL-1 β in inflamed paw tissue. We also demonstrated that EA significantly decreased the malondialdehyde (MDA) level in liver at 5 h after carrageenan injection. Moreover, histopathological studies indicated that EA significantly diminished migration of polymorphonuclear leukocytes into site of inflammation, as did indomethacin. CONCLUSIONS: Collectively, the anti-inflammatory mechanisms of EA might be related to the decrease in the level of MDA, iNOS, and COX-2 in the edema paw via the suppression of pro-inflammatory cytokines (TNFα, IL1 β), NO and PGE2 overproduction.

Individual pine and cedar tree saplings and branches were used to model the resistance to flow in a water flume for nonsubmerged and nonrigid vegetation to determine the amount that streamlining decreases the drag coefficient and reduces the momentum absorbing area. Currently, vegetation on floodplains is commonly assumed to behave as rigid roughness that can lead to large errors in the relationships between velocity and drag force. This presents a basic fluid mechanics problem. An extreme variation of roughness with depth of flow can result due to a large increase in the momentum absorbing area in nonsubmerged vegetation as depth is increased. This deems all the available roughness equations (which generally are based on relative roughness approach) useless for this application. In this paper a dimensional analysis, supported by experimental results, is developed to obtain a relationship between roughness conditions (i.e., density and flexural rigidity) and flow conditions (i.e., velocity and depth) for floodplains and vegetative zones of natural waterways.

The aim of this research was to investigate the relationship of job stress with turnover intention and job performance, consi dering the moderating role of organization-based self-esteem (OBSE). Data collected from 286 employees of Iranian National Drilling Company (INDC), who were selected by simple random sampling method. Pearson correlation and Moderated regression analysis through SPSS 19 software package were used for data analysis. Findings indicate the negative relationship between job stress and job performance and positive relationship between job stress and turnover intention. In addition, organization -based self-esteem (OBSE) significantly moderated the relationship of job stress with turnover intention and job performance.

On the basis of the side effects of detrimental synthetic chemicals, introducing healthy, available, and effective bioagents for pest management is critical. Due to this circumstance, several studies have been conducted that evaluate the pesticidal potency of plant-derived essential oils. This review presents the pesticidal efficiency of essential oils isolated from different genera of the Lamiaceae family including Agastache Gronovius, Hyptis Jacquin, Lavandula L., Lepechinia Willdenow, Mentha L., Melissa L., Ocimum L., Origanum L., Perilla L., Perovskia Kar., Phlomis L., Rosmarinus L., Salvia L., Satureja L., Teucrium L., Thymus L., Zataria Boissier, and Zhumeria Rech. Along with acute toxicity, the sublethal effects were illustrated such as repellency, antifeedant activity, and adverse effects on the protein, lipid, and carbohydrate contents, and on the esterase and glutathione S-transferase enzymes. Chemical profiles of the introduced essential oils and the pesticidal effects of their main components have also been documented including terpenes (hydrocarbon monoterpene, monoterpenoid, hydrocarbon sesquiterpene, and sesquiterpenoid) and aliphatic phenylpropanoid. Consequently, the essential oils of the Lamiaceae plant family and their main components, especially monoterpenoid ones with several bioeffects and multiple modes of action against different groups of damaging insects and mites, are considered to be safe, available, and efficient alternatives to the harmful synthetic pesticides.

Based on the local Shannon entropy concept in information theory, a new measure of aromaticity is introduced. This index, which describes the probability of electronic charge distribution between atoms in a given ring, is called Shannon aromaticity (SA). Using B3LYP method and different basis sets (6-31G**, 6-31+G** and 6-311++G**), the SA values of some five-membered heterocycles, C(4)H(4)X, are calculated. Significant linear correlations are observed between the evaluated SAs and some other criteria of aromaticity such as ASE, Lambda and NICS indices. According to the obtained relationships, the range of 0.003 < SA < 0.005 is chosen as the boundary of aromaticity/antiaromaticity. Using B3LYP/6-31+G** level of theory, the Shannon aromaticities for a series of mono-substituted benzene derivatives are calculated and analyzed. It is found that the least standard deviation between the aromaticities and the best linear correlation with the Hammett substituent constants are observed for the new index in comparison with the other indices. Also the values of the new index are evaluated for some substituted penta- and heptafulvenes, which successfully predict the order of aromaticity in these compounds. Applying this index to some non-benzonoids, linear and angular polyacenes also give satisfactory results and prove to be quite suitable for determining the local aromaticity of different rings in polyaromatic hydrocarbons.

A physically based model for estimating the resistance coefficient for coniferous trees in open-channel flow is modified to account for variations in the flexibility between species. The new model is based on the assumption of a linear increase in foliage area with height and a dimensional analysis. It is supported by experiments in air and water. The advantage of the new model over existing methods for estimating resistance factors is its ability to account for the interaction between the vegetation and the flow, taking into account velocity, depth of flow, and vegetative conditions (including type, size, stage of maturity, and density of vegetation). Based on the mathematical model, a table is provided to estimate Manning's n value for flow through vegetation.

Most of the biomedical materials printed using 3D bioprinting are static and are unable to alter/transform with dynamic changes in the internal environment of the body. The emergence of four-dimensional (4D) printing addresses this problem. By preprogramming dynamic polymer materials and their nanocomposites, 4D printing is able to produce the desired shapes or transform functions under specific conditions or stimuli to better adapt to the surrounding environment. In this review, the current and potential applications of 4D-printed materials are introduced in different aspects of the biomedical field, e.g., tissue engineering, drug delivery, and sensors. In addition, the existing limitations and possible solutions are discussed. Finally, the current limitations of 4D-printed materials along with their future perspective are presented to provide a basis for future research.