Showing papers by "University of Tabriz published in 2018"

Review on magnetically separable graphitic carbon nitride-based nanocomposites as promising visible-light-driven photocatalysts

Abstract: Graphitic carbon nitride (g-C3N4) has gained remarkable acceptance as a visible-light-driven photocatalyst with a distinctive 2D structure and great stability. Owing to its superior features, g-C3N4 has been engaged in various scientific activities for environmental pollution abatement, production and storage of energy, and gas sensors. However, the visible-light efficiency of pure g-C3N4 is very poor and its separation from the phototreated systems is difficult. The most promising method to improve the photocatalytic activity and facilitate separation process is to introduce a magnetic compound over the g-C3N4 sheets. This review has mainly focused on the recent advancement in fabrication, characterization and application of magnetic g-C3N4-based nanocomposites. Accordingly, four primary g-C3N4-based nanocomposites are discussed based on the type of integrated magnetic material. The effects on the structure, physico-chemical properties, photocatalytic activity towards degradation of pollutants, hydrogen generation, solid phase extraction, lithium-ion batteries, gas sensors, and supercapacitors are also discussed in detail.

Detailed analysis of the plasma extracellular vesicle proteome after separation from lipoproteins

Abstract: The isolation of extracellular vesicles (EVs) from blood is of great importance to understand the biological role of circulating EVs and to develop EVs as biomarkers of disease. Due to the concurrent presence of lipoprotein particles, however, blood is one of the most difficult body fluids to isolate EVs from. The aim of this study was to develop a robust method to isolate and characterise EVs from blood with minimal contamination by plasma proteins and lipoprotein particles. Plasma and serum were collected from healthy subjects, and EVs were isolated by size-exclusion chromatography (SEC), with most particles being present in fractions 8–12, while the bulk of the plasma proteins was present in fractions 11–28. Vesicle markers peaked in fractions 7–11; however, the same fractions also contained lipoprotein particles. The purity of EVs was improved by combining a density cushion with SEC to further separate lipoprotein particles from the vesicles, which reduced the contamination of lipoprotein particles by 100-fold. Using this novel isolation procedure, a total of 1187 proteins were identified in plasma EVs by mass spectrometry, of which several proteins are known as EV-associated proteins but have hitherto not been identified in the previous proteomic studies of plasma EVs. This study shows that SEC alone is unable to completely separate plasma EVs from lipoprotein particles. However, combining SEC with a density cushion significantly improved the separation of EVs from lipoproteins and allowed for a detailed analysis of the proteome of plasma EVs, thus making blood a viable source for EV biomarker discovery.

Breast cancer diagnosis: Imaging techniques and biochemical markers.

Abstract: Breast cancer is a complex disease which is found as the second cause of cancer-associated death among women. Accumulating of evidence indicated that various factors (i.e., gentical and envirmental factors) could be associated with initiation and progression of breast cancer. Diagnosis of breast cancer patients in early stages is one of important aspects of breast cancer treatment. Among of various diagnosis platforms, imaging techniques are main diagnosis approaches which could provide valuable data on patients with breast cancer. It has been showed that various imaging techniques such as mammography, magnetic resonance imaging (MRI), positron-emission tomography (PET), Computed tomography (CT), and single-photon emission computed tomography (SPECT) could be used for diagnosis and monitoring patients with breast cancer in various stages. Beside, imaging techniques, utilization of biochemical biomarkers such as proteins, DNAs, mRNAs, and microRNAs could be employed as new diagnosis and therapeutic tools for patients with breast cancer. Here, we summarized various imaging techniques and biochemical biomarkers could be utilized as diagnosis of patients with breast cancer. Moreover, we highlighted microRNAs and exosomes as new diagnosis and therapeutic biomarkers for monitoring patients with breast cancer.

Optimal management of energy hubs and smart energy hubs – A review

Abstract: The increase of environmental concerns, scarcity of fossil fuel resources, uncontrolled growth of demand, along with the development of efficient multi-generation systems have made the restructuring of current energy systems inevitable. Future energy systems will be in the form of sustainable multi-energy systems. The optimal operation of such systems requires an integrated energy management system for optimal planning, control and management. Energy hub is a new and promising concept for optimal management of systems with multiple energy carriers. Energy hub has a large potential for realization of energy system models and moving towards sustainable multi-energy systems. This paper provides a comprehensive overview of the concepts and different applications of energy hubs in various energy consumption sectors including residential, commercial, industrial, agricultural, and the integration of these systems. The potential role of energy hub as an integrated energy management system to solve the main challenges in these consumption sectors is evaluated. This study focuses on the benefits earned by integration of the options such as demand side management, distributed energy resources, renewable energy resources, multi-generation systems, storage systems as well as using the smart technologies by introducing the concept of smart energy hubs.

A Novel High Step-Up DC–DC Converter With Continuous Input Current Integrating Coupled Inductor for Renewable Energy Applications

Abstract: In this paper, a nonisolated dc–dc converter with high voltage gain is presented. Three diodes, three capacitors, an inductor, and a coupled inductor are employed in the presented converter. Since the inductor is connected to the input, the low input current ripple is achieved, which is important for tracking maximum power point of photovoltaic panels. The voltage stress across switch S is clamped by diode D 1 and capacitor C 1. Therefore, a main switch with low on-resistance RDS (on) can be employed to reduce the conduction loss. Besides, the main switch is turned on under zero current. This reduces the switching loss. The steady-state analysis of the proposed converter is discussed in this paper. Finally, the proposed converter prototype circuit is implemented to justify the validity of the analysis.

A comprehensive review of heuristic optimization algorithms for optimal combined heat and power dispatch from economic and environmental perspectives

Abstract: Combined heat and power economic dispatch (CHPED) aims to minimize the operational cost of heat and power units satisfying several equality and inequality operational and power network constraints. The CHPED should be handled considering valve-point loading impact of the conventional thermal plants, power transmission losses of the system, generation capacity limits of the production units, and heat-power dependency constraints of the cogeneration units. Several conventional optimization algorithms have been firstly presented for providing the optimal production scheduling of power and heat generation units. Recently, experience-based algorithms, which are called heuristic and meta-heuristic optimization procedures, are introduced for solving the CHPED optimization problem. In this paper, a comprehensive review on application of heuristic optimization algorithms for the solution of CHPED problem is provided. In addition, the most popular heuristic and meta-heuristic optimization algorithms are studied in this paper, and implementation of the optimization procedures for the solution of CHPED problem taking into account the objective functions and different constrains are discussed. The main contributions of the reviewed papers are studied and discussed in details. Additionally, main considerations of equality and inequality constraints handled by different research studies are reported in this paper. Five test systems are considered for evaluating the performance of different optimization techniques. Optimal solutions obtained by employment of multiple heuristic and meta-heuristic optimization methods for test instances are demonstrated and the introduced methods are compared in terms of convergence speed, attained optimal solutions, and constrains. The best optimal solutions for five test systems are provided in terms of operational cost by employment of different optimization methods.

Metabolomics and proteomics reveal drought-stress responses of leaf tissues from spring-wheat.

Abstract: To reveal the integrative biochemical networks of wheat leaves in response to water deficient conditions, proteomics and metabolomics were applied to two spring-wheat cultivars (Bahar, drought-susceptible; Kavir, drought-tolerant). Drought stress induced detrimental effects on Bahar leaf proteome, resulting in a severe decrease of total protein content, with impairments mainly in photosynthetic proteins and in enzymes involved in sugar and nitrogen metabolism, as well as in the capacity of detoxifying harmful molecules. On the contrary, only minor perturbations were observed at the protein level in Kavir stressed leaves. Metabolome analysis indicated amino acids, organic acids, and sugars as the main metabolites changed in abundance upon water deficiency. In particular, Bahar cv showed increased levels in proline, methionine, arginine, lysine, aromatic and branched chain amino acids. Tryptophan accumulation via shikimate pathway seems to sustain auxin production (indoleacrylic acid), whereas glutamate reduction is reasonably linked to polyamine (spermine) synthesis. Kavir metabolome was affected by drought stress to a less extent with only two pathways significantly changed, one of them being purine metabolism. These results comprehensively provide a framework for better understanding the mechanisms that govern plant cell response to drought stress, with insights into molecules that can be used for crop improvement projects.

MINLP Probabilistic Scheduling Model for Demand Response Programs Integrated Energy Hubs

Abstract: In this paper, an optimal probabilistic scheduling model of energy hubs operations is presented. The scheduling of energy hub determines the energy carriers to be purchased as input and converted or stored, in order to meet the energy requests, while minimizing the total hub's cost. However, as many other engineering endeavors, future operating criteria of energy hubs could not be forecasted with certainty. Load and price uncertainties are the most unclear parameters that hub operators deal with. In this regard, this paper proposes a 2 $m$ + 1 point estimation probabilistic scheduling scheme for energy hubs with multiple energy inputs and outputs. One of the applicable tools of energy hubs to have an efficient participation in the liberalized power market with volatile prices is demand response programs (DRPs). While there is plenty of experience in investigating the effect of DRP, it is electricity DRP that receives increasing attention by research and industry. Therefore, the proposed DRP investigates the effect of both responsive thermal and electric loads in reducing the total cost and participation of different facilities in supplying multiple loads. The proposed model envisages the most technical constraints of converters and storages. Several test systems have been investigated in order to confirm the effectiveness of the proposed model. The results verify the capability of the proposed model in covering the energy hub time-varying output demands as well as the economic advantages of implementing the suggested strategy. In addition, the results are compared with 2 $m$ point estimate method and Monte Carlo simulation.

Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran

Abstract: An accurate computational approach for the prediction of pan evaporation over daily time horizons is a useful decisive tool in sustainable agriculture and hydrological applications, particularly in designing the rural water resource systems, water use allocations, utilization and demand assessments, and the management of irrigation systems. In this study, a hybrid predictive model (Multilayer Perceptron-Firefly Algorithm (MLP-FFA)) based on the FFA optimizer that is embedded within the MLP technique is developed and evaluated for its suitability for the prediction of daily pan evaporation. To develop the hybrid MLP-FFA model, the pan evaporation data measured between 2012 and 2014 for two major meteorological stations (Talesh and Manjil) located at Northern Iran are employed to train and test the predictive model. The ability of the hybrid MLP-FFA model is compared with the traditional MLP and support vector machine (SVM) models. The results are evaluated using five performance criteria metrics: root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NS), and the Willmott’s Index (WI). Taylor diagrams are also used to examine the similarity between the observed and predicted pan evaporation data in the test period. Results show that an optimal MLP-FFA model outperforms the MLP and SVM model for both tested stations. For Talesh, a value of WI = 0.926, NS = 0.791, and RMSE = 1.007 mm day−1 is obtained using MLP-FFA model, compared with 0.912, 0.713, and 1.181 mm day−1 (MLP) and 0.916, 0.726, and 1.153 mm day−1 (SVM), whereas for Manjil, a value of WI = 0.976, NS = 0.922, and 1.406 mm day−1 is attained that contrasts 0.972, 0.901, and 1.583 mm day−1 (MLP) and 0.971, 0.893, and 1.646 mm day−1 (SVM). The results demonstrate the importance of the Firefly Algorithm applied to improve the performance of the MLP-FFA model, as verified through its better predictive performance compared to the MLP and SVM model.

Current developments in green synthesis of metallic nanoparticles using plant extracts: a review.

Abstract: Metal nanoparticles (MNPs) produced by green approaches have received global attention because of their physicochemical characteristics and their applications in the field of biotechnology In recent years, the development of synthesizing NPs by plant extracts has become a major focus of researchers because of these NPs have low hazardous effect in the environment and low toxicity for the human body Synthesized NPs from plants are not only more stable in terms of size and shape, also the yield of this method is higher than the other methods Moreover, some of these MNPs have shown antimicrobial activity which is consistently confirmed in past few years Plant extracts have been used as reducing agent and stabilizer of NPs in which we can reduce the toxicity in the environment as well as the human body only by not using chemical agents Furthermore, the presence of some specific materials in plant extracts could be extremely helpful and effective for the human body; for instance, polyphenol, which may have antioxidant effects has the capability for capturing free radicals before they can react with other biomolecules and cause serious damages In this article, we focused on of the most common plants which are regularly used to synthesize MNPs along with various methods for synthesizing MNPs from plant extracts

Epigenetic mechanisms as a new approach in cancer treatment: An updated review

Abstract: Epigenetic, along with genetic mechanisms, is essential for natural evolution and maintenance of specific patterns of gene expression in mammalians. Global epigenetic variation is inherited somatically and unlike genetic variation, it is dynamic and reversible. They are somatically associated with known genetic variations. Recent studies indicate the broad role of epigenetic mechanisms in the initiation and development of cancers, that they are including DNA methylation, histone modifications, nucleosomes changes, non-coding RNAs. The reversible nature of epigenetic changes has led to the emergence of novel epigenetic therapeutic approaches, so that several types of these medications have been approved by the FDA so far. In this review, we discuss the concept of epigenetic changes in diseases, especially cancers, the role of these changes in the onset and progression of cancers and the potential of using this knowledge in designing novel therapeutic strategies.

Calprotectin (S100A8/S100A9): a key protein between inflammation and cancer.

Abstract: Calprotectin (S100A8/S100A9), a heterodimeric EF-hand Ca2+ binding protein, are abundant in cytosol of neutrophils and are involved in inflammatory processes and several cancerous pathogens. The purpose of the present systematic review is to evaluate the pro- and anti-tumorigenic functions of calprotectin and its relation to inflammation. We conducted a review of studies published in the Medline (1966–2018), Scopus (2004–2018), ClinicalTrials.gov (2008–2018) and Google Scholar (2004–2018) databases, combined with studies found in the reference lists of the included studies. Elevated levels of S100A8/S100A9 were detected in inflammation, neoplastic tumor cells and various human cancers. Recent data have explained that many cancers arise from sites of infection, chronic irritation, and inflammation. The inflammatory microenvironment which largely includes calprotectin, has an essential role on high producing of inflammatory factors and then on neoplastic process and metastasis. Scientists have shown different outcomes in inflammation, malignancy and apoptosis whether the source of the aforementioned protein is extracellular or intracellular. These findings are offering new insights that anti-inflammatory therapeutic agents and anti-tumorigenic functions of calprotectin can lead to control cancer development.

High Step-Up Quasi- Z Source DC–DC Converter

Abstract: In this paper, a high step-up quasi- Z Source (QZS) dc–dc converter is proposed. This converter uses a hybrid switched-capacitors switched-inductor method in order to achieve high voltage gains. The proposed converter have resolved the voltage gain limitation of the basic QZS dc–dc converter while keeping its main advantages, such as continuous input current and low voltage stress on capacitors. Compared to the basic converter, the duty cycle is not limited, and the voltage stress on the diodes and switch is not increased. In addition to these features, the proposed converter has a flexible structure, and extra stages could be added to it in order to achieve even higher voltage gains without increasing the voltage stress on devices or limiting the duty cycle. The operation principle of the converter and related relationships and waveforms are presented in the paper. Also, a comprehensive comparison between the proposed and other QZS based dc–dc converters is provided which confirms the superiority of the proposed converter. Simulations are done in power systems computer aided design (PSCAD) in order to investigate the maximum power point tracking (MPPT) capability of the converter. In addition, the valid performance and practicality of the converter are studied through the results obtained from the laboratory built prototype.

Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing

Abstract: We present Memory Augmented Policy Optimization (MAPO), a simple and novel way to leverage a memory buffer of promising trajectories to reduce the variance of policy gradient estimate. MAPO is applicable to deterministic environments with discrete actions, such as structured prediction and combinatorial optimization tasks. We express the expected return objective as a weighted sum of two terms: an expectation over the high-reward trajectories inside the memory buffer, and a separate expectation over trajectories outside the buffer. To make an efficient algorithm of MAPO, we propose: (1) memory weight clipping to accelerate and stabilize training; (2) systematic exploration to discover high-reward trajectories; (3) distributed sampling from inside and outside of the memory buffer to scale up training. MAPO improves the sample efficiency and robustness of policy gradient, especially on tasks with sparse rewards. We evaluate MAPO on weakly supervised program synthesis from natural language (semantic parsing). On the WikiTableQuestions benchmark, we improve the state-of-the-art by 2.6%, achieving an accuracy of 46.3%. On the WikiSQL benchmark, MAPO achieves an accuracy of 74.9% with only weak supervision, outperforming several strong baselines with full supervision. Our source code is available at https://goo.gl/TXBp4e

Carbon quantum dots: recent progresses on synthesis, surface modification and applications

Abstract: Generally, carbon nanoparticles with a size of 10 nm (or less) are called carbon quantum dots (CQDs, C-dots or CD), which have created huge excitement due to their advantages in chemical inertness, high water solubility, excellent biocompatibility, resistance to photobleaching and various optical superiority. In this article, we describe the recent advancements in the area of CQDs; concentrating on their synthesis techniques, size control, surface modification approaches, optical properties, luminescent mechanism, and their applications in bioimaging, biosensing, drug delivery and catalysis.

Circulating microRNAs as diagnostic and therapeutic biomarkers in gastric and esophageal cancers

Abstract: Gastric and esophageal cancers are as main cancers of the gastrointestinal (GI) tract, which are associated with poor diagnosis and survival. Several efforts were made in the past few decades to finding effective therapeutic approaches, but these approaches had several problems. Finding new biomarkers is a critical step in finding new approaches for the treatment of these cancers. Finding new biomarkers that cover various aspects of the diseases could provide a choice of suitable therapies and better monitoring of patients with these cancers. Among several biomarkers tissue specific and circulating microRNAs (miRNAs) have emerged as powerful candidates in the diagnosis of gastric and esophageal cancers. MiRNAs are small noncoding single-stranded RNA molecules that are found in the blood and regulate gene expression. These have numerous characteristics that make them suitable for being used as ideal biomarkers in cancer diagnosis. Research has indicated that the level and profile of miRNA in serum and plasma are very high. They are potentially noninvasive and sensitive enough to detect tumors in their primary stages of infection. Multiple lines of evidence indicate that the presence, absence, or deregulation of several circulating miRNAs (i.e., let-7a, miR-21, miR-93, miR-192a, miR-18a, and miR-10b for gastric cancer, and miR-21, miR-375, miR-25-3p, miR-151a-3p, and miR-100-3p for esophageal cancer) are associated with initiation and progression of gastric and esophageal cancers. The aim of this review is to highlight the recent advances in the roles of miRNAs in diagnosis and treatment of gastric and esophageal cancers.