Shiraz University
  • Shiraz, Fars, Iran
Recent publications
Background Union between second cousins and closer relatives is called consanguineous marriage. Consanguineous marriage is associated with increased risk of autosomal recessive diseases and several multifactorial traits. In order to evaluate the association between prevalence/mortality of COVID-19 and the frequency of consanguineous marriage, the present ecologic study was carried out. For the present study, data of prevalence (per 10 ⁶ people) and mortality (per 10 ⁶ people) and number of performed laboratory diagnostic test (per 10 ⁶ people) of COVID-19 disease at four time points (December 2020; March, August and October 2021) of 65 countries were used. Results Univariable correlation and generalized estimating equation analysis were used. In analysis, prevalence and mortality of COVID-19 were used as dependent variables and human development index, number of performed diagnosis test and the mean of inbreeding coefficient (α-value) were introduced into model as covariates, and time point was used as a factor in analysis. The square root (SR) of prevalence ( P = 0.008) and SR-mortality ( P < 0.001) of COVID-19 negatively associated with the log-transformed of α-value. Conclusions The present finding means that in countries with high levels of consanguineous marriages, the prevalence of COVID-19 and mortality due to COVID-19 were lower than countries having low level of marriage with relatives.
Background Humans have altered fire regimes across ecosystems due to climate change, land use change, and increasing ignition. Unprecedented shifts in fire regimes affect animals and contribute to habitat displacement, reduced movement, and increased mortality risk. Mitigating these effects require the identification of habitats that are susceptible to wildfires. We designed an analytical framework that incorporates fire risk mapping with species distribution modeling to identify key habitats of Ursus arctos with high probability of fire in Iran. We applied the random forest algorithm for fire risk mapping. We also modeled brown bear habitats and predicted connectivity between them using species distribution models and connectivity analysis, respectively. Finally, the fire risk map, critical habitats, and corridors were overlaid to spatially identify habitats and corridors that are at high risk of fire. Results We identified 17 critical habitats with 5245 km ² of corridors connecting them, 40.06% and 11.34% of which are covered by conservation areas, respectively. Our analysis showed that 35.65% of key habitats and 23.56% of corridors are at high risk of fire. Conclusions Since bears habitat in this semi-arid landscape rely on forests at higher altitudes, it is likely that shifting fire regimes due to changing climate and land use modifications reduce the extent of habitats in the future. While it is not well known how fire affects bears, identifying its key habitat where wildfires are likely to occur is the first step to manage potential impacts from increasing wildfires on this species.
Background Hamster is widely used as an experimental model in the study of reproductive system. However, pregnancy diagnosis and aging always have been a challenge. ultrasonography have been used in diagnosis of pregnancy in some small laboratory animals, such as rabbits, rats, and mice. Current study describes use of trans-abdominal ultrasonography for pregnancy diagnosis and fetal age estimation in golden hamster. Furthermore, a macroscopic examination was performed to evaluate the embryonic vesicle diameter, crown-rump length, and fetal head diameter. Ten adult female golden hamsters were selected and maintained under controlled light conditions (14 h light/10 h darkness). The estrous cycle was synchronized using eCG and hCG. During estrous (18 h after hCG injection), the hamsters were naturally mated. After seven days of mating, the hamsters were examined daily for pregnancy diagnosis and aging with an ultrasound scanner equipped with an 8.5-MHZ linear probe. On each day of the experiment, at least one of the pregnant hamsters was euthanized and dissected for macroscopic fetal measurements using a digital caliper. Results The gestational sac and crown-rump length were identified and measured by ultrasonographicly on day 7 of pregnancy and head could be visible after day 10 of gestation. Statistical analysis revealed that the ultrasound estimation of gestational age was significantly correlated with the actual age of the fetus (r = 0.98; p < 0.05). Conclusions Real-time ultrasound can be used for the diagnosis of pregnancy and estimation of fetal age in golden hamster from day 7 of gestation.
SARS-CoV-2 (COVID-19) is the causative organism for a pandemic disease with a high rate of infectivity and mortality. In this study, we aimed to assess the affinity between several available small molecule and proteins, including Abl kinase inhibitors, Janus kinase inhibitor, dipeptidyl peptidase 4 inhibitors, RNA-dependent RNA polymerase inhibitors, and Papain-like protease inhibitors, using binding simulation, to test whether they may be effective in inhibiting COVID-19 infection through several mechanisms. The efficiency of inhibitors was evaluated based on docking scores using AutoDock Vina software. Strong ligand–protein interactions were predicted among some of these drugs, that included: Imatinib, Remdesivir, and Telaprevir, and this may render these compounds promising candidates. Some candidate drugs might be efficient in disease control as potential inhibitors or lead compounds against the SARS-CoV-2. It is also worth highlighting the powerful immunomodulatory role of other drugs, such as Abivertinib that inhibits pro-inflammatory cytokine production associated with cytokine release syndrome (CRS) and the progression of COVID-19 infection. The potential role of other Abl kinase inhibitors, including Imatinib in reducing SARS-CoV and MERS-CoV viral titers, immune regulatory function and the development of acute respiratory distress syndrome (ARDS), indicate that this drug may be useful for COVID-19, as the SARS-CoV-2 genome is similar to SARS-CoV.
Objective How do birds navigate their way? It is one of the interesting question about homing pigeons, however the genetic of navigation has reminded as a puzzle. Optic lobe, olfactory bulb, hippocampus and cere were collected for RNA sampling. The generated RNA-seq represent RNA resequencing data for racing homer (homing) pigeon and other rock pigeon breeds. The obtained data set can provide new insight about hippocampus role and GSR contribution to pigeon magnetoreception. Data description To investigate the navigation ability of rock pigeon breeds, 60 whole transcriptome sequence data sets related to homing pigeon, Shiraz tumblers, feral pigeons and Persian high flyers were obtained. RNA extraction was performed from three brain regions (optic lobe, olfactory bulb, hippocampus) and cere. Paired-end 150 bp short reads (Library size 350 bp) were sequenced by Illumina Hiseq 2000. In this way, about 342.1 Gbp and 130.3 Gb data were provided. The whole transcriptome data sets have been deposited at the NCBI SRA database (PRJNA532674). The submitted data set may play critical role to describe the mechanism of navigation ability of rock pigeon breeds.
Objective Wilt caused by Fusarium oxysporum f. sp. melonis (Fom) is one of the most widespread and destructive melon diseases worldwide. Whole-genome sequencing data of a diverse set of Fom strains, as well as several non-pathogenic strains isolated from melon from different parts of the world are described here. These data shed light on the genetic diversity, population structure and the potential evolutionary trajectories which have led to the emergence of different Fom races, and will facilitate identification of avirulence genes which will be helpful to develop resistant melon cultivars. Data description Genomic DNA was extracted from mycelium of 38 Fusarium oxysporum (Fo) strains collected from different parts of the world including Belgium, China, France, Iran, Israel, Japan, Mexico, New Zealand, Spain, the Netherlands, and the United States. The genomes were sequenced to ≈ 20 × coverage using the Illumina Hiseq Xten system, resulting in paired-end reads of 151 bp and assemblies of 1675 (Fom-18L) to 4472 (Fom-R12-13) scaffolds. The genome sequences are available in the National Center for Biotechnology Information (NCBI) and the Sequence Read Archive (SRA) under Project number PRJNA596396 and PRJNA596396, respectively. The presented data set can be useful to identify the genes associated with pathogenic strategies.
Phase change materials have wide applications as thermal energy storage systems. Due to the low thermal conductivity of these materials, lots of efforts have been made to increase the thermal conductivity and efficiency of thermal energy storage systems. In this numerical study, convective and radiation heat transfer on a cavity with transparent inner walls have been investigated considering the effects of buoyancy force and viscosity of phase change materials. For this purpose, computational fluid dynamics that have high accuracy in calculations have been used. The considered wall is made of brick, in which the cavity containing phase change materials is included and layers of cement and plaster are placed on the wall. The results indicate that increasing the heat flux increases the effects of convective heat transfer. In addition, by applying radiation effects, the melting process of paraffin materials increases by 31%. Due to the transparency of the inner walls of the cavity, the radiation process occurred surface to surface. Therefore, with increasing amount of molten material, the radiation effects increase, the reason for this is that RT24 material is more transparent in liquid form than solid material. Also, increasing the heat flux from 50 to 100 and 300 (w/m²) increases the speed of the melting process by 19.3% and 27.2%, respectively.
Electrospraying is a technique to improve the application and stability of bioactive compounds in food. Here, electrospraying was applied to fabricate gliadin particles incorporated γ-oryzanol. The round particles were obtained, with an average diameter of 481.56 ± 283.74 nm, from scanning electron microscopy. Simulations demonstrated how γ-oryzanol-loaded gliadin particles were unfolded in acetic acid and culminated in their globular shape under an electric field. The results also revealed that γ-oryzanol was present in gliadin particles. Moreover, there was a successful formation of particles with a homogeneous distribution and an enhanced thermostabilization of γ-oryzanol. In food simulants, γ-oryzanol demonstrated an initial burst release, followed by a subsequent, slower release that occurred gradually. Finally, MTT assays showed concentration- and time-dependent inhibitions of γ-oryzanol-loaded gliadin particles on HT-29 cells, with IC50 values of 0.47 and 0.40 mg/mL for 24 and 48 h, respectively. This study described a protocol for developing γ-oryzanol-loaded gliadin particles with enhanced stability, valuable release-behavior, and decreased HT-29 proliferation.
To determine the toxicity and bioaccumulation of copper, adult oribatid soil mites Oppia nitens were exposed for 28 days to LUFA 2.2 soil spiked at concentrations of 0–6400 mg Cu kg⁻¹ dry soil. Effects on survival and reproduction were related to total and available (0.01 M CaCl2 extractable and pore water) concentrations in the soil and concentrations in the animals. The mites showed a concentration-dependent uptake of copper, which, however, decreased at toxic concentrations. Overall bioaccumulation factors were low, suggesting a low tendency for copper bioaccumulation. The estimated median lethal concentration (LC50) values were 3251 mg Cu kg⁻¹ dry soil, 1130 mg Cu kg⁻¹ dry soil, 1977 mg Cu L⁻¹ pore water, and 592 mg Cu kg⁻¹ dry body weight, and the estimated 50 % effective concentrations (EC50) for effects of copper on reproduction were 589 mg Cu kg⁻¹ dry soil, 116 mg Cu kg⁻¹ dry soil, 78.5 mg Cu L⁻¹ pore water, and 413 mg Cu kg⁻¹ dry body weight, based on measured soil total concentrations, 0.01 M CaCl2 extractable, porewater, and internal concentrations, respectively. The results show that the mite O. nitens is a suitable test organism for measuring metal bioavailability and toxicity in soil.
Owing to the necessity of precise calculations in gas hydrates systems comprised of novel environmentally friendly materials such as natural amino acids (NAAs), as well as their blends with ionic liquids (ILs), alcohols, and salts, the present study reports a new method concerning the water activity computation coupled with the van der Waals-Platteeuw (vdW-P) model. The developed activity model consists of two terms, namely Free-Volume modification of the Flory-Huggins (FVFH) equation taking into account the molecular (short-range) interactions, and the extended Debye-Hückel (EDH) equation considering the ionic (long-range) interactions. The model's performance is then assessed against a comprehensive databank (11 NAAs, 9 blends, 475 data points) collected from open literature and data of 3 gaseous hydrate formers (CH4, CO2, and natural gas). The overall deviation of the determined gas hydrates dissociation temperatures for the whole databank is found to be 0.40 K (0.14%), while the most significant individual deviation does not exceed 1.76%, proving the remarkable performance of the developed calculation procedure. Not only does not the model benefit from parameter regression, but it also offers accurate predictions in the complex systems of the inhibitors’ blends. When NAAs are employed solely (409 data points), the deviations of the model results from real data of gas hydrates dissociation temperatures are 0.39 K. On the other hand, in the presence of NAAs mixtures with ILs, alcohols, and salts (66 data points), which represent highly complex systems, the deviation is 0.41 K.
Such a significant standpoint is the development of environmentally safe materials that any improvement in this regard is currently state of the art. Accordingly, this study aims to develop a precise calculation procedure based on solution thermodynamic regulations to determine hydrate dissociation conditions in the complex systems comprised of gaseous hydrate formers and sugar-derived materials aqueous solutions. The developed methodology employs free-volume modification of the Flory-Huggins model for water activity calculation and then modified van der Waals-Platteeuw approach for hydrate dissociation conditions modeling in the intended systems. To this end, 29 systems of 11 hydrate formers, 11 sugar-derived compounds, and 528 data points are appraised, the results of which indicate the AARDs (%) of 0.13 (AAD = 0.35 K) and 4.66 (AAD = 0.15 MPa) in predicting hydrate dissociation conditions when the best strategy is employed to determine interaction parameters. The impressive performance of the method in representing the experimental data of the hydrate systems is reflected in the accomplishment of accurate water activity calculation. Green hydrate inhibition strategy and enriching sugar-derived products are the systems that can benefit from the achievements of this study.
Parameter estimation of power plants is one of the main challenges of power system studies. Among different components of a power plant, excitation system (EXS) has great importance because of its effect on dynamic stability of power systems. Thus, it is vital to have accurate models of EXSs for power system dynamic studies. Since the field voltage and current are not accessible in brushless EXSs, parameter estimation of them is more difficult and challenging. Therefore, a new method is proposed in this paper to estimate field voltage signal using other measurements of the synchronous generator (SG). The proposed method is carried out through three stages; 1) parameter estimation of the SG using load rejection tests, 2) field voltage estimation, and 3) parameter estimation of EXS. The proposed method is applied to a 147 MVA industrial gas unit. The estimated model outputs are compared to the experimental results to show the accuracy and effectiveness of the proposed method.
Today, we are facing the COVID-19 pandemic. Accordingly, properly wearing face masks has become vital as an effective way to prevent the rapid spread of COVID-19. This research develops an Efficient Mask-Net method for low-power devices, such as mobile and embedding models with low-memory requirements. The method identifies face mask-wearing conditions in two different schemes: I. Correctly Face Mask (CFM), Incorrectly Face Mask (IFM), and Not Face Mask (NFM) wearing; II. Uncovered Chin IFM, Uncovered Nose IFM, and Uncovered Nose and Mouth IFM. The proposed method can also be helpful to unmask the face for face authentication based on unconstrained 2D facial images in the wild. In this study, deep convolutional neural networks (CNNs) were employed as feature extractors. Then, deep features were fed to a recently proposed large margin piecewise linear (LMPL) classifier. In the experimental study, lightweight and very powerful mobile implementation of CNN models were evaluated, where the novel “EffientNetb0” deep feature extractor with LMPL classifier outperformed well-known end-to-end CNN models, as well as conventional image classification methods. It achieved high accuracies of 99.53 and 99.64% in fulfilling the two mentioned tasks, respectively.
Residential electric vehicle (REV) is an advanced technology with a rapid growth rate in transportation and electric grids. One key challenge in the operation of REVs is the necessity of the accurate, reliable, and practical forecasting method to provide accurate information of the charging profile in the look-ahead hours. In power system, in order to optimize the production and consumption as much as possible, in addition to accurately predicting the amount of electricity consumption, it is necessary for the stability of the grid to take into account the imminent probabilities. This paper presents the main principle of the probability density function forecasting approach in residential electric vehicle (REV) charging profile. To this end, an end-to-end deep learning structure is designed and integrated with kernel density estimation (KDE). The designed network is composed of four major blocks, i.e., convolutional layers to extract full spatial features, gated recurrent unit (GRU) to fully understand the temporal features as a time-efficient version of the gated deep network, an autoregressive (AR) to model the long patterns including battery type, REV type, and number of REVs and kernel density estimator block. Furthermore, to improve the learning ability of the designed network, an attention mechanism is integrated into the design network. The numerical results on the actual REVs (about 348 REVs) demonstrate the effectiveness and superiority of the proposed network through several cases and comparison with several well-known deep and shallow-based methods.
Thermal comfort has been the main target of the ventilation in subway systems. However, pollutant concentration and aerosol dispersion could be the leading health issues in underground metro stations. This study numerically simulated a train movement inside a subway system using the Dynamic Mesh Technique for a 3-D computational domain consisting of four stations and connecting tunnels. The effects of both the ventilation system and the train-induced fluid flow inside the subway system were investigated. Then, the particle generation and dispersion due to train braking are considered, and the impact of the ventilation system on reducing the particle concentration inside the station was investigated. It is shown that the airflow inside the subway system is entirely affected by the piston effect. The airflow generated by the train movement is much higher than that generated by the operation of the ventilation system when only one train passes through the tunnel. The results show that the ventilation system, consisting of the supply and exhaust fans inside the tunnel and supply grilles inside the platform, can reduce the particle concentration by half, except for the platform beside the stopped train when the train enters the station and during half of the train stop time. The other design concept demonstrates that the under-platform exhaust system considerably reduces the concentration of the particles released by the train braking system on the trackside platform.
Arsenite (As(ΙΙΙ)) is one of the most toxic contaminants in surface and underground waters that needs to be removed from aquatic environments as it has harmful effects on human health and other living organisms. In this study, copper slag supported nanoscale zero-valent iron (nZVI/copper slag) adsorbent is used to reduce the concentration of As(ΙΙΙ) from an aqueous environment up to a permissible level. nZVI/copper slag adsorbent was prepared using Fe³⁺ reduction by BH⁻. FE-SEM, EDX, XRD, FTIR, BET, VSM, and Zeta potential were utilized for characterization of the adsorbent synthesis. In this study, effective parameters on the As(ΙΙΙ) removal were made into a function of initial concentration, adsorbent dosage, and pH of the solution at a constant time of 5 min using Design-Expert software. The results of experiments showed that only 7.9 g/L of nZVI/copper slag adsorbent in 5 min and pH of 5.4 can remove >99 % of As(ΙΙΙ) with the initial concentration of 20 mg/L in aqueous solution. Among different isotherms, the Langmuir model with a maximum adsorption capacity of 4.27 mg/g could describe As(ΙΙΙ) adsorption model. In addition, it was found that adsorption followed the pseudo-second order kinetics model. The amount of iron (Fe) leached from the adsorbent surface of nZVI/copper slag has been significantly reduced by using pure copper slag as a substrate in 3 consecutive cycles. Furthermore, nZVI/copper slag adsorbent can be separated from aqueous media by applying an external magnetic field due to its magnetic properties and high weight of pure copper slag.
Interatomic potentials for the Al-Ti, Al-Ta, Al-Zr, Al-Nb and Al-Hf binary systems have been developed based on the second nearest-neighbor modified embedded-atom method (2NN MEAM) formalism. The fundamental materials properties (structural, thermodynamic and elastic behaviors of different intermetallics) could be readily described with the potentials using molecular dynamic simulation (MD), in rational agreements with experimental or first principles data. The potentials are further utilized to develop an interatomic potential for the (TiZrNbHfTa)Al3 high entropy intermetallic compound (HEIC), which open the door to understand atomic scale behavior of HEICs.
Spatio-temporal variability of extreme precipitation characteristics (EPCs) were analyzed using clustering techniques to establish homogeneous sub-regions (clusters), the nonparametric Mann–Kendall (M–K) test to detect significant monotonic temporal trend and the nonparametric Lepage (LP) test to detect change-points (jumps) representing significant short-term temporal trend. The study area is Fars province (southern Iran), exhibiting diverse climatic conditions within relatively small area. Detailed clustering analysis involved utilization of eight algorithms and four validation indices. Consequently, one algorithm was selected, suggesting seven clusters (C1 to C7) for the study area. EPCs were identified by eight variables, which were used for temporal analysis. The M–K test utilizing within cluster EPC data did not detect any significant trend (5% or 1% levels), for the study period (1976–2013). However, the LP test conducted at 10- and 5-year time-steps showed significant change-points (5% level) in temporal behavior of EPCs in every cluster. Furthermore, “between-cluster variability” was strong as shown in the number of EPCs with significant change-point. For 10-year time-step, C1 and C2, respectively, had two and eight EPCs with significant change-points, representing minimum and maximum number of EPCs. Remaining clusters had between three and seven EPCs with significant change-point. The 5-year time-step also showed strong EPC variability (within and between clusters). According to results for regions with diverse climatic conditions, detailed spatio-temporal analyses of EPCs should include proper identification of homogenous sub-regions (clusters) and also detection of both long-term and short-term (change-point) temporal trends by tests such as the M–K and LP. Results from this type of research can be used as part of the information, which is necessary for flood mitigation/prevention planning at the regional scale.
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4,241 members
Seraj Katebi
  • School of Electrical and Computer Engineering
Amir Mootabi Alavi
  • Department of Pathobiology
Amin Bigham Sadegh
  • School of Veterinary Medicine
Alireza Raayat Jahromi
  • Department of Clinical Science
Mohsen Ghane
  • Department of Clinical Science
Eram, Eram Square, 71964-84334, Shiraz, Fars, Iran
Head of institution
Hamid Nadgaran