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A hybrid approach consisting of 3D depthwise separable convolution and depthwise squeeze‑and‑excitation network for hyperspectral image classifcation
(Earth Science Informatics, 2024) Güngör, Mustafa; Güngör, Mustafa
Hyperspectral image classifcation is crucial for a wide range of applications, including environmental monitoring, precision agriculture, and mining, due to its ability to capture detailed spectral information across numerous wavelengths. However, the high dimensionality and complex spatial-spectral relationships in hyperspectral data pose signifcant challenges. Deep learning, particularly Convolutional Neural Networks (CNNs), has shown remarkable success in automatically extracting relevant features from high-dimensional data, making them well-suited for handling the intricate spatial-spectral relationships in hyperspectral images.This study presents a hybrid approach for hyperspectral image classifcation, combining 3D Depthwise Separable Convolution (3D DSC) and Depthwise Squeeze-and-Excitation Network (DSENet). The 3D DSC efciently captures spatial-spectral features, reducing computational complexity while preserving essential information. The DSENet further refnes these features by applying channel-wise attention, enhancing the model's ability to focus on the most informative features. To assess the performance of the proposed hybrid model, extensive experimental studies were carried out on four commonly utilized HSI datasets, namely HyRANK-Loukia and WHU-Hi (including HongHu, HanChuan, and LongKou). As a result of the experimental studies, the HyRANK-Loukia achieved an accuracy of 90.9%, marking an 8.86% increase compared to its previous highest accuracy. Similarly, for the WHU-Hi datasets, HongHu achieved an accuracy of 97.49%, refecting a 2.11% improvement over its previous highest accuracy; HanChuan achieved an accuracy of 97.49%, showing a 2.4% improvement; and LongKou achieved an accuracy of 99.79%, providing a 0.15% improvement compared to its previous highest accuracy. Comparative analysis highlights the superiority of the proposed model, emphasizing improved classifcation accuracy with lower computational costs.
Allelic variations of glutenin subunits and their association with quality traits in bread wheat genotypes
(2017) Aktaş, Hüsnü; Baloch, Faheem Shehzad
The present study was conducted to evaluate the genotype × environment interaction of the yield and quality traits for five bread wheat varieties commonly grown in the Southeastern Anatolia Region of Turkey and 20 advanced lines developed within the framework of the International Winter Wheat Improvement Project. We also determined the allelic pattern of the Glu-1 and Glu-3 loci of these genotypes and examined whether these loci had an effect on the quality traits. There was a significant variation among the genotypes and environments in terms of grain yield, protein content, sedimentation volume (SV), and the extensograph dough energy value (EDEV). The results of the study indicated that genotypic effect was more influential on SV and EDEV than environmental effect; thus, both traits could be used in breeding programs to develop elite cultivars with better quality. Twelve different high-molecularweight (HMW) glutenin alleles were identified at the Glu-A1, Glu-B1, and Glu-D1 loci, resulting in 14 allelic combinations, and 17 different alleles were observed in 19 combinations for low-molecular-weight (LMW) subunits. Furthermore, among all the genotypes, 13 + 16 and 13 + 19 alleles at Glu-B1 and 5 + 12 at Glu-D1 were observed to have the lowest frequency. Our study indicated that the combinations of HMW glutenin alleles with 2* at Glu-A1, 17 + 18 and 13 + 16 at Glu-B1, and 5 + 10 at Glu-D1, as well as the combinations of LMW alleles with subunits c and d at Glu-A3; subunits d, b, c, and g at Glu-B3; and subunits a and b at Glu-D3 had positive effects on the quality traits.
Antibiotic Removal from the Aquatic Environment with Activated Carbon Produced from Pumpkin Seeds
(Molecules, 2022) Alacabey, İhsan
Antibiotics are among the most critical environmental pollutant drug groups. Adsorption is one of the methods used to eliminate these pollutants. In this study, activated carbon was produced from pumpkin seed shells and subsequently modified with KOH. The adsorbent obtained through this procedure was used to remove ciprofloxacin from aqueous systems. Fourier Transform-Infrared Spectroscopy (FT-IR), Scanning Electron Microscopy (SEM), elemental, X-ray Photoelectron Spectroscopy (XPS), Brunauer–Emmett–Teller (BET) and Zeta analyses were used to characterize the adsorbent. The surface area, in particular, was found to be a very remarkable value of 2730 m2/g. The conditions of the adsorption experiments were optimized based on interaction time, adsorbent amount, pH and temperature. Over 99% success was achieved in removal operations carried out under the most optimal conditions, with an absorption capacity of 884.9 mg·g−1. In addition, the Langmuir isotherm was determined to be the most suitable model for the adsorption interaction
Biological assays, electrochemical behavior, and theoretical DFT calculations of Ru(II) complexes of chiral phosphinite based based on β-amino alcohols: Transfer hyrogenation of ketones using a HCOOH/Et3N mixture
(Elsevier, 2024) Dündar, Abdurrahman; Meriç, Nermin; Ertekin Binbay, Nil; Okumuş, Veysi; Sever, M. Şerif; Belyankova, Yelizaveta; Tursynbek, Saniya; Dauletbakov, Anuar; Sarah, Bayazit; Zolotareva, Darya; Yerassyl, Kamshyger; Dündar, Abdurahman
Synthesis of two phosphinite ligands based on β-amino alcohols, in high yields has been demonstrated. When we treated [Ru(arene)(μ-Cl)Cl]2 {arene:p-cymene,benzene} with chelating phosphinite ligands, we obtained neutral Ru(II)-complexes possessing the general formula [Ru(arene)phosphiniteCl2]. The structure of the ligands and complexes was confirmed using analytical and spectroscopic techniques. The quantum chemical calculations were carried out for the ruthenium complexes at the DFT/CAM-B3LYP level of theory in gas phase. The phosphinite complexes were subjected to cyclic voltammetry studies in order to determine the energies of HOMO and LUMO levels and to estimate their electrochemical and some electronic properties. Organic complex-based memory substrates were immobilized using TiO2-modified ITO electrodes, and the memory functions of phosphinite-based organic complexes were verified by chronoamperometry (CA) and open-circuit potential amperometry (OCPA). In the present study, the antioxidant potentials of ruthenium-based p-cymene and benzene complexes through DPPH radical scavenging, metal chelating, and reducing power activities were also determined. In addition, DNA binding abilities and antimicrobial activities of these complexes against pathogenic bacteria were studied. Finally, the ruthenium complex, (2S)-1-{[(2S)-2-[(diphenylphosphanyl)oxy]propyl][(1R)-1-phenylethyl]amino}propan-2-yldiphenyl phosphinitobis[dichloro(η6-benzene)ruthenium(II)] also catalyzed asymmetric transfer hydrogenation of acetophenone with high conversion (up to 99%) and good enantioselectivity (ee up to 89 %), in the existence of formic acid and triethylamine in dichloromethane medium under air atmosphere.
Biosynthesis, characterization, and investigation of antimicrobial and cytotoxic activities of silver nanoparticles using Solanum tuberosum peel aqueous extract
(Elsevier, 2023) Keskin, Cumali; Baran, Ayşe; Baran, Mehmet Fırat; Keskin, Cumali; Baran, Ayşe; Baran, Mehmet Fırat; Eftekhari, Aziz; Aytuğ Ava, Canan; İrtegün Kandemir, Sevgi; Cebe, Deniz Barış; Dağ, Beşir; Beilerli, Aferin; Khalilov, Rovshan
Metallic nanoparticle biosynthesis is thought to offer opportunities for a wide range of biological uses. The green process of turning biological waste into utilizable products gaining attention due to its economical and eco-friendly approach in recent years. This study reported the ability of Solanum tuberosum (ST) peel extract to the green synthesis of non-toxic, stable, small-sized silver nanoparticles without any toxic reducing agent utilizing the phytochemical components present in its structure. UV-visible spectroscopy, X-ray diffraction analysis, Fourier transform infrared spectroscopy, flourier scanning electron microscopy, atomic force microscopy, transmission electron microscopy, and energy dispersive analysis X-ray confirmed the biosynthesis and char-acterization of silver nanoparticles. Also, dynamic light scattering and thermogravimetric ana-lyses showed stable synthesized nanoparticles. The antibacterial activity of the biosynthesized silver nanoparticles was evaluated against four different bacterial strains, Escherichia coli (E. coli), Pseudomonas aeruginosa (P. aeruginosa), Staphylococcus aureus (S. aureus) Bacillus subtilis (B. subtilis), and a yeast, Candida albicans (C. albicans) using the minimum inhibitory concen-tration technique. The cytotoxic activities were determined against Human dermal fibroblast (HDF), glioblastoma (U118), colorectal adenocarcinoma (CaCo-2), and human ovarian (Skov-3) cell lines cancer cells using MTT test. The nanoparticle capping agents that could be involved in the reduction of silver ions to Ag NPs and their stabilization was identified using FTIR. Nano -particles were spherical in shape and had a size ranging from 3.91 to 27.07 nm, showed crys-talline nature, good stability (-31.3 mV), and the presence of capping agents. ST-Ag NPs significantly decreased the growth of bacterial strains after treatment. The in vitro analysis showed that the ST-Ag NPs demonstrated dose-dependent cytotoxicity against cell lines. Based on the data, it is feasible to infer that biogenic Ag NPs were capped with functional groups and demonstrated considerable potential as antibacterial and anticancer agents for biomedical and industrial applications.
Canker and leaf scorch on olive (Olea europaea L.) caused by Neoscytalidium dimidiatum in Turkey
(ScienceDirect, 2022) Derviş, Sibel; Özer, Göksel; Türkölmez, Şahimerdan; Derviş, Sibel
In a recent survey of olive groves in the Southeast Anatolia Region of Turkey, a new and serious decline of olive trees, beginning with foliar scorching and then dieback of twigs, branches, and even whole trees, was observed for the first time. In more advanced stages of the disease, necrosis and cankers were observed on the bark of the trunk, branches, and twigs. Isolations from symptomatic tissues from multiple cultivars in diverse locations yielded Neoscytalidium dimidiatum, which were identified using ITS, tef1, and tub2 loci of genomic DNA, in combination with morphological data. In vitro studies showed that conidial germination, hyphal growth, and pycnidia formation of the pathogen were positively correlated with elevated temperatures. Wild type olive “Delice”, “Edincik Su”, and “Memecik” seemed like the most resistant cultivars on which disease severity values were the lowest among 14 screened olive cultivars in two experimental orchards under natural conditions. Pathogenicity tests showed that “Gemlik” was the most susceptible cultivar showing the largest cankers and extensive scorch lesions. Isolates caused canker but not leaf scorch on O. europaea cultivars “Arbequina” “Halhalı”, “Manzanilla”, “Nizip Yağlık”, and “Saurani”. Neoscytalidium isolates are likely to have a negative impact on the health of diverse olive groves, which are primarily confined to Mediterranean-type climatic regions. These findings suggest an increased risk of infection in environments with increasing temperatures, as is common in the Southeast Anatolia Region of Turkey. The information gathered in this study will be used to examine the disease's epidemiology and establish disease control initiatives. This is also the first report of N. dimidiatum infecting O. europaea in the world.
The characterization of phenolic compounds via LC-ESI-MS/MS, antioxidant, enzyme inhibitory activities of Salvia absconditiflora, Salvia sclarea, and Salvia palaestina: A comparative analysis
(ScienceDirect, 2022) İzgi, Mehmet Necat; İzgi, Mehmet Necat; Cinar, Ahsen Sevda; Zengin, Gökhan; Yılmaz, Mustafa Abdullah
Abstract Salvia L. genus, one of the medicinal and aromatic plants belonging to the Lamiaceae family, is represented by many endemic species in temperate regions worldwide. The Salvia species have plenty of usages in traditional medicine, important pharmacological effects and economic value. In this context, this study was conducted to evaluate the chemical profile and potential bioactivity of ethyl acetate extracts from 3 different Salvia species; Salvia absconditiflora (Salvia cryptantha- an endemic species), S. sclarea and S. palaestina collected from different localities in Anatolia. Total phenolic-flavonoid contents, antioxidant properties and enzyme inhibition experiments were investigated in tested Salvia species. The phenolic profiles were also determined by liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). The analysis revealed the presence of phenolic compounds in ethyl acetate extracts such as cynaroside, rosmarinic acid, cosmosiin, luteolin, apigenin and acacetin. The highest level of phenolic components was detected in S. absconditiflora with 74.16 mg GAE/g. The best radical scavenging and reducing abilities were found in S. absconditiflora (DPPH: 251.39 mg TE/g; ABTS: 340.16 mg TE/g; CUPRAC: 496.20 mg TE/g; FRAP: 322.94 mg TE/g). Moreover, the inhibition of AChE, BChE, and α-amylase has also been studied. The most potent AChE inhibitory ability was recorded in S. sclarea (4.00 mg GALAE/g), followed by S. palaestina (3.38 mg GALAE/g) and S. absconditiflora (3.01 mg GALAE/g). The extracts exhibited similar amylase inhibitory effects. These results reveal that Salvia species [S. absconditiflora (S. cryptantha), S. sclarea and S. palaestina] confirming their potential can be rich sources of promising bioactive compounds as a starting point for further analysis. In addition, it is thought that this study may be the new report for the construction of helpful databases in terms of shedding light on future research and researchers with the valuable information obtained about these economically important Salvia species.
Classification and analysis of epileptic EEG recordings using convolutional neural network and class activation mapping
(2021) Yildiz, Abdulnasir; Zan, Hasan; Said, Sherif; Zan, Hasan
Electrical bio-signals have the potential to be used in different applications due to their hidden nature and their ability to facilitate liveness detection. This paper investigates the feasibility of using the Convolutional Neural Network (CNN) to classify and analyze electroencephalogram (EEG) data with their time-frequency representations and class activation mapping (CAM) to detect epilepsy disease. Several types of pre-trained CNNs are employed for a multi-class classification task (AlexNet, GoogLeNet, ResNet-18, and ResNet-50) and their results are compared. Also, a novel convolutional neural network architecture comprised of two horizontally concatenated GoogLeNets is proposed with two inputs scalograms and spectrogram of the eplictic EEG signal. Four segment lengths (4097, 2048, 1024, and 512 sampling points) with three time-frequency representations (short-time Fourier, Wavelet, and Hilbert-Huang transform) are statistically evaluated. The dataset used in this research is collected at the University of Bonn. The dataset is reorganized as normal, interictal, and ictal. The maximum achieved accuracies for 4097, 2048, 1024, and 512 sampling points are 100 %, 100 %, 100 %, and 99.5 % respectively. The CAM method is used to analyze discriminative regions of time-frequency representations of EEG segments and networks' decisions. This method showed CNN models used different time and frequency regions of input images for each class with correct and incorrect predictions.
Classification and analysis of epileptic EEG recordings using convolutional neural network and class activation mapping
(Biomedical Signal Processing and Control, 2021) Zan, Hasan; Yıldız, Abdulnasir; Said, Sherif; Zan, Hasan
Electrical bio-signals have the potential to be used in different applications due to their hidden nature and their ability to facilitate liveness detection. This paper investigates the feasibility of using the Convolutional Neural Network (CNN) to classify and analyze electroencephalogram (EEG) data with their time-frequency representations and class activation mapping (CAM) to detect epilepsy disease. Several types of pre-trained CNNs are employed for a multi-class classification task (AlexNet, GoogLeNet, ResNet-18, and ResNet-50) and their results are compared. Also, a novel convolutional neural network architecture comprised of two horizontally concatenated GoogLeNets is proposed with two inputs scalograms and spectrogram of the eplictic EEG signal. Four segment lengths (4097, 2048, 1024, and 512 sampling points) with three time-frequency representations (short-time Fourier, Wavelet, and Hilbert-Huang transform) are statistically evaluated. The dataset used in this research is collected at the University of Bonn. The dataset is reorganized as normal, interictal, and ictal. The maximum achieved accuracies for 4097, 2048, 1024, and 512 sampling points are 100 %, 100 %, 100 %, and 99.5 % respectively. The CAM method is used to analyze discriminative regions of time-frequency representations of EEG segments and networks' decisions. This method showed CNN models used different time and frequency regions of input images for each class with correct and incorrect predictions.
Crystal structure, IR and NMR spectra of (E)-2‑methoxy-4-(2-morpholinovinyl)phenol molecule and its DFT calculations
(ScienceDirect, 2023) Arici, K.; Altınoz, D.E.; Cavusoglu, M.; Diken, S.
In this study; molecular structure, IR and NMR spectroscopic properties of (E)-2–methoxy-4-(2- morpholinovinyl)phenol are investigated both experimentally and theoretically for the first time. The structural characterization of the compound was carried out by X-ray diffraction technique. The bond lengths, bond angles and dihedral angles of the compound were optimized using DFT with the B3LYP 6–311G(dp) base set. Using the optimized results, the infrared vibration frequencies and intensities of the compound as well as the 1H and 13C chemical shift values in the NMR spectrum were calculated with the B3LYP 6–311G(dp) basis set. In addition, the potential energy distributions (PED) of the structure were calculated to determine some characteristic vibrational frequencies of the compound. It is found that the theoretically calculated data support the experimental results. As a result, there is a good agreement between the experimentally determined structural parameters, vibrational frequencies, chemical shifts and theoretically calculated data of the compound.
Design and Implementation of a Maximum Power Point Tracking System for a Piezoelectric Wind Energy Harvester Generating High Harmonicity
(Sustainability, 2021) Özhan, Davut; Özhan, Davut; Bizon, Nicu; Lopez-Guede, Jose Manuel
In this work, a maximum power point tracking (MPPT) system for its application to a new piezoelectric wind energy harvester (PWEH) has been designed and implemented. The motivation for such MPPT unit comes from the power scales of the piezoelectric layers being in the order of μW. In addition, the output generates highly disturbed voltage waveforms with high total harmonic distortion (THD), thereby high THD values cause a certain power loss at the output of the PWEH system and an intense motivation is given to design and implement the system. The proposed MPPT system is widely used for many different harvesting studies, however, in this paper it has been used at the first time for such a distorted waveform to our best knowledge. The MPPT consists of a rectifier unit storing the rectified energy into a capacitor with a certain voltage called VOC (i.e., the open circuit voltage of the harvester), then a dc-dc converter is used with the help of the MPPT unit using the half of VOC as the critical value for the performance of the control. It has been demonstrated that the power loss is nearly half of the power for the MPPT-free system, the efficiency has been increased with a rate of 98% and power consumption is measured as low as 5.29 μW
Determination of trace Bi by ICP-OES after magnetic solid phase extraction with fullerene C-60 modified gamma-Fe2O3 superparamagnetic iron oxide nanoparticles
(ROYAL SOC CHEMISTRY, 2015) Kılınç, Ersin
In this research, a procedure for the preconcentration of Bi at trace levels in milk samples prior to inductively coupled plasma optical emission spectrometry (ICP-OES) is proposed. It is based on the batch magnetic solid phase extraction of Bi ions by fullerene C-60 modified maghemite (gamma-Fe2O3 superparamagnetic iron oxide nanoparticles - SPION) nanoparticles. The various factors that influence the preconcentration of Bi such as pH, the amount of magnetic nanoparticles, initial Bi concentration, sample volume and the effects of possible interfering ions were investigated. A linear analytical curve was achieved in the concentration range of 0.25-10 ng mL(-1) with a limit of detection of 0.06 ng mL(-1). The precision expressed as a RSD was 7.5% for a concentration of 0.25 ng mL(-1). The method was validated by the analysis of a standard reference material (NIST SRM 1643e). In addition, 96.6-98.7% recoveries were achieved for the spiked samples. Bi concentrations in ten milk samples were successfully determined after the developed method was applied. Bi content in the milk samples ranged from 6.5 to 14.3 ng mL(-1).
Development of Novel Fe3O4/AC@SiO2@1,4-DAAQ Magnetic Nanoparticles with Outstanding VOC Removal Capacity: Characterization, Optimization, Reusability, Kinetics, and Equilibrium Studies
(Industrial and Engineering Chemistry Research, 2021) Ece, Mehmet Şakir; Kutluay, Sinan; Şahin, Ömer; Horoz, Sabit
The adsorption of pollutants to the surface of adsorbents plays a critical role in the effectiveness of adsorption technology for air purification applications. Herein, novel magnetic nanoparticles functionalized with 1,4-diaminoanthraquinone (1,4-DAAQ), namely, Fe3O4/activated carbon (AC)@SiO2@1,4-DAAQ, were innovatively synthesized via co-precipitation and sol-gel techniques. After that, these nanoparticles were used for high-efficiency removal of volatile organic compounds (VOCs) (i.e., benzene and toluene). The synthesized nanoparticles were characterized by various techniques such as Fourier transform IR spectroscopy, thermogravimetric analysis/differential thermal analysis, scanning electron microscopy, and Brunauer-Emmett-Teller analysis. The dynamic adsorption process of VOCs was optimized based on operating parameters. The adsorption experiments revealed that Fe3O4/AC@SiO2@1,4-DAAQ showed exceptional performance for the removal of VOCs. It was observed that for benzene, Fe3O4, AC, Fe3O4/AC, Fe3O4/AC@SiO2, and Fe3O4/AC@SiO2@1,4-DAAQ exhibited dynamic adsorption capacities of 180.25, 228.87, 295.84, 382.10, and 1232.77 mg/g, respectively. Additionally, for toluene, they exhibited dynamic adsorption capacities of 191.08, 274.53, 310.26, 421.30, and 1352.16 mg/g, respectively. This indicated that the modification of 1,4-DAAQ could greatly enhance the dynamic adsorption capacity of Fe3O4/AC@SiO2@1,4-DAAQ for VOCs. In addition to the apparent adsorptive behavior in removing VOCs, Fe3O4/AC@SiO2@1,4-DAAQ exhibited high repeatability. After ten consecutive adsorption/desorption cycles, for benzene and toluene, Fe3O4/AC@SiO2@1,4-DAAQ retained 79.36 and 78.24% of its initial adsorption capacity, respectively. According to the characterization results, the average pore diameter for Fe3O4/AC@SiO2@1,4-DAAQ was determined to be 24.46 nm, indicating that they were in the mesopore range. The adsorption mechanism of the VOCs on Fe3O4/AC@SiO2@1,4-DAAQ was clarified by investigating the isotherm and kinetic criteria in detail. Isotherm models suggested that the adsorption process of VOCs is physical. Moreover, from the analysis of diffusion-based rate-limiting kinetic models, the findings reveal a combination of intraparticle diffusion as well as film diffusion throughout the adsorption process of VOCs. In addition, it was concluded from the analysis of the mass transfer model factors that global mass transfer and internal diffusion are more effective than film diffusion. The results demonstrated that the Fe3O4/AC@SiO2@1,4-DAAQ nanoadsorbent is a promising material for the effective removal of VOCs.
Effect of Different Harvest Dates to Essential Oil Components of Oil-Bearing Rose (Rosa damascena Mill.) in Mardin
(Taylor & Francis Online, 2022) İzgi, Mehmet Necat
Rosa damascena Mill. is the most prominent rose source for the manufacturing of rose oil, rose water, concrete, and absolute from flower petals. The purpose of this study was to see how different harvest dates affected the essential oil components and rates obtained from flowers in oil rose planted for the first time in Turkey's Mardin Province. Blossoms were taken five times with three replications, at around one-week intervals, from a garden in the Yaylabaşı district at 6:00 a.m. (4, 11, 18, 24, and 31 May). Animal manure and leonardite were used instead of chemical fertilizers for plants. No chemical pesticides have been applied. Essential oil components of the oils obtained by a water distillation system (Clevenger apparatus) were determined by GC/MS. The citronellol ratio, which is the most important component for the quality of the essential oil in the plant, was found to be highest (35.90%) in the first harvest period and lowest in the last harvest period (17.57%). Geraniol levels decreased in the first three harvests, with the greatest level (27.16%) coming in the last harvest. The percentages of citronellol/geraniol varied between 1.51 and 0.65 depending on the different harvest dates. Nerol rates peaked at 15.44% on the first harvest, progressively declined until the fourth harvest (7.92%), then slightly increased on the fifth harvest. Nonadecane continuously increased from the first to the last harvest (4.10%-11.66%). No methyl eugenol was detected. Mardin is a promising location for oil-bearing rose cultivation, given the essential oil components and their ratios.
The effect of mindfulness on medication adherence in individuals diagnosed with Schizophrenia: A cross-sectional study
(PubMed, 2022) Yaşar Can, Sevinç; Dilmen Bayar, Behiye; Erkan, Fatma Melike; Dilmen Bayar, Behiye; Yaşar Can, Sevinç; Şanlı, Mehmet Emin
Objective: The aim of this study is to determine the effect of mindfulness on medication adherence in individuals diagnosed with schizophrenia. Materials and methods: This cross-sectional study was conducted with 147 individuals diagnosed with schizophrenia. The data were collected using the Descriptive Characteristics Form, Morisky Medication Adherence Scale, and Mindful Attention Awareness Scale. Results: A strong positive correlation was determined between the mindfulness level and medication adherence of the individuals diagnosed with schizophrenia. Furthermore, mindfulness of the individuals diagnosed with schizophrenia was statistically significant in explaining medication adherence (p < 0.05) and mindfulness predicted medication adherence by 64%. Conclusion: It was observed that the individuals' mindfulness level was low and half of the participants had low medication adherence. Moreover, as their mindfulness level increased, so did their medication adherence.
Effects of different nitrogen doses on thymoquinone and fatty acid composition in seed oil of black cumin (Nigella sativa L.)
(AOCS AND WILEY, 2021) İzgi, Mehmet Necat
Black cumin,Nigella sativaL. belonging to Ranunculaceae, is a valuablemedicinal plant because of the curative effects of seed and oil. Despite studieson black cumin, there are few studies on the effects of nitrogen on fatty acidcomposition, no records on thymoquinone yield, and the thymoquinone contentof the seed oil. In this study, the effects of different nitrogen doses on the seedoil yield, thymoquinone ratio/yield, and fatty acid compositions were investi-gated. Field studies with nitrogen doses (0, 40, 60, 80, and 100 kg/ha) werestudied using the randomized plot design. As a result of the research, nitrogendoses significantly affected the oil and thymoquinone yields while insignificanton the thymoquinone ratio. The highest oil yield with 501.6 kg/ha was obtainedfrom 80 kg/ha of the nitrogen doses, while the highest thymoquinone yield with10.24 kg/ha was obtained from the control plots. Thymoquinone yields were inthe same statistical group with other nitrogen doses, except for means of100 kg/ha nitrogen doses. Linoleic, oleic, and palmitic acids were major fattyacids of black cumin, and their variations were insignificant in nitrogen applica-tions. The values of butyric, capric, and linolenic acids varied significantly in dif-ferent nitrogen doses. As a result of the study, it was determined that thehighest oil yield was obtained from 80 kg/ha nitrogen application. It has beendetermined that nitrogen doses above 60–80 kg/ha should be avoided for theseed oil and thymoquinone yields in black cumin.
Efficacy of antimicrobial peptide LL-37 against biofilm forming Staphylococcus aureus strains obtained from chronic wound infections
(Elsevier, 2022) Demir, Cemil; Yigin, Akin; Demir, Cemil
The antimicrobial peptide LL-37 showed inhibitory effects against Staphylococcus aureus strains, which often responsible for wound infections. Understanding the molecular mechanisms of biofilm-containing wound infections is important. Thus, this study aimed to investigate both the antimicrobial and biofilm efficacy of LL-37 against biofilm-positive methicillin-susceptible S. aureus (MSSA) strains and biofilm-positive methicillin-resistant S. aureus (MRSA) strains obtained from chronic wound infections and its effect on different quorum sensing and virulence genes at suboptimal concentrations. Fifteen biofilm-forming MRSA and 15 biofilm-forming MSSA strains were included in this study. The minimum inhibitory concentration (MIC) values and biofilm formation were tested by microdilution methods. Real-time PCR was performed to determine gene expression levels. MIC values for LL-37 were 89.6 mg/L and 132.3 mg/L for MSSA and MRSA strains, respectively. No statistically significant difference was found between MRSA and MSSA strains in terms of the effect of LL-37 on biofilm formation. A statistically significant difference was found between MRSA and MSSA strains for atlA, RNAIII, and agrA gene expression levels following exposure to a suboptimal concentration of LL-37. Ultimately, the required LL-37 antimicrobial concentration was quite high; however, LL-37 antibiofilm concentration may be acceptable for use in humans against biofilm-forming MRSA and MSSA strains. This is the first study to investigate to effect of a suboptimal LL-37 concentration on gene expression levels of biofilm-forming MSSA and MRSA strains. LL-37 affected quorum sensing and biofilm producing mechanisms, even at suboptimal MIC concentrations.
Enantioseparation of Mandelic Acid Enantiomers With Magnetic Nano-Sorbent Modified by a Chiral Selector
(Wiley Online Library, 2015) Tarhan; Tural; Tural; Topal;, Tuba; Bilsen; Servet; Giray
In this study,R(+)-α-methylbenzylamine-modified magnetic chiral sorbent wassynthesized and assessed as a new enantioselective solid phase sorbent for separation ofmandelic acid enantiomers from aqueous solutions. The chemical structures and magnetic prop-erties of the new sorbent were characterized by vibrating sample magnetometry, transmissionelectron microscopy, Fourier transform infrared spectroscopy, and dynamic light scattering.The effects of different variables such as the initial concentration of racemic mandelic acid, dos-age of sorbent, and contact time upon sorption characteristics of mandelic acid enantiomers onmagnetic chiral sorbent were investigated. The sorption of mandelic acid enantiomers followeda pseudo-second-order reaction and equilibrium experiments were wellfitted to a Langmuir iso-therm model. The maximum adsorption capacity of racemic mandelic acid on to the magneticchiral sorbent was found to be 405 mg g 1. The magnetic chiral sorbent has a greater affinityfor (S)-(+)-mandelic acid compared to(R)-( )-mandelic acid. The optimum resolution wasachieved with 10 mL 30 mM of racemic mandelic acid and 110 mg of magnetic chiral sorbent.The best percent enantiomeric excess values (up to 64%) were obtained by use of a chiralpakAD-H column.
Endosulfan Elimination Using Amine-Modified Magnetic Diatomite as an Adsorbent
(Frontiers in Chemistry, 2022) Alacabey, İhsan
Pesticides are among the most dangerous developing toxins since they are very hazardous to the environment and threaten human health. In this study, researchers successfully manufactured surface-modified magnetic diatomite (m-DE-APTES) and used them as a sorbent to extract endosulfan from an aqueous solution. There is no other study like it in the scholarly literature, and the results are astounding. Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), energy dispersive X-ray (EDX), electron spin resonance (ESR), and surface area measurements were used to analyze magnetic diatomite particles with surface modification. According to the analysis results, magnetic diatomite has a wide surface area and a porous structure. Furthermore, m-DE-APTES has a higher endosulfan adsorption capacity (97.2 mg g-1) than raw diatomite (DE) (16.6 mg g-1). Adsorption statistics agree with Langmuir adsorption isotherm (R 2 = 0.9905), and the adsorption occurred spontaneously at -2.576 kj mol-1 in terms of ΔGo. Finally, m-DE-APTES are a viable alternative adsorbent for removing pesticides from aqueous solutions.
Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals
(MDPI, 2019) Türk, Ömer; Özerdem, Mehmet Siraç; Türk, Ömer
The studies implemented with Electroencephalogram (EEG) signals are progressing very rapidly and brain computer interfaces (BCI) and disease determinations are carried out at certain success rates thanks to new methods developed in this field. The effective use of these signals, especially in disease detection, is very important in terms of both time and cost. Currently, in general, EEG studies are used in addition to conventional methods as well as deep learning networks that have recently achieved great success. The most important reason for this is that in conventional methods, increasing classification accuracy is based on too many human efforts as EEG is being processed, obtaining the features is the most important step. This stage is based on both the time-consuming and the investigation of many feature methods. Therefore, there is a need for methods that do not require human effort in this area and can learn the features themselves. Based on that, two-dimensional (2D) frequency-time scalograms were obtained in this study by applying Continuous Wavelet Transform to EEG records containing five different classes. Convolutional Neural Network structure was used to learn the properties of these scalogram images and the classification performance of the structure was compared with the studies in the literature. In order to compare the performance of the proposed method, the data set of the University of Bonn was used. The data set consists of five EEG records containing healthy and epilepsy disease which are labeled as A, B, C, D, and E. In the study, A-E and B-E data sets were classified as 99.50%, A-D and B-D data sets were classified as 100% in binary classifications, A-D-E data sets were 99.00% in triple classification, A-C-D-E data sets were 90.50%, B-C-D-E data sets were 91.50% in quaternary classification, and A-B-C-D-E data sets were in the fifth class classification with an accuracy of 93.60%.

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