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Host-Induced Gene Silencing: Approaches in Plant Disease Management
No full textCrops exposed to the damage of various biotic and abiotic sources result in reduced yields and cause economic losses. There are prominent strategies such as breeding for improved resistance, the application of pesticides, and cultural practices that are available to mitigate damage resulting from abiotic/biotic sources. Genetic engineering suggests a new technique “host-induced gene silencing (HIGS)” which is a promising approach to the control of different negative biotic to abiotic stress factors affecting the plants. HIGS is a creative idea using RNA interference (RNAi) technology as an efficient strategy to control plant pathogens. These techniques involve shutting off one or few of the important genes playing role in pathogenesis that are related to pathogen growth, development, and /or the host genes responsible for invasion. In general, HIGS utilizes RNAi molecules generated by the plant, which then target the key genes of pathogens resulting in resistance formation. RNAi technology suggests a new insight by using small non-coding RNA sequences able to switch-off gene expression (blocking gene function) relied on introducing the sequence-specific technique. Inserting short sequences of RNA, which partly match the target gene’s sequence, contributes to the silence of the target-oriented proteins on the plant. Therefore, it suppresses a specific gene, eliminating or enhancing certain plant traits for the purpose of different know-how agro-biotechnological aspects. RNAi causes biochemical or phenotypic differentiation for generating new quality traits in the organisms. It has an important potential paves way for the control of pests and diseases. In this chapter, we will highlight the application of RNAi in the plant system to explore the novel traits for the better management of current problems related to plant cultivation involving abiotic stress, biotic stress, and plant disease management
A comparative study of magnetic, and magnetocaloric properties of different transition metal-doped La0.67Sr0.33AO3 (A: Mn, Co, Cr, and Fe) samples
No full textIn the present work, structural, magnetic, and magnetocaloric properties of La0.67Sr0.33AO3 (A: Mn, Co, Cr and Fe) samples were examined. XRD analyses revealed that all samples produced by the sol–gel method and subjected to the same grinding and heat treatment steps crystallized in the perovskite crystal structure. In order to examine the magnetic behavior of the samples, temperature-dependent magnetization (M(T)) measurements were carried out. It was found that La0.67Sr0.33MnO3, which has the highest magnetization value, has the highest Curie temperature (T C = 368.2 K). In order to determine the magnetic phase transition order and calculate magnetic entropy change (- Δ SM) value, field-dependent magnetization (M(H)) measurements in the transition temperature region were carried at zero field-cooled (ZFC) and field-cooled (FC) processes. The maximum magnetic entropy change (-ΔSMmax) of La0.67Sr0.33MnO3 sample was calculated as 1.5 Jkg−1 K−1 under 1 T magnetic field change. La0.67Sr0.33CoO3 sample, in which antiferromagnetic (AFM) and ferromagnetic (FM) interactions coexist, exhibits a paramagnetic (PM)-FM phase transition at 244.1 K. At 1 T magnetic field change, the (-ΔSMmax) was found to be 0.145 Jkg−1 K−1 for this sample. La0.67Sr0.33CrO3 and La0.67Sr0.33FeO3 samples showed weak FM properties due to the different magnetic interactions originating from the ions in the B-sites of the samples. The magnetic phase transition is second-order for all samples
The Diagnosis of Diabetes Mellitus with Boosting Methods
No full textIn addition to the damage, it can cause to various organs, diabetes mellitus (DM) also increases a person's risk of developing other serious health conditions. These can include heart disease, stroke, and nerve damage. Furthermore, DM is a leading cause of blindness and kidney failure. However, with proper management and treatment, many of the complications of DM can be prevented or delayed. Thus, early detection and treatment of DM are crucial. With the advancement of machine learning technology, new opportunities have emerged in the field of medicine. Many disease detection research relies on machine learning techniques, with a particular emphasis on boosting algorithms. Boosting algorithms are used to improve the accuracy of predictions made by other weak models such as decision trees. Using knowledge discovery methods, boosting algorithms are examined and compared on a diabetes dataset in this study. The performance of the boosting algorithms is evaluated by generating ROC curves and comparing average accuracy values. When the study's results were evaluated in terms of precision, Gradient Boosting, AdaBoost, CatBoost, LightGBM, and XGBoost algorithms gives success rates of %85, %83, %88, %86, and %87, respectively
The native and metastable defects and their joint density of states in hydrogenated amorphous silicon obtained from the improved dual beam photoconductivity method
No full textIn this study, undoped hydrogenated amorphous silicon (a-Si:H) thin films deposited under moderate dilution ratios of silane by radio frequency plasma-enhanced chemical vapor deposition (RF-PECVD) have been investigated using steady-state photoconductivity and improved dual beam photoconductivity (DBP) methods to identify changes in multiple gap states in annealed and light-soaked states. Four different gap states were identified in annealed state named as A, B, C, and X states. The peak energy positions of these Gaussian distributions are consistent with those recently identified by Fourier transform photocurrent spectroscopy (FTPS). After in situ light soaking, their density increases with different rates as peak energy positions and half-widths remain unaffected. The electron-occupied A and B states located below the dark Fermi level and their density and ratios in the annealed and light-soaked states correlate well with those defects detected by time-domain pulsed electron paramagnetic resonance (EPR) experiments. The A, B, and X states located closer to the middle of the bandgap anneal out at room temperature in dark and define the "fast"states. However, the C states show no sign of room temperature annealing such that they must define the "slow"states in undoped a-Si:H. The results found in this study indicate that the anisotropic disordered network is a more appropriate model than previously proposed defect models based on the continuous random network to define the nanostructure of undoped a-Si:H, where multiple defects, D0 and non-D0 defects, can be identified by using the improved DBP method
A novel model-based technique to improve design processes for microstrip antennas
No full textThe present work aims to prove the concept of a novel approach to designing microstrip antennas with desired radiation patterns without time-consuming trials and simulations. While it is pretty straightforward to design a microstrip antenna operating at a specific frequency, it requires repetitive trials to design an antenna with a specific radiation pattern. For this purpose, a unique model-based design technique is applied using the cavity model expressions in the present work. The behaviors and mutual influences of various parameters in the intended design problem are described by two graph models. The “employer model” oversees the limitations and freedoms of the antenna's parameters, while the “employee model” uses graph theory and machine learning to define the relationships between graph nodes. These two models have a dynamic structure that changes every calculation step to minimize design error. After two models are constructed, these models suggest physical parameter values according to the cavity model for the desired antenna radiation pattern. Then, the results for examples are demonstrated, and the validity of the proposed technique is proven. Finally, the developability of the method and its further works are discusse
External cost of pollutant emissions in Turkey
No full textExternal costs that occur in the use of energy resources are one of the most important criteria in determining energy and environmental policies. For this purpose, it is important to know the external costs of any energy sources. The purpose of this study is to calculate external costs due to air pollutants in Turkey for the years 2000 and 2019. The air pollutants namely ammonia, nonmethane volatile organic compounds, nitrogen oxides, particulate matter, and sulphur dioxide have been taken into consideration in the evaluation for the impact categories-human health damage, loss of biodiversity, crop losses, and buildings damaged. These pollutant emissions data were obtained from the European Environmental Agency database. Then, these pollutant emissions data were used in the monetization calculations for the years 2000 and 2019. According to the evaluation, air pollutant emissions' total external costs in Turkey were 25135,72 Million Euros and 24654,42 Million Euros for the years 2000 and 2019 respectively. © Published under licence by IOP Publishing Ltd
Copper based metal organic framework decorated with gold nanoparticles as a new electrochemical sensor material for the detection of L-Cysteine in milk samples
No full textA facile electrochemical sensor based on carbon felt electrode (CFE) modified with gold nanoparticles decorated copper based metal organic framework (AuNPs@Cu-MOF) was achieved for the electrochemical sensing of L-Cysteine (L-Cys). For this purpose, AuNPs@Cu-MOF was synthesized and characterized. The electrochemical behaviors of L-Cys at plain and modified CFEs were investigated via cyclic voltammetry (CV). According CV results, AuNPs@Cu-MOF structure showed a catalytic effect on the oxidation of L-Cys as well as increasing the active electrode surface area by 206% compared to bare CFE. In addition, the pH effect on electrochemical determination of L-Cys at AuNPs@Cu-MOF/CFE was widely examined, and it was determined that the best oxidation peak current of L-Cys was obtained in pH 5 acetate buffer. Moreover, a linear detection range of 30–400 µM for L-Cys with a limit of detection value of 2.21 µM (n = 3) was achieved with the proposed electrochemical sensor. The developed L-Cys sensor was also applied for L-Cys detection in various milk samples and acceptable recovery values were obtained ranging from 100.05 to 108.45%
Validation of a blood gas device for ionized calcium analysis in Holstein cows
No full textBackground: Accurate analysis of ionized calcium (iCa) is critical for the detection of hypocalcemia or subclinical hypocalcemia. The Edan i15 Vet (EDAN) blood gas device has not been validated for iCa in dairy cows. Objectives: We aimed to validate the EDAN blood gas device against the Gem Premier 3000 (GEM) analyzer by measuring iCa concentrations and evaluating the ability of these measurements, compared with serum total calcium (TCa) concentrations, to diagnose subclinical hypocalcemia. Methods: iCa concentrations were measured with the EDAN and GEM devices, and serum TCa concentrations were measured with a wet biochemistry method with blood from 125 lactating Holstein cows between calving to day 27 postpartum. Results: Bland-Altman plots showed a mean and total bias of 0.05 and 0.24 mmol/L for the EDAN device, respectively. The intercept did not include zero, but the slope included 1.0 in the Passing-Bablok regression. The sensitivity and specificity (Se/Sp) of the EDAN device were 93/94%, 93/90%, 91/93%, and 85/95% for iCa cut-off values of <1.00, 1.05, 1.10, and 1.15 mmol/L, respectively, as determined with the GEM device. The Se/Sp were 57/82% and 72/80% for EDAN and 57/80% and 72/79% for GEM at serum TCa cut-off points <2.15 and <2.00 mmol/L, respectively. The average iCa concentrations analyzed with the GEM and EDAN devices were 1.04 ± 0.18 and 1.09 ± 0.17, respectively. Conclusions: The EDAN device did not have satisfactory agreement with GEM and could not be used interchangeably, but it had satisfactory Se/Sp to diagnose subclinical hypocalcemia compared with the GEM-derived iCa cut-off points. Serum TCa concentration cut-off values were not suitable for diagnosing subclinical hypocalcemia because of unsatisfactory Se/Sp compared with iCa concentrations analyzed by the GEM and EDAN devices. The iCa values analyzed using the EDAN and GEM devices were consistent with previously reported data
How Did the Updated 2019 European Society of Cardiology/European Atherosclerosis Society Risk Categorization for Patients with Diabetes Affect the Risk Perception and Lipid Goals? A Simulated Analysis of Real-life Data from EPHESUS Study
No full textBackground: The recent 2019 European Society of Cardiology/European Atherosclerosis Society practice guidelines introduced a new risk categorization for patients with diabetes. We aimed to compare the implications of the 2016 and 2019 European Society of Cardiology/European Atherosclerosis Society guidelines with regard to the lipid-lowering treatment use, low-density lipoprotein cholesterol goal attainment rates, and the estimated proportion of patients who would be at goal in an ideal setting. Methods: Patients with diabetes were classified into 4 risk categories according to 2019 European Society of Cardiology/European Atherosclerosis Society dyslipidemia guidelines from the database of EPHESUS (cross-sectional, observational, countrywide registry of cardiology outpatient clinics) study. The use of lipid-lowering treatment and low-density lipoprotein cholesterol goal attainment rates were then compared according to previous and new guidelines. Results: This analysis included a total of 873 diabetic adults. Half of the study population (53.8%) were on lipid-lowering treatment and almost one-fifth (19.1%) were on high-intensity statins. While low-density lipoprotein cholesterol goal was achieved in 19.5% and 7.5% of patients, 87.4% and 69.6% would be on target if their lipid-lowering treatment was intensified according to 2016 and 2019 European Society of Cardiology/European Atherosclerosis Society lipid guidelines, respectively. The new target <55 mg/dL could only be achieved in 2.2% and 8.1% of very high-risk primary prevention and secondary prevention patients, respectively. Conclusion: The control of dyslipidemia was extremely poor among patients with diabetes. The use of lipid-lowering treatment was not at the desired level, and high-intensity lipid-lowering treatment use was even lower. Our simulation model showed that the high-dose statin plus ezetimibe therapy would improve goal attainment; however, it would not be possible to get goals with this treatment in more than one-third of the patients
The relationship between coronavirus disease-2019-positive patients and plasma interleukins and transforming growth factor-β levels
No full textObjective: The aim of this study is to reveal the relationship between the cytokine plasma levels and symptoms of coronavirus disease-2019 (COVID-19)-positive patients, which is characterized by serious respiratory syndromes. Materials and Methods: Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2)-positive patients were evaluated in four groups. Group 1 patients had no symptoms. Group 2 patients were mildly symptomatic. Group 3 patients had multiple symptoms. Group 4 patients had all symptoms of acute respiratory distress syndrome. Analysis of interleukin (IL)-17A, transforming growth factor-β1 (TGF-β1), and IL-6 concentrations in plasma samples taken from patients were examined by enzyme-linked immunosorbent assay method. Results: IL-17A levels were increased in parallel with the clinical condition in all patients. TGF-β1 was only observed in patients in Groups 3 and 4, and IL-6 was only observed in Group 4 patients. Conclusion: It is known that many cytokines are involved in the development of different viral infections and viral invasion always triggers an inflammatory response. The profile of inflammatory markers may be used to classify COVID-19 patients. In conclusion of this study, it is suggested that the level of cytokines which is changed according to the patient's clinical status should be used to evaluate the response of SARS-CoV-2 treatment. IL-17A, TGF-β1, and IL-6 concentrations in plasma levels could be good prognostic indicators of COVID-19