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Iran is one of the main diversity centers and origins of pistachios in the world. Pistachio cultivation spread first within the ancient Persian Empire and then moved gradually westward. Knowledge of the genetic relationships among wild and cultivated varieties of pistachio is important for the efficient utilization of the available germplasm resources. Three molecular marker strategies, namely, inter-simple sequence repeat (ISSR), inter-retrotransposon amplified polymorphism (IRAP), and retrotransposon microsatellite amplified polymorphism (REMAP), were used to study the genetic relationships among 35 pistachio accessions including 15 wild-type genotypes of Pistacia vera and 20 important cultivars from Iran. According to the results, high levels of polymorphism were observed for all three marker systems. REMAP and IRAP techniques had the higher mean values of genetic relationships parameters than ISSR technique. The results from this study showed that the 5′LTR2, Sukkula, Sukkula + UBC855, and 5′LTR2 + UBC811 primers were the most informative and could be used to evaluate the genetic relationships of pistachios accessions. Cluster analysis using the unweighted pair group method with arithmetic mean (UPGMA) properly separated the accessions and divided them into four main groups. The presence of most cultivated genotypes in a group indicates genetic erosion of cultivated pistachio in Iran. Wild-type genotypes of P. vera are located in different clusters indicating the high diversity of the genotypes. The results provide useful genetic information about wild pistachios in northeastern of Iran and indicate that the use of wild pistachios in breeding programs could be useful for generating new genotypes with interesting characters.

Background: The recent novel coronavirus disease 2019 (COVID-19) pandemic has underlined the importance of risk score models in public health emergencies. This study aimed to develop a risk prediction score to identify high-risk hospitalized patients for disease progression on admission. Methods: This prospective cohort study included 171 COVID-19 patients, identified through the reverse transcription polymerase chain reaction test, admitted to Bohlool Hospital in Gonabad City, Iran, between April 4 and June 5, 2021. The patients’ demographic, clinical, and laboratory data were collected upon admission, and clinical outcomes were monitored until the end of the study. The discovery dataset (80% of the data) was used to develop the risk score model based on clinical and laboratory features and patient characteristics to predict COVID-19 progression. An additive risk score model was developed based on the regression coefficients of the significant variables in a multiple logistic regression model. The performance of the risk score model was evaluated on the validation dataset (20% of the data) using the receiver operating characteristic (ROC) curve. Statistical analyses were performed with SPSS software, version 21. Results: The Mean±SD for age of participants was 59.54±20.52 years, and 48.6% were male. Most patients (82.5%) fully recovered or showed improvement, while 5.2% experienced disease progression and 12.3% died. Three variables, interleukin-6, neutrophil-to-lymphocyte ratio, and lung involvement, were found to be significant in predicting risk, with a good discriminatory ability, having an area under the ROC curve of 0.970 (95% CI, 0.935%, 1.00%) in the discovery set and 0.973 (95% CI, 0.923%, 1.00%) in the validation set. Conclusion: The developed risk score model in this study can be used as a clinical diagnostic tool to identify COVID-19 patients at higher risk of disease progression and aid in informed decision-making and resource utilization in similar situations, such as respiratory disease outbreaks in the post-corona era.

Introduction and GoalAmong natural disasters, flood is undoubtedly the most catastrophic hazard in the world. One of the basic strategies for reducing the damage caused by floods is to prepare a flood sensitivity map. Spatial prediction of the flooding probability using models created from spatial and historical data, which ultimately leads to the preparation of flood sensitivity maps is an appropriate solution for land management planners in different areas to prevent the occurrence of this phenomenon. In this research, in order to determine flood-prone areas, the hybrid model of adaptive neural and fuzzy inference and the metaexploratory optimization algorithm of imperial competition (ANFIS-ICA) and the hybrid model of adaptive neural and fuzzy inference and the metaexploratory optimization algorithm of particle swarm (ANFIS-PSO) are used.Materials and MethodsThe Zarine River watershed has an area of 4485 km2 and is located in the northwest of Kurdistan province between the longitude of 45°48ʹ30ʺand 46°48ʹ20ʺ east and the latitude of 35°42ʹ20ʺ and 36°23ʹ15ʺ north. The climate of the region is humid and the average annual rainfall is 480 mm. Locations of flood events were randomly divided into two groups: training (70%) and validation (30%). Various environmental factors (height, direction, slope, surface curvature, land use, lithology, rainfall, flow power index, distance from river and topographic wetness index) were selected as independent variables in the modeling and their digital layers were prepared. The ANFIS-ICA and ANFIS-PSO models were used in this research and their prediction results were evaluated based on the criterion (AUC) and the true skill statistic (TSS).Results and DiscussionOn the basis of these findings, in the validation stage, the model (ANFIS-PSO) with an AUC of 0.98 and a true skill statistic (TSS) of 0.89 had the highest accuracy. The results also showed that the factor of distance from the stream was identified as the most important environmental factor. In addition, ground slope and TWI were ranked second and third in importance, respectively.Conclusion and SuggestionsBased on the results, the hybridization approach, which combines machine learning models and meta-exploratory optimization algorithms, improves the learning power as well as the predictive power of the model. The results of this research showed that the distance from the stream and the slope of the land are the most important factors affecting flooding. Based on the results and analysis, it can be concluded that machine learning models have a high capability for predicting flood potential. The flood potential maps prepared in this research can be very useful for managers and experts and can be used in planning flood prevention measures. Directing flood control facilities and measures in situations with a high flood potential will improve flood management from an economic and technical point of view.

Abstract Z‐source‐based half‐bridge inverters are a type of Z‐source inverters whose outer stage is a half‐bridge inverter. This paper presents a generalized structure from which 16 configurations can be derived. Once this topology has been introduced, one of the derived topologies, out of 16, is analyzed in detail based on the appropriate control method. According to these analysis results, this topology's boost‐factor and some equations to design passive and active devices are obtained. Next, it will be tried to derive the equations for each topology based on the obtained results for one of the configurations. In the next step, the topology that has been analyzed is considered to evaluate the efficiency of this topology using the power losses study. Hereafter, the generalized configuration's merits and downsides are studied by comparing it with other similar topologies. Finally, the experiment in the final section checks the operation and validity of the obtained results.

Abstract Background Septic arthritis is an important differential diagnosis of hip joint pain. Joint aspiration analysis is a necessary diagnostic measure for septic arthritis. In order to reduce the need for joint aspiration, we compared the combination of ultrasound findings and laboratory findings to separate septic arthritis from reactive arthritis. Methods Children aged

Abstract Background Arrhythmia symptoms are frequent complaints in children and often require a pediatric cardiology evaluation. Data regarding the clinical utility of wearable technologies are limited in children. We hypothesize that an Apple Watch can capture arrhythmias in children. Methods We present an analysis of patients ≤18 years-of-age who had signs of an arrhythmia documented by an Apple Watch. We include patients evaluated at our center over a 4-year-period and highlight those receiving a formal arrhythmia diagnosis. We evaluate the role of the Apple Watch in arrhythmia diagnosis, the results of other ambulatory cardiac monitoring studies, and findings of any EP studies. Results We identify 145 electronic-medical-record identifications of Apple Watch, and find arrhythmias confirmed in 41 patients (28%) [mean age 13.8 ± 3.2 years]. The arrythmias include: 36 SVT (88%), 3 VT (7%), 1 heart block (2.5%) and wide 1 complex tachycardia (2.5%). We show that invasive EP study confirmed diagnosis in 34 of the 36 patients (94%) with SVT (2 non-inducible). We find that the Apple Watch helped prompt a workup resulting in a new arrhythmia diagnosis for 29 patients (71%). We note traditional ambulatory cardiac monitors were worn by 35 patients (85%), which did not detect arrhythmias in 10 patients (29%). In 73 patients who used an Apple Watch for recreational or self-directed heart rate monitoring, 18 (25%) sought care due to device findings without any arrhythmias identified. Conclusion We demonstrate that the Apple Watch can record arrhythmia events in children, including events not identified on traditionally used ambulatory monitors.

BACKGROUND: Sore throat and hoarseness are two common complications of intubation in patients with general anesthesia. This research aimed to compare the effect of different endotracheal cuff pressures on sore throat and hoarseness after general anesthesia. MATERIALS AND METHODS: The present double-blinded clinical trial was conducted on 45 patients who are candidates for surgery with general anesthesia in autumn and winter 2021. The participants were divided into three groups of 15 through a permuted block randomization. The 20–24 cm H2O (level of pressure) group was labeled as A, and the 25–29 cm H2O group B and the 30–34 cm H2O group was known as group C. All the patients were operated. The endotracheal intubation was done for men with tubes #8-8.5 and for women with tubes 7-7.5. The presence and severity sore throat and the hoarseness after operation were checked by a nurse after recovery. Data were recorded in a researcher-made checklist. The data were analyzed in SPSS 19. P .05). CONCLUSION: According to the results of the present study, despite the fact that the range of 20 to 34 cm of water is a safe and risk-free range in terms of causing sore throat and hoarseness, and there was no difference between the pressures in the three groups, but at higher pressure (groups 2 and 3), the amount of sore throat and hoarseness was more and there was a statistically significant difference at different times within group. Therefore, as much as possible, the amount of pressure should be adjusted according to the need and avoid applying excess pressure.

Background: The development of green chemistry methods involving plant-based nanoparticle synthesis presents an affordable and eco-friendly approach for wastewater treatment and color removal. This study aimed to synthesize ZnO nanoparticles using the sol-gel method with Salvia officinalis and Abelmoschus esculentus plants, examining their photocatalytic efficiency for organic dye removal. Methods: To compare the properties of ZnO nanoparticles, another type of ZnO-NPs was synthesized using the co-precipitation method. The characterization of synthesized nanoparticles was performed using thermogravimetric analysis (TGA-DTG), X-ray diffraction (XRD), Dynamic Light Scattering (DLS), Zeta potential (ZP), field emission scanning electron microscopy (FE-SEM), Energy Dispersive X-ray (EDX), Fourier transform infrared spectroscopy (FTIR), and UV–Vis spectrophotometry. Results: Based on XRD results, the average crystalline size of nanoparticles was calculated using the Debye-Scherer equation for synthesized nanoparticles using the S. officinalis at 22.99 nm and for the A. esculentus at 29.79 nm, and for the co-precipitation method at 18.83 nm. The FE-SEM images showed spherical ZnO nanoparticles. Photocatalytic properties of ZnO-NPs were investigated for remove of methylene blue organic dye in the presence of UV light. The pH 10 was identified as the best pH, which had the highest percentage of color degradation. All three types of nanoparticles were tested by up to 360 min to optimize the dyeing time. For A. esculentus, the highest percentage of color removal occurred in the first 90 min (41.0 %), for S. officinalis nanoparticles between 75 and 90 min (86.9 %), and for chemically synthesized nanoparticles between 30 and 45 min (100 %). Conclusions: In conclusion, the best MB dye degradation capacity belonged to co-precipitation ZnO nanoparticles followed by S. officinalis and A. esculentus nanoparticles.

Abstract Improving the visual explainability of medical artificial intelligence (AI) is fundamental to enabling reliable and transparent clinical decision-making. Medical image analysis systems are becoming increasingly prominent in the clinical setting as algorithms are learning to accurately classify diseases in various imaging modalities. Saliency heat-maps are commonly leveraged in the clinical setting and allow clinicians to visually interpret regions of an image that the model is focusing on. However, studies have shown that in certain scenarios, models do not attend to clinically significant regions of an image and perform inference using insignificant visual features. Here, we discuss the importance of focusing on visual explainability and an effective strategy that has the potential to improve a model's ability to focus more on clinically relevant regions of a given medical image using attention mechanisms.

As one of the largest super-saline lakes in the world, Lake Urmia in northwestern Iran has been facing severe drying in recent years. Drought and rapid expansion of agricultural activities are considered to be the main driving factors in the shrinking of the lake. To address this problem, an analysis of the spatiotemporal dynamics of land use/land cover (LULC) is important. This research implemented a multi-source satellite image analysis through support vector machine (SVM) for mapping LULC distributions for the years 2000, 2010, and 2020. Cellular automata (CA)–Markov was prepared for modeling the future landscape changes for 2030 and 2040. In the last step, the water requirement of agriculture in the catchment area of the Urmia Lake was simulated through the NETWAT model. Through the employed future LULC modeling, it was found that the areas covered by irrigated agriculture and gardens will grow from 1,450 and 395 km2 to 3,600 and 1,650 km2 in 2040, respectively, as deduced from the changes that occurred from 2000 to 2020. This will increase the water requirement of agriculture from 1.5 billion cubic metres in 2000 to more than 4.1 billion cubic metres in 2040. HIGHLIGHTS LULC modeling is implemented through a CA–Markov model to predict future LULC for 2030 and 2040.; The NETWAT model was used to simulate the water requirement of agriculture.; A significant increase in the areas covered by agricultural activities were identified.; A 200% increase in water requirement of agriculture was observed in the period of 40 years (2000–2040).;

Abstract With the rising demand for new cultivars of carnation, efficient transformation protocols are needed to enable the bioengineering of new traits. Here, we established a novel and efficient Agrobacterium-mediated transformation system using callus as the target explant for four commercial carnation cultivars. Leaf-derived calli of all cultivars were inoculated with Agrobacterium tumefaciens strain LBA4404 containing the plasmid pCAMBIA 2301 harboring genes for β-glucuronidase (uidA) and neomycin phosphotransferase (nptII). Polymerase chain reaction (PCR) and histochemical assays confirmed the presence of uidA and β-glucuronidase (GUS), respectively in transgenic shoots. The effect on transformation efficiency of medium composition and the presence of antioxidants during inoculation and co-cultivation was investigated. The transformation efficiency was increased in Murashige and Skoog (MS) medium lacking KNO3 and NH4NO3, and also in MS medium lacking macro and micro elements and Fe to 5% and 3.1% respectively, compared to 0.6% in full-strength medium. Transformation efficiency was increased dramatically to 24.4% across all carnation cultivars by the addition of 2 mg/l melatonin to nitrogen-depleted MS medium. Shoot regeneration was also doubled in this treatment. The establishment of this efficient and reliable transformation protocol can advance the development of novel carnation cultivars through molecular breeding approaches.