Effect of Winter Foliar Application of Urea on Changes in Leaf and Node Polyamines, Yield and Some Characteristics of Reproductive of Kinnow Mandarin Trees

Introduction Nitrogen plays an important role in the uniformity and quality of citrus trees. Several studies previously reported that a low level of nitrogen in citrus trees is generally caused a reduction in yield and fruit quality (Aziz, 1997; Khan et al., 2009). In this regard, applying urea is recommended as the most suitable form of nitrogen for foliar application. The polyamines are included; putrescine, spermidine, and spermine which have been considered as plant growth regulators (Alcazar et al., 2010; Khezri et al., 2010). The role of nitrogen in vegetative and reproductive growth and yield, as well as the correlation between polyamines, flower induction and fruit set in other plants, were proved in previous studies. In this regard, the results of the current study will increase our understanding about the role of polyamines in the morphology of the tree and also the mechanism of regulating the alternate bearing of Kinnow mandarin leading to an approach method to address this problem.   Materials and Methods To conduct this study a 17-year-old uniform of Kinnow Mandarin (Citrus reticulate Blanco) grafted onto Bitter orange (Citrus aurantium) rootstock, which grown in a commercial orchard, located in Dezful (Khuzestan Province in Iran). For sampling, the branches which possess leaves and nodes were selected from four sides of each tree, then leaves and nodes were collected at three different time points (one, three, and five weeks post-treatment). Samples were immediately frozen in liquid nitrogen after excision and transported to the Physiology Laboratory of fruit trees within 2h for determining the N fractions and polyamines. The concentration of N in dried leaves and nodes was determined using the colorimetry technique as described by Walling et al. (1989). The experiment was set up as a factorial treatment based on a randomized complete block design with three replications to investigate the effect of different concentrations of urea foliar application (0, 0.75%, 1.5%) on nitrogen and polyamines contents at different time points (Dec 22, Jan 5, Jan 20) followed by evaluating flower characteristics and yield in Kinnow mandarin plant. Data analysis including variance was carried out using MSTATC and SAS software. To compare the mean of polyamines and nitrogen in leaves and nodes, the cut-out method was used, and also for comparison of pistil dimensions, number of flowers, and yields, Duncan's multiple range test (DMRT) was performed.      Results and Discussion Results indicated that polyamines concentration and nitrogen decreased during the period of time and also, in most cases, polyamines concentration was lower in the nodes than the leaves. High levels of polyamines and nitrogen were obtained in leaves and nodes which were treated with the foliar application of 1.5 % urea concentration after Jan 20. The polyamines content in leaves and nodes was greatly dependent on the spraying time and urea concentration used. Spermine (Spm) was the dominant polyamines in leaves and nodes with the highest values of 44.01 nmol/gF.W, 34.41 nmol/gF.W, respectively. Regarding the fact that flower density was higher in trees that treated with urea concentration of 1.5 % after Jan 5 y than other treatments, however, their yield was lower than the trees that treated on Dec 22 with the same urea concentration. This was probably due to the flower abscission as well as the fruit abscission in June. The results of this study showed that the highest yield was obtained with 1.5 % urea concentration after foliar application on Dec 22 compared with other treatments. Regarding the fact that flower differentiation in mandarin occurs during the late January until late February (in Dezful conditions), it can be explained that the foliar application on Dec 22 was performed before differentiation and consequently, the trees have received their required nitrogen. Also, the low-temperature is considered as natural inducer of citrus flowering in the Dezful, likewise, foliar fertilizer application in winter along with the natural factor (low temperature) stimulates flowering in a larger number of buds resulting in increasing the flowering. As nitrogen promotes vegetative and reproductive growth, it can be said that increasing the nitrogen content of leaves followed by transfering to the nodes, increases the number of buds, especially reproductive buds, which leads to an increase in flowering and yield. According to this study, the foliar application of urea in winter with 1.5% concentration can increase yield in Kinnow mandarin trees. Therefore, polyamines can play an important physiological role in flower development of Kinnow mandarin.   Conclusion In this study, we focused on the effect of the foliar application urea during winter on leaves and nodes of Kinnow mandarin trees and investigated the polyamines, Put, Spm, and Spd contents upon treatments. In conclusion, the application of foliar urea in winter resulted in the significant endogenous increase of polyamines and N in the leaves and nodes of Kinnow mandarin trees. Also, yield, flower density, and pistil diameter were increased by spraying urea. There is a possibility that free polyamines affect on physiological processes

The Study of Abnormal Liver Ultrasound Findings in Candidate Patients Undergoing Renal Transplantation from Brain Dead Donors

BACKGROUND AND OBJECTIVE: Chronic kidney disease in addition to kidney involvement may cause abnormalities in various systems of the body, in which liver disorders are one of the most commonly encountered disorders. Failure to identify some of these disorders can cause a serious problem in transolant patient. This study was performed to determine the frequency and type of abnormal liver ultrasound findings in renal transplant patients. METHODS: In this cross-sectional study, recorded data of 480 kidney failure patients who had received kidney transplant from brain death donors during the last 6 years in three Mashhad hospitals were investigated. Ultrasonography was considered before the transplant and abnormal liver findings were recorded in a checklist and were assesed. FINDINGS: The mean age was 39.07±12.67 years of which 52.70% were male and 42.30% were female. Liver disorders were observed in 13.12% of patients. The highest prevalence was related to fatty liver grade I (2.5%), grade II (1.46%), gallstone (1.25%) and liver cysts (1.25%). CONCLUSION: The results of the study showed that liver asymptomatic disorders in renal transplant patients have significant prevalence and because some of these disorders require treatment before transplantation, enough attention to screening before transplantation can help to prevent post-transplant complications

Salient Arithmetic Data Extraction from Brain Activity via an Improved Deep Network

Data Availability Statement: The EEG dataset is available online at https://mindbigdata.com/opendb/ (Accessed on 12 February 2020).Interpretation of neural activity in response to stimulations received from the surrounding environment is necessary to realize automatic brain decoding. Analyzing the brain recordings corresponding to visual stimulation helps to infer the effects of perception occurring by vision on brain activity. In this paper, the impact of arithmetic concepts on vision-related brain records has been considered and an efficient convolutional neural network-based generative adversarial network (CNN-GAN) is proposed to map the electroencephalogram (EEG) to salient parts of the image stimuli. The first part of the proposed network consists of depth-wise one-dimensional convolution layers to classify the brain signals into 10 different categories according to Modified National Institute of Standards and Technology (MNIST) image digits. The output of the CNN part is fed forward to a fine-tuned GAN in the proposed model. The performance of the proposed CNN part is evaluated via the visually provoked 14-channel MindBigData recorded by David Vivancos, corresponding to images of 10 digits. An average accuracy of 95.4% is obtained for the CNN part for classification. The performance of the proposed CNN-GAN is evaluated based on saliency metrics of SSIM and CC equal to 92.9% and 97.28%, respectively. Furthermore, the EEG-based reconstruction of MNIST digits is accomplished by transferring and tuning the improved CNN-GAN’s trained weights.This research received no external funding

ASH1L-MRG15 methyltransferase deposits H3K4me3 and FACT for damage verification in nucleotide excision repair

To recognize DNA adducts, nucleotide excision repair (NER) deploys the XPC sensor, which detects damage-induced helical distortions, followed by engagement of TFIIH for lesion verification. Accessory players ensure that this factor handover takes place in chromatin where DNA is tightly wrapped around histones. Here, we describe how the histone methyltransferase ASH1L, once activated by MRG15, helps XPC and TFIIH to navigate through chromatin and induce global-genome NER hotspots. Upon UV irradiation, ASH1L adds H3K4me3 all over the genome (except in active gene promoters), thus priming chromatin for XPC relocations from native to damaged DNA. The ASH1L-MRG15 complex further recruits the histone chaperone FACT to DNA lesions. In the absence of ASH1L, MRG15 or FACT, XPC is misplaced and persists on damaged DNA without being able to deliver the lesions to TFIIH. We conclude that ASH1L-MRG15 makes damage verifiable by the NER machinery through the sequential deposition of H3K4me3 and FACT

Visual Saliency and Image Reconstruction from EEG Signals via an Effective Geometric Deep Network-Based Generative Adversarial Network

Data Availability Statement: The EEG-ImageNet dataset used in this study is publicly available in this address: https://tinyurl.com/eeg-visual-classification (accessed on 10 October 2022).Copyright © 2022 by the authors. Reaching out the function of the brain in perceiving input data from the outside world is one of the great targets of neuroscience. Neural decoding helps us to model the connection between brain activities and the visual stimulation. The reconstruction of images from brain activity can be achieved through this modelling. Recent studies have shown that brain activity is impressed by visual saliency, the important parts of an image stimuli. In this paper, a deep model is proposed to reconstruct the image stimuli from electroencephalogram (EEG) recordings via visual saliency. To this end, the proposed geometric deep network-based generative adversarial network (GDN-GAN) is trained to map the EEG signals to the visual saliency maps corresponding to each image. The first part of the proposed GDN-GAN consists of Chebyshev graph convolutional layers. The input of the GDN part of the proposed network is the functional connectivity-based graph representation of the EEG channels. The output of the GDN is imposed to the GAN part of the proposed network to reconstruct the image saliency. The proposed GDN-GAN is trained using the Google Colaboratory Pro platform. The saliency metrics validate the viability and efficiency of the proposed saliency reconstruction network. The weights of the trained network are used as initial weights to reconstruct the grayscale image stimuli. The proposed network realizes the image reconstruction from EEG signals.This research received no external funding

A Novel Approach for Automatic Detection of Driver Fatigue Using EEG Signals Based on Graph Convolutional Networks

Data Availability Statement: In this research, experimental data were not recorded.Copyright © 2024 by the authors. Nowadays, the automatic detection of driver fatigue has become one of the important measures to prevent traffic accidents. For this purpose, a lot of research has been conducted in this field in recent years. However, the diagnosis of fatigue in recent research is binary and has no operational capability. This research presents a multi-class driver fatigue detection system based on electroencephalography (EEG) signals using deep learning networks. In the proposed system, a standard driving simulator has been designed, and a database has been collected based on the recording of EEG signals from 20 participants in five different classes of fatigue. In addition to self-report questionnaires, changes in physiological patterns are used to confirm the various stages of weariness in the suggested model. To pre-process and process the signal, a combination of generative adversarial networks (GAN) and graph convolutional networks (GCN) has been used. The proposed deep model includes five convolutional graph layers, one dense layer, and one fully connected layer. The accuracy obtained for the proposed model is 99%, 97%, 96%, and 91%, respectively, for the four different considered practical cases. The proposed model is compared to one developed through recent methods and research and has a promising performance.This research received no external funding

Influence of modified air on combustion characteristics in meso-scale vortex combustor

The need to supply power for miniaturized mechanical devices opens exciting new opportunities for combustion, especially in the field of micro-power generation. Because of the need for power supply devices with high-specific energy (small-size, low weight, long duration) and power. Meso/micro scale combustion has been considered as a potential solution for many small-volumes and energy demanding systems, such as power supplies for portable device. In this study the structure of turbulent diffusion flames in a meso scale combustor with different oxygen concentration has been investigated using a new design of vortex combustor. Methane gas was used as a fuel. Numerical investigations have been performed on the temperature distribution, swirl number, heat loss, and emitter efficiency in vortex combustion. The results have been obtained for various O2 concentrations in the air as oxidizer. The results shows that thermal flame behaves depend strongly on the oxygen content in the oxidizer. When the oxygen concentration increases from 15% to 30%, the flame temperature of the meso-combustion rises in all cases. Emitter efficiency is very high in the meso-combustor with high O2 concentration in oxidizer

EEG-based functional connectivity analysis of brain abnormalities: A systematic review study

Several imaging modalities and many signal recording techniques have been used to study the brain activities. Significant advancements in medical device technologies like electroencephalographs have provided conditions for recording neural information with high temporal resolution. These recordings can be used to calculate the connections between different brain areas. It has been proved that brain abnormalities affect the brain activity in different brain regions and the connectivity patterns between them are changed as a result. This paper studies the electroencephalogram (EEG) functional connectivity methods and investigates the impacts of brain abnormalities on brain functional connectivities. The effects of different brain abnormalities including stroke, depression, emotional disorders, epilepsy, attention deficit hyperactivity disorder (ADHD), autism, and Alzheimer's disease on functional connectivity of the EEG recordings have been explored in this study. The EEG-based metrics and network properties of different brain abnormalities have been discussed to present a comparison of the connectivities affected by each abnormality. Also, the effects of therapy and medical intake on the EEG functional connectivity network of each abnormality have been reviewed.This research received no external funding

Prevalence and correlates of psychiatric disorders in a national survey of Iranian children and adolescents

Objective: Considering the impact of rapid sociocultural, political, and economical changes on societies and families, population-based surveys of mental disorders in different communities are needed to describe the magnitude of mental health problems and their disabling effects at the individual, familial, and societal levels. Method: A population-based cross sectional survey (IRCAP project) of 30 532 children and adolescents between 6 and 18 years was conducted in all provinces of Iran using a multistage cluster sampling method. Data were collected by 250 clinical psychologists trained to use the validated Persian version of the semi-structured diagnostic interview Kiddie-Schedule for Affective Disorders and Schizophrenia-PL (K-SADS-PL). Results: In this national epidemiological survey, 6209 out of 30 532 (22.31%) were diagnosed with at least one psychiatric disorder. The anxiety disorders (14.13%) and behavioral disorders (8.3%) had the highest prevalence, while eating disorders (0.13%) and psychotic symptoms (0.26%) had the lowest. The prevalence of psychiatric disorders was significantly lower in girls (OR = 0.85; 95% CI: 0.80-0.90), in those living in the rural area (OR = 0.80; 95% CI: 0.73-0.87), in those aged 15-18 years (OR = 0.92; 95% CI: 0.86-0.99), as well as that was significantly higher in those who had a parent suffering from mental disorders (OR = 1.96; 95% CI: 1.63-2.36 for mother and OR = 1.33; 95% CI: 1.07-1.66 for father) or physical illness (OR = 1.26; 95% CI: 1.17-1.35 for mother and OR = 1.19; 95% CI: 1.10-1.28 for father). Conclusion: About one fifth of Iranian children and adolescents suffer from at least one psychiatric disorder. Therefore, we should give a greater priority to promoting mental health and public health, provide more accessible services and trainings, and reduce barriers to accessing existing services. © 2019 Tehran University of Medical Scienc