Validation of aerosol optical depth using AERONET ground stations in marine areas (Case Study: Persian Gulf)

Dust prediction such as prediction of wind and rain needs to synoptic information to the earth's surface, upper layers of the atmosphere, the prediction maps of land surface and upper levels as well as using radar and satellites. In the meantime, radar and satellite observations included remote sensing can be useful in prediction accuracy. The purpose of this study, use of remote sensing technology and MODIS images to estimate dust optical depth on the Persian Gulf surface and estimating the linear correlation relationship between the dust measurements in ground and atmospheric. The dust optical depth calculated using the code developed in MATLAB software. Evaluation of extracted data conducted using Pearson correlation coefficient, RMSE and RMSD index. In this study, optical depth obtained from image processing compared with the optical depths obtained from AERONET network. The evaluation results showed a high and significant correlation between the obtained optical depth and optical depths obtained from AERONET network (R 2 =0.99). The best and most suitable mode demonstrated for 1.243 and 1.632 bonds. At all stations, AOD value obtained from satellite image is bigger than AOD amount corresponding to the AERONET station and the algorithm used has overestimated. The cause of this more estimate can be use of limited particle's effective radius, because the scope of this effective radius is limited at the distribution of particle size in log-normal. Error resources at the retrieving particulate matter was defined such as sensor calibration error, pollution on the radiation angle, or poor predictor of water reflection

Analysis of Methylation and Expression Profile of Foxp3 Gene in Patients with Behçet’s Syndrome

Forkhead box P3 (Foxp3) gene is an important means in the Treg cells function, in both maintenances of immune tolerance and regulation of response. Epigenetic modifications of the foxp3 gene at its regulatory regions control the chromatin accessibility for the transcription factors and other transcriptional regulators in order to control Foxp3 expression. In addition, the methylation status of CpG islands within the Foxp3 promoter and regulatory elements regulate the expression of Foxp3. This study was performed to assess the role of the foxp3 gene in patients with Behçet’s syndrome (BS). Venous blood samples were collected from all participants and peripheral blood mononuclear cells (PBMC) were extracted through Ficoll-Hypaque method. Genomic DNA was randomly sheared by sonication and immunoprecipitated with a monoclonal antibody. The status methylation of the foxp3 gene was estimated in 108 blood samples of active BS patients and healthy individuals (controls); using methylation DNA immunoprecipitation (MeDIP) technique. Expression analysis was carried out; using Real-time PCR. The expression of foxp3 gene in the patients' group (mean±SD: 1.79±1.12) was significantly lower than the healthy group (mean±SD: 2.73±1.33) (p<001). Also, the methylation levels of Foxp3 promoter showed that its level in patients (mean±SD: 2.3±1.16) was higher than the healthy group (mean±SD: 1.85±0.59). However, this increase was not statistically significant (p>0.05). Also, these results indicated that increasing the amount of methylation of the foxp3 gene by reducing its expression leads to an increase and intensifying of the disease. The decrease in Foxp3 expression is possibly associated with hypermethylation of the gene, and it can be considered as a risk factor for BS. Future studies may be needed to identify the capability of specific DNA methylation alterations in this syndrome

Study of the Factors Influencing the Process of Political Socialization of Students in Islamic Republic of Iran1

This paper examines the Factors influencing the process of political socialization of students. Effects of the political parties within the university and outside the university (such as political parties and political figures); and the policies of the Ministry of Science, Research and Technology, on the process of political socialization of students has been studied. Details of the survey have been collected from a sample size of 1210 individuals among public universities nationwide and purified with the method of Stratified sampling. Data analysis was also performed with "SPSS" software in 1392. The results of the study revealed these assumptions: The political parties within the college had a positive impact on the process of political socialization; and off-campus parties and political organizations such as parties and political figures and the policies of the Ministry of Science, Failed to have a major impact on political socialization of student

Tumor Necrosis Factor-Alpha and Interleukin-6 Gene Polymorphisms in Iranian Patients with Ischemic Heart Failure

Background: Proinflammatory cytokines have been known to be elevated in patients with Chronic Heart Failure (CHF). Given the importance of proinflammatory cytokines in the context of the failing heart, the prevalence of Tumor Necrosis Factor-α (TNF-α), Interleukin (IL)-6 polymorphisms in patients with CHF was studied due to ischemic heart disease. Methods: Forty three patients with ischemic heart failure were enrolled in this study and compared with 140 healthy individuals. The allele and genotype frequency of four Single Nucleotide Polymorphisms (SNPs) within the IL-6 (-174, nt565) and TNF-α (-308, -238) genes were determined, using Polymerase Chain Reaction with Sequence-Specific Primers (PCR-SSP) assay. Results: The frequency of the TNF-α (-238) A/A genotype was significantly higher in patients comparing to controls (p=0.043), while TNF-α G/A genotype at the same position decreased significantly, in comparison with controls (p=0.018). The most frequent haplotype for TNF-α was A/A in the patient group in comparison with controls (p=0.003). There was no significant difference in allele and genotype frequencies of IL-6 at positions -174 and nt565, and TNF-α at position -308. Conclusion: Certain alleles, genotypes, and haplotypes in TNF-α, but not IL-6, gene were overrepresented in patients with ischemic heart failure, which may, in turn, predispose individuals to this disease

Association of interleukin-4 gene polymorphisms with ischemic heart failure

Background: As of the potential immunomodulatory effects of interleukin-4 (IL-4) and its importance in inhibiting the production of proinflammatory cytokines by monocytes and activated T cells, the IL-4 gene polymorphisms were investigated in a group of patients with chronic heart failure due to ischemic heart disease.Methods: Forty three patients with ischemic heart failure (IHF) were enrolled in this study and compared with 139 healthy individuals. The allele and genotype frequency of 3 single nucleotide polymorphisms within the IL-4 gene were determined.Results: The frequency of the IL-4 –590/T allele in the patient group was significantly higher than in the control group (p &lt; 0.0001). The most frequent genotypes in patients with IHF were IL-4 (–590) CC (p &lt; 0.0001), IL-4 (–33) CC (p = 0.021), and IL-4 (–33) TT (p &lt; 0.0001). The frequency of the following genotypes was significantly lower in patients compared to controls: IL-4 (–1098) TG (p = 0.035), IL-4 (–590) TC (p &lt; 0.0001), and IL-4 (–33) TC (p &lt; 0.0001). The most frequent IL-4 haplotypes in the patient group, which were significantly higher than in the control group, were TCC (p &lt; 0.0001), TCT (p = 0.0242), and GCT (p = 0.0108) haplotypes. In contrast, the frequencies of the following haplotypes in the patient group were significantly lower than in the controls: GCC (p = 0.032), TTT (p = 0.0268), and TTC (p = 0.0399).Conclusions: Certain alleles, genotypes, and haplotypes in IL-4 gene were over represented inpatients with IHF, which may, in turn, predispose individuals to this disease

Pervasive AI in next generation wireless: The Hexa-X project perspective

International audienceThe European 6G flagship project Hexa-X has the objective to conduct exploratory research on the next generation of mobile networks with the intention to connect human, physical and digital worlds with a fabric of technology enablers. Within this scope, one of the main research challenges is the ambition for beyond 5G (B5G)/6G systems to support, enhance and enable real-time trustworthy control by transforming Artificial Intelligence (AI)/Machine Learning (ML) technologies into a vital and trusted tool for large-scale deployment of intelligence in the wider society. Hence, the study and development of concepts and solutions enabling AI-driven communication and computation co-design for a B5G /6G communication system is required. This paper focuses on describing the possibilities emerging with the application of AI/ML mechanisms to 6G networking, identifies the resulting challenges and describes some potential solution approaches

The Hexa-X project vision on Artificial Intelligence and Machine Learning-driven Communication and Computation co-design for 6G

International audienceThis paper provides an overview of the most recent advancements and outcomes of the European 6G flagship project Hexa-X, on the topic of in-network Artificial Intelligence (AI) and Machine Learning (ML). We first present a general introduction to the project and its ambitions in terms of use cases (UCs), key performance indicators (KPIs), and key value indicators (KVIs). Then, we identify the key challenges to realize, implement, and enable the native integration of AI and ML in 6G, both as a means for designing flexible, low-complexity, and reconfigurable networks (\textit{learning to communicate}), and as an intrinsic in-network intelligence feature (\textit{communicating to learn }or, 6G as an efficient AI/ML platform). We present a high level description of down selected technical enablers and their implications on the Hexa-X identified UCs, KPIs and KVIs. Our solutions cover lower layer aspects, including channel estimation, transceiver design, power amplifier and distributed MIMO related challenges, and higher layer aspects, including AI/ML workload management and orchestration, as well as distributed AI. The latter entails Federated Learning and explainability as means for privacy preserving and trustworthy AI. To bridge the gap between the technical enablers and the 6G targets, some representative numerical results accompany the high level description. Overall, the methodology of the paper starts from the UCs and KPIs/KVIs, to then focus on the proposed technical solutions able to realize them. Finally, a brief discussion of the ongoing regulation activities related to AI is presented, to close our vision towards an AI and ML-driven communication and computation co-design for 6G

Pervasive artificial intelligence in next generation wireless: The Hexa-X project perspective

The European 6G flagship project Hexa-X has the objective to conduct exploratory research on the next generation of mobile networks with the intention to connect human, physical and digital worlds with a fabric of technology enablers. Within this scope, one of the main research challenges is the ambition for beyond 5G (B5G)/6G systems to support, enhance and enable real-time trustworthy control by transforming Artificial Intelligence (AI) / Machine Learning (ML) technologies into a vital and trusted tool for large-scale deployment of interconnected intelligence available to the wider society. Hence, the study and development of concepts and solutions enabling AI-driven communication and computation co-design for a B5G /6G communication system is required. This paper focuses on describing the possibilities that emerge with the application of AI/ML mechanisms to 6G networks, identifying the resulting challenges and proposing some potential solution approaches

The Hexa-X project vision on Artificial Intelligence and Machine Learning-driven Communication and Computation co-design for 6G

International audienceThis paper provides an overview of the most recent advancements and outcomes of the European 6G flagship project Hexa-X, on the topic of in-network Artificial Intelligence (AI) and Machine Learning (ML). We first present a general introduction to the project and its ambitions in terms of use cases (UCs), key performance indicators (KPIs), and key value indicators (KVIs). Then, we identify the key challenges to realize, implement, and enable the native integration of AI and ML in 6G, both as a means for designing flexible, low-complexity, and reconfigurable networks (\textit{learning to communicate}), and as an intrinsic in-network intelligence feature (\textit{communicating to learn }or, 6G as an efficient AI/ML platform). We present a high level description of down selected technical enablers and their implications on the Hexa-X identified UCs, KPIs and KVIs. Our solutions cover lower layer aspects, including channel estimation, transceiver design, power amplifier and distributed MIMO related challenges, and higher layer aspects, including AI/ML workload management and orchestration, as well as distributed AI. The latter entails Federated Learning and explainability as means for privacy preserving and trustworthy AI. To bridge the gap between the technical enablers and the 6G targets, some representative numerical results accompany the high level description. Overall, the methodology of the paper starts from the UCs and KPIs/KVIs, to then focus on the proposed technical solutions able to realize them. Finally, a brief discussion of the ongoing regulation activities related to AI is presented, to close our vision towards an AI and ML-driven communication and computation co-design for 6G