Background: HELLP syndrome is one of the disorders characterized by hemolysis, increased liver enzymes and decreased platelet count. So far, many molecular pathways and genes have been identified in relation to the pathogenesis of this syndrome; however, the main cause of the incidence and progression of the disease has not been identified. Using the biological system approach is a way to target patients by identifying genes and molecular pathways. In this study, we investigated genes and important molecular factors in the pathogenesis of HELLP syndrome. Material and Methods: In this study, the microarray dataset was downloaded from Gene Expression Omnibus (GEO) database and analyzed using the GEO2R online tool for identifying differentially expressed genes (DEGs). Enrichment analysis of DEGs was evaluated using the Enrichr database. Then, protein-protein interaction (PPI) networks were constructed via the STRING database; they were visualized by Cytoscape. Then the STRING database was used to construct PPI networks. The hub genes were recognized using the cytoHubba. Ultimately, the interaction of the miRNA-hub genes and drug-hub genes were also evaluated. Result: After analysis, it was found that some genes with the highest degree of connectivity are involved in the pathogenesis of HELLP syndrome, which are known as the hub genes. These genes are as follows: KIT, JAK2, LEP, EP300, HIST1H4L, HIST1H4F, HIST1H4H, MMP9, THBS2, and ADAMTS3. Has-miR-34a-5p was also most associated with hub genes. Conclusion: Finally, it can be said, that the identification of genes and molecular pathways in HELLP syndrome can be helpful in identifying the pathogenesis pathways of the disease, and designing therapeutic targets.

In this study, we explore the possibility that the Drought Monitor database belongs to class of fractal process which can be characterized using a single scaling exponent. The Drought Monitor map identifies areas of drought and labels them by intensity: D0 abnormally dry, D1 moderate drought, D2 severe drought, D3 extreme drought, and D4 exceptional drought. The vibration analysis using power spectral densities (PSD) method has been carried out to discover whether some type of power-law scaling exists for various statistical moments at different scales of this database. We perform multi-fractal analysis to estimate the multi-fractal spectrum of each group. We apply Higuchi algorithm to find the fractal complexity of each group and then compare them for different time intervals. Our findings reveal that we have a wide range of exponents for D0-D4. Therefore, D0-D4 belong to class of multi-fractal process for which a large number of scaling exponents are required to characterize the scaling structure.

During the few last years, climate change, including global warming, which is attributed to human activities, and its long-term adverse effects on the planet’s functions have been identified as the most challenging discussion topics and have provoked significant concern and effort to find possible solutions. Since the warmth arising from the Earth’s landscapes affects the world’s weather and climate patterns, we decided to study the changes in Land Surface Temperature (LST) patterns in different seasons through nonlinear methods. Here, we particularly wanted to estimate the noninteger dimension and fractal structure of the Land Surface Temperature. For this study, the LST data were obtained during the daytime by a Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite. Depending on the time of the year data were collected, temperatures changed in different ranges. Since equatorial regions remain warm, and Antarctica and Greenland remain cold, and also because altitude affects temperature, we selected Riley County in the US state of Kansas, which does not belong to any of these location types, and we observed the seasonal changes in temperature in this county. According to our fractal analysis, the fractal dimension may provide a complexity index to characterize different LST datasets. The multifractal analysis confirmed that the LST data may define a self-organizing system that produces fractal patterns in the structure of data. Thus, the LST data may not only have a wide range of fractal dimensions, but also they are fractal. The results of the present study show that the Land Surface Temperature (LST) belongs to the class of fractal processes with a noninteger dimension. Moreover, self-organized behavior governing the structure of LST data may provide an underlying principle that might be a general outcome of human activities and may shape the Earth’s surface temperature. We explicitly acknowledge the important role of fractal geometry when analyzing and tracing settlement patterns and urbanization dynamics at various scales toward purposeful planning in the development of human settlement patterns.

During few last years, climate change including global warming which is attributed to human activities and also its long-term adverse effects on the planet’s functions have been identified as the most challenging discussion topics which have arisen many concerns and efforts to find the possible solutions. Since the warmth arising from Earth’s landscapes affects the world’s weather and climate patterns, we decided to study the changes in the Land Surface Temperature (LST) patterns in different seasons through non-linear methods. Here, we particularly want to estimate the non-integer dimension and fractal structure of the land surface temperature. For this study, the (LST) data has been obtained during the daytime by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite. Depending on what time of the year data has been collected, temperatures change in different ranges. Since equatorial regions remain warm, and Antarctica and Greenland remain cold, and also because altitude affects temperature, we selected Riley County in the U.S. state of Kansas, which does not belong to any of this type locations and we are interested to observe the seasonal changes in temperature in this county. The results of the present study show that the Land Surface Temperature (LST) belongs to the class of fractal process with non-integer dimension.