Gully Erosion Susceptibility Mapping Using Multivariate Adaptive Regression Splines—Replications and Sample Size Scenarios

Soil erosion is a serious problem affecting numerous countries, especially, gully erosion. In the current research, GIS techniques and MARS (Multivariate Adaptive Regression Splines) algorithm were considered to evaluate gully erosion susceptibility mapping among others. The study was conducted in a specific section of the Gorganroud Watershed in Golestan Province (Northern Iran), covering 2142.64 km2 which is intensely influenced by gully erosion. First, Google Earth images, field surveys, and national reports were used to provide a gully-hedcut evaluation map consisting of 307 gully-hedcut points. Eighteen gully erosion conditioning factors including significant geoenvironmental and morphometric variables were selected as predictors. To model sensitivity of gully erosion, Multivariate Adaptive Regression Splines (MARS) was used while the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC), drawing ROC curves, efficiency percent, Yuden index, and kappa were used to evaluate model efficiency. We used two different scenarios of the combination of the number of replications, and sample size, including 90%/10% and 80%/20% with 10 replications, and 70%/30% with five, 10, and 15 replications for preparing gully erosion susceptibility mapping (GESM). Each one involves a various subset of both positive (presence), and negative (absence) cases. Absences were extracted as randomly distributed individual cells. Therefore, the predictive competency of the gully erosion susceptibility model and the robustness of the procedure were evaluated through these datasets. Results did not show considerable variation in the accuracy of the model, with altering the percentage of calibration to validation samples and number of model replications. Given the accuracy, the MARS algorithm performed excellently in predictive performance. The combination of 80%/20% using all statistical measures including SST (0.88), SPF (0.83), E (0.79), Kappa (0.58), Robustness (0.01), and AUC (0.84) had the highest performance compared to the other combinations. Consequently, it was found that the performance of MARS for modelling gully erosion susceptibility is quite consistent while changes in the testing and validation specimens are executed. The intense acceptable prediction capability of the MARS model verifies the reliability of the method employed for use of this model elsewhere and gully erosion studies since they are qualified to quickly generating precise and exact GESMs (gully erosion sensitivity maps) to make decisions and management edaphic and hydrologic features.

Promoter methylation and expression pattern of DLX3, ATF4, and FRA1 genes during osteoblastic differentiation of adipose-derived mesenchymal stem cells

Introduction: Nowadays, mesenchymal stem cells are touted as suitable cell supply for the restoration of injured bone tissue. The existence of osteogenic differentiation makes these cells capable of replenishing damaged cells in the least possible time. It has been shown that epigenetic modifications, especially DNA methylation, contribute to the regulation of various transcription factors during phenotype acquisition. Hence, we concentrated on the correlation between the promoter methylation and the expression of genes DLX3, ATF4 , and FRA1 during osteoblastic differentiation of adipose-derived mesenchymal stem cells in vitro after 21 days. Methods: Adipose-derived mesenchymal stem cells were cultured in osteogenesis differentiation medium supplemented with 0.1 µM dexamethasone, 10 mM β-glycerol phosphate, and 50 µM ascorbate-2-phosphate for 21 days. RNA and DNA extraction was done on days 0, 7, 14, and 21. Promoter methylation and expression levels of genes DLX3 , ATF4 , and FRA1 were analyzed by methylation-specific quantitative PCR and real-time PCR assays, respectively. Results: We found an upward expression trend with the increasing time for genes DLX3, ATF4, and FRA1 in stem cells committed to osteoblast-like lineage compared to the control group ( P <0.05). On the contrary, methylation-specific quantitative PCR displayed decreased methylation rates of DLX3 and ATF4 genes, but no FRA1 , over time compared to the non-treated control cells ( P <0.05). Bright-field images exhibited red-colored calcified deposits around Alizarin Red S-stained cells after 21 days compared to the control group. Statistical analysis showed a strong correlation between the transcription of genes DLX3 and ATF4 and methylation rate ( P <0.05). Conclusion: In particular, osteoblastic differentiation of adipose-derived mesenchymal stem cells enhances DLX3 and ATF4 ranscriptions by reducing methylation rate for 21 days.

Survival and Factors Affecting it in Lung Cancer Patients Referred to Imam Khomeini Clinic in Hamadan Province

Background: Lung cancer is one of the most common cancers and the leading cause of death due to cancer in the world. It has the highest mortality rate compared to breast, prostate, and other cancers. Different factors can be effective in the survival of lung cancer patients. The present study has evaluated survival and its related factors. Materials and Methods: The present study was performed on 157 lung cancer patients referred to Imam Khomeini Specialized Clinic in Hamadan during 2003-2017. Patient follow-up was performed through periodic referrals and telephone calls with patients’ relatives. The survival rate was evaluated using Kaplan-Meier method and the log-rank test was used to compare the survival of different levels of variables. Data analysis was then carried out using SPSS version 23.0 and STATA version14.0. P value < 0.05 was considered statistically significant in all tests. Results: Of 157 patients with lung cancer, 86% were male. The mean and median survival times of patients were 15 and 11 months, respectively. The results of log-rank test indicated that metastasis and site of metastasis were effective in patient survival (P < 0.05). Conclusion: The results of the present study showed that the presence of metastasis is a strong factor for the survival of patients; therefore, it seems necessary to diagnose the disease as early as possible using a screening program.

Effect of valproic acid on cisplatin-resistant ovarian cancer cell lines

Background and aims: Platinum resistance has been one of the most important problems in the management of ovarian cancer. The effects of various chemotherapeutic agents are limited in patients with platinum resistance. Therefore, developing new anticancer drugs that can improve the effect of currently used cytostatics is critical. The current study investigated the effects of valproic acid (VPA) alone and in combination with cisplatin on ovarian cancer cells. Methods: In this experimental study, the human ovarian cancer cell lines (A2780-S and A2780-CP) were grown in RPMI-1640 medium in appropriate culture conditions. The cells were treated with various concentrations of cisplatin (0.15-400 µg/mL) or VPA (10-2000 µg/mL) and were incubated for 24, 48, and 72 hours. Moreover, A2780 cells were co-treated with different concentrations of cisplatin and VPA for 48 hours. Afterward, cell viability was investigated using MTT assay. GraphPad Prism statistical software was used for the data analysis and ANOVA and Duncan’s test were conducted. Results: A dose- and time-dependent reduction was observed in cell viability following the treatment with cisplatin or VPA. Moreover, cotreatment of the A2780 cells with cisplatin and VPA resulted in a significantly greater inhibition of cell viability compared to the treatment with either agent alone. Conclusion: Overall, it can be argued that VPA does not only cause inhibition of proliferation and induction of apoptosis in ovarian cancer cells but also helps to enhance the antiproliferative effects of cisplatin and results in the increased susceptibility to cisplatin in resistant cells. VPA may therefore be used to treat cancer in the future.

Induction of neural stem cell proliferation by iron oxide nanoparticles and magnetic field and Ki67 gene expression in rat hippocampus after ischemia/reperfusion

Background and aims: Ischemic stroke is considered as the second leading cause of death in the world and yet one of the causes of disability in adults. The present study aimed to evaluate the effects of iron oxide nanoparticles and the magnetic field on neural stem cells proliferation after ischemia/reperfusion in the rat model. Methods: This experimental study was conducted on a total of 50 male Wistar rats aged 6-7 weeks and weight of 220-250 g weight, which were divided into sham (i.e., ischemia-reperfusion model), control, iron oxide nanoparticles treated-, magnetic field exposed-, and simultaneously iron oxide nanoparticles and magnetic field exposed- groups. The brain ischemia/reperfusion was performed for 20 minutes by blocking the animal carotid arteries. In addition, neural stem cell proliferation was evaluated in the hippocampus of the 5 groups after 4 days by bromodeoxyuridine (BrdU) staining method. Then, the expression of Ki67 gene involved in the cell proliferation was quantitatively studied among the 5 groups by the quantitative real-time polymerase chain reaction (qRT-PCR). Results: The results of BrdU staining revealed that iron oxide nanoparticles and the magnetic field separately increased cell proliferation after ischemia/reperfusion after 4 days in the hippocampus. However, simultaneous treatment with nanoparticles and magnetic field failed to show a significant difference compared to the sham group for 4 days. Conversely, the expression of Ki67 gene increased significantly in the group treated with iron oxide nanoparticles or the group exposed to magnetic field compared to the ischemia-reperfusion model. Conclusion: In general, iron oxide nanoparticles and magnetic field can separately be regarded as 2 effective methods for increasing the neural stem cell proliferation after ischemia/reperfusion.

Pyruvate kinase deficiency and its gene mutations in newborns with jaundice in East Azerbaijan province year 2014

Multi Objective Geometric Programming with Interval Coefficients: A Parametric Approach

When we talk of optimization in industry we need to pay attention in searching for very powerful and flexible optimization techniques. One of such techniques which has attracted the interest of many researchers in the last few decades is called geometric programming that provides a powerful tool for solving nonlinear problems. As we know in the real world, many applications of geometric programming are engineering design problems. Generally, engineering design problems deal with multi-objective functions, in which their objectives are often in conflicts with each other. This paper considers a solution method when the cost, the constraint coefficients, and the right-hand sides in the multi-objective geometric programming problems are imprecise and represented as interval values. This problem is reduced with the method of weighted sum to a single objective function and further by applying interval-valued function, we solve the problem by geometric programming technique. The ability of calculating the bounds of the objective value developed in this paper might help lead to more realistic modeling efforts in engineering optimization areas. Finally a numerical example is given to illustrate the methodology of solution and efficiency of the present approach.

In silico analysis of signal peptides for secretory production of a-amylase in Bacillus subtilis

α-Amylases are important commercial enzymes and have a broad application in industrial processes and medicine. Gram-positive bacteria such as Bacillus subtilis are possible host organisms for αamylases secretory production. Secretion of α-amylases to the culture medium versus intracellular production has several advantages such as prevention of inclusion bodies accumulation, higher product stability and solubility. Signal peptides are considered as one of the most essential elements for successful secretory synthesis of the recombinant proteins. Therefore, by the selection of an efficient signal peptide, secretion of the recombinant protein can be enhanced. The goal of this investigation was the in silico evaluation of several peptides to find the most suitable leader peptides for secretory production of αamylase in B. subtilis. In present work, 30 signal peptides were selected, and numerous online servers such as SignalP, ProtParam, SOLpro, PRED-TAT and ProtComp was used for investigation of suitable signal peptides. According to in silico predictions all other signal peptides connected to α-amylase were stable and soluble except PPBD_BACSU. PPBD_BACSU because of having D-score below cut-off could not be recognized as a suitable signal peptide for α-amylase. Computational analysis identified QOX2_BACSU may direct protein into transmembrane location and was ignored. All 28 remained were predicted as secretory signal peptides which can excrete protein out of the bacteria. The signal peptides recommended by the present study are valuable for rational designing of secretory soluble α-amylase. Although, such information can be useful for future experimental production of these mentioned secretory proteins.

Yoga Improves Anxiety and Vital Signs of Women with Premenstrual Syndrome

DGO: Dice Game Optimizer

In recent years, optimization algorithms have been used in many applications. Most of these algorithms are inspired by physical processes or living beings' behaviors. This article suggests a new optimization method called “Dice Gaming Optimizer“ (DGO), which simulates dice gaming laws. This algorithm is inspired by an old game and the searchers are a set of players. Each player moves in the playground based on at least one and maximum six different players called guide’s players. The number of guide’s players for each player is determined by the number of dice. DGO is tested on 23 standard benchmark test functions and also compared with eight other algorithms such as: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuckoo Search (CS), Ant-Lion Optimizer (ALO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm and Emperor Penguin Optimizer (EPO). Moreover, a real-life engineering design problem is solved by DGO. The results indicate that DGO have better performance as compared to the other well-known optimization algorithms.