Search

Purpose: Considering the role that students' diverse identifications play in education, this paper aims to contribute to the developing discourse on multicultural education concerning the status of ethnic, religious and linguistic diversity in Iranian upstream educational documents (UEDs). Design/methodology/approach: This paper explores the status of ethnic, religious and linguistic diversity in Iranian UEDs through quantitative and qualitative content analysis. Findings: Findings show that three continuous patterns are repeated throughout the documents: (1) The so-called diversity: although there are promising and positive statements about diversity in the documents, they are indirect, and this level of attention is very superficial and limited; (2) Affirming assimilation: the documents are covered by presenting the Islamic-Iranian identity, and somehow almost all the issues raised in the documents end up in this pattern; and (3) Pattern of contradictions: content analysis shows that the content of documents does not have intellectual and logical coherence. Practical implications: This research paper points to the need for a systematic analysis of ignoring diversity operating in UEDs in Iran. Originality/value: To the best of the authors' knowledge, this paper broadens the document analysis as one of the first research to critically explore the depiction of diversity in important UEDs in Iran.

The index of Erodible Fraction (EF) component against wind is well known as a criteria of soil wind erodibility. This study was conducted to estimate the index of EF by utilizing support vector machine (SVM), artificial neural network (ANN) and genetic algorithm (GA-ANN) methods. For this reason, 95 soil samples were collected 10 cm above the soil surface of Allahabad plain in Qazvin province. Then the percentage of aggregates with a diameter of less than 0.84 mm, pH, EC, soil saturation capacity, SAR, equivalent calcium carbonate, organic matter, clay, sand and silt were measured and used in SVM, ANN and GA-ANNs model construction and testing. Results revealed that the EF had a significant correlation at the level of 1% with five soil characteristics, including pH, EC, SAR, clay and organic matter. On the other hand, the ANNs model developed for EF prediction had higher accuracy (R2 = 0.49) than the GA-ANNs model (R2 = 0.37) and SVMs model (0.02). All models were approximately overestimated and the GMER values for SVMs, ANNs and GA-ANNs models were 1.11, 1.15 and 1.08, respectively. However, according to the Akaike information criterion (AIC), all of them had similar power model estimation. Although SVMs offer good accuracy and perform fast prediction in this study, it did not perform well. This issue may be related to the complexity of the relationship between EF and soil properties. [ABSTRACT FROM AUTHOR]

Objective(s): Polycystic ovary syndrome (PCOS) is one of the main causes of infertility in women. This study was conducted to uncover the effects of lupeol as an anti-androgenic triterpene on experimentally-induced PCOS in mice. Materials and Methods: Eighty immature female mice were divided into 4 groups: Control (C), PCOS (P), Lupeol (L), and Flutamide (F). PCOS was induced in test groups by injection of Dehydroepiandrosterone (60 mg/kg/day, IP) for twenty days. Following the PCOS induction, the two groups of L and F were treated with lupeol (40 mg/kg/day) and/or flutamide (10 mg/kg/day) respectively and the two groups of C and P received sesame oil (0.1 ml/mouse/day) for 15 days. After the treatment period, ten animals in each group were selected for collecting blood and ovary samples. In vitro fertilization assessment was carried out on 10 remaining mice in each group. The hormonal assays and oxidative stress biomarker determination were performed on serum and tissue samples. Moreover, histopathological analyses were conducted on the ovaries. Results: PCOS-elevated concentration of LH and Testosterone was significantly (P<0.05) lowered in lupeol and flutamide-received animals. Lupeol and flutamide both reduced PCOS-induced fibrosis and the number of atretic follicles. Both compounds declined the PCOS-increased lipid peroxidation and protein oxidation in serum and the ovaries. Lupeol increased the PCOS-reduced fertility rate and decreased the number of arrested embryos by 12%. Conclusion: These findings indicate that lupeol could be a novel compound in the treatment of PCOS as it reduced PCOS-induced structural and also functional disorders. [ABSTRACT FROM AUTHOR]

Purpose: Quantitative MRI finds important applications in clinical and research studies. However, it is encoding intensive and may suffer from prohibitively long scan times. Accelerated MR parametermapping techniques have been developed to help address these challenges. Here, an accelerated joint T1, T2*, frequency and proton density mapping technique with scan-specific self-supervised network reconstruction is proposed to synergistically combine parallel imaging, model-based, and deep learning approaches to speed up parameter mapping. Methods: Proposed framework, Joint MAPLE, includes parallel imaging, signal modeling, and data consistency blocks which are optimized jointly in a combined loss function. A scan-specific self-supervised reconstruction is embedded into the framework, which takes advantage of multi-contrast data from a multi-echo, multi-flip angle, gradient echo acquisition. Results: In comparison with parallel reconstruction techniques powered by low-rank methods, emerging scan specific networks, and model-based T2* estimation approaches, the proposed framework reduces the reconstruction error in parameter maps by approximately two-fold on average at acceleration rates as high as R=16 with uniform sampling. It can outperform evaluated parallel reconstruction techniques up to four-fold on average in the presence of challenging sub-sampling masks. It is observed that Joint MAPLE performs well at extreme acceleration rates of R=25 and R=36 with error values less than 20%. Conclusion: Joint MAPLE enables higher fidelity parameter estimation at high acceleration rates by synergistically combining parallel imaging and model-based parameter mapping and exploiting multi-echo, multi-flip angle datasets. Utilizing a scan-specific self-supervised reconstruction obviates the need for large data sets for training while improving the parameter estimation ability. [ABSTRACT FROM AUTHOR]