Piezoelectrically transduced high-Q silica micro resonators,"

ABSTRACT In this paper, we report on high-performance piezoelectric-on-silica micromechanical resonators for integrated timing applications. Fused silica is used as the resonator structural material for its excellent material properties, and thin film aluminum nitride is used as the piezoelectric transduction layer. A silica resonator is demonstrated with a high quality factor (Q U ~25,841), low motional impedance (R m ~350 ȍ), and good power handling capability. The measured fuQ product of this resonator is the highest amongst reported micromachined silica/fused quartz resonators

Neuro-protective effects of cerium and yttrium oxide nanoparticles on high glucose-induced oxidative stress and apoptosis in undifferentiated PC12 cells

Objective: Oxidative stress has been recognized as the major factor for the development of diabetes and its complications. Cerium oxide and Yttrium oxide nanoparticles are known as free radicals scavengers. The aim of this study was to investigate the protective effect of CeO2and Y2O3on oxidative stress induced by high glucose in undifferentiated rat pheochromocytoma (PC12) cells. Methods: In this study, undifferentiated PC12 cells were exposed to high glucose (25 mg/ml, 24 hours) and the protective effects of CeO2and Y2O3nanoparticles were evaluated. The viability of undifferentiated PC12 cells was determined by MTT assay. The levels of reactive oxygen species (ROS) were measured using 2,7-dichlorodihydrofluorescein diacetate (DCF). The expression levels of pro-apoptotic Bax, antiapoptotic Bcl-2 and caspase3 proteins were also detected by western blotting. Total antioxidant power (TAP), total thiol molecules (TTM) and lipid peroxidation (LPO) were also evaluated. Results: CeO2and Y2O3increased survival of undifferentiated PC12 cells exposed to high glucoseinduced oxidative stress. CeO2and Y2O3pre-treatment decreased ROS production, LPO, Bax and caspase-3 proteins expression. Both nanoparticles have also increased the TTM and Bcl-2 protein expression. Discussion: These findings suggest that CeO2and Y2O3protect the undifferentiated PC12 cells against the oxidative stress and apoptosis induced by high glucose. © W. S. Maney and Son Ltd 2015

DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues

Background: P4 medicine (predict, prevent, personalize, and participate) is a new approach to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and treatment of diseases, prediction plays a fundamental role. One of the intelligent strategies is the design of deep learning models that can predict the state of the disease using gene expression data. Results: We create an autoencoder deep learning model called DeeP4med, including a Classifier and a Transferor that predicts cancer's gene expression (mRNA) matrix from its matched normal sample and vice versa. The range of the F1 score of the model, depending on tissue type in the Classifier, is from 0.935 to 0.999 and in Transferor from 0.944 to 0.999. The accuracy of DeeP4med for tissue and disease classification was 0.986 and 0.992, respectively, which performed better compared to seven classic machine learning models (Support Vector Classifier, Logistic Regression, Linear Discriminant Analysis, Naive Bayes, Decision Tree, Random Forest, K Nearest Neighbors). Conclusions: Based on the idea of DeeP4med, by having the gene expression matrix of a normal tissue, we can predict its tumor gene expression matrix and, in this way, find effective genes in transforming a normal tissue into a tumor tissue. Results of Differentially Expressed Genes (DEGs) and enrichment analysis on the predicted matrices for 13 types of cancer showed a good correlation with the literature and biological databases. This led that by using the gene expression matrix, to train the model with features of each person in a normal and cancer state, this model could predict diagnosis based on gene expression data from healthy tissue and be used to identify possible therapeutic interventions for those patients

Prevalence and determinants of chronic kidney disease in northeast of Iran: Results of the Golestan cohort study

The burden of chronic kidney disease (CKD) is increasing globally in particular in fast emerging economies such as Iran. Population-based studies on prevalence of CKD in Iran are scarce. The objective of the current study was to explore the prevalence and determinants of CKD in the setting of Golestan Cohort Study (GCS), the largest prospective cohort in the Middle East. Methods In this observational study, 11,409 participants enrolled in the second phase of GCS were included. Sex, age, literacy, residence, anthropometric measurements, smoking, opium use, self-reported history of cardiovascular diseases (heart disease and/or stroke), hypertension, diabetes, and lipid profile were the predictors of interest. The outcomes of interest were eGFR and CKD defined as eGFR 60 ml/min/1.73m2. Results Mean (SD) of GFR was 70.0 ± 14.7 ml/min/1.73m2 among all participants, 68.2 ± 14.2 among women, and 72.0 ± 15.0 among men. Prevalence of CKD was 23.7 (26.6 in women, 20.6 in men). The prevalence of CKD stages 3a, 3b, 4, and 5 were 20.0, 3.3, 0.4 and 0.1, respectively. Female sex, older age, urban residence, history of CVD, hypertension or diabetes, larger body mass and surrogates of body fat and opium use were all associated with CKD. Opium had a significant positive association with CKD in adjusted model. All anthropometric measurements had positive linear association with CKD. Being literate had inverse association. Sex had significant interaction with anthropometric indices, with higher odds ratios among men compared with women. A significantly high association was observed between the rate of change in waist circumference and systolic blood pressure with risk of CKD. Conclusion One in four people in this cohort had low eGFR. Obesity and overweight, diabetes, hypertension, and dyslipidemia are major risk factors for CKD. Halting the increase in waist circumference and blood pressure may be as important as reducing the current levels. © 2017 Sepanlou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Dependency of recrystallization mechanism to the initial grain size

The effect of initial grain size on the recrystallization behavior of a type 304 austenitic stainless steel during and following hot deformation was investigated using hot torsion. The refinement of the initial grain size to 8 μm, compared with an initial grain size of 35 μm, had considerable effects on the dynamic recrystallization (DRX) and post-DRX phenomena. For both DRX and post-DRX, microstructural investigations using electron backscattered diffraction confirmed an interesting transition from conventional (discontinuous) to continuous DRX with a decrease in the initial grain size. Also, there were unexpected effects of initial grain size on DRX and post-DRX grain sizes