Energy Metabolites as Biomarkers in Ischemic and Dilated Cardiomyopathy

With more than 25 million people affected, heart failure (HF) is a global threat. As energy production pathways are known to play a pivotal role in HF, we sought here to identify key metabolic changes in ischemic- and non-ischemic HF by using a multi-OMICS approach. Serum metabolites and mRNAseq and epigenetic DNA methylation profiles were analyzed from blood and left ventricular heart biopsy specimens of the same individuals. In total we collected serum from n = 82 patients with Dilated Cardiomyopathy (DCM) and n = 51 controls in the screening stage. We identified several metabolites involved in glycolysis and citric acid cycle to be elevated up to 5.7-fold in DCM (p = 1.7 × 10⁻⁶). Interestingly, cardiac mRNA and epigenetic changes of genes encoding rate-limiting enzymes of these pathways could also be found and validated in our second stage of metabolite assessment in n = 52 DCM, n = 39 ischemic HF and n = 57 controls. In conclusion, we identified a new set of metabolomic biomarkers for HF. We were able to identify underlying biological cascades that potentially represent suitable intervention targets.     

Behaviour of sand–clay mixtures for road pavement subgrade

Materials forming sand grains and colluvial soil deposits have a distinct structure, consisting of a composite matrix of coarse and fine soil grains. The influence of sand grains content on the behaviour of sand–clay mixtures was investigated by a series of intensive laboratory experiments. The California bearing ratio (CBR), unconfined compression strength (UCS) and compaction tests were carried out on various contents of sand and clay mixtures. The sand–clay mixtures were prepared with sand contents of 0, 10, 20, 30, 40, and 50% by weight. The laboratory tests on these mixtures have indicated that their behaviour will depend on the relative concentration of the sand and clay samples. The results of the tests showed a decrease in the UCS, and an increase the CBR values with an increase in the amount of sand. An increase in dry unit weight and a decrease in respective moisture content by an increase in the amount of sand were observed in the compaction tests.     

Genetic programming-based attenuation relationship: An application of recent earthquakes in turkey

This study investigates an application of genetic programming (GP) for the prediction of peak ground acceleration (PGA) using strong-ground-motion data from Turkey. The input variables in the developed GP model are the average shear-wave velocity, earthquake source to site distance and earthquake magnitude, and the output is the PGA values. The proposed GP model is based on the most reliable database compiled for earthquakes in Turkey. The results show that the consistency between the observed PGA values and the predicted ones by the GP model yields relatively high correlation coefficients (R²=0.75). The proposed model is also compared with an existing attenuation relationship and found to be more accurate.     

Generalized Regression Neural Networks and Feed Forward Neural Networks for prediction of scour depth around bridge piers

In this study, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN) approaches are used to predict the scour depth around circular bridge piers. Hundred and sixty five data collected from various experimental studies, are used to predict equilibrium scour depth. The model consisting of the combination of dimensional data involving the input variables is constructed. The performance of the models in training and testing sets are compared with observations. Then, the model is also tested by Multiple Linear Regression (MLR) and empirical formula. The results of all approaches are compared in order to get more reliable comparison. The results indicated that GRNN can be applied successfully for prediction of scour depth around circular bridge piers.     

Transferrin-Decorated Niosomes with Integrated InP/ZnS Quantum Dots and Magnetic Iron Oxide Nanoparticles: Dual Targeting and Imaging of Glioma

The development of multifunctional nanoscale systems that can mediate efficient tumor targeting, together with high cellular internalization, is crucial for the diagnosis of glioma. The combination of imaging agents into one platform provides dual imaging and allows further surface modification with targeting ligands for specific glioma detection. Herein, transferrin (Tf)-decorated niosomes with integrated magnetic iron oxide nanoparticles (MIONs) and quantum dots (QDs) were formulated (PEGNIO/QDs/MIONs/Tf) for efficient imaging of glioma, supported by magnetic and active targeting. Transmission electron microscopy confirmed the complete co-encapsulation of MIONs and QDs in the niosomes. Flow cytometry analysis demonstrated enhanced cellular uptake of the niosomal formulation by glioma cells. In vitro imaging studies showed that PEGNIO/QDs/MIONs/Tf produces an obvious negative-contrast enhancement effect on glioma cells by magnetic resonance imaging (MRI) and also improved fluorescence intensity under fluorescence microscopy. This novel platform represents the first niosome-based system which combines magnetic nanoparticles and QDs, and has application potential in dual-targeted imaging of glioma.     

Quality assessment of surface and groundwater in Solaklı Basin (Trabzon, Turkey)

The paper reports a study of the surface and ground water in Solakl? Basin, Trabzon, from an assessment of 33 sampling points. Many of the parameters analysed are only marginally acceptable as potable water, even though the samples were collected during the wet season. The highest concentrations of contaminants were found downstream of the centres of population. The results indicate regular monitoring is required and some measures should be taken to protect the limited resources.     

Application of an adaptive neuro-fuzzy inference system for classification of Behcet disease using the fast Fourier transform method

Abstract: In this study, ophthalmic arterial Doppler signals were obtained from 200 subjects, 100 of whom suffered from ocular Behcet disease while the rest were healthy subjects. An adaptive neuro-fuzzy inference system (ANFIS) was used to detect the presence of ocular Behcet disease. Spectral analysis of the ophthalmic arterial Doppler signals was performed by the fast Fourier transform method for determining the ANFIS inputs. The ANFIS was trained with a training set and tested with a testing set. All these data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ocular Behcet disease. Performance indicators and statistical measures were used for evaluating the ANFIS. The correct classification rate was 94% for healthy subjects and 90% for unhealthy subjects suffering from ocular Behcet disease. The classification results showed that the ANFIS was effective at detecting ophthalmic arterial Doppler signals from subjects with Behcet disease.     

Diffusion-Induced Void Evolution in Core–Shell Nanowires: Elaborated View on the Nanoscale Kirkendall Effect

In 2006, our group reported the fabrication of ultra-long single-crystal ZnAl₂O₄ spinel nanotubes, starting from ZnO/Al₂O₃ core–shell nanowires, involving the nanoscale Kirkendall effect. In this feature paper, we introduce our up-to-date results on the design of porous and hollow 1D nanostructures following this solid-state interfacial reaction route. In particular, we present our recent understandings on void evolution induced by the unbalanced diffusion. We first give a short introduction on the distinctions of core–shell nanowires in interfacial nanoreactions, in contrast to other systems. Then we discuss the roles of desorption, stress, and/or defects on void evolution and nanotube formation. Our results demonstrate that the stress- and defects-engineered diffusion mechanism can be used to specifically design hollow nanostructures, which will open up a new window in exploitation of the Kirkendall effect at the nanoscale.     

A notch strain calculation of a notched specimen under axial-torsion loadings

In this paper, a notch analysis model is presented for the numerical prediction of multiaxial strains of a notched 1070 steel specimen under combined axial and torsion loadings. The proposed model is based on the notion of a structural yield surface and uses a small-strain cyclic plasticity model to describe stress–strain relations. A notch load–strain curve is calculated with Neuber’s rule and incremental nonlinear finite element analysis. The presented model is applied to simulate the notch root deformations of a circumferentially notched specimen under cyclic tension–compression–torsion loading histories. The model predictions are evaluated with strain measurements at the notch root of the specimen in a comprehensive set of cyclic tests. The computed strain loops were in accord with experimental data and matched qualitatively with measured shear–axial strain histories irrespective of loading path of the test. In proportional balanced torsion-axial loading, the nonlinear shear strain–axial strain loops were calculated properly. The modeling errors were determined to be a function of the loading path shape, and compared to shear strains, axial strain predictions were more accurate.