Mental Health Changes and Its Predictors in Adolescents using the Path Analytic Model: A 7-Year Observational Study.

OBJECTIVE: This 7-year observational study examines the hours of TV-watching, phone conversation with friends, using the internet, and physical activity as predictors of mental health among adolescents in south of Iran. METHODS: At the baseline (in 2005), the participants were 2584 high school students in the 9th to 11th grade. At the baseline, 30% of the available participants (n = 775) were selected in the follow-up (2012) using convenience sampling method. This study used the path analysis to examine the predictors of mental health and to obtain direct, indirect and total effects of the independent variables. RESULTS: At the baseline (2005), female gender, internet use, maternal education, physical activity and father's education were associated with mental health (p<0.05). Baseline mental health, internet use and physical activity predicted mental health of the participants in the follow up (p<0.05). CONCLUSION: The findings of the study revealed that better mental health in later life is associated with better mental health at baseline, male gender, higher physical activity and phone communication with friends, and less use of the internet and TV

Distribution of the sheet current in a magnetically shielded superconducting filament

The distribution of the transport current in a superconducting filament aligned parallel to the flat surface of a semi-infinite bulk magnet is studied theoretically. An integral equation governing the current distribution in the Meissner state of the filament is derived and solved numerically for various filament-magnet distances and different relative permeabilities. This reveals that the current is depressed on the side of the filament adjacent to the surface of the magnet and enhanced on the averted side. Substantial current redistributions in the filament can already occur for low values of the relative permeability of the magnet, when the distance between the filament and the magnet is short, with evidence of saturation at moderately high values of this quantity, similar to the findings for magnetically shielded strips.Comment: 11 pages, 5 figures; submitted to Physica

Transport critical current density in Fe-sheathed nano-SiC doped MgB2 wires

The nano-SiC doped MgB2/Fe wires were fabricated using a powder-in-tube method and an in-situ reaction process. The depression of Tc with increasing SiC doping level remained rather small due to the counterbalanced effect of Si and C co-doping. The high level SiC co-doping allowed creation of the intra-grain defects and nano-inclusions, which act as effective pinning centers, resulting in a substantial enhancement in the Jc(H) performance. The transport Jc for all the wires is comparable to the magnetic Jc at higher fields despite the low density of the samples and percolative nature of current. The transport Ic for the 10wt% SiC doped MgB2/Fe reached 660A at 5K and 4.5T (Jc = 133,000A/cm2) and 540A at 20K and 2T (Jc = 108,000A/cm2). The transport Jc for the 10wt% SiC doped MgB2 wire is more than an order of magnitude higher than for the state-the-art Fe-sheathed MgB2 wire reported to date at 5K and 10T and 20K and 5T respectively. There is a plenty of room for further improvement in Jc as the density of the current samples is only 50%.Comment: 4 pages, 7 figures, presented at ASC 2002, Housto

A framework for intracranial saccular aneurysm detection and quantification using morphological analysis of cerebral angiograms

Reliable early prediction of aneurysm rupture can greatly help neurosurgeons to treat aneurysms at the right time, thus saving lives as well as providing significant cost reduction. Most of the research efforts in this respect involve statistical analysis of collected data or simulation of hemodynamic factors to predict the risk of aneurysmal rupture. Whereas, morphological analysis of cerebral angiogram images for locating and estimating unruptured aneurysms is rarely considered. Since digital subtraction angiography (DSA) is regarded as a standard test by the American Stroke Association and American College of Radiology for identification of aneurysm, this paper aims to perform morphological analysis of DSA to accurately detect saccular aneurysms, precisely determine their sizes, and estimate the probability of their ruptures. The proposed diagnostic framework, intracranial saccular aneurysm detection and quantification, first extracts cerebrovascular structures by denoising angiogram images and delineates regions of interest (ROIs) by using watershed segmentation and distance transformation. Then, it identifies saccular aneurysms among segmented ROIs using multilayer perceptron neural network trained upon robust Haralick texture features, and finally quantifies aneurysm rupture by geometrical analysis of identified aneurysmic ROI. De-identified data set of 59 angiograms is used to evaluate the performance of algorithms for aneurysm detection and risk of rupture quantification. The proposed framework achieves high accuracy of 98% and 86% for aneurysm classification and quantification, respectively

Modeling Influence of Sediment Heterogeneity on Nutrient Cycling in Streambeds

Rivers and their hyporheic zones play an important role in nutrient cycling. The fate of dissolved inorganic nitrogen is governed by reactions that occur in the water column and streambed sediments. Sediments are heterogeneous both in term of physical (e.g., hydraulic conductivity) and chemical (e.g., organic carbon content) properties, which influence water residence times and biogeochemical reactions. Yet few modeling studies have explored the effects of both physical and chemical heterogeneity on nutrient transport in the hyporheic zone. In this study, we simulated hyporheic exchange in physically and chemically heterogeneous sediments with binary distributions of sand and silt in a low-gradient meandering river. We analyzed the impact of different silt/sand patterns on dissolved organic carbon, oxygen, nitrate, and ammonium. Our results show that streambeds with a higher volume proportion of silt exhibit lower hyporheic exchange rates but more efficient nitrate removal along flow paths compared to predominantly sandy streambeds. The implication is that hyporheic zones with a mixture of inorganic sands and organic silts have a high capacity to remove nitrate, despite their moderate permeabilities

Effect of PVA doping on flux pinning in Bulk MgB2

The synthesis and characterization of PVA (Poly Vinyl Acetate) doped bulk MgB2 superconductor is reported here. PVA is used as a Carbon source. PVA doping effects made two distinguishable contributions: first enhancement of Jc field performance and second an increase in Hc2 value, both because of carbon incorporation into MgB2 crystal lattice. The susceptibility measurement reveals that Tc decreased from 37 to 36 K. Lattice parameter a decreased from 3.085 A to 3.081 A due to the partial substitution of Carbon at Boron site. PVA doped sample exhibited the Jc values greater than 10^5 A/cm2 at 5 & 10 K at low fields; which is almost 3 times higher than the pure one, while at high fields the Jc is increased by an order of magnitude in comparison to pure MgB2. From R(T)H measurements we found higher Tc values under magnetic field for doped sample; indicating an increase in Hc2. Also the magnetization measurements exhibited a significant enhancement in Hirr value. The improved performance of PVA doped MgB2 can be attributed to the substitution of carbon at boron site in parent MgB2 and the resulting impact on the carrier density and impurity scattering. The improved flux pinning behavior could easily be seen from reduced flux pinning force plots.Comment: 14 Pages of Text + Figs. To appear in Physica

Prediction of Glioblastoma Multiform Response to Bevacizumab Treatment Using Multi-Parametric MRI

Glioblastoma multiform (GBM) is a highly malignant brain tumor. Bevacizumab is a recent therapy for stopping tumor growth and even shrinking tumor through inhibition of vascular development (angiogenesis). This paper presents a non-invasive approach based on image analysis of multi-parametric magnetic resonance images (MRI) to predict response of GBM to this treatment. The resulting prediction system has potential to be used by physicians to optimize treatment plans of the GBM patients. The proposed method applies signal decomposition and histogram analysis methods to extract statistical features from Gd-enhanced regions of tumor that quantify its microstructural characteristics. MRI studies of 12 patients at multiple time points before and up to four months after treatment are used in this work. Changes in the Gd-enhancement as well as necrosis and edema after treatment are used to evaluate the response. Leave-one-out cross validation method is applied to evaluate prediction quality of the models. Predictive models developed in this work have large regression coefficients (maximum R2 = 0.95) indicating their capability to predict response to therapy