Cerebral amyloid angiopathy-related inflammation (CAA-RI) is a rare but increasingly recognized subtype of CAA. CAA-RI consists of two subtypes: inflammatory cerebral amyloid angiopathy and amyloid β (Aβ)-related angiitis. Acute or subacute onset of cognitive decline or behavioral changes is the most common symptom of CAA-RI. Rapid progressive dementia, headache, seizures, or focal neurological deficits, with patchy or confluent hyperintensity on T2 or fluid-attenuated inversion recovery sequences and evidence of strictly lobar microbleeds or cortical superficial siderosis on susceptibility-weighted imaging imply CAA-RI. The gold standard for diagnosis is autopsy or brain biopsy. However, biopsy is invasive; consequently, most clinically diagnosed cases have been based on clinical and radiological data. Other diagnostic indexes include the apolipoprotein E ε4 allele, Aβ and anti-Aβ antibodies in cerebral spinal fluid and amyloid positron emission tomography. Many diseases with similar clinical manifestations should be carefully ruled out. Immunosuppressive therapy is effective both during initial presentation and in relapses. The use of glucocorticoids and immunosuppressants improves prognosis. This article reviews the pathology and pathogenesis, clinical and imaging manifestations, diagnostic criteria, treatment, and prognosis of CAA-RI, and highlights unsolved problems in the existing research.

Pavement distortions, such as rutting and shoving, are the common pavement distress problems that need to be inspected and repaired in a timely manner to ensure ride quality and traffic safety. This paper introduces a real-time, low-cost inspection system devoted to detecting these distress features using high-speed 3D transverse scanning techniques. The detection principle is the dynamic generation and characterization of the 3D pavement profile based on structured light triangulation. To improve the accuracy of the system, a multi-view coplanar scheme is employed in the calibration procedure so that more feature points can be used and distributed across the field of view of the camera. A sub-pixel line extraction method is applied for the laser stripe location, which includes filtering, edge detection and spline interpolation. The pavement transverse profile is then generated from the laser stripe curve and approximated by line segments. The second-order derivatives of the segment endpoints are used to identify the feature points of possible distortions. The system can output the real-time measurements and 3D visualization of rutting and shoving distress in a scanned pavement.

No abstract

Objective Abdominal visceral adiposity is related to risks for insulin resistance and metabolic perturbations. Magnetic resonance imaging (MRI) and computed tomography are advanced instruments that quantify abdominal adiposity; yet field use is constrained by their bulkiness and costliness. The purpose of this study is to develop prediction equations for total abdominal, subcutaneous, and visceral adiposity via anthropometrics, stereovision body imaging (SBI), and MRI. Design and Methods Participants (67 men and 55 women) were measured for anthropometrics, and abdominal adiposity volumes evaluated by MRI umbilicus scans. Body circumferences and central obesity were obtained via SBI. Prediction models were developed via multiple linear regression analysis, utilizing body measurements and demographics as independent predictors, and abdominal adiposity as a dependent variable. Cross-validation was performed by the data-splitting method. Results The final total abdominal adiposity prediction equation was –470.28+7.10waist circumference–91.01gender+5.74sagittal diameter (R²=89.9%); subcutaneous adiposity was –172.37+8.57waist circumference–62.65gender–450.16stereovision waist-to-hip ratio (R²=90.4%); and visceral adiposity was –96.76+11.48central obesity depth–5.09 central obesity width+204.74stereovision waist-to-hip ratio–18.59gender (R²=71.7%). R² significantly improved for predicting visceral fat when SBI variables were included, but not for total abdominal or subcutaneous adiposity. Conclusions SBI is effective for predicting visceral adiposity and the prediction equations derived from SBI measurements can assess obesity.

Magnetic separatrix is an important boundary layer separating the inflow and outflow regions in magnetic reconnection. In this article, we investigate the sub-structures of the separatrix region by using two-and-half dimensional electromagnetic particle-in-cell simulation. The separatrix region can be divided into two sub-regions in terms of the ion and electron frozen-in conditions. Far from the neutral sheet, ions and electrons are magnetized in magnetic fields. Approaching the neutral sheet, ion frozen-in condition is broken in a narrow region ($c/x pi ) at the edge of a density cavity, while electrons are frozen-in to magnetic fields. In this region, electric field E z is around zero, and the convective term -(v i  B) is balanced by the Hall term in the generalized Ohm's law because ions carry the perpendicular current. Inside the density cavity, both ion and electron frozen-in conditions are broken. The region consists of two sub-ion or electron-scale layers, which contain intense electric fields. Formation of the two sub-layers is due to the complex electron flow pattern around the separatrix region. In the layer, E z is balanced by a combination of Hall term and the divergence of electron pressure tensor, with the Hall term being dominant. Our preliminary simulation result shows that the separatrix region in guide field reconnection also contains two sub-regions: the inner region and the outer region. However, the inner region contains only one current layer in contrast with the case without guide field. V C 2012 American Institute of Physics. [http://dx.