Systemic risk and spatiotemporal dynamics of the US housing market

Housing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market (1975–2011) at the state level based on the Random Matrix Theory (RMT). We identify richer economic information in the largest eigenvalues deviating from RMT predictions for the housing market than for stock markets and find that the component signs of the eigenvectors contain either geographical information or the extent of differences in house price growth rates or both. By looking at the evolution of different quantities such as eigenvalues and eigenvectors, we find that the US housing market experienced six different regimes, which is consistent with the evolution of state clusters identified by the box clustering algorithm and the consensus clustering algorithm on the partial correlation matrices. We find that dramatic increases in the systemic risk are usually accompanied by regime shifts, which provide a means of early detection of housing bubbles.HM, WJX, ZQJ and WXZ received support from the National Natural Science Foundation of China Grant 11075054 and 71131007, the Shanghai (Follow-up) Rising Star Program Grant 11QH1400800, the Shanghai "Chen Guang'' Project Grant 2012CG34, and Fundamental Research Funds for the Central Universities. BP and HES received support from the Defense Threat Reduction Agency (DTRA), the Office of Naval Research (ONR), and the National Science Foundation (NSF) Grant CMMI 1125290. (11075054 - National Natural Science Foundation of China; 71131007 - National Natural Science Foundation of China; 11QH1400800 - Shanghai (Follow-up) Rising Star Program; 2012CG34 - Shanghai "Chen Guang'' Project; Fundamental Research Funds for the Central Universities; Defense Threat Reduction Agency (DTRA); Naval Research (ONR); CMMI 1125290 - National Science Foundation (NSF))Published versio

Buccal Transmucosal Delivery System of Enalapril for Improved Cardiac Drug Delivery: Preparation and Characterization

Purpose: To prepare and characterize buccal transmucosal delivery system of enalapril maleate for overcoming its low bioavailability, and hence provide improved therapeutic efficacy and patient compliance.Methods: Transmucosal drug delivery systems of enalapril maleate were formulated as buccal films by solvent casting technique using polyvinylpyrrolidone K90, hydroxypropyl methylcellulose, sodium carboxymethylcellulose (high viscosity). The films were evaluated for film weight, thickness, folding endurance, drug content uniformity, surface pH, in vitro residence time, in vitro drug release and ex-vivo permeation.Results: All the formulations showed high drug content (96.45 to 98.49 %). Those with good swelling showed good residence time. In vitro drug release was highest for films prepared with high viscosity grade sodium carboxymethylcellulose (SCMC- HV,F2), releasing 92.24 % of drug in 1.5 h) followed by F4 (containing polyvinyl pyrrolidone K-90 1 % w/v and SCMC (HV) 1 % w/v). Ex-vivo drug permeation at the end of 10 h was 82.24 and 89.9 % for F2 and F4, respectively.Conclusion: Prompt drug release was obtained from the formulation (F2) containing SCMC 2 % w/v with 10 mg enalapril. However, on the basis of the highest swelling and residence time, and controlled drug release, formulation F4 (containing PVP K-90 and SCMC HV) would be suitable for the development of buccal film for effective therapy of cardiac diseases.Keywords: Cardiac disease, Transmucosal, Buccal films, Enalapril maleate, Drug release, Ex-vivo permeatio

Deep Latent Regularity Network for Modeling Stochastic Partial Differential Equations

Stochastic partial differential equations (SPDEs) are crucial for modelling dynamics with randomness in many areas including economics, physics, and atmospheric sciences. Recently, using deep learning approaches to learn the PDE solution for accelerating PDE simulation becomes increasingly popular. However, SPDEs have two unique properties that require new design on the models. First, the model to approximate the solution of SPDE should be generalizable over both initial conditions and the random sampled forcing term. Second, the random forcing terms usually have poor regularity whose statistics may diverge (e.g., the space-time white noise). To deal with the problems, in this work, we design a deep neural network called Deep Latent Regularity Net (DLR-Net). DLR-Net includes a regularity feature block as the main component, which maps the initial condition and the random forcing term to a set of regularity features. The processing of regularity features is inspired by regularity structure theory and the features provably compose a set of basis to represent the SPDE solution. The regularity features are then fed into a small backbone neural operator to get the output. We conduct experiments on various SPDEs including the dynamic Φ^{4}_{1} model and the stochastic 2D Navier-Stokes equation to predict their solutions, and the results demonstrate that the proposed DLR-Net can achieve SOTA accuracy compared with the baselines. Moreover, the inference time is over 20 times faster than the traditional numerical solver and is comparable with the baseline deep learning models

Diagnosis of pulmonary tuberculosis among asymptomatic HIV+ patients in Guangxi, China

BACKGROUND: Pulmonary tuberculosis (PTB) among asymptomatic Chinese patients with HIV infection has not been investigated despite high tuberculosis burden in China. This study was aimed to evaluate the prevalence, risk factors and clinical outcomes of PTB among asymptomatic patients with HIV/AIDS in Guangxi to facilitate the development of diagnostic and treatment strategies. METHODS: All asymptomatic adult HIV-infected patients with CD4 < 350 cells/µl who attended four HIV clinics in Guangxi between August 2006 and March 2008 were evaluated for active PTB with physical examination, chest X-ray (CXR), sputum smear and/or sputum liquid culture. Data were described using median (interquartile range, IQR) and frequencies. Univariate and multivariate Logistic regression analyses were performed to identify risk factors associated with PTB. RESULTS: Among 340 asymptomatic subjects, 15 (4%) were diagnosed with PTB, with 4 (27%) sputum smear positive and 8 (53%) sputum culture positive. CXR has higher diagnostic sensitivity (87%) than sputum smear (25%) and sputum culture (67%), but lower specificity (56%) compared with sputum smear (99%) and culture (100%). In univariate analysis, injection drug user, body mass index (BMI) < 18 kg/m(2), CD4 < 50 cells/µl and presence of peripheral lymphadenopathy were associated with an increased risk of asymptomatic PTB, while in multivariate analysis only peripheral lymphadenopathy maintained statistical significance (OR = 7.6, 95%CI 1.4 - 40). Patients with negative smear and minor or no abnormalities on CXR had longer interval between screening and TB treatment. CONCLUSIONS: PTB was relatively common in this group of HIV(+) asymptomatic Chinese patients. Diagnosis is challenging especially where sputum culture is unavailable. These findings suggest that an enhanced evaluation for PTB needs to be integrated with HIV care in China and transmission prevention in China to control at both households and health care facilities, especially for patients with factors associated with a higher risk of PTB

Numerical strategy on the grid orientation effect in the simulation for two-phase flow in porous media by using the adaptive artificial viscosity method

It is a challenge to numerically solve nonlinear partial differential equations whose solution involves discontinuity. In the context of numerical simulators for multi-phase flow in porous media, there exists a long-standing issue known as Grid Orientation Effect (GOE), wherein different numerical solutions can be obtained when considering grids with different orientations under certain unfavorable conditions. Our perspective is that GOE arises due to numerical instability near displacement fronts, where spurious oscillations accompanied by sharp fronts, if not adequately suppressed, lead to GOE. To reduce or even eliminate GOE, we propose augmenting adaptive artificial viscosity when solving the saturation equation. It has been demonstrated that appropriate artificial viscosity can effectively reduce or even eliminate GOE. The proposed numerical method can be easily applied in practical engineering problems.Comment: 10 page

Comparison of the subjective satisfaction of the donor site morbidity : free radial forearm flap versus anterolateral thigh flap for reconstruction in tongue cancer patients

The purpose of the study was to compare the differences of the subjective satisfaction of the donor site morbidity between the free radial forearm flap (FRFF) and anterolateral thigh flap (ALTF) for tongue reconstruction. One hundred and nineteen patients underwent FRFF or ALTF reconstruction were retrospectively evaluated by a standardized self-established donor site morbidity questionnaire which included 5 domains, sensibility, movement disabilities, cosmetics, social activities and general impacts on the quality of life. The Cronbach?s coefficient alpha of the questionnaire was 0.707. The exploratory factor analysis revealed that the 5 items of the questionnaire might load onto two distinct subscales. Patients with ALTF had higher scores in the sensibility, cosmetics and the composite score (P 0.05). ALTF has the advantage of better results of donor site morbidity, such as sensibility and cosmetics, over FRFF