Group-based trajectory modeling for longitudinal data of healthcare financial charges in patients with inflammatory bowel disease

Inflammatory bowel disease (IBD) is a heterogeneous group of lifelong chronic inflammatory diseases with variable and unpredictable disease courses which often require significant healthcare expenditures. There exists no uniform severity measure to capture the activity and the healthcare utilization of the disease. This study seeks to identify disease trajectories for the IBD patients based on their annual financial healthcare charges over time. We performed a longitudinal study of annual financial charges using a consented, prospective, natural history registry of 2,400 IBD patients at the University of Pittsburgh Medical Center from 2009 to 2013. The annual charges were calculated as the sum of inpatient admission charges and professional service charges, with (ChargeF) or without (ChargeR) biological medicine charges. Patients who completed a five-year follow-up were included in the study. The continuous financial charges were first categorized into sections of different price range, and then the data was fitted with a latent group-based zero-inflated Poisson model to identify different homogeneous trajectory patterns of financial charges. We identified six distinct trajectory groups of total annual charges obtained from each of the two calculation methods (ChargeF and ChargeR). We further compared between these trajectories for patient characteristics, disease activity indices (Harvey-Bradshaw Index and ulcerative colitis activity index), disease activity markers (high-sensitivity C-reactive protein and erythrocyte sedimentation rate), health-related quality of life index (short inflammatory bowel disease questionnaire, SIBDQ), healthcare utilization (emergency department, hospitalization, and surgery), and corticosteroid prescriptions. We concluded that the healthcare financial charge could be a novel and uniform metric to evaluate the disease severity and the response of IBD patients to treatments. The present study is the first of its kind using latent group-based trajectory modeling of financial charges to identify distinct subsets of IBD patients with their response to treatments. The model could be used to determine the genetic, environmental, and other factors that influence disease severity and the patient’s response to medical therapies. It will provide important information for the development of personalized or precision medical interventions for IBD patients and the reduction of their health care cost. Public Health Relevance: This study proposed a new metric which could be an accurate reflection of classic disease activity parameters, biochemical markers of inflammation, disease activity indices, and health-related quality of life in a cohort of patients with inflammatory bowel disease. The model developed would be of great significance to exploring the risk factors that influence the response to medical interventions. It will provide important information for the development of personalized or precision medical interventions for patients with inflammatory bowel disease and the reduction of their health care cost

Entity-sensitive attention and fusion network for entity-level multimodal sentiment classification

National Research Foundation (NRF) Singapor

CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images

With the powerfulness of convolution neural networks (CNN), CNN based face reconstruction has recently shown promising performance in reconstructing detailed face shape from 2D face images. The success of CNN-based methods relies on a large number of labeled data. The state-of-the-art synthesizes such data using a coarse morphable face model, which however has difficulty to generate detailed photo-realistic images of faces (with wrinkles). This paper presents a novel face data generation method. Specifically, we render a large number of photo-realistic face images with different attributes based on inverse rendering. Furthermore, we construct a fine-detailed face image dataset by transferring different scales of details from one image to another. We also construct a large number of video-type adjacent frame pairs by simulating the distribution of real video data. With these nicely constructed datasets, we propose a coarse-to-fine learning framework consisting of three convolutional networks. The networks are trained for real-time detailed 3D face reconstruction from monocular video as well as from a single image. Extensive experimental results demonstrate that our framework can produce high-quality reconstruction but with much less computation time compared to the state-of-the-art. Moreover, our method is robust to pose, expression and lighting due to the diversity of data.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence, 201

An integrated vertex model of the mesoderm invagination during the embryonic development of Drosophila

The mesoderm invagination of the Drosophila embryo is known as an archetypal morphogenic process. To explore the roles of the active cellular forces and the regulation of these forces, we developed an integrated vertex model that combines the regulation of morphogen expression with cell movements and tissue mechanics. Our results suggest that a successful furrow formation requires an apical tension gradient, decreased basal tension, and increased lateral tension, which corresponds to apical constriction, basal expansion, and apicobasal shortening respectively. Our model also considers the mechanical feedback which leads to an ectopic twist expression with external compression as observed in experiments. Our model predicts that ectopic invagination could happen if an external compressive gradient is applied

Stress and Strength Analysis of Non-Right Angle H-section Beam

In this paper, according to the design requirements of a steel structural project, based on the principle of structural mechanics of thin-walled bar, the non-right angle H-section, which is subjected to bending moment and shear force, is taken as the object of study, the formulas of bending normal stress and shear stress are deduced. On this basis, the distribution of bending stress and shear stress and the location of dangerous stress are analyzed, the calculation method of section strength is discussed, and the FEA software ABAQUS is used to verify the above.&nbsp

Precessing cylinders at the second and third resonance: Turbulence controlled by geostrophic flow

Copyright © 2015 American Physical SocietyWe investigate, via both asymptotic analysis and direct numerical simulation, precessionally driven flow of a homogeneous fluid confined in fluid-filled circular cylinders that rotate rapidly about their symmetry axis and precess about a different axis and that are marked by radius-height aspect ratios Γ=1.045945 and Γ=1.611089 . At these radius-height aspect ratios, the Poincaré force resonates directly with the two special inertial modes that have the simplest vertical structure. An asymptotic analytical solution in closed form describing weakly precessing flow is derived in the mantle frame of reference for asymptotically small Ekman numbers, showing quantitative agreement with the result of direct nonlinear numerical simulation. Our numerical simulation makes use of a finite-element method with the three-dimensional tetrahedralization of a cylindrical cavity that allows the construction of dense nodes in the vicinity of the bounding surface of the cavity for resolving the thin viscous boundary layer. It is found that axisymmetric geostrophic flow in the alternating eastward and westward direction can be generated and maintained by nonlinear and viscous effects in the viscous boundary layer. It is also found that, when the precessing rate is moderate and, consequently, the geostrophic flow is weak, nonlinear interaction between the resonant inertial mode and the nonesonant inertial modes driven by the Poincaré force and the boundary-layer influx leads to strongly turbulent flow with irregular temporal-spatial fluctuation. When the cylinders are strongly precessing such that the geostrophic flow becomes predominant, however, the effect of the geostrophic flow controls/stabilizes its nonlinear dynamics, leading to weakly turbulent flow that can be largely described by a dominant quasisteady geostrophic component and a weak nonaxisymmetric component localized in the region where the geostrophic flow is weak.Leverhulme TrustMacau FDCTNSF

Investigating the Effects of Dimension-Specific Sentiments on Product Sales: The Perspective of Sentiment Preferences

While the literature has reached a consensus on the awareness effect of online word-of-mouth (eWOM), this paper studies its persuasive effect—specifically, dimension-specific sentiment effects on product sales.We examine the sentiment information in eWOM along different product dimensions and reveal different persuasive effects on consumers’ purchase decisions based on consumers’ sentiment preference, which is defined as the relative importance that consumers place on various dimension-specific sentiments. We use an aspect-level sentiment analysis to derive dimension-specific sentiment and PVAR (panel vector auto-regression) models, and estimate their effects on product sales using a movie panel dataset. The findings show that three dimension-specific sentiments (star, genre, and plot) are positively related to movie sales.Regarding consumers’ sentiment preferences, we find a positive relationship to movie sales that is stronger for plot sentiment, relative to star sentiment for low-budget movies. For high-budget movies, we find a positive relationship to movie sales that is stronger for star sentiment, relative to plot or genre sentiment

AntFuzzer: A Grey-Box Fuzzing Framework for EOSIO Smart Contracts

In the past few years, several attacks against the vulnerabilities of EOSIO smart contracts have caused severe financial losses to this prevalent blockchain platform. As a lightweight test-generation approach, grey-box fuzzing can open up the possibility of improving the security of EOSIO smart contracts. However, developing a practical grey-box fuzzer for EOSIO smart contracts from scratch is time-consuming and requires a deep understanding of EOSIO internals. In this work, we proposed AntFuzzer, the first highly extensible grey-box fuzzing framework for EOSIO smart contracts. AntFuzzer implements a novel approach that interfaces AFL to conduct AFL-style grey-box fuzzing on EOSIO smart contracts. Compared to black-box fuzzing tools, AntFuzzer can effectively trigger those hard-to-cover branches. It achieved an improvement in code coverage on 37.5% of smart contracts in our benchmark dataset. AntFuzzer provides unified interfaces for users to easily develop new detection plugins for continually emerging vulnerabilities. We have implemented 6 detection plugins on AntFuzzer to detect major vulnerabilities of EOSIO smart contracts. In our large-scale fuzzing experiments on 4,616 real-world smart contracts, AntFuzzer successfully detected 741 vulnerabilities. The results demonstrate the effectiveness and efficiency of AntFuzzer and our detection p