Population Density-based Hospital Recommendation with Mobile LBS Big Data

The difficulty of getting medical treatment is one of major livelihood issues in China. Since patients lack prior knowledge about the spatial distribution and the capacity of hospitals, some hospitals have abnormally high or sporadic population densities. This paper presents a new model for estimating the spatiotemporal population density in each hospital based on location-based service (LBS) big data, which would be beneficial to guiding and dispersing outpatients. To improve the estimation accuracy, several approaches are proposed to denoise the LBS data and classify people by detecting their various behaviors. In addition, a long short-term memory (LSTM) based deep learning is presented to predict the trend of population density. By using Baidu large-scale LBS logs database, we apply the proposed model to 113 hospitals in Beijing, P. R. China, and constructed an online hospital recommendation system which can provide users with a hospital rank list basing the real-time population density information and the hospitals' basic information such as hospitals' levels and their distances. We also mine several interesting patterns from these LBS logs by using our proposed system

Mitochondrial redox signaling by p66Shc is involved in regulating androgenic growth stimulation of human prostate cancer cells.

p66Shc is shown to negatively regulate the life span in mice through reactive oxygen species (ROS) production. Recent reports, however, revealed that p66Shc protein level is significantly elevated in several human cancer tissues and growth-stimulated carcinoma cells, suggesting a mitogenic and carcinogenic role for p66Shc. In this communication, we demonstrate for the first time that p66Shc mediates androgenic growth signals in androgen-sensitive human prostate cancer cells through mitochondrial ROS production. Growth stimulation of prostate cancer cells with 5alpha-dihydrotestosterone (DHT) is accompanied by increased p66Shc level and ROS production, which is abolished by antioxidant treatments. However, antioxidant treatments do not affect the transcriptional activity of androgen receptor (AR) as observed by its inability to block DHT-induced prostate-specific antigen expression, an AR-dependent correlate of prostate cancer progression. Elevated expression of p66Shc by cDNA transfection increases the basal cell proliferation and, thus, reduces additional DHT-induced cell proliferation. Furthermore, DHT increases the translocation of p66Shc into mitochondria and its interaction with cytochrome c. Conversely, both redox-negative p66Shc mutant (W134F), which is deficient in cytochrome c interaction, and p66Shc small interfering RNA decrease DHT-induced cell proliferation. These results collectively reveal a novel role for p66Shc-ROS pathway in androgen-induced prostate cancer cell proliferation and, thus, may play a role in early prostate carcinogenesis

Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks

Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics. As a result, these models suffer from accuracy decay over a long time and thus require frequent calibration. In this work, we address this issue by formulating BP estimation as a sequence prediction problem in which both the input and target are temporal sequences. We propose a novel deep recurrent neural network (RNN) consisting of multilayered Long Short-Term Memory (LSTM) networks, which are incorporated with (1) a bidirectional structure to access larger-scale context information of input sequence, and (2) residual connections to allow gradients in deep RNN to propagate more effectively. The proposed deep RNN model was tested on a static BP dataset, and it achieved root mean square error (RMSE) of 3.90 and 2.66 mmHg for systolic BP (SBP) and diastolic BP (DBP) prediction respectively, surpassing the accuracy of traditional BP prediction models. On a multi-day BP dataset, the deep RNN achieved RMSE of 3.84, 5.25, 5.80 and 5.81 mmHg for the 1st day, 2nd day, 4th day and 6th month after the 1st day SBP prediction, and 1.80, 4.78, 5.0, 5.21 mmHg for corresponding DBP prediction, respectively, which outperforms all previous models with notable improvement. The experimental results suggest that modeling the temporal dependencies in BP dynamics significantly improves the long-term BP prediction accuracy.Comment: To appear in IEEE BHI 201

Beyond the design of automated writing evaluation: Pedagogical practices and perceived learning effectiveness in EFL writing classes

Automated writing evaluation (AWE) software is designed to provide instant computer-generated scores for a submitted essay along with diagnostic feedback. Most studies on AWE have been conducted on psychometric evaluations of its validity; however, studies on how effectively AWE is used in writing classes as a pedagogical tool are limited. This study employs a naturalistic classroom-based approach to explore the interaction between how an AWE program, MY Access!, was implemented in three different ways in three EFL college writing classes in Taiwanand how students perceived its effectiveness in improving writing. The findings show that, although the implementation of AWE was not in general perceived very positively by the three classes, it was perceived comparatively more favorably when the program was used to facilitate students’ early drafting and revising process, followed by human feedback from both the teacher and peers during the later process. This study also reveals that the autonomous use of AWE as a surrogate writing coach with minimal human facilitation caused frustration to students and limited their learning of writing. In addition, teachers’ attitudes toward AWE use and their technology-use skills, as well as students’ learner characteristics and goals for learning to write, may also play vital roles in determining the effectiveness of AWE. With limitations inherent in the design of AWE technology, language teachers need to be more critically aware that the implementation of AWE requires well thought-out pedagogical designs and thorough considerations for its relevance to the objectives of the learning of writing

Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation

BACKGROUND: During the last few years, the knowledge of drug, disease phenotype and protein has been rapidly accumulated and more and more scientists have been drawn the attention to inferring drug-disease associations by computational method. Development of an integrated approach for systematic discovering drug-disease associations by those informational data is an important issue. METHODS: We combine three different networks of drug, genomic and disease phenotype and assign the weights to the edges from available experimental data and knowledge. Given a specific disease, we use our network propagation approach to infer the drug-disease associations. RESULTS: We apply prostate cancer and colorectal cancer as our test data. We use the manually curated drug-disease associations from comparative toxicogenomics database to be our benchmark. The ranked results show that our proposed method obtains higher specificity and sensitivity and clearly outperforms previous methods. Our result also show that our method with off-targets information gets higher performance than that with only primary drug targets in both test data. CONCLUSIONS: We clearly demonstrate the feasibility and benefits of using network-based analyses of chemical, genomic and phenotype data to reveal drug-disease associations. The potential associations inferred by our method provide new perspectives for toxicogenomics and drug reposition evaluation

Prevalence and determinants of resistant hypertension among hypertensive patients attending a cardiology clinic in China: a prospective cross-sectional study

Purpose: To determine occurrence and determinants of resistant hypertension (RHT) among patients attending cardiology clinic of the affiliated hospital of Hangzhou Normal University, China.Methods: An observational prospective cross-sectional study was conducted among patients with hypertension attending the cardiology clinic over a period of 6 months. After identification of patients with RHT, various independent co-variants were tested by logistic regression in order to evaluate the determinants of RHT.Results: Out of 556 patients, 104 (18.7 %) patients had RHT while 67 (12.1 %) patients had uncontrolled blood pressure (BP) in spite of treatment with three antihypertensive drugs including a diuretic; 37 (6.6 %) patients had controlled BP with > three drugs. Obesity (OR: 2.7, p = 0.002], duration of hypertension (OR: 1.8, p = 0.015], presence of diabetes mellitus (OR: 3.6, p < 0.001) and ischemic heart disease (OR: 3.2, p = 0.001) were significant determinants of resistant hypertension in the study cohort.Conclusion: The prevalence of RHT found in this study is significantly high, thus indicating a need for greater attention of clinicians to this highly morbid condition. Obese patients and those suffering from diabetes mellitus, ischemic heart disease and chronic diseases should be evaluated for the presence of RHT. Early identification of such patients will provide sufficient time for clinicians to refer patients, as well as modify and/or intensify therapy.Keywords: Resistant hypertension, Risk factors, Hypertension, Stroke, Diabetes mellitus, Ischemic heart diseas