Statistical methods for fitting dengue disease models, and related issues

Dengue is currently the world’s fastest growing vector-borne disease which causes fever, headache, muscle aches and other flu-like symptoms, affecting 50-100 million people worldwide yearly. Modeling dengue incidence over time is challenging because of multiple virus serotypes, high asymptomaticity, and the limited data availability. Different dengue modeling approaches have been explored in the public health literature such as economic models, agent-based (AB) models, and ordinary differential equation (ODE) models. ODE models are the standard to model dynamic systems involving interactions between various populations because of their solid mathematical/statistical foundation and ease of implementation in standard software packages. However, the homogeneous and perfectly mixing assumptions of the ODE model may not accurately represent the real world. On the other hand, AB models may lack the solid mathematical/statistical theory, but can model heterogeneity at the individual level. In the first part of this dissertation, we propose a simplified new ODE model (vSEIR) and compare this model with three existing ODE models. We also compare two discretization methods for initial value problems: derivative-free mesh adaptive direct search method with quadratic models (MADSQ) and derivative trust region (DTR) method. The simulation studies show that MADSQ can provide a better solution to the ODE compared to DTR when the parameter space has many local minima. We also demonstrate that the proposed vSEIR ODE model provides a better fit to the data than the other existing ODE models. In the second part of this dissertation, we validate a dengue ComputationaL ARthropod Agents (CLARA) AB model, by comparing with its corresponding ODE model and the real world data. We not only show the similarity between the two models, but also contrast them. Our future plan is to continue to improve dengue ODE models by providing a stochastic version. Improved dengue models will provide public health researchers tools to better understand dengue disease outbreaks

Tetraodon nigroviridis as a nonlethal model of infectious spleen and kidney necrosis virus (ISKNV) infection

AbstractInfectious spleen and kidney necrosis virus (ISKNV) is the type species of the genus Megalocytivirus, family Iridoviridae. We have previously established a high mortality ISKNV infection model of zebrafish (Danio rerio). In this study, a nonlethal Tetraodon nigroviridis model of ISKNV infection was established. ISKNV infection did not cause lethal disease in Tetraodon but could infect almost all the organs of this species. Electron microscopy showed ISKNV particles were present in infected tissues. Immunofluorescence and quantitative real-time PCR analysis showed that nearly all the virions and infected cells were cleared at 14d postinfection. The expression profiles of interferon-γ and tumor necrosis factor-α gene in response to ISKNV infection were significantly different in Tetraodon and zebrafish. The establishment of the nonlethal Tetraodon model of ISKNV infection can offer a valuable tool complementary to the zebrafish infection model for studying megalocytivirus disease, fish immune systems, and viral tropism

Double-blind comparison of ziprasidone and risperidone in the treatment of Chinese patients with acute exacerbation of schizophrenia

Background: The aim of the study was to evaluate the efficacy and safety of ziprasidone versus risperidone in Chinese subjects with acute exacerbation of schizophrenia. Methods: In patients meeting the Chinese Classification of Mental Disorders criteria for schizophrenia and with a Positive and Negative Syndrome Scale (PANSS) total score >= 60 were randomly assigned to six weeks of double-blind treatment with ziprasidone 40-80 mg twice daily or risperidone 1-3 mg bid, flexibly dosed. Noninferiority was demonstrated if the upper limit of the two-sided 95% confidence interval (CI) for the difference in PANSS total score improvement from baseline in the evaluable population was smaller than the prespecified noninferiority margin of 10 units. Results: The intent-to-treat population comprised 118 ziprasidone-treated and 121 risperidone-treated subjects. Improvement (reduction) from baseline to week 6 in PANSS total score was (-35.6 [95% CI: -38.6, -32.6]) for ziprasidone and (-37.1 [95% CI: -39.9, -34.4]) for risperidone. Noninferiority was demonstrated in the evaluable population with a difference score of 1.5 [95% CI: -2.5, 5.5]. Mean prolactin levels decreased at week 6 compared with baseline for ziprasidone (-3.5 ng/mL), but significantly increased for risperidone (61.1 ng/mL; P < 0.001). More risperidone-treated subjects (14.9%) than ziprasidone-treated subjects (4.2%) reported weight gain >= 7%. Akathisia and somnolence in the ziprasidone group and akathisia and insomnia in the risperidone group were the most common side effects. Treatment-related/treatment-emergent adverse events were reported by 79.7% and 71.1% of ziprasidone-treated and risperidone-treated subjects, respectively. Conclusion: In Chinese subjects, ziprasidone was as effective as risperidone, with less weight gain and less prolactin elevation.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000294955100009&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Clinical NeurologyPsychiatrySCI(E)PubMed3ARTICLE77-85

A Deep Learning Method Using Gender-Specific Features for Emotion Recognition

Speech reflects people’s mental state and using a microphone sensor is a potential method for human–computer interaction. Speech recognition using this sensor is conducive to the diagnosis of mental illnesses. The gender difference of speakers affects the process of speech emotion recognition based on specific acoustic features, resulting in the decline of emotion recognition accuracy. Therefore, we believe that the accuracy of speech emotion recognition can be effectively improved by selecting different features of speech for emotion recognition based on the speech representations of different genders. In this paper, we propose a speech emotion recognition method based on gender classification. First, we use MLP to classify the original speech by gender. Second, based on the different acoustic features of male and female speech, we analyze the influence weights of multiple speech emotion features in male and female speech, and establish the optimal feature sets for male and female emotion recognition, respectively. Finally, we train and test CNN and BiLSTM, respectively, by using the male and the female speech emotion feature sets. The results show that the proposed emotion recognition models have an advantage in terms of average recognition accuracy compared with gender-mixed recognition model

miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions

MicroRNAs (miRNAs) are small non-coding RNA molecules capable of negatively regulating gene expression to control many cellular mechanisms. The miRTarBase database (http://mirtarbase.mbc.nctu.edu.tw/) provides the most current and comprehensive information of experimentally validated miRNA-target interactions. The database was launched in 2010 with data sources for >100 published studies in the identification of miRNA targets, molecular networks of miRNA targets and systems biology, and the current release (2013, version 4) includes significant expansions and enhancements over the initial release (2010, version 1). This article reports the current status of and recent improvements to the database, including (i) a 14-fold increase to miRNA-target interaction entries, (ii) a miRNA-target network, (iii) expression profile of miRNA and its target gene, (iv) miRNA target-associated diseases and (v) additional utilities including an upgrade reminder and an error reporting/user feedback system

Epidemiological trend in inflammatory bowel disease in Taiwan from 2001 to 2015: a nationwide populationbased study

Background/Aims Incidences of inflammatory bowel disease (IBD), ulcerative colitis (UC), and Crohn’s disease (CD), have been increasing in Asia. In this study, we report the relevant clinical characteristics and determined the epidemiological trend of IBD in Taiwan from 2001 to 2015. Methods A retrospective study was conducted to analyze data recorded from January 2001 through December 2015 in the registered database compiled by the National Health Insurance and provided by the Ministry of Health and Welfare, Taiwan. Results A total of 3,806 patients with catastrophic IBD illness were registered from 2001 to 2015 in Taiwan (CD, 919; UC, 2,887). The crude incidence of CD increased from 0.17/100,000 in 2001 to 0.47/100,000 in 2015, whereas that of UC increased from 0.54/100,000 in 2001 to 0.95/100,000 in 2015. The prevalence of CD increased from 0.6/100,000 in 2001 to 3.9/100,000 in 2015, whereas that of UC increased from 2.1/100,000 in 2001 to 12.8/100,000 in 2015. The male-to-female ratio in the study sample was 2.19 for CD and 1.62 for UC. The median age of those registered with CD was lower than that of those registered for UC: 38.86 and 44.86 years, respectively. A significantly greater increase in CD incidence rate was identified among 20 to 39-year-old compared with other age groups. Conclusions Using Taiwan’s nationwide insurance database, we determined that the number of patients with CD increased more rapidly during the study period than the number of patients with UC, especially among age 20 to 39-year-old, resulting in a decreased UC-to-CD ratio

Excavatoids O and P, New 12-Hydroxybriaranes from the Octocoral Briareum excavatum

Two new 12-hydroxybriarane diterpenoids, designated as excavatoids O (1) and P (2), were isolated from the octocoral Briareum excavatum. The structures of briaranes 1 and 2 were established on the basis of extensive spectral data analysis. Excavatoid P (2) is the first metabolite which possesses a 6β -chlorine atom in briarane analogues

Detection of Cartilage Oligomeric Matrix Protein Using a Quartz Crystal Microbalance

Current methods for diagnosing early stage osteoarthritis (OA) based on the magnetic resonance imaging and enzyme-linked immunosorbent assay methods are specific, but require specialized laboratory facilities and highly trained personal to obtain a definitive result. In this work, a user friendly and non-invasive quartz crystal microbalance (QCM) immunosensor method has been developed to detect Cartilage Oligomeric Matrix Protein (COMP) for early stage OA diagnosis. This QCM immunosensor was fabricated to immobilize COMP antibodies utilizing the self-assembled monolayer technique. The surface properties of the immunosensor were characterized by its FTIR and electrochemical impedance spectra (EIS). The feasibility study was based on urine samples obtained from 41 volunteers. Experiments were carried out in a flow system and the reproducibility of the electrodes was evaluated by the impedance measured by EIS. Its potential dynamically monitored the immunoreaction processes and could increase the efficiency and sensitivity of COMP detection in laboratory-cultured preparations and clinical samples. The frequency responses of the QCM immunosensor changed from 6 kHz when testing 50 ng/mL COMP concentration. The linear regression equation of frequency shift and COMP concentration was determined as: y = 0.0872 x + 1.2138 (R2 = 0.9957). The COMP in urine was also determined by both QCM and EIS for comparison. A highly sensitive, user friendly and cost effective analytical method for the early stage OA diagnosis has thus been successfully developed

A cytoplasmic RNA virus generates functional viral small RNAs and regulates viral IRES activity in mammalian cells

The roles of virus-derived small RNAs (vsRNAs) have been studied in plants and insects. However, the generation and function of small RNAs from cytoplasmic RNA viruses in mammalian cells remain unexplored. This study describes four vsRNAs that were detected in enterovirus 71-infected cells using next-generation sequencing and northern blots. Viral infection produced substantial levels (\u3e105 copy numbers per cell) of vsRNA1, one of the four vsRNAs. We also demonstrated that Dicer is involved in vsRNA1 generation in infected cells. vsRNA1 overexpression inhibited viral translation and internal ribosomal entry site (IRES) activity in infected cells. Conversely, blocking vsRNA1 enhanced viral yield and viral protein synthesis. We also present evidence that vsRNA1 targets stem-loop II of the viral 5′ untranslated region and inhibits the activity of the IRES through this sequence-specific targeting. Our study demonstrates the ability of a cytoplasmic RNA virus to generate functional vsRNA in mammalian cells. In addition, we also demonstrate a potential novel mechanism for a positive-stranded RNA virus to regulate viral translation: generating a vsRNA that targets the IRES