Parameter inference and model selection for differential equation models

Includes bibliographical references.2015 Summer.Firstly, we consider the problem of estimating parameters of stochastic differential equations with discrete-time observations that are either completely or partially observed. The transition density between two observations is generally unknown. We propose an importance sampling approach with an auxiliary parameter when the transition density is unknown. We embed the auxiliary importance sampler in a penalized maximum likelihood framework which produces more accurate and computationally efficient parameter estimates. Simulation studies in three different models illustrate promising improvements of the new penalized simulated maximum likelihood method. The new procedure is designed for the challenging case when some state variables are unobserved and moreover, observed states are sparse over time, which commonly arises in ecological studies. We apply this new approach to two epidemics of chronic wasting disease in mule deer. Next, we consider the problem of selecting deterministic or stochastic models for a biological, ecological, or environmental dynamical process. In most cases, one prefers either deterministic or stochastic models as candidate models based on experience or subjective judgment. Due to the complex or intractable likelihood in most dynamical models, likelihood-based approaches for model selection are not suitable. We use approximate Bayesian computation for parameter estimation and model selection to gain further understanding of the dynamics of two epidemics of chronic wasting disease in mule deer. The main novel contribution of this work is that under a hierarchical model framework we compare three types of dynamical models: ordinary differential equation, continuous time Markov chain, and stochastic differential equation models. To our knowledge model selection between these types of models has not appeared previously. The practice of incorporating dynamical models into data models is becoming more common, the proposed approach may be useful in a variety of applications. Lastly, we consider estimation of parameters in nonlinear ordinary differential equation models with measurement error where closed-form solutions are not available. We propose a new numerical algorithm, the data driven adaptive mesh method, which is a combination of the Euler and 4th order Runge-Kutta methods with different step sizes based on the observation time points. Our results show that both the accuracy in parameter estimation and computational cost of the new algorithm improve over the most widely used numerical algorithm, the 4th Runge-Kutta method. Moreover, the generalized profiling procedure proposed by Ramsay et al. (2007) doesn't have good performance for sparse data in time as compared to the new approach. We illustrate our approach with both simulation studies and ecological data on intestinal microbiota

Effect of ganoderic acid on diethylnitrosamine-induced liver cancer in mice

Purpose: To investigate the hepatoprotective role of ganoderic acid A (GAA) on liver cancer induced by diethylnitrosamine (DEN) via Nrf-2/HO-1/NF-κB signal pathway in mice. Methods: Sixty male C57BL/6J mice were randomly divided into 4 groups: (1) control group, (2) DEN (25 mg/kg) group, (3) GAA (20 mg/kg) + DEN group, (4) GAA (40 mg/kg) + DEN group. The protective effect of GAA on liver was evaluated by determining malondialdehyde (MDA), superoxide dismutase (SOD), inflammatory cytokines including interleukin-6 (IL-6), interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and the expression of heme oxygenase-1 (HO-1), nuclear factor erythroid- 2-related factor-2 (Nrf-2), IκBα, p-IκBα, p65, p-p65, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) in serum. Results: The results demonstrate that GAA treatment significantly suppressed the generation of MDA, proinflammatory cytokines, and restored the activity of SOD in the serum of DEN-induced liver cancer in mice. Western blots analysis revealed that GAA significantly restored Nrf-2/HO-1/NF-κB signal pathwayrelated protein levels in DEN-induced mice liver cancer model. Conclusion: This research reveals the anticancer activity of GAA in liver tissue, and suggests that GAA counters DEN-induced liver  cancer through Nrf-2/HO-1/NF-κB signal pathway. Keywords: Ganoderic acid A, Nrf-2/HO-1/NF-κB pathway, Liver cancer, MDA, GAPDH, SO

Sialyltransferase Inhibition and Recent Advances

Sialic acids, existing as terminal sugars of glycoconjugates, play important roles in various physiological and pathological processes, such as cell–cell adhesion, immune defense, tumor cell metastasis, and inflammation. Sialyltransferases (STs) catalyze the transfer of sialic acid residues to non-reducing oligosaccharide chains of proteins and lipids, using cytidine monophosphate N-acetylneuraminic acid (CMP-Neu5Ac) as the donor. Elevated sialyltransferase activity leads to overexpression of cell surface sialic acids and contributes to many disease developments, such as cancer and inflammation. Therefore, sialyltransferases are considered as potential drug targets for disease treatment. Inhibitors of sialyltransferases thus are of medicinal interest, especially for the cancer therapy. In addition, sialyltransferase inhibitors are useful tool to study sialyltransferase function and related mechanisms. This review highlights recent development of inhibitors of sialyltransferases reported since 2004. The inhibitors are summarized as eight groups: 1) sialic acid analogs, 2) CMP-sialic acid analogs, 3) cytidine analogs, 4) oligosaccharide derivatives, 5) aromatic compounds, 6) flavonoids, 7) lithocholic acid analogs, and 8) others. This article is part of a Special Issue entitled: Physiological Enzymology and Protein Functions

Sialyltransferase Inhibition and Recent Advances

Sialic acids, existing as terminal sugars of glycoconjugates, play important roles in various physiological and pathological processes, such as cell–cell adhesion, immune defense, tumor cell metastasis, and inflammation. Sialyltransferases (STs) catalyze the transfer of sialic acid residues to non-reducing oligosaccharide chains of proteins and lipids, using cytidine monophosphate N-acetylneuraminic acid (CMP-Neu5Ac) as the donor. Elevated sialyltransferase activity leads to overexpression of cell surface sialic acids and contributes to many disease developments, such as cancer and inflammation. Therefore, sialyltransferases are considered as potential drug targets for disease treatment. Inhibitors of sialyltransferases thus are of medicinal interest, especially for the cancer therapy. In addition, sialyltransferase inhibitors are useful tool to study sialyltransferase function and related mechanisms. This review highlights recent development of inhibitors of sialyltransferases reported since 2004. The inhibitors are summarized as eight groups: 1) sialic acid analogs, 2) CMP-sialic acid analogs, 3) cytidine analogs, 4) oligosaccharide derivatives, 5) aromatic compounds, 6) flavonoids, 7) lithocholic acid analogs, and 8) others. This article is part of a Special Issue entitled: Physiological Enzymology and Protein Functions

Photonic crystal fiber half-taper probe based refractometer

A compact singlemode - photonic crystal fiber - singlemode fiber tip (SPST) refractive index sensor is demonstrated in this paper. A CO2 laser cleaving technique is utilised to provide a clean-cut fiber tip which is then coated by a layer of gold to increase reflection. An average sensitivity of 39.1 nm/RIU and a resolvable index change of 2.56 x 10-4 are obtained experimentally with a ~3.2 µm diameter SPST. The temperature dependence of this fiber optic sensor probe is presented. The proposed SPST refractometer is also significantly less sensitive to temperature and an experimental demonstration of this reduced sensitivity is presented in the paper. Because of its compactness, ease of fabrication, linear response, low temperature dependency, easy connectivity to other fiberized optical components and low cost, this refractometer could find various applications in chemical and biological sensing

Safety and efficacy of early radiofrequency catheter ablation in patients with paroxysmal atrial fibrillation complicated with amiodarone-induced thyrotoxicosis

Background: Amiodarone is an antiarrhythmic drug that is frequently used to control atrial fibrillation (AF). Many patients with AF are afraid of the risk of ablation and take amiodar­one, some patients develop amiodarone-induced thyrotoxicosis (AIT). The purpose of the study was to investigate the safety and efficacy of early radiofrequency catheter ablation in patients with paroxysmal AF complicated with AIT. Methods: From the 146 consecutive patients with paroxysmal AF who had been treated with amiodarone and underwent 3-dimensional mapping system guided circumferential pulmonary vein isolation (PVI) at our center from January 2013 to June 2014, 20 had developed AIT. Thirty controls with normal thyroid function and matched for baseline characteristics were selected. Results: Pulmonary vein isolation was completed in all patients without serious complications and with similar procedural (170.60 ± 14.80 vs. 158.18 ± 9.06 min; p = 0.062) and X-ray exposure (16.48 ± 2.15 vs. 15.36 ± 1.57 min; p = 0.058) time in AIT vs. control groups; however, upon coronary sinus catheter pacing (from 300 ms to 200 ms) after intrave­nous isoproterenol administration 30 min post PVI, rates of induction of AF (35% vs. 3.33%; p = 0.005) and of non-pulmonary vein-related atrial tachyarrhythmias (50% vs. 6.67%; p = 0.01) were higher, while those for atrial flutter (15% vs. 3.33%; p = 0.17) and atrial tachycardia (15% vs. 6.67%; p = 0.31) were similar, as was the recovery of conduction of pulmonary vein potential (15% vs. 30%; p = 0.191). In AIT vs. control group, atrial tachyarrhythmia recurrence rate was higher at 3 months (45% vs. 16.67%, p = 0.032) but not between 3 and 12 months (30% vs. 23.33%; p = 0.418) follow-up. Conclusions: Early catheter ablation for paroxysmal AF in patients with AIT appeared safe and effective albeit with higher atrial tachyarrhythmia recurrence rate up to 3 months but not beyond 12 months after PVI relative to controls.

A Survey on Causal Reinforcement Learning

While Reinforcement Learning (RL) achieves tremendous success in sequential decision-making problems of many domains, it still faces key challenges of data inefficiency and the lack of interpretability. Interestingly, many researchers have leveraged insights from the causality literature recently, bringing forth flourishing works to unify the merits of causality and address well the challenges from RL. As such, it is of great necessity and significance to collate these Causal Reinforcement Learning (CRL) works, offer a review of CRL methods, and investigate the potential functionality from causality toward RL. In particular, we divide existing CRL approaches into two categories according to whether their causality-based information is given in advance or not. We further analyze each category in terms of the formalization of different models, ranging from the Markov Decision Process (MDP), Partially Observed Markov Decision Process (POMDP), Multi-Arm Bandits (MAB), and Dynamic Treatment Regime (DTR). Moreover, we summarize the evaluation matrices and open sources while we discuss emerging applications, along with promising prospects for the future development of CRL.Comment: 29 pages, 20 figure