Characteristics of the ligand-binding site interaction for a series of arecoline-derived muscarinic agonists: a quantum chemical study

This work focuses on electronic and conformational structure of bicyclic analogues of arecoline and sulfoarecoline--muscarinic receptor agonists structurally related to 4,5,6,7-tetrahydroisoxazolo[4,5-c]pyridin-3-ol (THPO) and its S-methylsulfonium derivative (DHTO). Conformational freedom of six-member rings containing sulphur and nitrogen has been investigated by means of semiempirical AM1 method. Interaction between 'cationic heads' of two representative compounds and HCO2- ion serving as a model of carboxyl group of Asp in the muscarinic receptor has been modelled using DFT method in local approximation (LDA with VWN exchange correlation functional). Electrostatic potential (ESP) around studied complexes and ligands with added electron (simulation of complex formation) are presented and analysed. Position and depth of ESP minima in a series of studied ligands correlate well with their activity as muscarinic agonists. On the basis of our results the mechanism of ligand-binding site interaction may be elucidated. The calculations allow also for the comparison of bicyclic analogues of arecoline with already existing model for muscarinic pharmacophore and for rationalization of model parameters.     

苏打碱化土壤盐分离子与相关性分析

选取典型苏打碱化土壤剖面,分析了0～200cm深度主要盐分离子(Ca2 、Mg2 、Na 、K 、CO32-、HCO3-、Cl-和SO42-)的垂直分布规律。对各离子之间的相关性进行了统计分析。结果表明,测试土样离子以Na 和HCO3-离子为主,其剖面分布规律是表层含量较低,在80cm左右逐渐增至最大值,其后离子含量随深度而降低,至160cm以下则又随深度而增加,即在整个土壤剖面上大致呈倒"S"曲线分布。CO32-、HCO3-、Cl-与Na ,CO32-、HCO3-与Cl-之间均是相关系数大于0.80的极显著相关;除K 离子外,其它7个离子间均具有显著相关性,这表明该苏打碱化土壤盐分离子组成具有复杂性的特点。     

A hybrid artificial bee colony for a nurse rostering problem

The nurse rostering problem (NRP) is a combinatorial optimization problem tackled by assigning a set of shifts to a set of nurses, each has specific skills and work contract, to a predefined rostering period according to a set constraints. The metaheuristics are the most successful methods for tackling this problem. This paper proposes a metaheuristic technique called a hybrid artificial bee colony (HABC) for NRP. In HABC, the process of the employed bee operator is replaced with the hill climbing optimizer (HCO) to empower its exploitation capability and the usage of HCO is controlled by hill climbing rate (HCR) parameter. The performance of the proposed HABC is evaluated using the standard dataset published in the first international nurse rostering competition 2010 (INRC2010). This dataset consists of 69 instances which reflect this problem in many real-world cases that are varied in size and complexity. The experimental results of studying the effect of HCO using different value of HCR show that the HCO has a great impact on the performance of HABC. In addition, a comparative evaluation of HABC is carried out against other eleven methods that worked on INRC2010 dataset. The comparative results show that the proposed algorithm achieved two new best results for two problem instances, 35 best published results out of 69 instances as achieved by other comparative methods, and comparable results in the remaining instances of INRC2010 dataset.     

Computer-aided optimal design of custom scoliosis braces considering clinical and patient evaluations

Scoliosis causes an abnormal three dimensional curvature of the spine that is often treated by an orthotic device called brace. The objective of this research was to develop a new approach to automatically identify the optimal design of custom-built brace, based on clinical and patient evaluations. In this approach, torso geometry of the scoliosis patient was achieved using a 3-D imaging system that generated a 3-D torso surface model, which was modified using a custom CAD system to design the 3-D brace surface model. Two design parameters, a translational correction factor and a rotational correction factor, were selected to design the brace geometry from the torso geometry. The 3-D digital brace was evaluated by three clinical evaluation measures (imbalance, rib hump and principal axis angle reduction) and one patient evaluation measure (discomfort). A multi-objective optimization method was employed to identify the optimal design parameters considering both clinical and patient evaluations.     

Development of a multi-parametric model predictive control algorithm for insulin delivery in type 1 diabetes mellitus using clinical parameters

A multi-parametric model predictive control (mpMPC) algorithm for subcutaneous insulin delivery for individuals with type 1 diabetes mellitus (T1DM) that is computationally efficient, robust to variations in insulin sensitivity, and involves minimal burden for the user is proposed. System identification was achieved through impulse response tests feasible for ambulatory conditions on the UVa/Padova simulator adult subjects with T1DM. An alternative means of system identification using readily available clinical parameters was also investigated. A safety constraint was included explicitly in the algorithm formulation using clinical parameters typical of those available to an attending physician. Closed-loop simulations were carried out with daily consumption of 200 g carbohydrate. Controller robustness was assessed by subject/model mismatch scenarios addressing daily, simultaneous variation in insulin sensitivity and meal size with the addition of Gaussian white noise with a standard deviation of 10%. A second-order-plus-time-delay transfer function model fit the validation data with a mean (coefficient of variation) root-mean-square-error (RMSE) of 26 mg/dL (19%) for a 3 h prediction horizon. The resulting control law maintained a low risk Low Blood Glucose Index without any information about carbohydrate consumption for 90% of the subjects. Low-order linear models with clinically meaningful parameters thus provided sufficient information for a model predictive control algorithm to control glycemia. The use of clinical knowledge as a safety constraint can reduce hypoglycemic events, and this same knowledge can further improve glycemic control when used explicitly as the controller model. The resulting mpMPC algorithm was sufficiently compact to be implemented on a simple electronic device.     

CdTe/CdS量子点在Nafion/羟基磷灰石膜上的电化学发光及其应用于肌红蛋白的高灵敏检测

利用羟基磷灰石(HAp)和Nation将CdTe/CdS量子点(QDs)修饰到玻碳电极表面,研究了固定在复合膜内的CdTe/CdS量子点的电化学发光行为.相比水溶液中的量子点在裸电极上的电化学发光,CdTe/CdS修饰到复合膜内后的电化学发光更加稳定,且发光强度增加数十倍.在pH为6.5的磷酸盐缓冲溶液(PBS)中,以...     

Identification and experimental validation of an HIV model for HAART treated patients

The objective of this paper is to identify the parameters of a human immunodeficiency virus (HIV) evolution model from a clinical data set of patients treated with two different highly active antiretroviral therapy (HAART) protocols. After introducing a model with six state variables, a preliminary step considers the reduction of the number of parameters to be identified by means of sensitivity analysis, and then identifiability items are discussed. A nonlinear optimization-based procedure for identification is developed, which divides the unknown parameters into two families: the group dependent and the patient dependent parameters. Numerical results show that the identified model can be individually adapted to each patient and this result is promising for predicting the effects (e.g., failures or successes) of therapeutic actions.     

A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients

Intensive insulin therapy (IIT) and tight glycaemic control (TGC), particularly in intensive care unit (ICU), are the subjects of increasing and controversial debate in recent years. Model-based TGC has shown potential in delivering safe and tight glycaemic management, all the while limiting hypoglycaemia. A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition-Glucose (ICING) model is presented and validated using data from critically ill patients. Two existing glucose-insulin models are reviewed and formed the basis for the ICING model. Model limitations are discussed with respect to relevant physiology, pharmacodynamics and TGC practicality. Model identifiability issues are carefully considered for clinical settings. This article also contains significant reference to relevant physiology and clinical literature, as well as some references to the modeling efforts in this field. Identification of critical constant population parameters was performed in two stages, thus addressing model identifiability issues. Model predictive performance is the primary factor for optimizing population parameter values. The use of population values are necessary due to the limited clinical data available at the bedside in the clinical control scenario. Insulin sensitivity, S(I), the only dynamic, time-varying parameter, is identified hourly for each individual. All population parameters are justified physiologically and with respect to values reported in the clinical literature. A parameter sensitivity study confirms the validity of limiting time-varying parameters to S(I) only, as well as the choices for the population parameters. The ICING model achieves median fitting error of <1% over data from 173 patients (N=42,941 h in total) who received insulin while in the ICU and stayed for ≥ 72 h. Most importantly, the median per-patient 1-h ahead prediction error is a very low 2.80% [IQR 1.18, 6.41%]. It is significant that the 75th percentile prediction error is within the lower bound of typical glucometer measurement errors of 7-12%. These results confirm that the ICING model is suitable for developing model-based insulin therapies, and capable of delivering real-time model-based TGC with a very tight prediction error range. Finally, the detailed examination and discussion of issues surrounding model-based TGC and existing glucose-insulin models render this article a mini-review of the state of model-based TGC in critical care.     

Integral-based identification of patient specific parameters for a minimal cardiac model

A minimal cardiac model has been developed which accurately captures the essential dynamics of the cardiovascular system (CVS). However, identifying patient specific parameters with the limited measurements often available, hinders the clinical application of the model for diagnosis and therapy selection. This paper presents an integral-based parameter identification method for fast, accurate identification of patient specific parameters using limited measured data. The integral method turns a previously non-linear and non-convex optimization problem into a linear and convex identification problem. The model includes ventricular interaction and physiological valve dynamics. A healthy human state and four disease states, valvular stenosis, pulmonary embolism, cardiogenic shock and septic shock are used to test the method. Parameters for the healthy and disease states are accurately identified using only discretized flows into and out of the two cardiac chambers, the minimum and maximum volumes of the left and right ventricles, and the pressure waveforms through the aorta and pulmonary artery. These input values can be readily obtained non-invasively using echo-cardiography and ultra-sound, or invasively via catheters that are often used in Intensive Care. The method enables rapid identification of model parameters to match a particular patient condition in clinical real time (3-5 min) to within a mean value of 4-10% in the presence of 5-15% uniformly distributed measurement noise. The specific changes made to simulate each disease state are correctly identified in each case to within 10% without false identification of any other patient specific parameters. Clinically, the resulting patient specific model can then be used to assist medical staff in understanding, diagnosis and treatment selection.     

Superpixel segmentation for hyperspectral images based on false colour composition with colour histogram driving

Hyperspectral images (HSIs) segmentation has gradually become an important basis for HSIs processing, such as the classification and unmixing. In this paper, we proposed a superpixel segmentation algorithm based on principal component (PC) weighted false colour composition (FCC) with colour histogram driving (FCC-CHD). First, the dimensionality of HSIs is reduced by using FCC algorithm based on weighted PC, so that the main spectral information of HSIs is transformed to colour information of false colour images. To scale difference of ground objects in HSIs, we propose colour histogram driving (CHD) function to guarantee the accuracy of metric functions and computational efficiency. In segmentation process, the hill climbing optimization (HCO) algorithm is used to transform a global optimization problem into a local optimization problem, ensuring the high efficiency of the algorithm. Finally, parameters selection experiments provide reasonable parameter selection references, and the comparison experiments on two datasets demonstrate that the proposed algorithm is more effective and accurate than other superpixel segmentation methods.     

Selection of genetic algorithm operators for river water quality model calibration

Model calibration is the most important step in the overall model development. Genetic algorithm (GA) was used in this study to optimise model parameters of river water quality models. In general, the efficiency of using GA depends on the proper selection of GA operators, which are the components that make up the overall GA process. A comprehensive investigation on the importance of GA operators on model parameter optimisation was conducted in this study (and presented in this paper) based on the hypothesis that the selection of GA operators depends on the sensitivity of model parameters to model output. A numerical experiment on GA operators was conducted first considering a hypothetical river network water quality model with both insensitive and sensitive model parameters and were later validated using Yarra River Water Quality Model (YRWQM). It was found that a robust GA operator set obtained from the literature was capable of achieving a near-optimum model parameter set for the sensitive model but not for the insensitive model. However, due to insensitivity of water quality model parameters on model output, the ‘not so’ near-optimum parameter set did not contribute a great difference to the overall water quality predictions in the insensitive model. Therefore, based on these numerical experiments, it was concluded that a GA operator set obtained from the literature is adequate for calibration of model parameters of river water quality model using GA.     

Empirical Bayes estimation of random effects of a mixed-effects proportional odds Markov model for ordinal data

The objective of this work was to investigate the factors influencing the quality of empirical Bayes estimates (EBEs) of individual random effects of a mixed-effects Markov model for ordered categorical data. It was motivated by an attempt to develop a model-based dose adaptation tool for clinical use in colorectal cancer patients receiving capecitabine, which induces severe hand-and-foot syndrome (HFS) toxicity in more than a half of the patients. This simulation-based study employed a published mixed-effects model for HFS. The quality of EBEs was assessed in terms of accuracy and precision, as well as shrinkage. Three optimization algorithms were compared: simplex, quasi-Newton and adaptive random search. The investigated factors were amount of data per patient, distribution of categories within patients, magnitude of the inter-individual variability, and values of the effect model parameters. The main factors affecting the quality of EBEs were the values of parameters governing the dose–response relationship and the within-subject distribution of categories. For the chosen HFS toxicity model, the accuracy and precision of EBEs were rather low, and therefore the feasibility of their use for individual model-based dose adaptation seemed limited.     

基于 Markov 随机场的脑部三维磁共振血管造影数据的分割

文章提出了脑部核磁共振血管造影(Magnetic Resonance Angiography, MRA)的全自动分割方法,该方法 有效增强了现有的基于 Markov 随机场(Markov Random Field, MRF)的分割技术。现有的三维 Markov 分割模型通 常面临的挑战是：(1)低级 MRF 模型参数初始化不够准确;(2)普通的 MRF 邻域系统无法探测精细的血管结构。 针对这两类问题,分别提出了基于多尺度滤波响应阈值分析和多模式邻域系统进行解决,使得 MRF 模型的血管 分辨率提高到 2 个体素的细小血管。实验中,低级模型参数的精确估计采用了最大期望算法,高阶 MRF 参数的 估计采用最大伪似然估计方法;通过三维仿真数据和实际脑部 MRA 数据进行验证,分割结果显示了较小的全局 误差     

Modeling Obesity Using Abductive Networks

This paper investigates the use of abductive-network machine learning for modeling and predicting outcome parameters in terms of input parameters in medical survey data. Here we consider modeling obesity as represented by the waist-to-hip ratio (WHR) risk factor to investigate the influence of various parameters. The same approach would be useful in predicting values of clinical parameters that are difficult or expensive to measure from others that are more readily available. The AIM abductive network machine learning tool was used to model the WHR from 13 other health parameters. Survey data were collected for a randomly selected sample of 1100 persons aged 20 yr and over attending nine primary health care centers at Al-Khobar, Saudi Arabia. Models were synthesized by training on a randomly selected set of 800 cases, using both continuous and categorical representations of the parameters, and evaluated by predicting the WHR value for the remaining 300 cases. Models for WHR as a continuous variable predict the actual values within an error of 7.5% at the 90% confidence limits. Categorical models predict the correct logical value of WHR with an error in only 2 of the 300 evaluation cases. Analytical relationships derived from simple categorical models explain global observations on the total survey population to an accuracy as high as 99%. Simple continuous models represented as analytical functions highlight global relationships and trends. Results confirm the strong correlation between WHR and diastolic blood pressure, cholesterol level, and family history of obesity. Compared to other statistical and neural network approaches, AIM abductive networks provide faster and more automated model synthesis. A review is given of other areas where the proposed modeling approach can be useful in clinical practice.     

A system dynamics model for the analysis of clinical laboratory productivity

This study describes the initial stages of the development of a system dynamics (SD) model that will be used to simulate the dynamics of various factors that impact the productivity of clinical laboratories. Facing a staff capacity constraint, a clinical laboratory can undertake a number of strategies: (a) hiring new staff, (b) working overtime, or (c) doing both. In this preliminary research, we use a dynamic model to (a) study the impact of these strategies on laboratory productivity, including cost control, and (b) simulate the laboratory human resource utilization process. An SD approach is used for model development because it enables modelers to understand and discuss complex problems by illuminating the relationships among the variables involved. We use a laboratory's test backlog and the test turnaround time as main productivity parameters for this preliminary study. We run the simulation for six months and study laboratory productivity performance behavior over this time period.     

引力搜索算法在青霉素发酵模型参数估计中的应用

针对生物发酵过程难以精确估计模型参数的问题,提出一种利用引力搜索算法(GSA)对青霉素发酵非构造式动力学模型参数进行估计的方法。在分析发酵过程反应机理的基础上,选取合适的青霉素发酵非构造式动力学模型的状态方程式;然后利用GSA良好的全局搜索能力,对状态方程式的参数进行估计,从而得到精确的发酵模型。仿真结果表明：GSA实现了对青霉素发酵过程模型参数的准确估计,所得到的模型精度能够满足青霉素发酵过程的状态估计和控制需求。因此,GSA可有效地应用于模型参数估计。     

Performance of variations of the dynamic elastance model in lung mechanics

Acute respiratory distress syndrome (ARDS) is associated with high mortality and it is a major clinical problem. A common therapy for ARDS patients is mechanical ventilation (MV). However, poorly applied MV can be potentially fatal and optimal MV settings are patient specific. Thus, choosing a good positive end expiratory pressure (PEEP)-level compromise is a clinical challenge. Physiological modeling of the lung is one way to support the selection of the optimal settings for mechanical ventilation.This research makes the reasonably well-supported assumption that optimal PEEP is in the region of minimal elastance of the lung-tissue. The first order model of pulmonary mechanics (FOM) was modified in two differing ways in order to determine the patient-specific pressure range that coincides with minimal elastance. The extensions to the FOM (multiplicative elastance correction and additive volume correction parameters) are compared and evaluated.The addition of the correction parameters ultimately improved the consistency of the modeled elastance across PEEP levels for most patients tested. The results for minimal elastance were in very similar ranges for both approaches. Although this consistency offers a partial validation of the robustness of the approaches, discernment of the optimal approach cannot be determined. Further validation across differing patient states and experimental inputs must be undertaken to determine which method is more representative of true patient physiology.     

Reactions in complex biologically relevant systems: challenges for computational approaches

The ever increasing power of computational architectures allows to investigate reactive phenomena in complex biomolecular systems and environments. Results from such calculations can be compared with experiment and give important insight into microscopic aspects of chemical reactions. We discuss two examples of biologically relevant reaction mechanisms: double proton transfer in a DNA–base pair analogue for which detailed experimental information is available and ligand (re)binding in myoglobin (Mb). For the double proton transfer in the DNA–base pair analogue ab initio molecular dynamics simulations provide direct information on the infrared spectrum that has also been observed experimentally. In the case of ligand rebinding in MbNO, we discuss results from reactive molecular dynamics simulations to investigate the rebinding probability after photodissociation of the NO from myoglobin and compare the findings with experiment. Both reaction types pose different challenges and we highlight and address the computational difficulties in each case. In particular, we explore and discuss the possibilities and limitations of current computational methods to understand reactive processes in systems where several degrees of freedom are important.     

Computer models of albumin and haemoglobin glycation

Two first-order mathematical models were developed to mimic the glycation of haemoglobin (H) and albumin (A). The total concentrations of A and H were assumed to be constant. The responses of the two models to varying blood glucose level were compared. The parameters of the haemoglobin model were not numerically estimated, only an informal fit was performed using clinical and published data. Nonlinear regression analysis was used to estimate the parameters of the albumin model. The level of glycated A (GA) was derived from the measured fructosamine level. Three diabetics were monitored daily for the level of fructosamine and blood glucose profile over a period of 10, 16 and 21 days, respectively. Conclusions: (1) The difference between GA and glycated H (GH) resulting from different elimination rates is decreased by the stratification of erythrocyte population. (2) Both GA and GH seem to have higher elimination rates than their nonglycated equivalents.     

基于TVARMA的飞行器结构响应序列参数谱估计

针对时变自回归滑动平均(TVARMA)模型参数谱估计容易出现谱峰漂移的问题,提出一种基于组合目标函数和遗传算法的TVARMA模型参数估计方法,并将之应用于飞行器结构响应序列的谱估计。首先,通过长自回归方法和增广最小二乘方法获得TVARMA模型参数初始估计值;其次,依据连续函数极值条件推导模型参数的频域约束条件并结合罚函数方法构造组合目标函数;最后,采用遗传算法对模型参数进行优化获得使组合目标函数最小的参数值作为TVARMA模型参数的最优估计。应用结果表明:该方法可以克服谱峰漂移现象,提高模型在时域和时频域的建模精度。