Simulation of head movement trajectories: model and fit to main sequence

A sixth order nonlinear model for horizontal head rotations in humans is presented and investigated using experimental results on head movement trajectories and neck muscle EMG. The controller signals, structured in accordance with time optimal control theory, are parameterized, and controller signal parameter variations show a dominating influence on different aspects of the head movement trajectory. The model fits the common head acceleration types over a wide range of amplitudes, and also less common (dynamic overshoot) trajectories.On leave from Department of Neurology, University of Hamburg, F.R.G.; supported by Deutsche Forschungsgemeinschaft Bonn, FRG     

Three algorithms for interpreting models consisting of ordinary differential equations: Sensitivity coefficients,sensitivity functions,global optimization

This paper reports the development and application of three powerful algorithms for the analysis and simulation of mathematical models consisting of ordinary differential equations. First, we describe an extended parameter sensitivity analysis: we measure the relative sensitivities of many dynamical behaviors of the model to perturbations of each parameter. We check sensitivities to parameter variation over both small and large ranges. These two extensions of a common technique have applications in parameter estimation and in experimental design. Second, we compute sensitivity functions, using an efficient algorithm requiring just one model simulation to obtain all sensitivities of state variables to all parameters as functions of time. We extend the analysis to a behavior which is not a state variable. Third, we present an unconstrained global optimization algorithm, and apply it in a novel way: we determine the input to the model, given an optimality criterion and typical outputs. The algorithm itself is an efficient one for high-order problems, and does not get stuck at local extrema. We apply the sensitivity analysis, sensitivity functions, and optimization algorithm to a sixth-order nonlinear ordinary differential equation model for human eye movements. This application shows that the algorithms are not only practicable for high-order models, but also useful as conceptual tools.     

单纯形加速法拟合生态学中的非线性模型

本文以Logistic模型,Taylor幂法则模型,Holling功能反应模型,以及种群内禀增长力Rm等模型的拟合和参数估计为例,探讨单纯形加速法在生态模型优化拟合和参数估计中的应用.结果表明,单纯形加速法拟合生态学中的非线性模型不仅适用广泛,而且拟合过程是直接求原来非线性模型的最优拟合,因而优于生态学中通常使用的将原模型“线性化后再拟合”的方法,而与其它一些最优化方法,如:麦夸方法、枚举选优法等比较,由于单纯形法不需计算目标函数的偏导数,因而计算不受目标函数及其偏导函数复杂程度的限制,而且对于各种模型其求优计算过程十分相似,可以编制统一的计算程序.本研究所编制的计算机程序对于本文未提到的其它一些模型也是完全适用的,在应用时仅需修改定义目标函数的自定义函数语句即可.研究也发现,在求优过程中,只要搜索系数选择适当和实际数据合理,是可以保证寻优成功的.     

A fast global fitting algorithm for fluorescence lifetime imaging microscopy based on image segmentation

Global fitting algorithms have been shown to improve effectively the accuracy and precision of the analysis of fluorescence lifetime imaging microscopy data. Global analysis performs better than unconstrained data fitting when prior information exists, such as the spatial invariance of the lifetimes of individual fluorescent species. The highly coupled nature of global analysis often results in a significantly slower convergence of the data fitting algorithm as compared with unconstrained analysis. Convergence speed can be greatly accelerated by providing appropriate initial guesses. Realizing that the image morphology often correlates with fluorophore distribution, a global fitting algorithm has been developed to assign initial guesses throughout an image based on a segmentation analysis. This algorithm was tested on both simulated data sets and time-domain lifetime measurements. We have successfully measured fluorophore distribution in fibroblasts stained with Hoechst and calcein. This method further allows second harmonic generation from collagen and elastin autofluorescence to be differentiated in fluorescence lifetime imaging microscopy images of ex vivo human skin. On our experimental measurement, this algorithm increased convergence speed by over two orders of magnitude and achieved significantly better fits.     

Fluorescence-microscopy-based image analysis for analyte-dependent particle doublet detection in a single-step immunoagglutination assay

A novel fluorescence-microscopy-based image analysis method for classification of singlet and doublet latex particles is demonstrated and applied to a particle-based immunoagglutination assay for quantification of biomolecules in microliter-volume bulk samples. The image analysis method, verified by flow cytometric agglutination analysis, is based on a pattern recognition algorithm employing Gaussian-base-function fitting which allows robust identification and counting of singlets, doublets, and higher agglomerates of fluorescent microparticles. The immunoagglutination assay is experimentally modeled by a biotin-streptavidin interaction, with the goal of both theoretically and experimentally investigating the performance of a general immunoagglutination-based assay. For this purpose a theoretical model of the initial agglutination kinetics, based on particle diffusion combined with a steric factor determined by the level of specific and nonspecific agglutination, was developed. The theoretical model combined with the experimental data can be used to optimize an agglutination-based assay with regard to sensitivity and dynamic range and to estimate the affinity, receptor surface density, molecular and binding site sizes, and level of nonspecific binding that is present in the assay. The experimental results are in good agreement with the theoretical model, indicating the usefulness of the model for immunoagglutination assay optimization.     

Optimal estimation of ion-channel kinetics from macroscopic currents

Markov modeling provides an effective approach for modeling ion channel kinetics. There are several search algorithms for global fitting of macroscopic or single-channel currents across different experimental conditions. Here we present a particle swarm optimization(PSO)-based approach which, when used in combination with golden section search (GSS), can fit macroscopic voltage responses with a high degree of accuracy (errors within 1%) and reasonable amount of calculation time (less than 10 hours for 20 free parameters) on a desktop computer. We also describe a method for initial value estimation of the model parameters, which appears to favor identification of global optimum and can further reduce the computational cost. The PSO-GSS algorithm is applicable for kinetic models of arbitrary topology and size and compatible with common stimulation protocols, which provides a convenient approach for establishing kinetic models at the macroscopic level.     

Evaluation of a particle swarm algorithm for biomechanical optimization

Optimization is frequently employed in biomechanics research to solve system identification problems, predict human movement, or estimate muscle or other internal forces that cannot be measured directly. Unfortunately, biomechanical optimization problems often possess multiple local minima, making it difficult to find the best solution. Furthermore, convergence in gradient-based algorithms can be affected by scaling to account for design variables with different length scales or units. In this study we evaluate a recently-developed version of the particle swarm optimization (PSO) algorithm to address these problems. The algorithm's global search capabilities were investigated using a suite of difficult analytical test problems, while its scale-independent nature was proven mathematically and verified using a biomechanical test problem. For comparison, all test problems were also solved with three off-the-shelf optimization algorithms--a global genetic algorithm (GA) and multistart gradient-based sequential quadratic programming (SQP) and quasi-Newton (BFGS) algorithms. For the analytical test problems, only the PSO algorithm was successful on the majority of the problems. When compared to previously published results for the same problems, PSO was more robust than a global simulated annealing algorithm but less robust than a different, more complex genetic algorithm. For the biomechanical test problem, only the PSO algorithm was insensitive to design variable scaling, with the GA algorithm being mildly sensitive and the SQP and BFGS algorithms being highly sensitive. The proposed PSO algorithm provides a new off-the-shelf global optimization option for difficult biomechanical problems, especially those utilizing design variables with different length scales or units.     

Parameter estimation with a novel gradient-based optimization method for biological lattice-gas cellular automaton models

Lattice-gas cellular automata (LGCAs) can serve as stochastic mathematical models for collective behavior (e.g. pattern formation) emerging in populations of interacting cells. In this paper, a two-phase optimization algorithm for global parameter estimation in LGCA models is presented. In the first phase, local minima are identified through gradient-based optimization. Algorithmic differentiation is adopted to calculate the necessary gradient information. In the second phase, for global optimization of the parameter set, a multi-level single-linkage method is used. As an example, the parameter estimation algorithm is applied to a LGCA model for early in vitro angiogenic pattern formation.     

A computational framework to simulate the endolymph flow due to vestibular rehabilitation maneuvers assessed from accelerometer data

Vertiginous symptoms are one of the most common symptoms in the world, therefore investing in new ways and therapies to avoid the sense of insecurity during the vertigo episodes is of great interest. The classical maneuvers used during vestibular rehabilitation consist in moving the head in specific ways, but it is not fully understood why those steps solve the problem. To better understand this mechanism, a three-dimensional computational model of the semicircular ducts of the inner ear was built using the finite element method, with the simulation of the fluid flow being obtained using particle methods. To simulate the exact movements performed during rehabilitation, data from an accelerometer were used as input for the boundary conditions in the model. It is shown that the developed model responds to the input data as expected, and the results successfully show the fluid flow of the endolymph behaving coherently as a function of accelerometer data. Numerical results at specific time steps are compared with the corresponding head movement, and both particle velocity and position follow the pattern that would be expected, confirming that the model is working as expected. The vestibular model built is an important starting point to simulate the classical maneuvers of the vestibular rehabilitation allowing to understand what happens in the endolymph during the rehabilitation process, which ultimately may be used to improve the maneuvers and the quality of life of patients suffering from vertigo.     

Meta-models as a straightforward approach to the sensitivity analysis of complex models

Complex simulation models are important tools in applied ecological and conservation research. However sensitivity analysis of this important class of models can be difficult to conduct. High level interactions and non-linear responses are common in complex simulations, and this necessitates a global sensitivity analysis, where each parameter is tested at a range of values, and in combination with changes in many other parameters. We reviewed the literature, searching for population viability analyses that used simulation models. We found only 9 out of the 122 simulation population viability analysis used global sensitivity analysis. This result is typical of other simulation models in applied ecology, where global sensitivity analysis is rare. We then demonstrate how to conduct a meta-modeling sensitivity analysis, where a simpler statistically fit function (the meta-model, also known as the surrogate model or emulator) is used to approximate the behavior of the complicated simulation. This simpler meta-model is interrogated to inform on the behavior of simulation model. We fit two example meta-models, a generalized linear model and a boosted regression tree, to exemplify the approach. Our hope is that by going through these techniques thoroughly they will become more widely adopted.     

Optimality in forward dynamics simulations

This paper discusses a methodology and an algorithm for the analysis of dynamics of bio-mechanical systems, and in particular of optimal movement patterns between initial and target configurations. The basic formulation utilizes a finite element time discretization and establishes a large set of equations in displacements and forces. These are solved simultaneously for the whole time interval considered. The algorithm allows different optimization criteria for the movement, based on either the smoothness of the movement or the minimization of needed controls or control rates. It is primarily aimed at musculoskeletal simulations with either the joint resultant moments or the redundant muscular tensions as unknowns. Kinetic and kinematic constraints can be introduced for the obtained movement. Examples show that the obtained results are strongly dependent on the optimality criterion used. Systematic usage of the algorithm can improve knowledge about optimal motion planning.     

In vivo determination of the anatomical axes of the ankle joint complex: An optimization approach

This study investigates the feasibility of a subject-specific three-dimensional model of the ankle joint complex for kinematic and dynamic analysis of movement. The ankle joint complex was modelled as a three-segment system, connected by two ideal highe joints: the talocrural and the subtalar joint. A mathematical formulation was developed to express the three-dimensional translation and rotation between the foot and shank segments as a function of the two joint angles, and 12 model parameters describing the locations of the joint axes. An optimization method was used to fit the model parameters to three-dimensional kinematic data of foot and shank markers, obtained during test movements throughout the entire physiological range of motion of the ankle joint. The movement of the talus segment, which cannot be measured non-invasively, is not necessary for the analysis.

This optimization method was used to determine the position and orientation of the joint axes in 14 normal subjects. After optimization, the discrepancy between the best fitting model and actual marker kinematics was between 1 and 3 mm for all subjects. The predicted inclination of the subtalar joint axis from the horizontal plane was 37.4±2.7°, and the medial deviation was 18.0±16.2°. The lateral side of the talucrural axis was directed slightly posteriorly (6.8±8.1°), and inclined downward by 7.0±5.4°. These results are similar to previously reported typical results from anatomical, in vitro, studies. Reproducibility was evaluated by repeated testing of one subject, which resulted in variations of about one-fifth of the standard deviation within the group, the inclination of the subtalar joint axis was significantly correlated to the arch height and a radiographic ‘tarsal index’. It is concluded that this optimization method provides the opportunity to incorporate inter-individual anatomical differences into kinematic and dynamic analysis of the ankle joint complex. This allows a more functional interpretation of kinematic data, and more realistic estimates of internal forces.     

分子对接的随机聚点搜索算法

随机聚点搜索算法是一种普遍的全局极小化方法,在目标函数自变量数目不很大时,计算效率较高。将该算法应用于分子对接,首先要通过模型分子对接,反复调整算法各控制参数使效率最高。对于HIV－1蛋白酶与苯甲醚配体的刚性对接,算法成功的找到了相互作用能量全局极小,与晶体结构的均方根偏差(RMSD)仅0.2?。这表明,该算法可高效率找到分子对接的能量最适构型。     

A Global Optimization Approach to Multi-Polarity Sentiment Analysis

Following the rapid development of social media, sentiment analysis has become an important social media mining technique. The performance of automatic sentiment analysis primarily depends on feature selection and sentiment classification. While information gain (IG) and support vector machines (SVM) are two important techniques, few studies have optimized both approaches in sentiment analysis. The effectiveness of applying a global optimization approach to sentiment analysis remains unclear. We propose a global optimization-based sentiment analysis (PSOGO-Senti) approach to improve sentiment analysis with IG for feature selection and SVM as the learning engine. The PSOGO-Senti approach utilizes a particle swarm optimization algorithm to obtain a global optimal combination of feature dimensions and parameters in the SVM. We evaluate the PSOGO-Senti model on two datasets from different fields. The experimental results showed that the PSOGO-Senti model can improve binary and multi-polarity Chinese sentiment analysis. We compared the optimal feature subset selected by PSOGO-Senti with the features in the sentiment dictionary. The results of this comparison indicated that PSOGO-Senti can effectively remove redundant and noisy features and can select a domain-specific feature subset with a higher-explanatory power for a particular sentiment analysis task. The experimental results showed that the PSOGO-Senti approach is effective and robust for sentiment analysis tasks in different domains. By comparing the improvements of two-polarity, three-polarity and five-polarity sentiment analysis results, we found that the five-polarity sentiment analysis delivered the largest improvement. The improvement of the two-polarity sentiment analysis was the smallest. We conclude that the PSOGO-Senti achieves higher improvement for a more complicated sentiment analysis task. We also compared the results of PSOGO-Senti with those of the genetic algorithm (GA) and grid search method. From the results of this comparison, we found that PSOGO-Senti is more suitable for improving a difficult multi-polarity sentiment analysis problem.     

A new particle swarm optimization algorithm for noisy optimization problems

We propose a new particle swarm optimization algorithm for problems where objective functions are subject to zero-mean, independent, and identically distributed stochastic noise. While particle swarm optimization has been successfully applied to solve many complex deterministic nonlinear optimization problems, straightforward applications of particle swarm optimization to noisy optimization problems are subject to failure because the noise in objective function values can lead the algorithm to incorrectly identify positions as the global/personal best positions. Instead of having the entire swarm follow a global best position based on the sample average of objective function values, the proposed new algorithm works with a set of statistically global best positions that include one or more positions with objective function values that are statistically equivalent, which is achieved using a combination of statistical subset selection and clustering analysis. The new PSO algorithm can be seamlessly integrated with adaptive resampling procedures to enhance the capability of PSO to cope with noisy objective functions. Numerical experiments demonstrate that the new algorithm is able to consistently find better solutions than the canonical particle swarm optimization algorithm in the presence of stochastic noise in objective function values with different resampling procedures.     

Constrained global optimization for estimating molecular structure from atomic distances.

Finding optimal three-dimensional molecular configurations based on a limited amount of experimental and/or theoretical data requires efficient nonlinear optimization algorithms. Optimization methods must be able to find atomic configurations that are close to the absolute, or global, minimum error and also satisfy known physical constraints such as minimum separation distances between atoms (based on van der Waals interactions). The most difficult obstacles in these types of problems are that 1) using a limited amount of input data leads to many possible local optima and 2) introducing physical constraints, such as minimum separation distances, helps to limit the search space but often makes convergence to a global minimum more difficult. We introduce a constrained global optimization algorithm that is robust and efficient in yielding near-optimal three-dimensional configurations that are guaranteed to satisfy known separation constraints. The algorithm uses an atom-based approach that reduces the dimensionality and allows for tractable enforcement of constraints while maintaining good global convergence properties. We evaluate the new optimization algorithm using synthetic data from the yeast phenylalanine tRNA and several proteins, all with known crystal structure taken from the Protein Data Bank. We compare the results to commonly applied optimization methods, such as distance geometry, simulated annealing, continuation, and smoothing. We show that compared to other optimization approaches, our algorithm is able combine sparse input data with physical constraints in an efficient manner to yield structures with lower root mean squared deviation.     

Modeling vegetation greenness and its climate sensitivity with deep‐learning technology

Climate sensitivity of vegetation has long been explored using statistical or process‐based models. However, great uncertainties still remain due to the methodologies’ deficiency in capturing the complex interactions between climate and vegetation. Here, we developed global gridded climate–vegetation models based on long short‐term memory (LSTM) network, which is a powerful deep‐learning algorithm for long‐time series modeling, to achieve accurate vegetation monitoring and investigate the complex relationship between climate and vegetation. We selected the normalized difference vegetation index (NDVI) that represents vegetation greenness as model outputs. The climate data (monthly temperature and precipitation) were used as inputs. We trained the networks with data from 1982 to 2003, and the data from 2004 to 2015 were used to validate the models. Error analysis and sensitivity analysis were performed to assess the model errors and investigate the sensitivity of global vegetation to climate change. Results show that models based on deep learning are very effective in simulating and predicting the vegetation greenness dynamics. For models training, the root mean square error (RMSE) is <0.01. Model validation also assure the accuracy of our models. Furthermore, sensitivity analysis of models revealed a spatial pattern of global vegetation to climate, which provides us a new way to investigate the climate sensitivity of vegetation. Our study suggests that it is a good way to integrate deep‐learning method to monitor the vegetation change under global change. In the future, we can explore more complex climatic and ecological systems with deep learning and coupling with certain physical process to better understand the nature.     

Numerical simulation of fibrous biomaterials with randomly distributed fiber network structure

This paper presents a computational framework to simulate the mechanical behavior of fibrous biomaterials with randomly distributed fiber networks. A random walk algorithm is implemented to generate the synthetic fiber network in 2D used in simulations. The embedded fiber approach is then adopted to model the fibers as embedded truss elements in the ground matrix, which is essentially equivalent to the affine fiber kinematics. The fiber–matrix interaction is partially considered in the sense that the two material components deform together, but no relative movement is considered. A variational approach is carried out to derive the element residual and stiffness matrices for finite element method (FEM), in which material and geometric nonlinearities are both included. Using a data structure proposed to record the network geometric information, the fiber network is directly incorporated into the FEM simulation without significantly increasing the computational cost. A mesh sensitivity analysis is conducted to show the influence of mesh size on various simulation results. The proposed method can be easily combined with Monte Carlo (MC) simulations to include the influence of the stochastic nature of the network and capture the material behavior in an average sense. The computational framework proposed in this work goes midway between homogenizing the fiber network into the surrounding matrix and accounting for the fully coupled fiber–matrix interaction at the segment length scale, and can be used to study the connection between the microscopic structure and the macro-mechanical behavior of fibrous biomaterials with a reasonable computational cost.     

Following FRET through five energy transfer steps: spectroscopic photobleaching, recovery of spectra, and a sequential mechanism of FRET.

We report the acquisition and analysis of spectrally resolved photobleaching data from a model system designed to exhibit FRET. Spectrally resolved photobleaching can be used to determine the presence of FRET in these systems and to investigate multi-step mechanisms of energy transfer. The model system was a previously described set of fluorescent beads consisting of a system of six fluorophores. In standard photobleaching experiments to determine FRET, bleaching of an acceptor molecule resulting in recovery of donor intensity or changes in photobleaching kinetics are used as indicators of FRET. Here, we use the Bateman equations to model growth and decay in a photobleaching experiment. Linked donor-acceptor growth and decay is used as an indicator of FRET. The apparatus required is relatively simple when compared to lifetime imaging systems. Several data analysis strategies, rigorous model building, global fitting procedures, and error analysis are presented. Using these procedures a five-step sequential mechanism of energy transfer was selected for these beads.