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。     

Optimization of feed rate profile for the monoclonal antibody production

The optimal control algorithm to calculate the optimal feed rate profile of nutrient solution containing two limiting nutrients was proposed. Different from other conventional optimization methods, the proposed algorithm calculated the optimal control profiles for different initial and feed conditions. The singular optimal control algorithm, dynamic programming, and nonsingular transformation algorithm were used for the optimization of simple problems of the 4th order and the performances were compared. With the proposed transformation algorithm, the final MAb concentration increased and the CPU time decreased. For the different initial glucose and glutamine conditions, the optimal control profiles were calculated with the proposed transformation algorithm. As the initial glutamine concentration increased, the final MAb concentration also increased due to the cell viability increase. This was also applied to the different feed compositions. When the glutamine concentration was increased in the feed stream, the final MAb concentration also increased.     

Efficiencies of maximum likelihood methods of phylogenetic inferences when different substitution models are used

Choice of a substitution model is a crucial step in the maximum likelihood (ML) method of phylogenetic inference, and investigators tend to prefer complex mathematical models to simple ones. However, when complex models with many parameters are used, the extent of noise in statistical inferences increases, and thus complex models may not produce the true topology with a higher probability than simple ones. This problem was studied using computer simulation. When the number of nucleotides used was relatively large (1000 bp), the HKY+Gamma model showed smaller d(T) topological distance between the inferred and the true trees) than the JC and Kimura models. In the cases of shorter sequences (300 bp) simpler model and search algorithm such as JC model and SA+NNI search were found to be as efficient as more complicated searches and models in terms of topological distances, although the topologies obtained under HKY+Gamma model had the highest likelihood values. The performance of relatively simple search algorithm SA+NNI was found to be essentially the same as that of more extensive SA+TBR search under all models studied. Similarly to the conclusions reached by Takahashi and Nei [Mol. Biol. Evol. 17 (2000) 1251], our results indicate that simple models can be as efficient as complex models, and that use of complex models does not necessarily give more reliable trees compared with simple models.     

Simulation of L/A control in fed-batch culture of baker's yeast

L/A controllers have extended their use from continuous to fed-batch fermentation where the control is applied from the start of an initial batch phase. As opposed to proportional integral derivative (PID) controllers where even a startup procedure is recommended prior to fed-batch, the L/A controller is not upset by an early connection. It is easily retuned continuously by means of ethanol measurements and can cope with a large range of output conditions. The performance of an L/A algorithm, which uses biomass concentration as the controlled variable, is assessed through simulation. The self-contained algorithm is relatively simple with no greater intrinsic complexity than modern PID stand alone controllers.     

Framework for online optimization of recombinant protein expression in high-cell-density Escherichia coli cultures using GFP-fusion monitoring

A framework for the online optimization of protein induction using green fluorescent protein (GFP)-monitoring technology was developed for high-cell-density cultivation of Escherichia coli. A simple and unstructured mathematical model was developed that described well the dynamics of cloned chloramphenicol acetyltransferase (CAT) production in E. coli JM105 was developed. A sequential quadratic programming (SQP) optimization algorithm was used to estimate model parameter values and to solve optimal open-loop control problems for piecewise control of inducer feed rates that maximize productivity. The optimal inducer feeding profile for an arabinose induction system was different from that of an isopropyl-beta-D-thiogalactopyranoside (IPTG) induction system. Also, model-based online parameter estimation and online optimization algorithms were developed to determine optimal inducer feeding rates for eventual use of a feedback signal from a GFP fluorescence probe (direct product monitoring with 95-minute time delay). Because the numerical algorithms required minimal processing time, the potential for product-based and model-based online optimal control methodology can be realized.     

Use of Approximate Bayesian Computation to Assess and Fit Models of Mycobacterium leprae to Predict Outcomes of the Brazilian Control Program

Hansen’s disease (leprosy) elimination has proven difficult in several countries, including Brazil, and there is a need for a mathematical model that can predict control program efficacy. This study applied the Approximate Bayesian Computation algorithm to fit 6 different proposed models to each of the 5 regions of Brazil, then fitted hierarchical models based on the best-fit regional models to the entire country. The best model proposed for most regions was a simple model. Posterior checks found that the model results were more similar to the observed incidence after fitting than before, and that parameters varied slightly by region. Current control programs were predicted to require additional measures to eliminate Hansen’s Disease as a public health problem in Brazil.     

The dromotropic pacemaker: system analysis and design considerations.

Patients suffering from chronotropic incompetence are generally treated with a rate-responsive pacemaker that stimulates the heart at a rate derived from a strain related sensor signal. The pacemaker concept described here uses a well-defined time interval in the electrogram as sensor parameter (AVCT: atrio-ventricular conduction time). AVCT is directly controlled by the autonomic nervous system. The design of the new algorithm was based on a thorough experimental analysis of AVCT subject to variations of the exercise rate and the pacing frequency. There it was demonstrated that AVCT is disturbed by the respiratory activity. The new rate-responsive algorithm which uses the internal model control principle explicitly takes into account the closed-loop nature of the underlying system. The major design objectives were: a) extended range of the individual heart rate, b) effective attenuation of the respiratory related disturbance and c) dynamic stability. Seven patients undergoing an incremental exercise test were paced with the new AVCT-based algorithm. When paced with this algorithm the paced heart rate was 126 +/- 12 min(-1) whereas the peak intrinsic heart rate was only 100 +/- 20 min(-1). The increase which was significant (26 +/- 13 min(-1); 15.53 min(-1)) clearly demonstrated the potential of this concept to restore chronotropic competence. A reanalysis of the experiments was undertaken in order to facilitate the individual parameterization in clinical practice. It could be demonstrated that a rather simple screening test is sufficient to gain the knowledge necessary for the individual parameterisation.     

Development of an above-knee prosthesis equipped with a microcomputer-controlled knee joint: first test results

The shortcomings of conventional above-knee prostheses are due to their lack of adaptive control. Implementation of a microcomputer controlling the knee joint in a passive way has been suggested to enhance the patient's gait comfort, safety and cosmesis. This approach was used in the design of a new prosthetic system for the above-knee amputee, and tested on one patient. The knee joint of a conventional, modular prosthesis was replaced by a knee joint mechanism, equipped with a controllable brake on the knee joint axis. Sensors and a microcomputer were added, keeping the system self-contained. The modularity of the design permits the use of an alternative, external, PC-based control unit, emulating the self-contained one, and offering extended data monitoring and storage facilities. For both units an operating environment was written, including sensor/actuator interfacing and the implementation of a real-time interrupt, executing the control algorithm. A double finite state approach was used in the design of the control algorithm. On a higher level, the mode identification algorithm reveals the patient's intent. Within a specific mode (lower level), the relevant mode control algorithm looks for the current phase within the gait cycle. Within a particular phase, a specific simple control action with the brake replaces normal knee muscle activity. Test were carried out with one prosthetic patient using a basic control algorithm for level walking, allowing controlled knee flexion during stance phase. The technical feasibility of such a concept is illustrated by the test results, even though only flexion during early stance phase was controlled during the trials. Patient acceptance is not straightforward since knee flexion during stance phase is associated with knee buckling.     

Comparison of brain–computer interface decoding algorithms in open-loop and closed-loop control

Neuroprosthetic devices such as a computer cursor can be controlled by the activity of cortical neurons when an appropriate algorithm is used to decode motor intention. Algorithms which have been proposed for this purpose range from the simple population vector algorithm (PVA) and optimal linear estimator (OLE) to various versions of Bayesian decoders. Although Bayesian decoders typically provide the most accurate off-line reconstructions, it is not known which model assumptions in these algorithms are critical for improving decoding performance. Furthermore, it is not necessarily true that improvements (or deficits) in off-line reconstruction will translate into improvements (or deficits) in on-line control, as the subject might compensate for the specifics of the decoder in use at the time. Here we show that by comparing the performance of nine decoders, assumptions about uniformly distributed preferred directions and the way the cursor trajectories are smoothed have the most impact on decoder performance in off-line reconstruction, while assumptions about tuning curve linearity and spike count variance play relatively minor roles. In on-line control, subjects compensate for directional biases caused by non-uniformly distributed preferred directions, leaving cursor smoothing differences as the largest single algorithmic difference driving decoder performance.     

Simple adaptive pH control in bioreactors using gain-scheduling methods

A simple well-performing adaptive control technique for pH control in fermentations of recombinant protein production processes is described and its design procedure is explained. First, the entire control algorithm was simulated and parameterized. Afterwards it was tested in real cultivation processes. The results show that this simple technique leads to significant reductions in the fluctuations of the pH values in microbial cultures at a minimum of expenditures. The signal-to-noise ratio and thus the information captured by the pH signal were increased by about an order of magnitude. This leads to a substantial improvement in the noise of many other process signals that are used to monitor and control the process. For instance, respiratory off-gas data of CO₂ and its derived carbon dioxide production rate signals from the cultures carry much less noise as compared to those values obtained with conventional pH control. Detailed process analysis revealed that even very small pH jumps of 0.03 values during the fermentation were shown to result in pronounced deflections in CO₂-volume fraction of 8% (peak to peak). The proposed controller, maintaining the pH within the interval of 0.01 around the setpoint, reduces the noise considerably.     

Improving elbow torque output of stroke patients with assistive torque controlled by EMG signals

This paper develops an assistive torque system which uses homogeneic surface electromyogram (EMG) signals to improve the elbow torque capability of stroke patients by applying an external time-varying assistive torque. In determining the magnitude of the torque to apply, the incorporated assistive torque algorithm considers the difference between the weighted biceps and triceps EMG signals such that the applied torque is proportional to the effort supplied voluntarily by the user. The overall stability of the assistive system is enhanced by the incorporation of a nonlinear damping element within the control algorithm which mimics the physiological damping of the elbow joint and the co-contraction between the biceps and triceps. Adaptive filtering of the control signal is employed to achieve a balance between the bandwidth and the system adaptability so as to ensure a smooth assistive torque output. The innovative control algorithm enables the provision of an assistive system whose operation is both natural to use and simple to learn. The effectiveness of the proposed assistive system in assisting elbow movement performance is investigated in a series of tests involving five stroke patients and five able-bodied individuals. The results confirm the ability of the system to assist all of the subjects in performing a number of reaching and tracking tasks with reduced effort and with no sacrifice in elbow movement performance.     

Limited sampling of conformational space by the distance geometry algorithm: implications for structures generated from NMR data

Calculations with a metric matrix distance geometry algorithm were performed that show that the standard implementation of the algorithm generally samples a very limited region of conformational space. This problem is most severe when only a small amount of distance information is used as input for the algorithm. Control calculations were performed on linear peptides, disulfide-linked peptides, and a double-stranded DNA decamer where only distances defining the covalent structures of the molecules (as well as the hydrogen bonds for the base pairs in the DNA) were included as input. Since the distance geometry algorithm is commonly used to generate structures of biopolymers from distance data obtained from NMR experiments, simulations were performed on the small globular protein basic pancreatic trypsin inhibitor (BPTI) that mimic calculations performed with actual NMR data. The results on BPTI and on the control peptides indicate that the standard implementation of the algorithm has two main problems: first, that it generates extended structures; second, that it has a tendency to consistently produce similar structures instead of sampling all structures consistent with the input distance information. These results also show that use of a simple root-mean-square deviation for evaluating the quality of the structures generated from NMR data may not be generally appropriate. The main sources of these problems are identified, and our results indicate that the problems are not a fundamental property of the distance geometry algorithm but arise from the implementations presently used to generate structures from NMR data. Several possible methods for alleviating these problems are discussed.     

基于复杂性度量的表面肌电信号分类方法

提取表面肌电信号的复杂性测度信息,利用原始数据的复杂度指标构造特征矢量对四种前臂动作进行分类,取得了较好的识别效果.通过比较,发现基于原始数据的复杂度指标在分类性能上要优于基于重构序列的复杂度.肌电信号的复杂度算法简单,适合短数据运算,能够满足实时处理的要求.作为一种新的肌电信号特征,复杂性测度也为生理与病理分析提供了新的思路.     

Modular neuromuscular control of human locomotion by central pattern generator

The central pattern generators (CPG) in the spinal cord are thought to be responsible for producing the rhythmic motor patterns during rhythmic activities. For locomotor tasks, this involves much complexity, due to a redundant system of muscle actuators with a large number of highly nonlinear muscles. This study proposes a reduced neural control strategy for the CPG, based on modular organization of the co-active muscles, i.e., muscle synergies. Four synergies were extracted from the EMG data of the major leg muscles of two subjects, during two gait trials each, using non-negative matrix factorization algorithm. A Matsuoka׳s four-neuron CPG model with mutual inhibition, was utilized to generate the rhythmic activation patterns of the muscle synergies, using the hip flexion angle and foot contact force information from the sensory afferents as inputs. The model parameters were tuned using the experimental data of one gait trial, which resulted in a good fitting accuracy (RMSEs between 0.0491 and 0.1399) between the simulation and experimental synergy activations. The model׳s performance was then assessed by comparing its predictions for the activation patterns of the individual leg muscles during locomotion with the relevant EMG data. Results indicated that the characteristic features of the complex activation patterns of the muscles were well reproduced by the model for different gait trials and subjects. In general, the CPG- and muscle synergy-based model was promising in view of its simple architecture, yet extensive potentials for neuromuscular control, e.g., resolving redundancies, distributed and fast control, and modulation of locomotion by simple control signals.     

Adaptive control of anaerobic digestion processes-a pilot-scale application

A simple adaptive control algorithm, for which theoretical stability and convergence properties had been previously demonstrated, has been successfully implemented on a biomethanation pilot reactor. The methane digester, operated in the CSTR mode was submitted to a shock load, and successfully computer controlled during the subsequent transitory state.     

Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function.

Due to the recent progress of the DNA microarray technology, a large number of gene expression profile data are being produced. How to analyze gene expression data is an important topic in computational molecular biology. Several studies have been done using the Boolean network as a model of a genetic network. This paper proposes efficient algorithms for identifying Boolean networks of bounded indegree and related biological networks, where identification of a Boolean network can be formalized as a problem of identifying many Boolean functions simultaneously. For the identification of a Boolean network, an O(mnD+1) time naive algorithm and a simple O (mnD) time algorithm are known, where n denotes the number of nodes, m denotes the number of examples, and D denotes the maximum in degree. This paper presents an improved O(momega-2nD + mnD+omega-3) time Monte-Carlo type randomized algorithm, where omega is the exponent of matrix multiplication (currently, omega < 2.376). The algorithm is obtained by combining fast matrix multiplication with the randomized fingerprint function for string matching. Although the algorithm and its analysis are simple, the result is nontrivial and the technique can be applied to several related problems.     

Aid to diagnosis of melanoma in primary medical care.

OBJECTIVE--To evaluate an intervention designed to reduce the number of benign melanocytic lesions excised from the skin. DESIGN--A randomised controlled field trial based in the medical practices of two cities. Examination of histopathological reports of 5823 melanocytic skin lesions excised over the intervention period and in the preceding six months. INTERVENTION--Medical practitioners were offered an algorithm and use of an instant developing camera. SETTING AND SUBJECTS--Over 50 medical practitioners, mostly in general practice, in each of two cities in tropical Queensland, Australia. MAIN OUTCOME MEASURES--Percentages of benign (neither malignant nor potentially malignant) melanocytic lesions excised during the two year intervention period. RESULTS--There were no significant differences in the percentages of benign lesions reported in the intervention and control cities before the intervention started (93.6% and 94.0%, respectively), but there was a significant difference afterwards (88.8% and 93.8%, P < 0.001). There was no difference in the percentage of invasive melanomas excised per month in the intervention city (3.4%) compared with control city (3.4%). CONCLUSION--Clinical diagnostic accuracy may be enhanced by offering to clinicians managing suspicious melanocytic skin lesions a simple algorithm and a camera with which to record the appearance of lesions objectively.     

A novel efficient learning algorithm for self-generating fuzzy neural network with applications

In this paper, a novel efficient learning algorithm towards self-generating fuzzy neural network (SGFNN) is proposed based on ellipsoidal basis function (EBF) and is functionally equivalent to a Takagi-Sugeno-Kang (TSK) fuzzy system. The proposed algorithm is simple and efficient and is able to generate a fuzzy neural network with high accuracy and compact structure. The structure learning algorithm of the proposed SGFNN combines criteria of fuzzy-rule generation with a pruning technology. The Kalman filter (KF) algorithm is used to adjust the consequent parameters of the SGFNN. The SGFNN is employed in a wide range of applications ranging from function approximation and nonlinear system identification to chaotic time-series prediction problem and real-world fuel consumption prediction problem. Simulation results and comparative studies with other algorithms demonstrate that a more compact architecture with high performance can be obtained by the proposed algorithm. In particular, this paper presents an adaptive modeling and control scheme for drug delivery system based on the proposed SGFNN. Simulation study demonstrates the ability of the proposed approach for estimating the drug's effect and regulating blood pressure at a prescribed level.     

Adaptive control of specific growth rate based on proton production in anaerobic fed-batch culture

Summary This paper develops a practical and useful computer control scheme to control the specific growth rate as accurately as possible by measuring the released protons in anaerobic alcohol fermentation. In the case of current study, most of the released protons are due to the uptake of cationic ammonium ion via the conversion: NH₄ ⁺ NH₃(cell) + H⁺. Correlating the Proton Production (PP) and the Proton Production Rate (PPR) with specific growth rate () proved PP as a better measured variable. Using a simple adaptive control algorithm, was successfully controlled in a Zymomonas mobilis fed-batch culture.