A modified model for predicting the performance of a compact artificial kidney.

A modified urea transfer model is presented in this work for predicting the urea removal in a compact artificial kidney. The modified model represents a departure from the previous one in two aspects. A simpler plug flow equation instead of a general dispersion one is employed for describing the urea transport in the blood flow. This is justified by the rather large Peclet number for the present system. Furthermore, the internal and external urea diffusion resistances in the microencapsulated urease particle are incorporated into the urea balance equations in this work. Results of numerical simulation indicate that the urea diffusion resistances play a dominating role in the determination of urea removal from the artificial kidney. Effects of other physical parameters, such as the urea concentration in microencapsule, the membrane thickness and the partition coefficient between the membrane and the urease solution, on the performance of the artificial kidney are found to be of less significance.     

A spatial exploring model for urban land ecological security based on a modified artificial bee colony algorithm

Urban land ecological security plays an important role in alleviating human–land conflicts and in coordinating the development of natural and human resources and the environment. However, the subject of the present urban land ecological security is poorly studied. Given the circumstances, this paper presents a spatial exploring model for urban land ecological security (ULESM) based on the modified artificial bee colony algorithm (MABCA). The paper improves ABCA with a spatial grid, designed a grid-based ABCA calculation framework, ABCA's data structure, information-sharing mechanism and position selection strategy, introduces the model of spatial coupling and coordination degree for urban ecological security, and achieves spatial detection of ecologically hazardous areas. Taking Guangzhou as an example, this paper simulated the hazardous regions of the urban ecological land (assumed area: 50% of total area of Guangzhou), yielding favorable results. The results show that the ULESM based on MABCA exhibits an excellent global search and a rapid global-convergence speed, preventing a bee colony from being trapped in a local optimum. It can also effectively detect regions with urban land ecological hazards and provide intelligence-aided methods for decision making in urban ecological security and spatial planning     

An artificial immune network based algorithm for diabetes diagnosis

A novel artificial immune network based algorithm for the diagnosis of diabetes is presented. The algorithm's implementation includes: (1) creating the initial immune antibody network; (2) the network is evolved with the learning from foreign antigens; (3) diagnosis process is accomplished by majority vote of the k nearest neighbor antibodies.     

基于多智能体模型的兰州市城镇用地扩展模拟

利用1995、2005、2015、2018年4期兰州市土地利用数据和社会经济统计数据,运用扩展强度指数、Logistic回归、主成分分析等方法揭示兰州市城镇用地演变特征及驱动力,基于多智能体模型用Netlogo软件实现了2025年多情景下兰州市城镇用地扩展模拟.结果 表明:1995-2018年,兰州市城镇用地扩展强度和...     

A formal model of an artificial immune system

The paper presents a mathematical model based on the features of antigen-antibody bindings in the immune system. In the natural immune system, local binding of immune cells and molecules to antigenic peptides is based generally on the behavior of surface proteins. In particular, immune cells contain proteins on their receptors, and apparently, these proteins play the key role both in immune response and recognition processes. In this work, we consider the immune cells in the form of formal B-cell and formal T-cell and develop a mathematical model of their interactions. We refer this model as the formal immune system (FIS). The paper provides an analysis of a network of bindings (or interactions) among the formal proteins of the FIS.     

Scale invariance of immune system response rates and times: perspectives on immune system architecture and implications for artificial immune systems

Most biological rates and times decrease systematically with increasing organism body size. We use an ordinary differential equation (ODE) model of West Nile Virus in birds to show that pathogen replication rates decline with host body size, but natural immune system (NIS) response rates do not change systematically with body size. The scale-invariant detection and response of the NIS is surprising since the NIS has to search for small quantities of pathogens through larger physical spaces in larger organisms, and also respond by producing larger absolute quantities of antibody in larger organisms. We hypothesize that the NIS has evolved an architecture to efficiently neutralize pathogens. We investigate three different hypothesized NIS architectures using an Agent Based Model (ABM). We find that a sub-modular NIS architecture, in which lymph node number and size both increase sublinearly with body size, efficiently balances the tradeoff between local pathogen detection and global response. This leads to nearly scale-invariant detection and response consistent with experimental data. Similar to the NIS, physical space and resources are also important constraints on distributed systems, for example low-powered robots connected by short-range wireless communication. We show that the sub-modular design principles of the NIS can be applied to problems such as distributed robot control to efficiently balance the tradeoff between local search for a solution and global response or proliferation of the solution. We demonstrate that the lymphatic network of the NIS efficiently balances local and global communication, and we suggest a new approach for Artificial Immune Systems (AIS) that uses a sub-modular architecture to facilitate distributed search.     

Magnesia-zirconia based mimetic biomembrane chromatography for predicting human drug absorption

In this paper, a novel mimetic biomembrane chromatography stationary phase of magnesia-zirconia composite matrix were prepared with the Lewis acid-base interaction between phosphatidylcholine's residue phosphonate group and Lewis acid sites of magnesia-zirconia composite; the retention factors of a chemically diverse set of drugs on the new stationary phase were determined; the drugs logK(mbm) values were correlationed with the absorbed fraction of drugs orally administered in humans (%F(a)) and a hyperbolic relationship was obtained. Meanwhile, the relationship between the logK(mbm) values and hydrophobic parameters (logP(oct) and logD(oct)) were discussed. The usefulness of the new column for predicting oral drug absorption in humans is demonstrated by comparing this model with IAM, ILC and BMC models. Results show that the logK(mbm) values have good relationship with logK(W)(IAM), logK(BMC) and have moderate to fair relationship with logK(s) determined on four different ILC column (EPL, PC, PC-PE, PC-PS). Therefore, the logK(mbm) values can provide key information about the transport properties of drugs and this chromatographic model may be applicable for prediction of drug uptake through epithelial cell membranes during the drug discovery process.     

A computational model for predicting protein interactions based on multidomain collaboration

Recently, several domain-based computational models for predicting protein-protein interactions (PPIs) have been proposed. The conventional methods usually infer domain or domain combination (DC) interactions from already known interacting sets of proteins, and then predict PPIs using the information. However, the majority of these models often have limitations in providing detailed information on which domain pair (single domain interaction) or DC pair (multidomain interaction) will actually interact for the predicted protein interaction. Therefore, a more comprehensive and concrete computational model for the prediction of PPIs is needed. We developed a computational model to predict PPIs using the information of intraprotein domain cohesion and interprotein DC coupling interaction. A method of identifying the primary interacting DC pair was also incorporated into the model in order to infer actual participants in a predicted interaction. Our method made an apparent improvement in the PPI prediction accuracy, and the primary interacting DC pair identification was valid specifically in predicting multidomain protein interactions. In this paper, we demonstrate that 1) the intraprotein domain cohesion is meaningful in improving the accuracy of domain-based PPI prediction, 2) a prediction model incorporating the intradomain cohesion enables us to identify the primary interacting DC pair, and 3) a hybrid approach using the intra/interdomain interaction information can lead to a more accurate prediction.     

Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample

Previous work from our laboratory provided a "proof of concept" for use of artificial neural networks (nnets) to estimate metabolic equivalents (METs) and identify activity type from accelerometer data (Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P, J Appl Physiol 107: 1330-1307, 2009). The purpose of this study was to develop new nnets based on a larger, more diverse, training data set and apply these nnet prediction models to an independent sample to evaluate the robustness and flexibility of this machine-learning modeling technique. The nnet training data set (University of Massachusetts) included 277 participants who each completed 11 activities. The independent validation sample (n = 65) (University of Tennessee) completed one of three activity routines. Criterion measures were 1) measured METs assessed using open-circuit indirect calorimetry; and 2) observed activity to identify activity type. The nnet input variables included five accelerometer count distribution features and the lag-1 autocorrelation. The bias and root mean square errors for the nnet MET trained on University of Massachusetts and applied to University of Tennessee were +0.32 and 1.90 METs, respectively. Seventy-seven percent of the activities were correctly classified as sedentary/light, moderate, or vigorous intensity. For activity type, household and locomotion activities were correctly classified by the nnet activity type 98.1 and 89.5% of the time, respectively, and sport was correctly classified 23.7% of the time. Use of this machine-learning technique operates reasonably well when applied to an independent sample. We propose the creation of an open-access activity dictionary, including accelerometer data from a broad array of activities, leading to further improvements in prediction accuracy for METs, activity intensity, and activity type.     

BioPatRec: A modular research platform for the control of artificial limbs based on pattern recognition algorithms

Background

Processing and pattern recognition of myoelectric signals have been at the core of prosthetic control research in the last decade. Although most studies agree on reporting the accuracy of predicting predefined movements, there is a significant amount of study-dependent variables that make high-resolution inter-study comparison practically impossible. As an effort to provide a common research platform for the development and evaluation of algorithms in prosthetic control, we introduce BioPatRec as open source software. BioPatRec allows a seamless implementation of a variety of algorithms in the fields of (1) Signal processing; (2) Feature selection and extraction; (3) Pattern recognition; and, (4) Real-time control. Furthermore, since the platform is highly modular and customizable, researchers from different fields can seamlessly benchmark their algorithms by applying them in prosthetic control, without necessarily knowing how to obtain and process bioelectric signals, or how to produce and evaluate physically meaningful outputs.

Results

BioPatRec is demonstrated in this study by the implementation of a relatively new pattern recognition algorithm, namely Regulatory Feedback Networks (RFN). RFN produced comparable results to those of more sophisticated classifiers such as Linear Discriminant Analysis and Multi-Layer Perceptron. BioPatRec is released with these 3 fundamentally different classifiers, as well as all the necessary routines for the myoelectric control of a virtual hand; from data acquisition to real-time evaluations. All the required instructions for use and development are provided in the online project hosting platform, which includes issue tracking and an extensive “wiki”. This transparent implementation aims to facilitate collaboration and speed up utilization. Moreover, BioPatRec provides a publicly available repository of myoelectric signals that allow algorithms benchmarking on common data sets. This is particularly useful for researchers lacking of data acquisition hardware, or with limited access to patients.

Conclusions

BioPatRec has been made openly and freely available with the hope to accelerate, through the community contributions, the development of better algorithms that can potentially improve the patient’s quality of life. It is currently used in 3 different continents and by researchers of different disciplines, thus proving to be a useful tool for development and collaboration.

     

Convergence properties of a modified Hopfield-Tank model

The neural network model of Hopfield and Tank applied to the Travelling Salesman Problem, has been analyzed in order to improve its convergence properties. A simple change of the parameter sets always allows to reach states corresponding to valid tours. Besides a more interesting modification has been presented by adding a new term to force expression. This modified model has a high value of convergence and it is able to find short tours. So, more confidence can be given to these type of models, and real applications could be performed.     

人工神经网络与相容性生物量模型预测单木地上生物量的比较

森林生物量是林业生产经营和森林资源监测的重要指标,为探索高效低偏的单木生物量估测方法,引入人工神经网络.本研究采用黑龙江省东折棱河林场的101株长白落叶松地上生物量数据,基于不同变量(胸径、树高、冠幅)组合建立了4个聚合模型体系(AMS),采用加权回归消除模型的异方差.然后,基于最优的变量组合建立人工神经网络(ANN)...     

Real and artificial immune systems: computing the state of the body

Here I present the idea that the immune system uses a computational strategy to carry out its many functions in protecting and maintaining the body. Along the way, I define the concepts of computation, Turing machines and system states. I attempt to show that reframing our view of the immune system in computational terms is worth our while.     

A note on a method for determining advantageous properties of an anti-arrhythmic drug based on a mathematical model of cardiac cells

Regional hyperkalemia during acute ischemia may provoke cardiac arrhythmias such as ventricular fibrillation. Despite intense research efforts over the last decades, the problem of finding an efficient anti-arrhythmic drug without dangerous side effects is still open. One approach to analyze the effect of anti-arrhythmic drugs is to do simulations based on mathematical models of collections of cardiomyocytes. Such simulations have recently illuminated the pro-arrhythmic capability of well-established anti-arrhythmic drugs.The purpose of the present note is to introduce a method intended for computing advantageous properties of an anti-arrhythmic drug. For a given model of a normal and an ischemic cell, we introduce a drug as a vector of non-negative real numbers whose components are multiplied by individual terms representing specific ionic currents. The drug vector is computed such that the action potentials of the resulting drugged cells are as close as possible to the action potential of a normal (not drugged) cell. Numerical simulations based on the Luo-Rudy I model and the Hund-Rudy model show that the classical shortened action potential obtained due to hyperkalemia is prolonged by using the drug computed by this method. Furthermore, for both models a 2D collection of spatially coupled ischemic cells give arrhythmogenic solutions before the drug is applied, and stable solutions after the drug is applied. It is emphasized that we do not address the possibility of realizing a drug with the properties computed in this note.     

A multi-label learning model for predicting drug-induced pathology in multi-organ based on toxicogenomics data

Drug-induced toxicity damages the health and is one of the key factors causing drug withdrawal from the market. It is of great significance to identify drug-induced target-organ toxicity, especially the detailed pathological findings, which are crucial for toxicity assessment, in the early stage of drug development process. A large variety of studies have devoted to identify drug toxicity. However, most of them are limited to single organ or only binary toxicity. Here we proposed a novel multi-label learning model named Att-RethinkNet, for predicting drug-induced pathological findings targeted on liver and kidney based on toxicogenomics data. The Att-RethinkNet is equipped with a memory structure and can effectively use the label association information. Besides, attention mechanism is embedded to focus on the important features and obtain better feature presentation. Our Att-RethinkNet is applicable in multiple organs and takes account the compound type, dose, and administration time, so it is more comprehensive and generalized. And more importantly, it predicts multiple pathological findings at the same time, instead of predicting each pathology separately as the previous model did. To demonstrate the effectiveness of the proposed model, we compared the proposed method with a series of state-of-the-arts methods. Our model shows competitive performance and can predict potential hepatotoxicity and nephrotoxicity in a more accurate and reliable way. The implementation of the proposed method is available at https://github.com/RanSuLab/Drug-Toxicity-Prediction-MultiLabel.     

Application of an artificial neural network (ANN) model for predicting mosquito abundances in urban areas

The mosquito species is one of most important insect vectors of several diseases, namely, malaria, filariasis, Japanese encephalitis, dengue, and so on. In particular, in recent years, as the number of people who enjoy outdoor activities in urban areas continues to increase, information about mosquito activity is in demand. Furthermore, mosquito activity prediction is crucial for managing the safety and the health of humans. However, the estimation of mosquito abundances frequently involves uncertainty because of high spatial and temporal variations, which hinders the accuracy of general mechanistic models of mosquito abundances. For this reason, it is necessary to develop a simpler and lighter mosquito abundance prediction model. In this study, we tested the efficacy of the artificial neural network (ANN), which is a popular empirical model, for mosquito abundance prediction. For comparison, we also developed a multiple linear regression (MLR) model. Both the ANN and the MLR models were applied to estimate mosquito abundances in 2-year observations in Yeongdeungpo-gu, Seoul, conducted using the Digital Mosquito Monitoring System (DMS). As input variables, we used meteorological data, including temperature, wind speed, humidity, and precipitation. The results showed that performances of the ANN model and the MLR model are almost same in terms of R and root mean square error (RMSE). The ANN model was able to predict the high variability as compared to MLR. A sensitivity analysis of the ANN model showed that the relationships between input variables and mosquito abundances were well explained. In conclusion, ANNs have the potential to predict fluctuations in mosquito numbers (especially the extreme values), and can do so better than traditional statistical techniques. But, much more work needs to be conducted to assess meaningful time delays in environmental variables and mosquito numbers.