Simultaneously Identify Three Different Attributes of Proteins by Fusing their Three Different Modes of Chou's Pseudo Amino Acid Compositions.

Most published articles always applied a certain model or arithmetic to only a certain dataset. Considering the avalanche of biological data created in the post-genomic age, this type of research shows many shortcomings and inefficient characteristics, because it is always have difficulties to apply the same model to different datasets. So we proposed a multifunctional ensemble classifier which combines several individual classifiers. Each of them was trained in different parameter system. The final outcomes were combined through a weighted voting system. This classifier was conducted on several strictly constructed biological datasets. Based on the testing result from three different types of biological dataset, this new predictor can deal with more sweeping range of biological data, and receives more efficient and robust results in comparison with other published methods tentatively.

Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou's pseudo amino acid compositions.

Owing to the fact that location information can indicate important functionalities of proteins, developing computational tools to predict protein subcellular localization is one of the most efficient and meaningful tasks with no doubt. The existence methods dealing with prediction of protein subchloroplast locations can only handle the case of single location site. Therefore, it is meaningful and challenging to make effort in how to deal with the proteins with multiple subchloroplast location sites instead of just excluding them. To solve this problem, new systems for predicting protein subchloroplast localization with single or multiple sites are developed and discussed in the paper. Three different editions of KNN algorithms and four different types of feature extraction were adopted to construct the prediction systems. This is the first effort to predict the proteins with multiple subchloroplast locations. The overall jackknife success rates achieved by the best combination (features+classifier) on three dataset with different levels of homology were 89.08%, 81.29% and 71.11%. The performance of the prediction models indicate that the proposed methods might be applied as a useful and efficient assistant tool for the prediction of sub-subcellular localizations.

A multilabel model based on Chou's pseudo-amino acid composition for identifying membrane proteins with both single and multiple functional types.

Predicting membrane protein type is a meaningful task because this kind of information is very useful to explain the function of membrane proteins. Due to the explosion of new protein sequences discovered, it is highly desired to develop efficient computation tools for quickly and accurately predicting the membrane type for a given protein sequence. Even though several membrane predictors have been developed, they can only deal with the membrane proteins which belong to the single membrane type. The fact is that there are membrane proteins belonging to two or more than two types. To solve this problem, a system for predicting membrane protein sequences with single or multiple types is proposed. Pseudo-amino acid composition, which has proven to be a very efficient tool in representing protein sequences, and a multilabel KNN algorithm are used to compose this prediction engine. The results of this initial study are encouraging.

Observer-based H(infinity) control for networked nonlinear systems with random packet losses.

This paper investigates the observer-based H(infinity) control problem of networked nonlinear systems with global Lipschitz nonlinearities and random communication packet losses. The random packet loss is modelled as a Bernoulli distributed white sequence with a known conditional probability distribution. In the presence of random packet losses, sufficient conditions for the existence of an observer-based feedback controller are derived, such that the closed-loop networked nonlinear system is exponentially stable in the mean-square sense, and a prescribed H(infinity) disturbance-rejection-attenuation performance is also achieved. Then a linear matrix inequality (LMI) approach for designing such an observer-based H(infinity) controller is presented. Finally, a simulation example is used to demonstrate the effectiveness of the proposed method.

A two pathway model for tonic suppressed-by-contrast cells in the cat retina.

A two pathway spatiotemporal model is proposed to describe the function of tonic suppressed-by-contrast cells of the cat retina. The model is able to describe the experimentally determined responses of such neurons to drifting sinusoidal gratings. It is also able to predict their responses to alternating sinusoidal gratings and flashing or moving spots of light, and these predictions resemble experimental observations, at least qualitatively. The model is physiologically plausible, it can be used to summarize the dynamic responses of the tonic suppressed-by-contrast cells of the cat and potentially to account for the responses of the suppressed-by-contrast cells of other species.

A simple kinetic model for myeloma cell culture with consideration of lysine limitation.

A simple kinetic model is developed to describe the dynamic behavior of myeloma cell growth and cell metabolism. Glucose, glutamine as well as lysine are considered as growth limiting substrates. The cell growth was restricted as soon as the extracellular lysine is exhausted and then intracellular lysine becomes a growth limiting substrate. In addition, a metabolic regulator model together with the Monod model is used to deal with the growth lag phase after inoculation or feeding. By using these models, concentrations of substrates and metabolites, as well as densities of viable and dead cells are quantitatively described. One batch cultivation and two fed-batch cultivations with pulse feeding of nutrients are used to validate the model.

A population balance model describing the cell cycle dynamics of myeloma cell cultivation.

A multi-staged population balance model is proposed to describe the cell cycle dynamics of myeloma cell cultivation. In this model, the cell cycle is divided into three stages, i.e., G1, S, and G2M phases. Both DNA content and cell volume are used to differentiate each cell from other cells of the population. The probabilities of transition from G1 to S and division of G2M are assumed to be dependent on cell volume, and transition probability from S to G2M is determined by DNA content. The model can be used to simulate the dynamics of DNA content and cell volume distributions, phase fractions, and substrate and byproduct concentrations, as well as cell densities. Measurements from myeloma cell cultivations, especially the FACS data with respect to DNA distribution and cell fractions in different stages, are employed for model validation.

[Fermentation process monitoring and fault detection based on dynamic MPCA].

A dynamic multiway principle component analysis for on-line batch process monitoring and fault detection was proposed. It integrates the time-lagged windows of process dynamic behavior with the multiway principle component analysis (MPCA). Using multi-model instead of single model, the dynamic MPCA approach emphasizes particularly on-line process performance monitoring and fault defecting. On-line process monitoring of cephalosporin C fermentation was studied, the results demonstrate that the dynamic MPCA method is able to efficiently monitor performance of the fermentation process and exactly detect faults which results in extraordinary behavior of processes.

[Development of a portable glucose meter based on PIC microcomputer].

A portable glucose meter based on PIC microcomputer with data storage, analysis and historical data curve display functions, is presented. The concentration of blood glucose is detected with a glucose oxidase electrode. Test records may be stored and historic data can be displayed on the LCD with a fitting curve.

[Software and hardware design for the temperature control system of quantitative polymerase chain reaction].

A temperature control system for quantitive polymerase chain reaction (PCR) is presented in the paper with both software and hardware configuration. The performance of the control system has been improved by optimizing the software and hardware design according to the system's properties. The control system has been proven to have a good repeatability and reliability as well as high control precision.

[Neural network detection of abnormalities in fed-batch fermentation].

During fermentation, it is often difficult to detect the abnormalities, for example, caused by contamination on-line. Instead, the faults were detected usually by off-line laboratory analysis or other ways, which in most cases, is too late to remedy the situation. In this paper, a simple three-layers BP network was used for the early prediction of the amount of product, based on the difference in prediction errors between normal and abnormal charges and other accessorial information, such as profit function and pH value. In addition, three indications characteristic to abnormal charge are incorporated in practical operation. The prediction for Cephalosporin C Fed-batch Fermentation in a Chinese pharmaceutical factory was studied in details as an example and the result shows the abnormal charge can be discovered early successfully using the method.

The cell cycle related apoptotic susceptibility to arsenic trioxide is associated with the level of reactive oxygen species.

Double staining flow cytometry was performed using 7-amino actinomycin D and 6-carboxy-2', 7'-dichlorodihydrofluorescein diacetate, to detect the level fluctuation of reactive oxygen species (ROS) during the cell cycle of normal NB4 cells. Our results showed that NB4 cells possessed higher level of ROS in G2/M phase than in G1 and S phases. Double staining flow cytometry, with TdT mediated dUTP nick end labeling (Tunel) and propidium iodide (PI), indicated that As2O3 (2 microM) could induce apoptosis in NB4 cells prevailingly from G2/M phase, and this efficacy was enhanced upon co-administration of 2, 3-dimethoxy-1, 4-naphthoquinone (DMNQ) (2.5 microM) which could produce the endogenous ROS. These results suggested that different ROS level in different cell cycle phases of NB4 cells might determine the selective induction of G2/M apoptosis and the cells' susceptibility to apoptosis by As2O3.

Spike sorting based on automatic template reconstruction with a partial solution to the overlapping problem.

A new method for spike sorting is proposed which partly solves the overlapping problem. Principal component analysis and subtractive clustering techniques are used to estimate the number of neurons contributing to multi-unit recording. Spike templates (i.e. waveforms) are reconstructed according to the clustering results. A template-matching procedure is then performed. Firstly all temporally displaced templates are compared with the spike event to find the best-fitting template that yields the minimum residue variance. If the residue passes the chi(2)-test, the matching procedure stops and the spike event is classified as the best-fitting template. Otherwise the spike event may be an overlapping waveform. The procedure is then repeated with all possible combinations of two templates, three templates, etc. Once one combination is found, which yields the minimum residue variance among the combinations of the same number of component templates and makes the residue pass the chi(2)-test, the matching procedure stops. It is unnecessary to check the remaining combinations of more templates. Consequently, the computational effort is reduced and the over-fitting problem can be partly avoided. A simulated spike train was used to assess the performance of the proposed method, which was also applied to a real recording of chicken retina ganglion cells.