Peroxiredoxin 2 as a chemotherapy responsiveness biomarker candidate in osteosarcoma revealed by proteomics

Purpose : We aimed to identify novel chemotherapy responsiveness biomarkers for osteosarcoma (OS) by investigating the global protein expression profile of 12 biopsy samples from OS patients. Experimental design : Six patients were classified as good responders and six as poor responders, according to the Huvos grading system. The protein expression profiles obtained by 2‐D DIGE consisted of 2250 protein spots. Results : Among them, we identified 55 protein spots whose intensity was significantly different (Bonferroni adjusted p‐value<0.01) between the two patient groups. Mass spectrometric protein identification demonstrated that the 55 spots corresponded to 38 distinct gene products including peroxiredoxin 2 (PRDX 2). Use of a specific antibody against PRDX 2 confirmed the differential expression of PRDX 2 between good and poor responders, while PRDX 2 levels as measured by Western blotting correlated highly with their corresponding 2‐D DIGE values. The predictive value of PRDX 2 expression was further confirmed by examining an additional four OS cases using Western blotting. Conclusions and clinical relevance : These results establish PRDX 2 as a candidate for chemotherapy responsiveness marker in OS. Measuring PRDX 2 in biopsy samples before treatment may contribute to more effective management of OS.     

Chain packing in graphs

This paper discusses the complexity of packingk-chains (simple paths of lengthk) into an undirected graph; the chains packed must be either vertex-disjoint or edge-disjoint. Linear-time algorithms are given for both problems when the graph is a tree, and for the edge-disjoint packing problem when the graph is general andk = 2. The vertex-disjoint packing problem for general graphs is shown to be NP-complete even when the graph has maximum degree three andk = 2. Similarly the edge-disjoint packing problem is NP-complete even when the graph has maximum degree four andk = 3.     

Controller design for parabolic distributed parameter systems using finite integral transform techniques

A procedure is presented for designing a control system for distributed parameter systems of parabolic type based on the reduced-order decoupled state-space model obtained by a finite integral-transform technique. A Kalman filter is used as an observer to estimate the state variables, and state feedback control is performed. The method was applied to a one-dimensional heat conduction process and a moving bed adsorber. Both state estimation and control performances were satisfactory in spite of the model and parameter uncertainties. Following this controller design approach, the searching algorithms for the optimal sensors' and the optimal actuators' allocation problems were solved. These algorithms were applied to a one-dimensional heat conduction process in order to confirm the state estimator and controller performance. The fastest state estimation could be achieved by assigning the sensors at the optimal locations and the desired state distribution was realized with a few actuators located at the optimal positions.     

The thermodynamic database malt

The objectives of the development of the thermodynamic database MALT are described together with its main characteristic features. The database, which contains about 5000 species, also composes software for calculation of chemical equilibria and construction of generalized chemical potential diagrams. Discussion is made on the quality of the thermodynamic data to be used in the advanced software for calculating complex chemical equilibria in multicomponent systems and the importance of the chemical potential diagrams for checking the interconsistency of data in a visual manner is stressed.     

Optimal initial production control period based on a quality growth model

The initial production phase of new products or the initial installation phase of new manufacturing facilities is often unstable because of inexperienced workers and many defective products. An initial production process control, in which the defects in design, production technologies and products are fully fixed and removed, is switched to a normal process control whenever it is ready for actual mass production. This paper discusses a method of deciding the optimal initial production control period, based on a quality growth model. It is determined by the number of products with the minimum expected total quality control cost. Finally a penalty cost due to unattainable loss to the quality goal is introduced in the quality control cost: the realized stabilization level of the initial production process control is lower than the original quality objective. Numerical illustrations of the optimal policy are also presented. Copyright © 1999 John Wiley & Sons, Ltd.     

Approximate calculation method for integral of mean square value of nonstationary response

The response of the structure subjected to nonstationary random vibration such as earthquake excitation is nonstationary random vibration. Calculating method for statistical characteristics of such a response is complicated. Mean square value of the response is usually used to evaluate random response. Integral of mean square value of the response corresponds to total energy of the response. In this paper, a simplified calculation method to obtain integral of mean square value of the response is proposed. As input excitation, nonstationary white noise and nonstationary filtered white noise are used. Integrals of mean square value of the response are calculated for various values of parameters. It is found that the proposed method gives exact value of integral of mean square value of the response.     

A study on a quantum-inspired evolutionary algorithm based on pair swap

A quantum-inspired evolutionary algorithm (QEA) is proposed as a stochastic algorithm to perform combinatorial optimization problems. The QEA is evolutionary computation that uses quantum bits and superposition states in quantum computation. Although the QEA is a coarse-grained parallel algorithm, it involves many parameters that must be adjusted manually. This paper proposes a new method, named pair swap, which exchanges each best solution information between two individuals instead of migration in the QEA. Experimental results show that our proposed method is a simpler algorithm and can find a high quality solution in the 0-1 knapsack problem. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

Skeleton clustering by multi-robot monitoring for fall risk discovery

This paper tackles the problem of discovering subtle fall risks using skeleton clustering by multi-robot monitoring. We aim to identify whether a gait has fall risks and obtain useful information in inspecting fall risks. We employ clustering of walking postures and propose a similarity of two datasets with respect to the clusters. When a gait has fall risks, the similarity between the gait which is being observed and a normal gait which was monitored in advance exhibits a low value. In subtle fall risk discovery, unsafe skeletons, postures in which fall risks appear slightly as instabilities, are similar to safe skeletons and this fact causes the difficulty in clustering. To circumvent this difficulty, we propose two instability features, the horizontal deviation of the upper and lower bodies and the curvature of the back, which are sensitive to instabilities and a data preprocessing method which increases the ability to discriminate safe and unsafe skeletons. To evaluate our method, we prepare seven kinds of gait datasets of four persons. To identify whether a gait has fall risks, the first and second experiments use normal gait datasets of the same person and another person, respectively. The third experiments consider that how many skeletons are necessary to identify whether a gait has fall risks and then we inspect the obtained clusters. In clustering more than 500 skeletons, the combination of the proposed features and our preprocessing method discriminates gaits with fall risks and without fall risks and gathers unsafe skeletons into a few clusters.     

Bubble and liquid flow characteristics during horizontal cold gas injection into a water bath

In refining processes such as the AOD process cold gas is blown horizontally into the molten metal bath of the processes. The spatial distribution of bubbles in the bath is one of the important factors influencing the efficiency of the processes. In this study, a water model study was carried out to understand the characteristics of bubbles and liquid flow generated by horizontal gas injection. The bubble and liquid flow characteristics were measured using an electro‐resistivity probe and a laser Doppler velocimeter, respectively. In the flow field near the nozzle the bubble characteristics for the horizontal cold gas injection can be predicted by empirical equations derived for isothermal gas injection systems. The liquid flow characteristics could not be measured in this region. On the other hand, in the region far from the nozzle the two characteristics for the cold gas injection became different from those for the isothermal gas injection because of enhanced buoyancy force acting on expanding cold bubbles due to heat transfer.     

Estimation of time-dependent input from neuronal membrane potential

The set of firing rates of the presynaptic excitatory and inhibitory neurons constitutes the input signal to the postsynaptic neuron. Estimation of the time-varying input rates from intracellularly recorded membrane potential is investigated here. For that purpose, the membrane potential dynamics must be specified. We consider the Ornstein-Uhlenbeck stochastic process, one of the most common single-neuron models, with time-dependent mean and variance. Assuming the slow variation of these two moments, it is possible to formulate the estimation problem by using a state-space model. We develop an algorithm that estimates the paths of the mean and variance of the input current by using the empirical Bayes approach. Then the input firing rates are directly available from the moments. The proposed method is applied to three simulated data examples: constant signal, sinusoidally modulated signal, and constant signal with a jump. For the constant signal, the estimation performance of the method is comparable to that of the traditionally applied maximum likelihood method. Further, the proposed method accurately estimates both continuous and discontinuous time-variable signals. In the case of the signal with a jump, which does not satisfy the assumption of slow variability, the robustness of the method is verified. It can be concluded that the method provides reliable estimates of the total input firing rates, which are not experimentally measurable.