Optimizing time histograms for non-Poissonian spike trains

The time histogram is a fundamental tool for representing the inhomogeneous density of event occurrences such as neuronal firings. The shape of a histogram critically depends on the size of the bins that partition the time axis. In most neurophysiological studies, however, researchers have arbitrarily selected the bin size when analyzing fluctuations in neuronal activity. A rigorous method for selecting the appropriate bin size was recently derived so that the mean integrated squared error between the time histogram and the unknown underlying rate is minimized (Shimazaki & Shinomoto, 2007 ). This derivation assumes that spikes are independently drawn from a given rate. However, in practice, biological neurons express non-Poissonian features in their firing patterns, such that the spike occurrence depends on the preceding spikes, which inevitably deteriorate the optimization. In this letter, we revise the method for selecting the bin size by considering the possible non-Poissonian features. Improvement in the goodness of fit of the time histogram is assessed and confirmed by numerically simulated non-Poissonian spike trains derived from the given fluctuating rate. For some experimental data, the revised algorithm transforms the shape of the time histogram from the Poissonian optimization method.     

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.     

Parsing human skeletons in an operating room

Multiple human pose estimation is an important yet challenging problem. In an operating room (OR) environment, the 3D body poses of surgeons and medical staff can provide important clues for surgical workflow analysis. For that purpose, we propose an algorithm for localizing and recovering body poses of multiple human in an OR environment under a multi-camera setup. Our model builds on 3D Pictorial Structures and 2D body part localization across all camera views, using convolutional neural networks (ConvNets). To evaluate our algorithm, we introduce a dataset captured in a real OR environment. Our dataset is unique, challenging and publicly available with annotated ground truths. Our proposed algorithm yields to promising pose estimation results on this dataset.     

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     

Self-organizing maps with asymmetric neighborhood function

The self-organizing map (SOM) is an unsupervised learning method as well as a type of nonlinear principal component analysis that forms a topologically ordered mapping from the high-dimensional data space to a low-dimensional representation space. It has recently found wide applications in such areas as visualization, classification, and mining of various data. However, when the data sets to be processed are very large, a copious amount of time is often required to train the map, which seems to restrict the range of putative applications. One of the major culprits for this slow ordering time is that a kind of topological defect (e.g., a kink in one dimension or a twist in two dimensions) gets created in the map during training. Once such a defect appears in the map during training, the ordered map cannot be obtained until the defect is eliminated, for which the number of iterations required is typically several times larger than in the absence of the defect. In order to overcome this weakness, we propose that an asymmetric neighborhood function be used for the SOM algorithm. Compared with the commonly used symmetric neighborhood function, we found that an asymmetric neighborhood function accelerates the ordering process of the SOM algorithm, though this asymmetry tends to distort the generated ordered map. We demonstrate that the distortion of the map can be suppressed by improving the asymmetric neighborhood function SOM algorithm. The number of learning steps required for perfect ordering in the case of the one-dimensional SOM is numerically shown to be reduced from O(N(3)) to O(N(2)) with an asymmetric neighborhood function, even when the improved algorithm is used to get the final map without distortion.     

Simulation of postoperative 3D facial morphology using a physics-based head model

We propose a prototype of a facial surgery simulation system for surgical planning and the prediction of facial deformation. We use a physics-based human head model. Our head model has a 3D hierarchical structure that consists of soft tissue and the skull, constructed from the exact 3D CT patient data. Anatomic points measured on X-ray images from both frontal and side views are used to fire the model to the patient's head. The purposes of this research is to analyze the relationship between changes of mandibular position and facial morphology after orthognathic surgery, and to simulate the exact postoperative 3D facial shape. In the experiment, we used our model to predict the facial shape after surgery for patients with mandibular prognathism. Comparing the simulation results and the actual facial images after the surgery shows that the proposed method is practical.     

De Haas-van Alphen effect study of magnetic interaction in lead

Frequency and amplitude modulation (FM and AM) of a high-frequency (HF) de Haas-van Alphen oscillation due to magnetic interaction (MI) with a low-frequency (LF) oscillation in lead has been examined for field directions parallel to and close to 110, in the light of the old and of a new treatment of MI based on uniform and inhomogeneous inductions over the sample, respectively. The de Haas-van Alphen signals have been measured by the standard low-frequency field modulation method. The strength of AM and the sideband around HF have been examined as a function of the strength of FM and it is found that the experimental data deviate significantly from the prediction by the old treatment. Moreover, the deviation depends not only on the particular sample chosen to study, but also on the orientation, suggesting that the MI effect is influenced by the microscopic inhomogeneity of the sample. The absolute amplitude of LF also has been measured and is found again to deviate significantly from the prediction by the old treatment. However, the measured amplitude shows a satisfactory agreement with the prediction by the new treatment. In conclusion, the new treatment is a plausible theory which can describe all our experimental results, not only qualitatively but also quantitatively.     

Nitrogen and phosphorus effluent loads from a paddy-field district adopting collective crop rotation

Japanese paddy rice systems commonly adopt the rotation of vegetables, wheat and soybeans with paddy rice. Crop rotation may, however, increase the nutrient load in effluent discharged from the district because more fertilizer is applied to the rotation crops than is applied to paddy crops. We investigated a paddy-field district subject to collective crop rotation and quantified the annual nutrient load of effluent from the district in three consecutive years. The total annual exports of nitrogen and phosphorus over the investigation period ranged from 30.3 to 40.6 kg N ha(-1) and 2.62 to 3.13 kg P ha(-1). The results suggest that rotation cropping increases the effluent nutrient load because applied fertilizer is converted to nitrate, and surface runoff is increased due to the absence of shuttering boards at the field outlets.     

Helium gas permeability of montmorillonite/epoxy nanocomposites

Helium gas permeability of silicate clay (montmorillonite) particles/epoxy nanocomposites was examined. The incorporation of increasing amounts of montmorillonite particles reduced the helium gas permeability. Based on Fick’s law, gas permeation behavior of the nanocomposite was evaluated. With the increase of montmorillonite loading, gas diffusivity decreased, while gas solubility increased. Helium diffusion behavior is in agreement to the numerical results based on the Hatta–Taya–Eshelby theory. It has been revealed that dispersion of nanoscale platelets in polymer is effective in improving gas barrier property.     

Effect of atomic oxygen exposure on bubble formation in aluminum alloy

In order to investigate the effect of atomic oxygen exposure of an aluminum alloy on bubble formation during welding, electron beam welding was performed on the samples exposed to atomic oxygen produced by the oxygen plasma method. The change in the aluminum surface due to the exposure was analyzed by auger electron spectroscopy. Due to the exposure of atomic oxygen, the thickness of the aluminum oxide film increases on the surface, and pores are formed during welding. The pores can be formed by the formation of the Al₂O gas through the reaction between the aluminum oxide and aluminum in a high vacuum.