Lectin-like characteristics of recombinant human interleukin-1beta recognizing glycans of the glycosylphosphatidylinositol anchor

We found that 35S-labeled recombinant human interleukin-1beta (rhIL-1beta) binds phosphatidylinositol-specific phospholipase C-treated human placental alkaline phosphatase, phosphatidylinositol-specific phospholipase C-treated trypanosome surface variant glycoproteins, and urinary uromodulin immobilized on plates or immobilized on CNBr-activated Sepharose 4B. The interaction between rhIL-1beta and these glycoproteins was lectin-like, since it was inhibited in the presence of specific saccharides, i.e. mannose 6-phosphate or synthetic Ac-NH.CH2.CH2. PO4--->6Manalpha1-->(+/-2Manalpha1-->+/-6Manalpha1-->) propyl at about 1 microM. On the other hand, a wide variety of compounds including biantennary sugar chains derived from these glycoproteins as well as ethanolamine phosphate, inositol phosphate, mannose 6-sulfate, mannose 1-phosphate, glucose 6-phosphate, and mannitol 6-phosphate did not show any inhibitory effect at concentrations up to 1 mM. These results indicate that rhIL-1beta interacts with these glycoproteins via the mannose 6-phosphate diester of glycans on the glycosylphosphatidylinositol (GPI) anchor. Furthermore, when monolayers of polarized Madin-Darby canine kidney cells on polycarbonate filter membranes were incubated with 35S-rhIL-1beta in either the apical or basolateral chamber, 35S-interleukin-1beta was found to bind specifically to the apical membranes with a Ka value of 4.6 x 10(7) M-1, and the specific interaction was inhibited by 1 microM mannose 6-phosphate. Since the mannose 6-phosphate diester moiety exists only in the GPI glycans on plasma membranes, it was evident that interleukin-1beta can directly interact with the mannose 6-phosphate diester component of the intact glycan of GPI anchors on plasma membranes.     

Modeling of the pharyngeal muscle in Caenorhabditis elegans based on FitzHugh-Nagumo equations

The pharyngeal pumping motion to send food to the bowel is a rhythmic movement in Caenorhabditis elegans. This paper proposes a simulation-based approach to investigate the mechanisms of rhythm phenomena in the pharyngeal pumping motion. To conduct the simulations, first, we developed a pharyngeal muscle model including 29 cell models which simulate the activity of each cell as a membrane potential based on FitzHugh-Nagumo equations. Then, to compare the response of the model with that of C. elegans, we calculated the electropharyngeogram (EPG), which represents the electrophysiological responses of the pharyngeal cells, using the simulated membrane potentials. The results confirmed that our model could generate the EPG similar to that measured from C. elegans. We proposed a computer simulation of the pumping motion to investigate the mechanisms of rhythm phenomena in living organisms.     

Self-generation of reward by logarithmic transformation of multiple sensor evaluations

Artificial Life and Robotics - Although the design of the reward function in reinforcement learning is important, it is difficult to design a system that can adapt to a variety of environments and...     

Probabilistic nearest neighbor query processing on distributed uncertain data

A nearest neighbor (NN) query, which returns the most similar object to a user-specified query object, plays an important role in a wide range of applications and hence has received considerable attention. In many such applications, e.g., sensor data collection and location-based services, objects are inherently uncertain. Furthermore, due to the ever increasing generation of massive datasets, the importance of distributed databases, which deal with such data objects, has been growing. One emerging challenge is to efficiently process probabilistic NN queries over distributed uncertain databases. The straightforward approach, that each local site forwards its own database to the central server, is communication-expensive, so we have to minimize communication cost for the NN object retrieval. In this paper, we focus on two important queries, namely top-k probable NN queries and probabilistic star queries, and propose efficient algorithms to process them over distributed uncertain databases. Extensive experiments on both real and synthetic data have demonstrated that our algorithms significantly reduce communication cost.     

Theoretical and evolutionary parameter tuning of neural oscillators with a double-chain structure for generating rhythmic signals

A neural oscillator with a double-chain structure is one of the central pattern generator models used to simulate and understand rhythmic movements in living organisms. However, it is difficult to reproduce desired rhythmic signals by tuning an enormous number of parameters of neural oscillators. In this study, we propose an automatic tuning method consisting of two parts. The first involves tuning rules for both the time constants and the amplitude of the oscillatory outputs based on theoretical analyses of the relationship between parameters and outputs of the neural oscillators. The second involves an evolutionary tuning method with a two-step genetic algorithm (GA), consisting of a global GA and a local GA, for tuning parameters such as neural connection weights that have no exact tuning rule. Using numerical experiments, we confirmed that the proposed tuning method could successfully tune all parameters and generate sinusoidal waves. The tuning performance of the proposed method was less affected by factors such as the number of excitatory oscillators or the desired outputs. Furthermore, the proposed method was applied to the parameter-tuning problem of some types of artificial and biological wave reproduction and yielded optimal parameter values that generated complex rhythmic signals in Caenorhabditis elegans without trial and error.     

Detection method of emerging leading papers using time transition

To survive worldwide competitions of research and development in the current rapid increase of information, decision-makers and researchers need to be supported to find promising research fields and papers. But finding those fields from an available data in too much heavy flood of information becomes difficult. We aim to develop a methodology supporting to find emerging leading papers with a bibliometric approach. The analyses in this work are about four academic domains using our time transition analysis. In the time transition analysis, after citation networks are constructed, centralities of each paper are calculated and their changes are tracked. Then, the centralities are plotted, and the features of the leading papers are extracted. Based on the features, we proposed ways to detect the leading papers by focusing on in-degree centrality and its transition. This work will contribute to finding the leading paper, and it is useful for decision-makers and researchers to decide the worthy research topic to invest their resources.     

Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases

There has been an increasing prevalence of neurodegenerative diseases with the rapid increase in aging societies worldwide. Biomarkers that can be used to detect pathological changes before the development of severe neuronal loss and consequently facilitate early intervention with disease-modifying therapeutic modalities are therefore urgently needed. Diffusion magnetic resonance imaging (MRI) is a promising tool that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, as well as axonal density, order, and myelination, through the utilization of water molecules that are diffused within the tissue, with displacement at the micron scale. Diffusion tensor imaging is the most commonly used diffusion MRI technique to assess the pathophysiology of neurodegenerative diseases. However, diffusion tensor imaging has several limitations, and new technologies, including neurite orientation dispersion and density imaging, diffusion kurtosis imaging, and free-water imaging, have been recently developed as approaches to overcome these constraints. This review provides an overview of these technologies and their potential as biomarkers for the early diagnosis and disease progression of major neurodegenerative diseases.