Quantitative determination of cardiac glycosides in Digitalis lanata leaves by reversed-phase thin-layer chromatography

We have recently chosen to undertake a comprehensive evaluation of the modulation of gene expression by oxidative stress, using mRNA as a marker. Our model system is HA-1 hamster fibroblasts, using conditions under which we observe an adaptive response. Under these conditions, the HA-1 cells respond to a minimally toxic "pretreatment" dose of hydrogen peroxide by synthesizing RNAs and proteins that protect them against subsequent exposure to a highly cytotoxic concentration of hydrogen peroxide. Using the differential display technique to screen for modulated RNAs, we have recently reported an RNA species, adapt15/gadd7, whose steady-state level is significantly induced by a pretreatment dose of hydrogen peroxide (D. R. Crawford, G. P. Schools, S. L. Salmon, and K. J. A. Davies (1996) Arch. Biochem. Biophys. 325, 256-264). Here we report a second induced mRNA, designated adapt33. Two homologous adapt33 mRNAs were revealed by Northern blot hybridization. Both of these species were inducible by hydrogen peroxide, and they were sized at 1.46 and 0.99 kb. These inductions appeared to be dependent upon calcium, occurred as early as 90 min, and were maximal at 5 h. Cell fractionation revealed that a significant proportion of adapt33 RNA is associated with active translation. adapt33 is a novel sequence, as determined by cloning, sequencing, and GenBank analysis. adapt33 represents a new oxidant-inducible RNA and marker of cellular oxidative stress and a potential aid in the study, detection, and possible therapy of oxidant-related disorders.     

Designing high-level decision making systems based on fuzzy if–then rules for a point-to-point car racing game

In this paper, fuzzy rule-based systems are applied to a point-to-point car racing game. In the point-to-point car racing game, two car agents compete with each other for taking waypoints. There are three waypoints in the car racing field, each of which is assigned a number that indicates the order to take. The control process of car agents is modeled as a non-holonomic system where there are two input variables (acceleration and steering) for controlling the position, angle and velocity of the car agents. Fuzzy rule-based systems are used to make a high-level decision where the target waypoint to take is determined. Since a fuzzy rule-based system for the high-level decision making is generated in the manner of supervised learning, a set of training patterns should be given for the construction of the fuzzy rule-based systems. In this paper we examine two methods to obtain such a set of training patterns. We also examine two representations of input vectors for the fuzzy rule-based systems. We discuss the effect of obtained training patterns and the input representation on the performance of the fuzzy rule-based systems. After discussing and analyzing the experimental results, we present an adaptive framework of fuzzy rule-based systems. The performance of adaptive fuzzy rule-based systems is then examined based on the results of their non-adaptive version. A series of computational experiments are performed to show the learning ability of the adaptive fuzzy rule-based systems.     

A study of Q-learning considering negative rewards

In the reinforcement learning system, the agent obtains a positive reward, such as 1, when it achieves its goal. Positive rewards are propagated around the goal area, and the agent gradually succeeds in reaching its goal. If you want to avoid certain situations, such as dangerous places or poison, you might want to give a negative reward to the agent. However, in conventional Q-learning, negative rewards are not propagated in more than one state. In this article, we propose a new way to propagate negative rewards. This is a very simple and efficient technique for Q-learning. Finally, we show the results of computer simulations and the effectiveness of the proposed method.     

Unravel four hairpins!

DNA machines consisting of consecutive hairpins, which we have previously described, have various potential applications in DNA computation. In the present study, a 288-base DNA machine containing four consecutive hairpins was successfully constructed by ligation and PCR. PAGE and fluorescence spectroscopy experiments verified that all four hairpins were successfully opened by four opener oligomers, and that hairpin opening was dependent on the proper openers added in the correct order. Quantitative analysis of the final results by fluorescence spectroscopy indicated that all four hairpins were open in about 1/4 to 1/3 of the DNA machines.     

Cooperation of categorical and behavioral learning in a practical solution to the abstraction problem

Real robots should be able to adapt autonomously to various environments in order to go on executing their tasks without breaking down. They achieve this by learning how to abstract only useful information from a huge amount of information in the environment while executing their tasks. This paper proposes a new architecture which performs categorical learning and behavioral learning in parallel with task execution. We call the architectureSituation Transition Network System (STNS). In categorical learning, it makes a flexible state representation and modifies it according to the results of behaviors. Behavioral learning is reinforcement learning on the state representation. Simulation results have shown that this architecture is able to learn efficiently and adapt to unexpected changes of the environment autonomously. Atsushi Ueno, Ph.D.: He is a research associate in the Artificial Intelligence Laboratory at the Graduate School of Information Science at the Nara Institute of Science and Technology (NAIST). He received the B.E., the M.E., and the Ph.D. degrees in aeronautics and astronautics from the University of Tokyo in 1991, 1993, and 1997 respectively. His research interest is robot learning and autonomous systems. He is a member of Japan Association for Artificial Intelligence (JSAI). Hideaki Takeda, Ph.D.: He is an associate professor in the Artificial Intelligence Laboratory at the Graduate School of Information Science at the Nara Institute of Science and Technology (NAIST). He received his Ph.D. in precision machinery engineering from the University of Tokyo in 1991. He has conducted research on a theory of intelligent computer-aided design systems, in particular experimental study and logical formalization of engineering design. He is also interested in multiagent architectures and ontologies for knowledge base systems.     

Japanese/English Cross-Language Information Retrieval: Exploration of Query Translation and Transliteration

Cross-language information retrieval (CLIR), where queriesand documents are in different languages, has of late become one ofthe major topics within the information retrieval community. Thispaper proposes a Japanese/English CLIR system, where we combine aquery translation and retrieval modules. We currently target theretrieval of technical documents, and therefore the performance of oursystem is highly dependent on the quality of the translation oftechnical terms. However, the technical term translation is stillproblematic in that technical terms are often compound words, and thusnew terms are progressively created by combining existing basewords. In addition, Japanese often represents loanwords based on itsspecial phonogram. Consequently, existing dictionaries find itdifficult to achieve sufficient coverage. To counter the firstproblem, we produce a Japanese/English dictionary for base words, andtranslate compound words on a word-by-word basis. We also use aprobabilistic method to resolve translation ambiguity. For the secondproblem, we use a transliteration method, which corresponds wordsunlisted in the base word dictionary to their phonetic equivalents inthe target language. We evaluate our system using a test collectionfor CLIR, and show that both the compound word translation andtransliteration methods improve the system performance.     

Construction of a brain–machine hybrid system to evaluate adaptability of an insect

Insects perform adaptive behavior according to changing environmental conditions using comparatively small brains. Because adaptability is generated through the relationship among brain, body and environment, it is necessary to examine how a brain works under these conditions. In this study, to understand neural processing involved in adaptive behavior, we constructed a brain–machine hybrid system using motor signals related to the steering behavior of the male silkworm moth for controlling a two-wheeled mobile robot. We developed this hybrid system according to the following steps. (1) We selected steering signals corresponding to walking direction that were activated during neck swinging induced by optic flow and pheromone stimuli. (2) To control a robot by neural activity, we implemented a spike-behavior conversion rule such that frequency of the left and right neck motor neurons’ spikes was linearly converted into rotation of the wheels. (3) For electrophysiological multi-unit recordings on a robot, we developed small amplifiers. Using this hybrid system, we could observe the programmed behavioral pattern and orientation toward a pheromone source. Moreover, we compared the orientation behavior of moths and that of the hybrid system at different pheromone stimulus frequencies. From these experiments, we concluded that we could reconstruct silkworm moth behavior on the hybrid system.     

Dynamic alternation of primate response properties during trial-and-error knowledge updating

Humans and animals seek appropriate solutions to novel problems through trial-and-error (TE) actions and observation of their outcomes. Once an individual has obtained the knowledge (rule) to solve a problem, knowledge-based (KB) actions may be applied in a stereotypical manner. Solutions can thus be based on TE or KB actions. To characterize this learning process at the behavioral level, we developed a new cognitive task for a laboratory monkey (Macaca fuscata) to perform. In this task, a search array consisting of six elements of different colors was presented, one of which was the behaviorally relevant target. The target color was changed unpredictably with no instruction or signal, requiring the monkey to use a TE search strategy to find the target color. We found that once the monkey identified the relevant color by chance after a color change, correct performance increased in a step-like manner and at the same time, other response properties (reaction time and color-choice tendency) also changed discontinuously. These step-like alternations in behavioral performance may be attributed to the subject’s switching between TE and KB search strategies in the two phases. The present study has therefore provided behavioral evidence for the timing and manner of switching between search strategies during the process of updating knowledge.     

Effect of heparin on high glucose induced proliferation and expression of matrix metalloproteinases in normal human mesangial cells

Background The pathogenesis of diabetic nephropathy (DN) is a complex pathophysiological process. Its precise mechanism is not fully known. In recent years it has been recognized that synthesis of various extracelluar matrix (ECM) components may increase, and that degradation of ECM may decrease in DN. It was reported heparin could inhibit mesangial cells proliferation in vitro. The main aim of this study is to explore whether heparin inhibits proliferation of mesangial cells grown in high glucose concentration and to measure the effect of heparin on matrix metalloproteinases (MMPs) expression in mesangial cells. Methods The medium contained either low glucose (5 mmol/L) or high glucose (25 mmol/L). The concentrations of heparin in the culture medium were 0, 25, 50, 100, 200 or 400 μg/mL. A metabolic (WST-1) assay was used to measure mesangial cell proliferation and Western blot analysis was used to measure MMPs expression of mesangial cells. Results Normal human mesangial cell (NHMC) proliferation was higher in high glucose (HG) medium than in low glucose (LG) medium. They showed a 1.93 fold expansion after 72 h in high glucose in contrast to a 1.63 fold expansion in low glucose. In the presence of heparin, mesangial cells proliferation was inhibited, which was more obvious at high glucose concentrations than at low glucose concentrations. In high glucose, with heparin concentration of 50, 100, 200 and 400 μg/mL, the mesangial cells showed a 0.61 fold, 0.52 fold, 0.52 fold and 0.41 fold reductions in cell number compared to cells grown without heparin. In low glucose, only concentrations of 200 μg/mL and 400 μg/mL showed reduction in cell number, namely 0.54 fold and 0.45 fold, when compared to cells grown without heparin. In Western blot analysis, MMP₁, MMP₂, MMP₃ and MMP₉ was expressed by mesangial cells expressed in both high and low glucose concentrations, which was more prominent in high glucose medium. Incubation of heparin further increased expression of MMP₁, MMP₂, MMP₃ and MMP₉. Conclusions This study suggests that glucose can accelerate mesangial cell proliferation while heparin can reduce proliferation, being more obvious at high glucose concentrations. Higher glucose concentrations led to increased MMP expression, which may take part in the regulation of mesangial matrix synthesis and degradation. Addition of heparin resulted in a corresponding increase in MMP expression, most notably at high glucose concentrations, indicating a potentially renoprotective role in DN. Foundation item: Project (30370663) supported by the National Natural Science Foundation of China