Comparison of commercially available kits with standard methods for the detection of coliforms and Escherichia coli in foods.

Three commercially available kits that were supplemented with substrates for enzyme reactions were evaluated to determine their abilities to detect coliforms and fecal coliforms in foods. Japanese and U.S. Food and Drug Administration standard methods, as well as two agar plate methods, were compared with the three commercial kits. A total of 50 food samples from various retailers were examined. The levels of detection of coliforms were high with the commercial kits (78 to 98%) compared with the levels of detection with the standard methods (80 to 83%) and the agar plate methods (56 to 83%). Among the kits tested, the Colilert kit had highest level of recovery of coliforms (98%), and the level of recovery of Escherichia coli as determined by beta-glucuronidase activity with the Colilert kit (83%) was comparable to the level of recovery obtained by the U.S. Food and Drug Administration method (87%). Isolation of E. coli on the basis of the beta-glucuronidase enzyme reaction was found to be good. Levine's eosine methylene blue agar, which has been widely used in various laboratories to isolate E. coli was compared with 4-methylumbelliferyl-beta-D-glucuronide (MUG)-supplemented agar for isolation of E. coli. Only 47% of the E. coli was detected when eosine methylene blue agar was used; however, when violet red bile (VRB)-MUG agar was used, the E. coli detection rate was twice as high. Of the 200 E. coli strains isolated, only 2 were found to be MUG negative, and the gene responsible for beta-glucuronidase activity (uidA gene) was detected by the PCR method in these 2 strains. Of the 90 false-positive strains isolated that exhibited various E. coli characteristic features, only 2 non-E.coli strains hydrolyzed MUG and produced fluorescent substrate in VRB-MUG agar. However, the PCR did not amplify uidA gene products in these VRB-MUG fluorescence-positive strains.     

(+)-5,17-dehydromatrine N-oxide,a new alkaloid in Euchresta japonica

A new lupin alkaloid, (+)-5,17-dehydromatrine N-oxide, was isolated from the fresh aerial parts of Euchresta japonica. Its structure was confirmed by spectrometric data and by direct comparison with a synthetic sample, prepared from (+)-sophoranol ((+)-5-hydroxymatrine). It was also concluded that (+)-5,17-dehydromatrine N-oxide and (+)-matrine N-oxide possess the same configuration with respect to the asymmetric nitrogen by NMR spectra.     

Two cage-type lupin alkaloids from Sophora franchetiana

Two new cage-type lupin alkaloids, (?)-tsukushinamine-B and tsukushinamine-C, have been isolated from the fresh epigeal parts of Sophora franchetiana, along with (?)-cytisine, (?)-N-formylcytisine, (?)-rhombifoline, (?)-anagyrine, (?)-baptifoline and (±)-ammodendrine, as well as (?)-tsukushinamine-A. The structures of these novel tsukushinamine-type lupin alkaloids were determined by spectroscopic data and partly by a chemical reaction. Variations of the alkaloid contents in the seeds, seedlings and various parts of S. franchetiana were also examined.     

Isolation and Structure Elucidation of Growth Inhibitors on Silkworm Larvae from Magnolia kobus DC

Two lignans were isolated from leaves of Magnolia kobus DC. as growth inhibitors on silkworm larvae and structurally elucidated as sesamin (I) and kobusin (II) which has been hitherto unknown.     

A parameter control method in reinforcement learning to rapidly follow unexpected environmental changes

In order to rapidly follow unexpected environmental changes, we propose a parameter control method in reinforcement learning that changes each of learning parameters in appropriate directions. We determine each appropriate direction on the basis of relationships between behaviors and neuromodulators by considering an emergency as a key word. Computer experiments show that the agents using our proposed method could rapidly respond to unexpected environmental changes, not depending on either two reinforcement learning algorithms (Q-learning and actor-critic (AC) architecture) or two learning problems (discontinuous and continuous state-action problems).     

A neural circuit model forming semantic network with exception using spike-timing-dependent plasticity of inhibitory synapses

We propose a neural circuit model forming a semantic network with exceptions using the spike-timing-dependent plasticity (STDP) of inhibitory synapses. To evaluate the proposed model, we conducted nine types of computer simulation by combining the three STDP rules for inhibitory synapses and the three spike pairing rules. The simulation results obtained with the STDP rule for inhibitory synapses by Haas et al. [Haas, J.S., Nowotny, T., Abarbanel, H.D.I., 2006, Spike-timing-dependent plasticity of inhibitory synapses in the entorhinal cortex. J. Neurophysiol. 96, 3305–3313] are successful, whereas, the other results are unsuccessful. The results and examinations suggested that the inhibitory connection from the concept linked with an exceptional feature to the general feature is necessary for forming a semantic network with an exception.     

The mechanisms underlying the spatial spreading of signaling activity

During the induction of plasticity of dendritic spines, many intracellular signaling pathways are spatially and temporally regulated to co-ordinate downstream cellular processes in different dendritic micron-domains. Recent advent of imaging technology based on fluorescence resonance energy transfer (FRET) has allowed the direct monitoring of the spatiotemporal regulation of signaling activity in spines and dendrites during synaptic plasticity. In particular, the activity of three small GTPase proteins HRas, Cdc42, and RhoA, which share similar structure and mobility on the plasma membrane, displayed different spatial spreading patterns: Cdc42 is compartmentalized in the stimulated spines while RhoA and HRas spread into dendrites over 5-10 μm. These measurements thus provide the basis for understanding the mechanisms underlying the spatiotemporal regulation of signaling activity. Further, using spatiotemporally controlled spine stimulations, some of the roles of signal spreading have been revealed.     

Purification and characterization of β-(pyrazol-1-yl)-l-alanine synthase from Citrullus vulgaris

From seedlings of Citrullus vulgaris the enzyme β-(pyrazol-1-yl)-l-alanine synthase was purified 200-fold, when it showed electrophoretic homogeneity (MW 58 000) and could be dissociated into identical subunits (MW 32 000) each containing one molecule of pyridoxal 5′-phosphate. The K_m value was 2.5 × 10^?3 M for O-acetyl-l-serine and 7.4 × 10^?2 M for pyrazole. The enzyme did not catalyse the formation of related β-substituted alanines, such as l-mimosine and l-quisqualic acid, and significant differences were found between the β-(pyrazol-1-yl)-l-alanine synthase and β-substituted alanine syntheses and cysteine synthase from other sources.     

Simulation of rat behavior by a reinforcement learning algorithm in consideration of appearance probabilities of reinforcement signals

Brown and Wanger [Brown, R.T., Wanger, A.R., 1964. Resistance to punishment and extinction following training with shock or nonreinforcement. J. Exp. Psychol. 68, 503-507] investigated rat behaviors with the following features: (1) rats were exposed to reward and punishment at the same time, (2) environment changed and rats relearned, and (3) rats were stochastically exposed to reward and punishment. The results are that exposure to nonreinforcement produces resistance to the decremental effects of behavior after stochastic reward schedule and that exposure to both punishment and reinforcement produces resistance to the decremental effects of behavior after stochastic punishment schedule. This paper aims to simulate the rat behaviors by a reinforcement learning algorithm in consideration of appearance probabilities of reinforcement signals. The former algorithms of reinforcement learning were unable to simulate the behavior of the feature (3). We improve the former reinforcement learning algorithms by controlling learning parameters in consideration of the acquisition probabilities of reinforcement signals. The proposed algorithm qualitatively simulates the result of the animal experiment of Brown and Wanger.