Selectivity and stability via dendritic nonlinearity

Inspired by recent studies regarding dendritic computation, we constructed a recurrent neural network model incorporating dendritic lateral inhibition. Our model consists of an input layer and a neuron layer that includes excitatory cells and an inhibitory cell; this inhibitory cell is activated by the pooled activities of all the excitatory cells, and it in turn inhibits each dendritic branch of the excitatory cells that receive excitations from the input layer. Dendritic nonlinear operation consisting of branch-specifically rectified inhibition and saturation is described by imposing nonlinear transfer functions before summation over the branches. In this model with sufficiently strong recurrent excitation, on transiently presenting a stimulus that has a high correlation with feed- forward connections of one of the excitatory cells, the corresponding cell becomes highly active, and the activity is sustained after the stimulus is turned off, whereas all the other excitatory cells continue to have low activities. But on transiently presenting a stimulus that does not have high correlations with feedforward connections of any of the excitatory cells, all the excitatory cells continue to have low activities. Interestingly, such stimulus-selective sustained response is preserved for a wide range of stimulus intensity. We derive an analytical formulation of the model in the limit where individual excitatory cells have an infinite number of dendritic branches and prove the existence of an equilibrium point corresponding to such a balanced low-level activity state as observed in the simulations, whose stability depends solely on the signal-to-noise ratio of the stimulus. We propose this model as a model of stimulus selectivity equipped with self-sustainability and intensity-invariance simultaneously, which was difficult in the conventional competitive neural networks with a similar degree of complexity in their network architecture. We discuss the biological relevance of the model in a general framework of computational neuroscience.     

Towards understanding the mechanism of action of the multidrug resistance-linked half-ABC transporter ABCG2: a molecular modeling study

The ATP-binding cassette protein ABCG2 is a member of a broad family of ABC transporters with potential clinical importance as a mediator of multidrug resistance. We carried out a homology and knowledge-based, and mutationally improved molecular modeling study to establish a much needed structural framework for the protein, which could serve as guidance for further genetic, biochemical, and structural analyses. Based on homology with known structures of both full-length and nucleotide-binding domains (NBD) of ABC transporters and structural knowledge of integral membrane proteins, an initial model of ABCG2 was established. Subsequent refinement to conform to the lipophilic index distributions in the transmembrane domain (TMD) and to the results of site-directed mutagenesis experiments led to an improved model. The complete ABCG2 model consists of two identical subunits facing each other in a closed conformation. The dimeric interface in the nucleotide-binding domain (NBD) involves a characteristic nucleotide sandwich and the interface in the TMD consists of the TM helices 1–3 of one subunit and the helices 5 and 6 of the other. The interface between the NBD and the TMD is bridged by the conserved structural motif between TM2 and TM3, the intracellular domain 1 (ICD1), and the terminal β-strand (S6) of the central β-sheet in the NBD. The apparent flexibility of the ICD1 may play a role in transmitting conformational changes from the NBD to the TMD or from the TMD to the NBD.     

Patient views of adverse events: comparisons of self-reported healthcare staff attitudes with disclosure of accident information

In the present paper, we report results of surveys in 2003 in Japan and Denmark about patients' views about adverse events, focusing on the actions of healthcare staff involved in a medical accident. Results show that patients were more likely to indicate negative expectations to a doctor's reactions after a medical accident when asked in general terms than when asked in relation to concrete case stories. When asked in general terms, 66% (47%) of Japanese (Danish) respondents expected that doctors sometimes hold back on providing information to patients about a medical accident, while 37% (7%) did so when asked about a concrete, mild-outcome case. We examine some possible reasons for the relatively high level of distrust of Japanese patients, and we discuss whether the seemingly lower level of disclosure in Japan than in Denmark and the negative stories in the Japanese press may have an impact. We also suggest some implications for introducing a patient-centred or customer-centred approach to risk management in healthcare and other domains.     

Switching in polystyrene and polymethyl methacrylate thin films: effect of preparation conditions of the polymers

Switching without any break in the electrodes was observed in polystyrene and polymethyl methacrylate films by controlling the current using a high resistance connected in series with the sample. The effect of the method of polymerization on the switching characteristics were studies for both polymers. Polymers were prepared by -ray irradiation or by catalytic or noncatalytic thermal polymerization. The switching characteristics gave poor reproducibility for the catalytically polymerized polystyrene, but no difference was observed for the three types of polymethyl methacrylate from different origins. In the case of polystyrene, the lower the molecule weight, the higher was the threshold voltage. With polymethyl methacrylate such a dependence was not observed. In both polymers the on-state is relatively stable. When the samples in the on-state were heated above 100° C, they were converted to the offstate. No dots or breaks on the sample were detected by scanning electron microscopy (SEM) after switching has occurred.     

Further investigation on the thermal decomposition of aluminium dialkylamides

Both dimethylamino- (I) and diethylamino-alane dimers (II), [(R₂N)₂AlH]₂ (I, R=Me; II, R=Et) decompose above 800° C under a few hundred Pa of hydrogen to dark greyish, hard (Vickers hardness larger than 2000), oxidation-resistive and oxidation-protective deposits which are tentatively identified as Al₅C₃N. The deposits are stable to moisture and diluted hydrochloric acid in contrast to those obtained below 800° C, but they easily dissolve in sodium hydroxide solution at room temperature evolving a gas. The deposits on a stainless steel substrate adhere strongly to the substrate and remain so on rapid heating and cooling. The electrical resistivity of the deposits is in the range 10² to 10⁴ cm.     

High rate nitrogen removal by the CANON process at ambient temperature

Completely autotrophic nitrogen removal over nitrite (CANON) is a cost-effective nitrogen removal process. Implementation of the CANON process relies on the cooperation of ammonium-oxidizing and Anammox bacteria, as well as the inhibition of nitrite-oxidizing bacteria. Strict limitations on dissolved oxygen (DO) concentration in the reactor, and the addition of sufficient inorganic carbon in the influent, were adopted as the main operational strategies. The reactor was fed with synthetic inorganic wastewater composed mainly of NH(4)(+)-N, and operated for 106 days. Stable nitrogen removal rates (NRR) of around 1.4 kg N m(-3) d(-1) were obtained at ambient temperature. Morphological characteristics and analysis of bacterial community confirmed the formation of functional outer aerobic and inner anaerobic granular sludge, providing evidence of stable nitrogen removal.     

Polyphenols and fatty acids responsible for anti-cyanobacterial allelopathic effects of submerged macrophyte Myriophyllum spicatum

Myriophyllum spicatum is known to inhibit the growth of cyanobacteria such as Microcystis aeruginosa by releasing anti-cyanobacterial allelochemicals. The allelochemicals possibly responsible for the inhibition include five polyphenols and three fatty acids, but the extent to which these are indeed responsible for the anti-cyanobacterial effects is unclear. The goal of this research was to determine the contribution of these compounds to the allelopathic effect of M. spicatum on M. aeruginosa. We first collected information on the release rates of these compounds and then added the compounds to a cyanobacterial medium on the basis of their release rates so as to simulate their excretion by M. spicatum. Addition of the polyphenols and fatty acids inhibited the growth of M. aeruginosa, and the interaction of the polyphenols and fatty acids was additive. The EC50 of a polyphenol and fatty acid mixture was compared with that of M. spicatum itself as previously determined in a mixed culture system in which M. spicatum and M. aeruginosa were incubated. The former was about 1.9 times higher than that of the latter, the implication being that the inhibitory effect of the polyphenols and fatty acids contributed about 53% of the allelopathic effect of M. spicatum. This paper is the first to describe allelochemicals that account for a half of the anti-cyanobacterial allelopathic effect of a macrophyte.     

MOSAIC for multiple-reward environments

Reinforcement learning (RL) can provide a basic framework for autonomous robots to learn to control and maximize future cumulative rewards in complex environments. To achieve high performance, RL controllers must consider the complex external dynamics for movements and task (reward function) and optimize control commands. For example, a robot playing tennis and squash needs to cope with the different dynamics of a tennis or squash racket and such dynamic environmental factors as the wind. In addition, this robot has to tailor its tactics simultaneously under the rules of either game. This double complexity of the external dynamics and reward function sometimes becomes more complex when both the multiple dynamics and multiple reward functions switch implicitly, as in the situation of a real (multi-agent) game of tennis where one player cannot observe the intention of her opponents or her partner. The robot must consider its opponent's and its partner's unobservable behavioral goals (reward function). In this article, we address how an RL agent should be designed to handle such double complexity of dynamics and reward. We have previously proposed modular selection and identification for control (MOSAIC) to cope with nonstationary dynamics where appropriate controllers are selected and learned among many candidates based on the error of its paired dynamics predictor: the forward model. Here we extend this framework for RL and propose MOSAIC-MR architecture. It resembles MOSAIC in spirit and selects and learns an appropriate RL controller based on the RL controller's TD error using the errors of the dynamics (the forward model) and the reward predictors. Furthermore, unlike other MOSAIC variants for RL, RL controllers are not a priori paired with the fixed predictors of dynamics and rewards. The simulation results demonstrate that MOSAIC-MR outperforms other counterparts because of this flexible association ability among RL controllers, forward models, and reward predictors.