Disinfection efficacy and mechanism of olanexidine gluconate against norovirus.

BACKGROUND: The purpose of this study was to evaluate the virucidal activity of a new olanexidine-containing formulation for hand hygiene (olanexidine gluconate hand rub; OLG-HR) against non-enveloped viruses and to understand its mechanism of action. METHODS: The virucidal activities of OLG-HR against two strains of caliciviruses and three adenovirus serotypes were evaluated through suspension tests. Also, virus-like particles were used to predict the effect of olanexidine gluconate on virus particle structure. RESULTS: The results of suspension tests under conditions with and without interfering substances (1.5% BSA) indicated that OLG-HR had a broad-spectrum effect against non-enveloped viruses, and the virucidal effect was unaffected by organic contaminants. Furthermore, olanexidine inhibited the binding ability of virus-like particles to the binding receptor of human norovirus and increased the aggregation of virus-like particles in a dose-dependent manner. Transmission electron microscopy showed that the morphology of the virus-like particles was affected by exposure to olanexidine, indicating that the protein-denaturing effect of olanexidine gluconate caused the loss of receptor-binding capability of the viral capsid protein. CONCLUSIONS: This study suggests that olanexidine gluconate is a potential biological and environmental disinfectant against norovirus and adenovirus.

Connecting PM and MAP in Bayesian spectral deconvolution by extending exchange Monte Carlo method and using multiple data sets.

Nagata et al. proposed a parameter estimation method using Markov chain Monte Carlo (MCMC) for the spectral deconvolution of observed data. However, a systematic error occurs when the parameters to be estimated are close. In this paper, we first clarify that the exchange symmetry of parameters, which is essentially included in the spectral deconvolution problem, causes the systematic error. In particular, we show that estimation from a single data set is inherently difficult because the posterior distribution becomes unimodal or multimodal depending on the data set when the parameters to be estimated are close. Second, we alleviate the problem to the case of using multiple data sets and propose an extension of the exchange Monte Carlo method to low temperatures. This extension corresponds to bridging the gap between posterior mean (PM) estimation and maximum a posteriori (MAP) estimation. The above alleviation and bridging achieve a good estimation even when the parameters are close.

Non-monotonic convergence of online learning algorithms for perceptrons with noisy teacher.

Learning curves of simple perceptron were derived here. The learning curve of the perceptron learning with noisy teacher was shown to be non-monotonic, which has never appeared even though the learning curves have been analyzed for half a century. In this paper, we showed how this phenomenon occurs by analyzing the asymptotic property of the perceptron learning using a method in systems science, that is, calculating the eigenvalues of the system matrix and the corresponding eigenvectors. We also analyzed the AdaTron learning and the Hebbian learning in the same way and found that the learning curve of the AdaTron learning is non-monotonic whereas that of the Hebbian learning is monotonic.

Metastatic hemangiosarcoma of the liver in a young rat.

Spontaneous hemangiosarcoma is generally uncommon in rats. Furthermore, there are only a few case reports in young rats. This report describes a spontaneous hemangiosarcoma in a young 7-week-old rat. At necropsy, no remarkable changes were noted in any organs including the liver. Histopathologically, multifocal small tumors were located mainly in the perilobular region of the liver. The tumors comprised polygonal to short spindle-shaped cells that showed cellular atypia and local infiltration. In the foci, there were blood-filled spaces lined by spindle-shaped cells. Reticular fibers, which were loosely connected together and formed an irregular network within the foci, were noticed with Azan and silver stain. Immunohistochemistry revealed that tumor cells expressed vascular endothelial cell markers: von willebrand factor and CD34, respectively. These features indicate that the tumor originated from vascular endothelial cells. Although the primary lesion was uncertain, the foci were all small and multicentrically located mainly in the perilobular region, indicating that the liver lesion likely formed by hematogenic metastasis. Taken together, we diagnosed this case as a metastatic hemangiosarcoma.

Unchanged distribution density of anionic sites on the glomerular wall in rats with active heymann nephritis.

In various kinds of glomerulonephritis, alteration of anionic charge on the glomerular basement membrane (GBM) and podocytes has been controversial for more than decade. To elucidate the relation between glomerular protein leakage and anionic sites on the glomerular wall, we examined the distribution of anionic sites on the GBM and podocytes of rats with active Heymann nephritis (AHN). Urinalysis for protein levels was conducted, and the kidneys were examined using electron microscopic cytochemistry for the assessment of anionic charge with two cationic probes. The anionic sites on podocytes were decreased in number in the AHN rats; however, the distributions of anionic sites on the GBM were similar in density to those seen in the control animals. From these results, we consider that the decrease in anionic charge density on podocytes might be attributable to protein leakage and that the charge barrier of the GBM is irrelevant to the protein leakage in AHN rats.

Spontaneous vacuolar degeneration of the thyroid follicular epithelium in cynomolgus monkeys.

Vacuolar degeneration of the thyroid follicular epithelium was observed in two untreated female cynomolgus monkeys assigned to control groups. In light microscopy, large vacuoles containing a homogenous substance occupied the basal region of the epithelium, and the nuclei had shifted toward the apical region. The vacuoles showed negative reactions to PAS and thyroglobulin. Electron microscopic observation revealed dilatation of the rough endoplasmic reticulum corresponding to the vacuoles. The plasma TSH, T3 and T4 levels determined for the samples kept frozen were within the normal ranges, suggesting that the thyroid function was kept intact.

Hyaluronan deficiency in tumor stroma impairs macrophage trafficking and tumor neovascularization.

Despite the importance of stromal cells in tumor progression, our overall understanding of the molecular signals that regulate the complex cellular interactions within tumor stroma is limited. Here, we provide multiple lines of evidence that tumor-associated macrophages (TAM) preferentially traffic to stromal areas formed within tumors in a manner dependent on a hyaluronan (HA)-rich tumor microenvironment. To address the role of stroma-derived HA in macrophage recruitment, we disrupted the HA synthase 2 (Has2) gene in stromal fibroblasts using conditional gene targeting. The Has2 null fibroblasts showed severe impairment in recruiting macrophages when inoculated with tumor cells into nude mice, which shows the contribution of stroma-derived HA in intratumoral macrophage mobilization. Furthermore, a deficiency in stromal HA attenuated tumor angiogenesis and lymphangiogenesis concomitantly with impaired macrophage recruitment. Taken together, our results suggest that stromal HA serves as a microenvironmental signal for the recruitment of TAMs, which are key regulatory cells involved in tumor neovascularization.

Novel immunosuppressive therapy by M2000 in experimental multiple sclerosis.

The therapeutic potency of M2000 (beta-D-mannuronic acid), a novel designed nonsteroidal anti-inflammatory drug with immunosuppressive property in T-cell-mediated autoimmune disease, was tested. The influence of M2000 on myelin basic protein (MBP)-induced experimental autoimmune encephalomyelitis (EAE), an animal model of multiple sclerosis, was assessed. M2000 at two doses, 40 and 80 mg/kg/day, was administered intraperitoneally (i.p.) to prevention and treatment groups, respectively. Onset of i.p. injections of M2000 to prophylactic and therapeutic groups was day-1 and day-7 postimmunization. The WEHI-164 cell line was used for assaying the tolerability against M2000. The results of this experiment showed that the treatment of EAE with M2000 could significantly suppress disease development both prophylactically and therapeutically; the onset and symptoms of EAE in Lewis rats could be suppressed following the administration of M2000. Clinical improvement was accompanied by a marked decrease in mean numbers of vessels with perivascular cellular infiltration in M2000-treated rats compared with nontreated control. Disease suppression was associated with a marked suppression of MBP-specific T-cell reactivity in vitro, without any evidence for a generalized impairment of T-cell activity. Moreover, M2000 also showed a very high tolerability compared with certain steroidal and nonsteroidal anti-inflammatory drugs. Collectively, our data suggest that M2000 may provide a novel therapeutic option for T-cell-mediated autoimmune diseases in animal models and possibly in humans.

Analysis of ensemble learning using simple perceptrons based on online learning theory.

Ensemble learning of K nonlinear perceptrons, which determine their outputs by sign functions, is discussed within the framework of online learning and statistical mechanics. One purpose of statistical learning theory is to theoretically obtain the generalization error. This paper shows that ensemble generalization error can be calculated by using two order parameters, that is, the similarity between a teacher and a student, and the similarity among students. The differential equations that describe the dynamical behaviors of these order parameters are derived in the case of general learning rules. The concrete forms of these differential equations are derived analytically in the cases of three well-known rules: Hebbian learning, perceptron learning, and AdaTron (adaptive perceptron) learning. Ensemble generalization errors of these three rules are calculated by using the results determined by solving their differential equations. As a result, these three rules show different characteristics in their affinity for ensemble learning, that is "maintaining variety among students." Results show that AdaTron learning is superior to the other two rules with respect to that affinity.

Storage capacity diverges with synaptic efficiency in an associative memory model with synaptic delay and pruning.

It is known that storage capacity per synapse increases by synaptic pruning in the case of a correlation-type associative memory model. However, the storage capacity of the entire network then decreases. To overcome this difficulty, we propose decreasing the connectivity while keeping the total number of synapses constant by introducing delayed synapses. In this paper, a discrete synchronous-type model with both delayed synapses and their prunings is discussed as a concrete example of the proposal. First, we explain the Yanai-Kim theory by employing statistical neurodynamics. This theory involves macrodynamical equations for the dynamics of a network with serial delay elements. Next, considering the translational symmetry of the explained equations, we rederive macroscopic steady-state equations of the model by using the discrete Fourier transformation. The storage capacities are analyzed quantitatively. Furthermore, two types of synaptic prunings are treated analytically: random pruning and systematic pruning. As a result, it becomes clear that in both prunings, the storage capacity increases as the length of delay increases and the connectivity of the synapses decreases when the total number of synapses is constant. Moreover, an interesting fact becomes clear: the storage capacity asymptotically approaches 2/pi due to random pruning. In contrast, the storage capacity diverges in proportion to the logarithm of the length of delay by systematic pruning and the proportion constant is 4/pi. These results theoretically support the significance of pruning following an overgrowth of synapses in the brain and may suggest that the brain prefers to store dynamic attractors such as sequences and limit cycles rather than equilibrium states.

Associative memory by recurrent neural networks with delay elements.

The synapses of real neural systems seem to have delays. Therefore, it is worthwhile to analyze associative memory models with delayed synapses. Thus, a sequential associative memory model with delayed synapses is discussed, where a discrete synchronous updating rule and a correlation learning rule are employed. Its dynamic properties are analyzed by the statistical neurodynamics. In this paper, we first re-derive the Yanai-Kim theory, which involves macrodynamical equations for the dynamics of the network with serial delay elements. Since their theory needs a computational complexity of O(L4t) to obtain the macroscopic state at time step t where L is the length of delay, it is intractable to discuss the macroscopic properties for a large L limit. Thus, we derive steady state equations using the discrete Fourier transformation, where the computational complexity does not formally depend on L. We show that the storage capacity alphaC is in proportion to the delay length L with a large L limit, and the proportion constant is 0.195, i.e. alphaC=0.195L. These results are supported by computer simulations.