Revealing microbial community structures in large- and small-scale activated sludge systems by barcoded pyrosequencing of 16S rRNA gene

The diversity of bacterial groups in activated sludge from large- and small-scale wastewater treatment plants was explored by barcoded pyrosequencing of 16S rRNA gene. Activated sludge samples (three small and 17 large scale) were collected from 12 wastewater treatment plants to clarify precise taxonomy and relative abundances. DNA was extracted, and amplified by 4 base barcoded 27f/519r primer set. The 454 Titanium (Roche) pyrosequences were obtained and analyses performed by Quantitative Insight Into Microbial Ecology (QIIME) with around 100,000 reads. Sequence statistics were computed, while constructing a phylogenetic tree and heatmap. Computed results explained total microbial diversity at phylum and class level and resolution was further extended to Operational Taxonomic Unit (OTU) based taxonomic assignment for investigating community distribution based on individual sample. Composition of sequence reads were compared and microbial community structures for large- and small-scale treatment plants were identified as major phyla (Proteobacteria and Bacteroidetes) and classes (Betaproteobacteria and Bacteroidetes). Also, family level breakdowns were explained and differences in family Nitrospiraceae and phylum Actinobacteria found at their species level were also illustrated. Thus, the pyrosequencing method provides high resolution insight into microbial community structures in activated sludge that might have been unnoticed with conventional approaches.     

Helium gas permeability of montmorillonite/epoxy nanocomposites

Helium gas permeability of silicate clay (montmorillonite) particles/epoxy nanocomposites was examined. The incorporation of increasing amounts of montmorillonite particles reduced the helium gas permeability. Based on Fick’s law, gas permeation behavior of the nanocomposite was evaluated. With the increase of montmorillonite loading, gas diffusivity decreased, while gas solubility increased. Helium diffusion behavior is in agreement to the numerical results based on the Hatta–Taya–Eshelby theory. It has been revealed that dispersion of nanoscale platelets in polymer is effective in improving gas barrier property.     

Rolling and swaying motion estimation for a mobile robot by using omnidirectional optical flows

Described here is a method for estimating rolling and swaying motions of a mobile robot using optical flow. We have proposed an image sensor with a hyperboloidal mirror for the vision-based navigation of a mobile robot. Its name is HyperOmni Vision. The radial component of optical flow in HyperOmni Vision has a periodic characteristic. The circumferential component of optical flow has a symmetric characteristic. The proposed method makes use of these characteristic to estimate robustly the rolling and swaying motion of the mobile robot. Correspondence to: Y. Yagi e-mail: y-yagi@sys.es.osaka-u.ac.jp     

Iron Loss Analysis of Permanent Magnet Motors Fed by PWM Inverter

Carrier harmonic losses that consist of iron loss, eddy current loss, and ac copper loss are produced in a permanent magnet machine driven by a PWM inverter. It is already known that a higher motor inductance can lead to lower carrier harmonic losses. This paper investigates the carrier harmonic loss composition of two motors with identical dimensions but different inductances. The results of finite element analysis (FEA) showed that the eddy current loss in the iron core accounts for most of the carrier harmonic loss. It is also shown that the carrier harmonic loss of the iron core is quantifiable using theoretical calculations.     

Navigation behavior based on self-organized spatial representation in hierarchical recurrent neural network

ABSTRACT

A cognitive map is an internal model of the external world and contains the spatial representation of the surrounding environment. The existence of the cognitive map was first identified in rats; rats can navigate to their desired destination using cognitive maps while dealing with environmental uncertainty. We performed a mobile robot navigation experiment where obstacles were randomly placed using hierarchical recurrent neural network (HRNN) with multiple timescales. The HRNN was trained to navigate the mobile robot to the destination indicated by a snapshot image. After the training, the HRNN was able to successfully avoid the obstacles and navigate to the destination from any location in the environment. Analysis of the internal states of the HRNN showed that the module with fast timescale handles obstacle avoidance and the one with slow timescale has spatial representation corresponding to the spatial position of the destination. Moreover, in the experiment wherein the novel path appeared, the trained HRNN performed shortcut behavior. The shortcut behavior shows that the HRNN performed navigation using the self-organized spatial representation in the slow recurrent neural network. This indicates that training of goal-oriented navigation, i.e. the navigation motivated by a snapshot image of the destination results in the self-organization of cognitive map-like representation.     

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.