大規模多元接続ネットワークのためのグラフ構造に基づくランダムアクセス方式

電気通信大学201

Classification of Known and Unknown Environmental Sounds Based on Self-Organized Space Using a Recurrent Neural Network

Our goal is to develop a system to learn and classify environmental sounds for robots working in the real world. In the real world, two main restrictions pertain in learning. (i) Robots have to learn using only a small amount of data in a limited time because of hardware restrictions. (ii) The system has to adapt to unknown data since it is virtually impossible to collect samples of all environmental sounds. We used a neuro-dynamical model to build a prediction and classification system. This neuro-dynamical model can self-organize sound classes into parameters by learning samples. The sound classification space, constructed by these parameters, is structured for the sound generation dynamics and obtains clusters not only for known classes, but also unknown classes. The proposed system searches on the basis of the sound classification space for classifying. In the experiment, we evaluated the accuracy of classification for both known and unknown sound classes

Activation Process of [NiFe] Hydrogenase Elucidated by High-Resolution X-Ray Analyses: Conversion of the Ready to the Unready State

SummaryHydrogenases catalyze oxidoreduction of molecular hydrogen and have potential applications for utilizing dihydrogen as an energy source. [NiFe] hydrogenase has two different oxidized states, Ni-A (unready, exhibits a lag phase in reductive activation) and Ni-B (ready). We have succeeded in converting Ni-B to Ni-A with the use of Na2S and O2 and determining the high-resolution crystal structures of both states. Ni-B possesses a monatomic nonprotein bridging ligand at the Ni-Fe active site, whereas Ni-A has a diatomic species. The terminal atom of the bridging species of Ni-A occupies a similar position as C of the exogenous CO in the CO complex (inhibited state). The common features of the enzyme structures at the unready (Ni-A) and inhibited (CO complex) states are proposed. These findings provide useful information on the design of new systems of biomimetic dihydrogen production and fuel cell devices

Developmental Human-Robot Imitation Learning of Drawing with a Neuro Dynamical System

Abstract—This paper mainly deals with influences of teach-ing style and developmental processes in learning model to the acquired representations (primitives). We investigate these in-fluences by introducing a hierarchical recurrent neural network for robot model, and a form of motionese (a caregiver’s use of simpler and more exaggerated motions when showing a task to an infants). We modified a Multiple Timescales Recurrent Neural Network (MTRNN) for robot’s self-model. The number of layers in the MTRNN increases according to learn complex events. We investigate our approach with a humanoid robot “Actroid ” through conducting an imitation experiment in which a human caregiver gives the robot a task of pushing two buttons. Experiment results and analysis confirm that learning with phased teaching and structuring enables to acquire the clear motion primitives as the activities in the fast context layer of MTRNN and to the robot to handle unknown motions. I

LDL-C/HDL-C Ratio Predicts Carotid Intima-Media Thickness Progression Better Than HDL-C or LDL-C Alone

High-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) are strong predictors of atherosclerosis. Statin-induced changes in the ratio of LDL-C to HDL-C (LDL-C/HDL-C) predicted atherosclerosis progression better than LDL-C or HDL-C alone. However, the best predictor of subclinical atherosclerosis remains unknown. Our objective was to investigate this issue by measuring changes in carotid intima-media thickness (IMT). A total of 1,920 subjects received health examinations in 1999, and were followed up in 2007. Changes in IMT (follow-up IMT/baseline IMT × 100) were measured by ultrasonography. Our results showed that changes in IMT after eight years were significantly related to HDL-C (inversely, P < 0.05) and to LDL-C/HDL-C ratio (P < 0.05). When the LDL-C/HDL-C ratios were divided into quartiles, analysis of covariance showed that increases in the ratio were related to IMT progression (P < 0.05). This prospective study demonstrated the LDL-C/HDL-C ratio is a better predictor of IMT progression than HDL-C or LDL-C alone

地方清酒製造に貢献できる清酒酵母の育種

In order to contribute regional sake production, we have been trying to isolate yeast strains from nature. Yeast strain MITOY20 had a good sake fermentation performance compared to sake yeast strain K701. The meiotic segregant MITOY66 which exhibited mating type a was isolated from sake yeast K701 which was treated with rapamycin

PETREL: Platform for Extra and Terrestrial Remote Examination with LCTF

A small satellite ”PETREL” for UV astronomy and remote sensing with ”tunable” multi-spectral cameras conducted by an academia-industrial collaboration is presented. This project was originally proposed by an astronomer who desired a satellite for exploration of explosive objects in ultraviolet. To avoid the earthshine the astronomical observations are scheduled only in the nighttime. To utilize the daytime more electively we conceived a plan of ”satellite sharing” with the industrial collaborators, that can also reduce the developing cost drastically. The daytime mission is spectroscopy that is one of the potential fields in terms of data business, because that can provide chemical and biological information on the surface of the earth. We employ multi-spectral cameras making use of liquid crystal tunable filters (LCTFs) that enable adaptive observations at the optimized wave-bands for each targets. In 2020, this remote-sensing project and ultraviolet astronomy mission were accepted as a small satellite project of JAXA’s Innovative Satellite Technology Demonstration program and as an ISAS/JAXA’s small-scale program, respectively. This satellit

The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force

「コロナ制圧タスクフォース」COVID-19患者由来の血液細胞における遺伝子発現の網羅的解析 --重症度に応じた遺伝子発現の変化には、ヒトゲノム配列の個人差が影響する--. 京都大学プレスリリース. 2022-08-23.Coronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection