Investigation of automotive light blinking pattern conveying a driver's intention to yield

This paper proposes a method of conveying a driver’s intention to yield by blinking automotive lighting. Generally, headlight flashing indicates a driver’s intention to yield to another driver or pedestrians in Japan. However, this signaling method can have several meanings, including warning other drivers of road dangers such as crashed cars, or informing other drivers of the presence of a pedestrian. There is a possibility of a misunderstanding, increasing the risk of accidents. Therefore, we have investigated an effective method for facilitating communication in traffic. This study focuses on nonverbal communication in which blinking lights convey the driver’s intentions to others. In this study, visibility is not considered. Nine blinking patterns that changed the light colors, waveform, and blinking cycle were presented to fifteen healthy participants, and visual impressions were evaluated to identify the most suitable blinking patterns. These nine patterns indicate a driver’s intention to yield to pedestrians and other drivers. The results indicated that a blue-green blinking pattern in which luminance changes with a triangular wave pattern with a one-second cycle was best conveyed the intention to yield

Multi-Wavelength Photometric and Polarimetric Observations of the Outburst of 3C 454.3 in Dec. 2009

In December 2009, the bright blazar, 3C 454.3 exhibited a strong outburst in the optical, X-ray and gamma-ray regions. We performed photometric and polarimetric monitoring of this outburst in the optical and near-infrared bands with TRISPEC and HOWPol attached to the Kanata telescope. We also observed this outburst in the infrared band with AKARI, and the radio band with the 32-m radio telescope of Yamaguchi University. The object was in an active state from JD 2455055 to 2455159. It was 1.3 mag brighter than its quiescent state before JD 2455055 in the optical band. After the end of the active state in JD 2455159, a prominent outburst was observed in all wavelengths. The outburst continued for two months. Our optical and nearinfrared polarimetric observations revealed that the position angle of the polarization (PA) apparently rotated clockwise by 240 degrees within 11 d in the active state (JD 2455063-2455074), and after this rotation, PA remained almost constant during our monitoring. In the outburst state, PA smoothly rotated counterclockwise by 350 degrees within 35 d (JD 2455157-2455192). Thus, we detected two distinct rotation events of polarization vector in opposite directions. We discuss these two events compared with the past rotation events observed in 2005, 2007 and 2008.Comment: 9 pages, 6 figures, accepted for publication in PAS

Catalytic Direct Oxidation of Methane to Methanol by Redox of Copper Mordenite

Expectations for industrial implementation of direct conversion of CH4 to CH3OH are growing with the increasing demand for energy-efficient chemical processes. In this study, catalytic production of CH3OH by direct oxidation of CH4 with O2 was performed using Cu zeolite catalysts in a CH4/O2/H2O flow reaction. Among the various Cu zeolites investigated, the Cu-MOR catalyst exhibited relatively high CH3OH production with a turnover number of 7.4 molCH3OH/molCu over 24 h (CH4 conversion: 0.011%). The catalytically active Cu species and catalytic cycle were investigated by in-situ simultaneous X-ray absorption and infrared spectroscopy. The results suggest that the key to the catalytic cycle over Cu-MOR is the redox of Cu(I)/Cu(II) species. Furthermore, H2O-adsorption-included dynamic Cu species are revealed to be catalytically active

An antisense amido-bridged nucleic acid gapmer oligonucleotide targeting SRRM4 alters REST splicing and exhibits anti-tumor effects in small cell lung cancer and prostate cancer cells

Abstract Background Antisense oligonucleotide (ASO) medicine for clinical applications has been becoming a reality. We previously developed a gapmer ASO targeting Ser/Arg repetitive matrix 4 (SRRM4) that is abnormally expressed in small cell lung cancer (SCLC). However the detailed mechanism of ASO through repressing SRRM4 has not been completely elucidated. Further, effectiveness of SRRM4 ASO to prostate cancer (PCa) cells expressing SRRM4 similar to SCLC remains to be elucidated. RE1-silencing transcription factor (REST) is a tumor suppressor, and its splicing isoform (sREST) is abnormally expressed by SRRM4 and causes carcinogenesis with neuroendocrine phenotype in SCLC. The present study aimed to understand the contribution of REST splicing by SRRM4 ASO administration. Methods SRRM4 expression and REST splicing were analyzed by RT-qPCR and conventional RT-PCR after treating SRRM4 ASO, and cell viability was analyzed in vitro. Exogenous reconstitution of Flag-tagged REST plasmid in SCLC cells and the splice-switching oligonucleotide (SSO) specific for REST was analyzed for cell viability. Furthermore, we expanded the application of SRRM4 ASO in PCa cells abnormally expressing SRRM4 mRNA in vitro. Results SRRM4 ASO successfully downregulated SRRM4 expression, followed by repressed cell viability of SCLC and PCa cells in a dose-dependent manner. Administration of SRRM4 ASO then modified the alternative splicing of REST, resulting reduced cell viability. REST SSO specifically modified REST splicing increased REST expression, resulting in reduced cell viability. Conclusions Our data demonstrate that a gapmer ASO targeting SRRM4 (SRRM4 ASO) reduces cell viability through splicing changes of REST, followed by affecting REST-controlled genes in recalcitrant tumors SCLC and PCa cells

Direct Design of Active Catalysts for Low Temperature Oxidative Coupling of Methane via Machine Learning and Data Mining

Direct design of low temperature oxidative coupling of methane (OCM) catalysts is proposed via machine learning and data mining. 58 OCM catalysts are experimentally synthesized and evaluated. Collected 58 data are then classified by unsupervised machine learning in multi-dimensional space where active catalysts group for low temperature OCM is identified. Data mining then identifies the physical rule within the group. Catalysts satisfying such physical rule is designed where 2 undiscovered low temperature OCM catalysts are found and experimentally validated. Thus, machine learning and data mining reveal the hidden physical rule behind the catalysis leading to the direct design of catalysts. Hence, machine learning and data mining open up the insight of powerful strategy for designing catalysts