Development of Safety Measures of Bicycle Trafflc by Observation wffh Deep-Leamlng, Drive Recorder Data, Probe Blcycle wlth LIDAR, and Connected Simulators

This research outlines the development of evaluating safety measures for bicycle traffic using state-of-the-art technology, which was started since 2020 as a four-year project. The project is funded by the Commission on Advanced Road Technology in the Ministry of Land, Infrastructure, Transport and Tourism(MLIT). While Japan has a high bicycle modal share of 12% (2010), bicycle-related fatalities are relatively high among other countries in the IRTAD database (2019). Under these circumstances, since 2007, various measures for bicycle traffic measures have been implemented to improve the safe bicycle traffic environment, including the revision of the Road Traffic Act and the formulation of a national plan to promote bicycle use. However, serious accidents involving bicycles are remained in some specific cases. According to the government's traffic accident analysis results (2019), right-hook crash at signalized intersections are one of the most serious types of collision involving bicycles, along with accidents at unsignalized intersections involving vehicles turning left, rear-end collisions, and single vehicle accidents due to off-road deviation. In particular, proactive safety measures are required at signalized intersections along arterial roads, where electric personal mobility vehicles traveling at speeds of up to 20 km/h are expected to share with bicycles in the future. In order to evaluate safety measures for bicycle-vehicle crashes, this project set the following goals. 1) Identify factors influencing near-miss incidents and collisions through analysis of drive recorder data and accident statistical data. 2) Detailed analysis of traffic conditions from the cyclist's perspective using a probe bicycle equipped with a LiDAR sensor. 3) Development of an experimental environment using a connected simulator for evaluation of cooperative driving behavior. 4) Clarification of experimental conditions to evaluate different scenarios and conditions with and without intervention. 5) Proposal of effective interventions to improve crash cases based on experiments

Giant Retroperitoneal Mucinous Tumor Supportively Diagnosed as a Dedifferentiated Liposarcoma by Fluorescence In Situ Hybridization of MDM2 Gene

Surgical resection was performed on a 47-year-old woman for a retroperitoneal mass that weighed 8.5 kg. Histological examination revealed a myxoid sarcomatous tumor. Because diagnosis could not be determined by immunohistochemistry, attention was focused on MDM2 (murine double minute) gene amplification by fluorescence in situ hybridization (FISH) analysis. The tumor was finally determined to be a dedifferentiated liposarcoma. We experienced a case of a giant retroperitoneal dedifferentiated liposarcoma. FISH analysis was useful for the diagnosis and determination of the therapeutic strategy

fRNAdb: a platform for mining/annotating functional RNA candidates from non-coding RNA sequences

There are abundance of transcripts that code for no particular protein and that remain functionally uncharacterized. Some of these transcripts may have novel functions while others might be junk transcripts. Unfortunately, the experimental validation of such transcripts to find functional non-coding RNA candidates is very costly. Therefore, our primary interest is to computationally mine candidate functional transcripts from a pool of uncharacterized transcripts. We introduce fRNAdb: a novel database service that hosts a large collection of non-coding transcripts including annotated/non-annotated sequences from the H-inv database, NONCODE and RNAdb. A set of computational analyses have been performed on the included sequences. These analyses include RNA secondary structure motif discovery, EST support evaluation, cis-regulatory element search, protein homology search, etc. fRNAdb provides an efficient interface to help users filter out particular transcripts under their own criteria to sort out functional RNA candidates. fRNAdb is available a

A New Deep State-Space Analysis Framework for Patient Latent State Estimation and Classification from EHR Time Series Data

Many diseases, including cancer and chronic conditions, require extended treatment periods and long-term strategies. Machine learning and AI research focusing on electronic health records (EHRs) have emerged to address this need. Effective treatment strategies involve more than capturing sequential changes in patient test values. It requires an explainable and clinically interpretable model by capturing the patient's internal state over time. In this study, we propose the "deep state-space analysis framework," using time-series unsupervised learning of EHRs with a deep state-space model. This framework enables learning, visualizing, and clustering of temporal changes in patient latent states related to disease progression. We evaluated our framework using time-series laboratory data from 12,695 cancer patients. By estimating latent states, we successfully discover latent states related to prognosis. By visualization and cluster analysis, the temporal transition of patient status and test items during state transitions characteristic of each anticancer drug were identified. Our framework surpasses existing methods in capturing interpretable latent space. It can be expected to enhance our comprehension of disease progression from EHRs, aiding treatment adjustments and prognostic determinations.Comment: 21 pages, 6 figure

Development of hydroxyapatite-coated nonwovens for efficient isolation of somatic stem cells from adipose tissues

Adipose-derived stem cells (ASCs) are an attractive cell source for cell therapy. Despite the increasing number of clinical applications, the methodology for ASC isolation is not optimized for every individual. In this study, we developed an effective material to stabilize explant cultures from small-fragment adipose tissues. Methods: Polypropylene/polyethylene nonwoven sheets were coated with hydroxyapatite (HA) particles. Adipose fragments were then placed on these sheets, and their ability to trap tissue was monitored during explant culture. The yield and properties of the cells were compared to those of cells isolated by conventional collagenase digestion. Results: Hydroxyapatite-coated nonwovens immediately trapped adipose fragments when placed on the sheets. The adhesion was stable even in culture media, leading to cell migration and proliferation from the tissue along with the nonwoven fibers. A higher fiber density further enhanced cell growth. Although cells on nonwoven explants could not be fully collected with cell dissociation enzymes, the cell yield was significantly higher than that of conventional monolayer culture without impacting stem cell properties. Conclusions: Hydroxyapatite-coated nonwovens are useful for the effective primary explant culture of connective tissues without enzymatic cell dissociation

Results of the search for inspiraling compact star binaries from TAMA300's observation in 2000-2004

We analyze the data of TAMA300 detector to search for gravitational waves from inspiraling compact star binaries with masses of the component stars in the range 1-3Msolar. In this analysis, 2705 hours of data, taken during the years 2000-2004, are used for the event search. We combine the results of different observation runs, and obtained a single upper limit on the rate of the coalescence of compact binaries in our Galaxy of 20 per year at a 90% confidence level. In this upper limit, the effect of various systematic errors such like the uncertainty of the background estimation and the calibration of the detector's sensitivity are included.Comment: 8 pages, 4 Postscript figures, uses revtex4.sty The author list was correcte

RNA Editing Genes Associated with Extreme Old Age in Humans and with Lifespan in C. elegans

The strong familiality of living to extreme ages suggests that human longevity is genetically regulated. The majority of genes found thus far to be associated with longevity primarily function in lipoprotein metabolism and insulin/IGF-1 signaling. There are likely many more genetic modifiers of human longevity that remain to be discovered.Here, we first show that 18 single nucleotide polymorphisms (SNPs) in the RNA editing genes ADARB1 and ADARB2 are associated with extreme old age in a U.S. based study of centenarians, the New England Centenarian Study. We describe replications of these findings in three independently conducted centenarian studies with different genetic backgrounds (Italian, Ashkenazi Jewish and Japanese) that collectively support an association of ADARB1 and ADARB2 with longevity. Some SNPs in ADARB2 replicate consistently in the four populations and suggest a strong effect that is independent of the different genetic backgrounds and environments. To evaluate the functional association of these genes with lifespan, we demonstrate that inactivation of their orthologues adr-1 and adr-2 in C. elegans reduces median survival by 50%. We further demonstrate that inactivation of the argonaute gene, rde-1, a critical regulator of RNA interference, completely restores lifespan to normal levels in the context of adr-1 and adr-2 loss of function.Our results suggest that RNA editors may be an important regulator of aging in humans and that, when evaluated in C. elegans, this pathway may interact with the RNA interference machinery to regulate lifespan