Constructing Model of Bicycle Behavior on Non-signalized lntersection Using Nonlinear Autoregressive Exogenous Model

This study focuses on bicycle travel flow to prevent traffic accidents at non-signalized intersections. A bicycle's behavior can be characterized by various parameters, such as travel speed position, trajectory, acceleralion, and deceleration. The prevention of vehicle collisions with bicycles traveling at 10-15 km/h was regulated in the Advanced Emergency Braking System (AEBS) for passenger cars in regulation No. 152 of the World Forum for Harmonization of Vehicle Regulations in the United Nations. Therefore, it is essential to analyze the characteristics of bicycles in a reall trafflc environment to prevent traffic accidents involving cyclists. Meijer et. al. (2017) investigated bicycle behavior and charactericics using measurement devices installed on biccycles [1 ]. Ma et al. (2016) conducted a model of acceleration behavior on eleven cyclists using GPS data [2]. And it was pointed out that there was a need for modeling research for more cyclists.Hirose et al. (2021) examined bicycles' both travel speed and trajectory as bicycle travel flows based on data obtained from fixed-point observations at a non-signalized intersection in Tokyo, Japan [3]. This used fixed-point observalions to obtain raw data of bicycle travel flows in real traffic environment and reported various traffel speed, trajectory, and acceleration/deceleration patterns for bicycles entering intersections. The purpose of this study was to construct a model of bicycle travel flows based on fixed-point observations. It could simulate actual bicycle behaviors based on data that was obtained from measuring bicycle travel flows for 2828 cases from fixed-point observations. Furthermore, the data was divided into five patterns of bicycles entering intersections, and the accuracy of the model was evaluated for each pattern

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects

CRISPRa-mediated NEAT1 lncRNA upregulation induces formation of intact paraspeckles

Long noncoding RNAs (lncRNAs) are fundamental genomic regulatory factors under various physiological and pathological conditions. A class of lncRNAs termed architectural RNAs (arcRNAs) plays an essential scaffolding role in building nuclear bodies. NEAT1 arcRNA is an abundant, nuclear-retained lncRNA that constructs paraspeckle nuclear bodies. NEAT1 is upregulated in various developmental and disease conditions including cancer and virus infection. However, it remains unclear how elevated expression of NEAT1 influences such conditions. Here, we set up an experimental method to selectively increase NEAT1 expression. We applied the synergistic activation mediator (SAM) system using catalytically dead Cas9 (dCas9) proteins to activate transcription of the NEAT1 gene. We examined 10 pre-designed and 15 originally designed single-guide RNAs (sgRNAs) in the NEAT1 promoter region for CRISPR activation (CRISPRa). We validated several sgRNAs that we designed for the SAM system to strongly activate NEAT1 expression in two human cell lines and induced formation of paraspeckles with intact core-shell structures. Thus, this selective NEAT1 upregulation method using the SAM system would be useful for further functional analyses of NEAT1 lncRNA in both basic and applied research

Constructing Model of Bicycle Behavior on Non-signalized lntersection Using Nonlinear Autoregressive Exogenous Model

This study focuses on bicycle travel flow to prevent traffic accidents at non-signalized intersections. A bicycle's behavior can be characterized by various parameters, such as travel speed position, trajectory, acceleralion, and deceleration. The prevention of vehicle collisions with bicycles traveling at 10-15 km/h was regulated in the Advanced Emergency Braking System (AEBS) for passenger cars in regulation No. 152 of the World Forum for Harmonization of Vehicle Regulations in the United Nations. Therefore, it is essential to analyze the characteristics of bicycles in a reall trafflc environment to prevent traffic accidents involving cyclists. Meijer et. al. (2017) investigated bicycle behavior and charactericics using measurement devices installed on biccycles [1 ]. Ma et al. (2016) conducted a model of acceleration behavior on eleven cyclists using GPS data [2]. And it was pointed out that there was a need for modeling research for more cyclists.Hirose et al. (2021) examined bicycles' both travel speed and trajectory as bicycle travel flows based on data obtained from fixed-point observations at a non-signalized intersection in Tokyo, Japan [3]. This used fixed-point observalions to obtain raw data of bicycle travel flows in real traffic environment and reported various traffel speed, trajectory, and acceleration/deceleration patterns for bicycles entering intersections. The purpose of this study was to construct a model of bicycle travel flows based on fixed-point observations. It could simulate actual bicycle behaviors based on data that was obtained from measuring bicycle travel flows for 2828 cases from fixed-point observations. Furthermore, the data was divided into five patterns of bicycles entering intersections, and the accuracy of the model was evaluated for each pattern

Constructing Model of Bicycle Behavior on Non-signalized lntersection Using Nonlinear Autoregressive Exogenous Model

This study focuses on bicycle travel flow to prevent traffic accidents at non-signalized intersections. A bicycle's behavior can be characterized by various parameters, such as travel speed position, trajectory, acceleralion, and deceleration. The prevention of vehicle collisions with bicycles traveling at 10-15 km/h was regulated in the Advanced Emergency Braking System (AEBS) for passenger cars in regulation No. 152 of the World Forum for Harmonization of Vehicle Regulations in the United Nations. Therefore, it is essential to analyze the characteristics of bicycles in a reall trafflc environment to prevent traffic accidents involving cyclists. Meijer et. al. (2017) investigated bicycle behavior and charactericics using measurement devices installed on biccycles [1 ]. Ma et al. (2016) conducted a model of acceleration behavior on eleven cyclists using GPS data [2]. And it was pointed out that there was a need for modeling research for more cyclists.Hirose et al. (2021) examined bicycles' both travel speed and trajectory as bicycle travel flows based on data obtained from fixed-point observations at a non-signalized intersection in Tokyo, Japan [3]. This used fixed-point observalions to obtain raw data of bicycle travel flows in real traffic environment and reported various traffel speed, trajectory, and acceleration/deceleration patterns for bicycles entering intersections. The purpose of this study was to construct a model of bicycle travel flows based on fixed-point observations. It could simulate actual bicycle behaviors based on data that was obtained from measuring bicycle travel flows for 2828 cases from fixed-point observations. Furthermore, the data was divided into five patterns of bicycles entering intersections, and the accuracy of the model was evaluated for each pattern

A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images

Liquid-based cytology (LBC) for cervical cancer screening is now more common than the conventional smears, which when digitised from glass slides into whole-slide images (WSIs), opens up the possibility of artificial intelligence (AI)-based automated image analysis. Since conventional screening processes by cytoscreeners and cytopathologists using microscopes is limited in terms of human resources, it is important to develop new computational techniques that can automatically and rapidly diagnose a large amount of specimens without delay, which would be of great benefit for clinical laboratories and hospitals. The goal of this study was to investigate the use of a deep learning model for the classification of WSIs of LBC specimens into neoplastic and non-neoplastic. To do so, we used a dataset of 1605 cervical WSIs. We evaluated the model on three test sets with a combined total of 1468 WSIs, achieving ROC AUCs for WSI diagnosis in the range of 0.89–0.96, demonstrating the promising potential use of such models for aiding screening processes