Search

Arabinoxylan (AX) and arabinoxylooligosaccharides (AXOs) are carbohydrate sources utilized by Bifidobacterium longum subsp. longum. However, their degradation pathways are poorly understood. In this study, we characterized two genes, BLLJ_1850 and BLLJ_1851, in the hemicellulose-degrading gene cluster (BLLJ_1836-BLLJ_1859) of B. longum subsp. longum JCM 1217. Both recombinant enzymes expressed in Escherichia coli exhibited exo-α-L-arabinofuranosidase activity toward p-nitrophenyl-α-L-arabinofuranoside. BlArafE (encoded by BLLJ_1850) contains the glycoside hydrolase family 43 (GH43), subfamily 22 (GH43_22), and GH43_34 domains. The BlArafE GH43_22 domain was demonstrated to release α1,3-linked Araf from AX, but the function of BlArafE GH43_34 could not be clearly identified in this study. BlArafD (encoded by BLLJ_1851) contains GH43 unclassified subfamily (GH43_UC) and GH43_26 domains. The BlArafD GH43_UC domain showed specificity for α1,2-linked Araf in α1,2- and α1,3-Araf double-substituted structures in AXOs, while BlArafD GH43_26 was shown to hydrolyze α1,5-linked Araf in the arabinan backbone. Co-incubation of BlArafD and BlArafE revealed that these two enzymes sequentially removed α1,2-Araf and α1,3-Araf from double-substituted AXOs in this order. B. longum strain lacking BLLJ_1850-BLLJ_1853 did not grow in the medium containing α1,2/3-Araf double-substituted AXOs, suggesting that BlArafE and BlArafD are important for the assimilation of AX. KEY POINTS: • BlArafD GH43 unclassified subfamily domain is a novel α1,2-L-arabinofuranosidase. • BlArafE GH43 subfamily 22 domain is an α1,3-L-arabinofuranosidase. • BlArafD and BlArafE cooperatively degrade α1,2/3-Araf double-substituted arabinoxylan.

Japan has become a more aged society and there are more drivers, 65 years of age and above. Cars represent an important mode of transportation for the elderly; however, in recent years, the number of traffic accidents caused by elderly drivers has been on the rise, and this has become a social issue. Thus, for the elderly drivers to encourage them to improve their driving, we study a driver agent system which consists of smartphone, communication robot and cloud service and provides the driving support by attention awakening and the feedback support based on driving behavior evaluation. In this paper, we presented a summary of the proposed agent and reported on a set of preliminary experiments using our agent in an actual car environment. From the analysis of subjective evaluations and fixation points during driving, the results revealed the possibility that the drivers accept the agent and supports from the agent during driving and that the agent in an actual car environment did not distract the driver.

Older drivers experience a high rate of crashes due to road intersections, unseen objects, and failure to find the traffic signals. These characteristics would be recognized if a system could watch and monitor the behaviors of the drivers inside their car. Therefore, in this study, current advances in driver monitoring cameras are used for measuring the head movements of older drivers to evaluate the visual intent of the driver. Several quantitative metrics that compute the temporal and spatial aspects of head movements allow for assessing the behaviors of older and middle-aged drivers. A driving simulator study using urban road scenarios shows that at high vehicle speed, on average, older drivers move their heads more slowly within a narrower range and glance too quickly to recognize surrounding traffic correctly. Correlation analysis validated the high prediction capabilities of head-movement measures towards the future evaluation of driving risks.

Japan has become a more aged society and there are more drivers, 65 years of age and above. Cars represent an important mode of transportation for the elderly; however, in recent years, the number of traffic accidents caused by elderly drivers has been on the rise, and this has become a social issue. Thus, to ensure driving safety, we study a driver agent system that provides driving and feedback support to the elderly drivers for encouraging them to improve their driving. In this paper, we present a summary of the proposed agent and report on a set of experiments using our agent for the elderly and non-elderly drivers in an actual environment with car on public roads. From the analysis of driving operations and fixation points during driving, the results revealed that the acceptability of the agent was high, the agent in the actual car environment did not distract the driver, and the agent could improve driving behavior.

Automated driving systems are envisioned as the future mode of transportation owing to their projected ability to reduce human error and achieve more efficient and comfortable transportation. Accordingly, designing an interface that ensures the situational awareness of the human operator to reduce confusion, false expectations, and over-reliance on the automated system is important. When a human operator is in control, the automated system is expected to handle troublesome situations that the human is unable to manage. Thus, an interface is required to provide the appropriate information when necessary so that the human operator can easily perceive the reason for the sudden automated intervention. In this study, such a scenario is highlighted, in which a simulated automated intervention avoided a potential collision with a pedestrian who suddenly appeared on the roadside. To convey the reason for the automated intervention, an augmented reality-based head-up display (AR-HUD) cue that targets the pedestrian is developed. To understand the effects of the AR-HUD cue on the speed at which a human operator can recognize a pedestrian and the contribution of this visual cue to the perception of acceptability and credibility of the automated intervention, we compared AR-HUD with a static head-up display (S-HUD) that displays a pedestrian symbol at the bottom portion of the windshield. The results showed that the AR-HUD cue yielded faster recognition of the targeted pedestrian and provided a relatively more acceptable perception of the automated intervention.

The T-cell immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain (TIGIT) is a co-inhibitory receptor mainly expressed on T cells. Although TIGIT plays an important role in various autoimmune diseases, its role in atopic dermatitis (AD) remains unclear. In this study, we examined the expression levels of TIGIT and their association with clinical features in patients with AD. TIGIT expression on CD4+ T cells, central memory T cells, effector memory T cells and regulatory T cells was determined by flow cytometry. CD4+ T cells exhibited enhanced TIGIT expression in patients with AD compared with healthy individuals. In particular, effector memory T cells and regulatory T cells, but not central memory T cells, exhibited higher TIGIT expression in patients with AD than in healthy individuals. The frequency of TIGIT+ cells among CD4+ T cells was significantly increased in patients with mild AD compared with healthy individuals, while decreased in patients with severe AD. Consistently, the frequency of TIGIT+ cells among CD4+ T cells was negatively correlated with both serum thymus and activation-regulated chemokine levels and immunoglobulin E levels in patients with AD. Furthermore, TIGIT expression on CD4+ T cells inhibited cell proliferation in patients with AD. These results suggest that TIGIT expression on CD4+ T cells in patients with AD may be increased to suppress chronic cutaneous inflammation. Moreover, TIGIT expression may be impaired in a subset of patients with AD, leading to a deterioration of skin inflammation. Our study may provide new insight into a TIGIT pathway-based therapeutic approach for AD.

The purpose of this research is to analyze overconfidence and dependence caused by receiving the utterance from a driving support robot when a driver drives a car. Comfortable driving can be performed by receiving support from the driving support robot. However, it is expected that drivers will lead to accidents by the dependence or overconfidence bias on support robots. The dependence bias is that driver decreases the quality of driving behavior with respect to the function of the safety system, and the overconfidence bias is that driver embraces expectations more than the performance of the driving support robot. Although this problem includes the fuzziness of human characteristics, the conventional driving support system does not take into consideration such influence of the overconfidence or dependent bias. In order to verify whether driver has the bias against driving support robot in this paper, we investigated the impression of the robot when the driving support with driving animation and robot and report the results.

Recently, traffic accidents caused by elderly drivers have been increasing. It is thought that the reasons of accidents are functions such as decline of eyesight, cognition and physical strength caused by aging. In order to assist safe driving for elderly drivers, it is necessary to sufficiently understand any possible relationship of various senior drivers’ cognition, physical strength and driving behavior. In this paper relationship of elderly driver’s cognition, physical strength and driving behaviors were analyzed using the result of driving instructions on urban road by driving school instructors.

The interaction of programmed death-1 (PD-1) with its ligand, programmed death ligand-1 (PD-L1), has been considered to play a key role in the negative regulation of immune responses. Patients with diffuse cutaneous systemic sclerosis (SSc) had higher levels of soluble PD-1 (sPD-1) than those with limited cutaneous SSc and healthy individuals. Serum sPD-1 levels positively correlated with the severity of skin sclerosis. In contrast, serum sPD-L1 levels were significantly increased in patients with SSc compared with healthy individuals. Moreover, serum sPD-L1 levels were not associated with the extent of skin sclerosis and were elevated not only in patients with diffuse cutaneous SSc, but also in those with limited cutaneous SSc. These results suggested that serum sPD-1 levels may increase in patients with SSc and correlate with the severity of skin sclerosis. PD-1/PD-L1 interaction may contribute to the development of skin sclerosis in SSc.

Backgrounds Interleukin (IL)-10-producing regulatory B cells (B10 cells) have been shown to ameliorate psoriasis in mice. Human B10 progenitor cells are characterized as CD19 + CD24 hi CD38 hi B cells that exert their regulatory functions via the production of IL-10. However, the role of B10 cells in the pathogenesis of psoriasis remains unclear. Objectives We examined B10 cells in patients with psoriasis and healthy controls. Methods Peripheral blood mononuclear cells were isolated from psoriasis patients without a history of receiving any immunosuppressants during the 6-month period before enrollment in the study. Using flow cytometry, we determined the frequencies of blood B cell subsets, B10 progenitor cells, and B10 cells for 31 patients with psoriasis and 26 healthy controls. Results Both psoriasis patients and healthy controls showed similar frequencies of total B cells, IgD + CD27 − naive B cells, and IgD − CD27 + memory B cells. However, the frequency of CD19 + CD24 hi CD38 hi B10 progenitor cells was significantly higher in patients with psoriasis than in the healthy controls. In contrast, the frequency of B10 cells in patients with psoriasis was significantly lower than that in healthy controls. Furthermore, treatment with immunosuppressants resulted in a decrease in B10 progenitor cells and an increase in B10 cells. Conclusion B10 progenitor cells were increased, while IL-10-producing regulatory B10 cells were decreased in patients with psoriasis, suggesting that B10 cells may be functionally impaired in patients with psoriasis.

This paper presents an approach for predictive driving when facing blind intersections based on expert data. Expert drivers anticipate and avoid potential dangerous situations. In most cases these complex behaviors cannot be reproduced by state of the art planning approaches. We present an analysis of expert drivers while passing through blind intersections, we extract useful driving features to model the intersection and propose a cost function based on those features. We use inverse reinforcement learning to extract a feature-based cost function and learn its parameters from driver data. Finally, feature weights are computed based on collected expert driving data (using 211 trajectories). Evaluation was performed in terms of trajectory and speed using modified Hausdorff distance. Experimental results show that the planner is capable of computing trajectories comparable to those ones of the expert drivers when facing blind intersections.

The T-cell immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain (TIGIT) is a co-inhibitory receptor mainly expressed on T cells. Although TIGIT plays an important role in various autoimmune diseases, its role in atopic dermatitis (AD) remains unclear. In this study, we examined the expression levels of TIGIT and their association with clinical features in patients with AD. TIGIT expression on CD4

In this study, we developed an autonomous driving system for mountainous public roads. Three-dimensional normal distribution transform (NDT) scan matching is employed for localization and a model predictive controller is utilized for vehicle motion control. In order to increase the robustness of the localization method, the estimated poses are computed by an extended Kalman filter using dead reckoning and NDT information. The uncertainty of the pose estimated by NDT is determined by using the Hessian matrix computed in the optimization process for scan matching. We conducted experiments in a public road environment over 20 times and all of the tests were successful. The experimental results confirmed that the autonomous driving system can operate reliably in mountainous public roads. In addition, the evaluation results obtained for the localization method showed that accurate and robust localization can be achieved in mountainous rural environments.

This paper presents a model for safe driving at blind intersections and its integration to a local planner based on a Frenet frame. The model predicts potential moving obstacles from blind intersections to proactively slow down to avoid potential collisions. The derivation of the model is described and its parameters are detailed. The local planner computes smooth trajectories with smooth velocity profiles so that the vehicle can follow the paths without jerk and sudden accelerations resulting in safe and comfortable navigation. Experimental results in simulation and in the real field with an autonomous car, show that the proposed predictive driving framework can reproduce human expert driver's trajectories and velocities when facing blind intersections.

This paper presents a model for velocity control in blind corners and intersections based on expert driver data. Accurate expert driver data was collected with a car equipped with a 3D LiDAR and high definition maps. A model based on human expert driver data is used to control the velocity of the ego-vehicle when facing blind intersections. The model regulates ego-vehicle velocity based on the visibility of the road at the blind intersection. As the vehicle approximates the intersection and crossing roads are not visible, the vehicle slows down, then as the roads become visible the vehicle accelerates. Experimental results show the performance of the velocity model compared towards 270 trajectories taken from 7 expert drivers towards 6 different intersections without mandatory stops.