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Development of innovative therapeutic modalities would address an unmet clinical need in the treatment of triple negative breast cancer (TNBC). Activation of retinoic acid‐inducible gene‐I (RIG‐I)‐like receptors (RLRs) such as melanoma differentiation‐associated gene 5 (MDA5) and RIG‐I in cancer cells is suggested to suppress tumor progression by inducing cell death. Transfection of polyI:C, a conventionally used synthetic double‐stranded RNA (dsRNA) analogue that activates RLRs, has been evaluated in clinical trials. However, detailed mechanisms of tumor suppression by RLRs, especially interactions with other signaling pathways, remain elusive. Here, we showed that transfection of polyI:C suppressed transforming growth factor‐β (TGF‐β) signaling in a MDA5‐ and RIG‐I‐dependent manner. We found that suppression of TGF‐β signaling by polyI:C promoted cancer cell death, which was attenuated by forced expression of constitutively active Smad3. More detailed analysis suggested that cell death by polyI:C transfection exhibited characteristics of pyroptosis, which is distinct from apoptosis. Therapeutic efficacy of polyI:C transfection was also demonstrated using a mouse model. These results indicated that intratumor administration of polyI:C and related dsRNA analogues may be promising treatments for TNBC through inhibition of the anti‐pyroptotic function of TGF‐β.

Abstract The sense of agency refers to the feeling of control over one’s own actions, and through them, the external events. This study examined the effect of modified visual feedback on the sense of agency over one’s body movements using virtual reality in healthy individuals whose motor control was disturbed. Participants moved a virtual object using their right hand to trace a trajectory (Experiment 1) or a leading target (Experiment 2). Their motor control was disturbed by a delay in visual feedback (Experiment 1) or a 1-kg weight attached to their wrist (Experiment 2). In the offset conditions, the virtual object was presented at the median point between the desired position and the participants’ actual hand position. In both experiments, participants reported improved sense of agency in the offset condition compared to the aligned condition where the visual feedback reflected their actual body movements, despite their motion being less precise in the offset condition. The results show that sense of agency can be enhanced by modifying feedback to motor tasks according to the goal of the task, even when visual feedback is discrepant from the actual body movements. The present study sheds light on the possibility of artificially enhancing body agency to improve voluntary motor control.

Abstract In robot teleoperation, a lack of depth information often results in collisions between the robots and obstacles in its path or surroundings. To address this issue, free viewpoint images can greatly benefit the operators in terms of collision avoidance as the operators are able to view the robot’s surrounding from the images at arbitrary points, giving them a better depth information. In this paper, a novel free viewpoint image generation system is proposed. One approach to generate free viewpoint images is to use multiple cameras and Light Detection and Ranging (LiDAR). Instead of using the expensive LiDAR, this study utilizes a cost-effective laser rangefinder (LRF) and a characteristic of man-made environments. In other words, we install multiple fisheye cameras and an LRF on a robot. Free viewpoint images are generated under the assumption that walls are perpendicular to the floor. Furthermore, an easy calibration for estimating the poses of the multiple fisheye cameras, the LRF, and the robot model is proposed. Experimental results show that the proposed method can generate free viewpoint images using cameras and an LRF. Finally, the proposed method is primarily implemented using OpenGL Shading Language to utilize a graphics processing unit computation to achieve a real-time processing of the multiple high-resolution images. Supplementary videos and our source code are available at our project page ( https://matsuren.github.io/fvp ).

Thyroid transcription factor‐1 (TTF‐1, encoded by the NKX2‐1 gene) is highly expressed in small‐cell lung carcinoma (SCLC) and lung adenocarcinoma (LADC), but how its functional roles differ between SCLC and LADC remains to be elucidated. Here, we compared the genome‐wide distributions of TTF‐1 binding regions and the transcriptional programs regulated by TTF‐1 between NCI‐H209 (H209), a human SCLC cell line, and NCI‐H441 (H441), a human LADC cell line, using chromatin immunoprecipitation‐sequencing (ChIP‐seq) and RNA‐sequencing (RNA‐seq). TTF‐1 binding regions in H209 and H441 cells differed by 75.0% and E‐box motifs were highly enriched exclusively in the TTF‐1 binding regions of H209 cells. Transcriptome profiling revealed that TTF‐1 is involved in neuroendocrine differentiation in H209 cells. We report that TTF‐1 and achaete‐scute homolog 1 (ASCL1, also known as ASH1, an E‐box binding basic helix–loop–helix transcription factor, and a lineage‐survival oncogene of SCLC) are coexpressed and bound to adjacent sites on target genes expressed in SCLC, and cooperatively regulate transcription. Furthermore, TTF‐1 regulated expression of the Bcl‐2 gene family and showed antiapoptotic function in SCLC. Our findings suggest that TTF‐1 promotes SCLC growth and contributes to neuroendocrine and antiapoptotic gene expression by partly coordinating with ASCL1.

In this paper, we propose the octave deep plane-sweeping network (OctDPSNet). OctDPSNet is a novel learning-based plane-sweeping stereo, which drastically reduces the required GPU memory and computation time while achieving a state-of-the-art depth estimation accuracy. Inspired by octave convolution, we divide image features into high and low spatial frequency features, and two cost volumes are generated from these using our proposed plane-sweeping module. To reduce spatial redundancy, the resolution of the cost volume from the low spatial frequency features is set to half that of the high spatial frequency features, which enables the memory consumption and computational cost to be reduced. After refinement, the two cost volumes are integrated into a final cost volume through our proposed pixel-wise “squeeze-and-excitation” based attention mechanism, and the depth maps are estimated from the final cost volume. We evaluate the proposed model on five datasets: SUN3D, RGB-D SLAM, MVS, Scenes11, and ETH3D. Our model outperforms previous methods on five datasets while drastically reducing the memory consumption and computational cost. Our source code is available at https://github.com/matsuren/octDPSNet.

We developed a path-planning system for radiation source identification devices using 4π gamma imaging. The estimated source location and activity were calculated by an integrated simulation model by using 4π gamma images at multiple measurement positions. Using these calculated values, a prediction model to estimate the probability of identification at the next measurement position was created by via random forest analysis. The path-planning system based on the prediction model was verified by integrated simulation and experiment for a 137Cs point source. The results showed that 137Cs point sources were identified using the few measurement positions suggested by the path-planning system.

We propose a method to predict trajectories of surrounding vehicles considering individual drivingcharacteristics. Trajectory prediction of surrounding vehicles is attracting a lot of attention now, and it is expected to apply to advanced driver assistance systems. However, previous methods perform the trajectory prediction based on common driving patterns even though each driver shows a different driving characteristic. The proposed method focuses on the following behavior behind the preceding vehicle and estimates a driving characteristic of each driver using machine learning techniques. Based on the estimation result, the proposed method adjusts the prediction model and appropriately generates a trajectory. As the result, the performance of trajectory prediction can be dramatically improved.

In this paper, a novel method is proposed to estimate the driving styles of other drivers based on a driving risk feature. This new feature is proposed using a dynamic potential field method wherein the distribution changes depending on the relative number of adjacent vehicles. A more appropriate description of driving risk is obtained compared to other indices. The proposed feature dramatically improves the accuracy of estimating the driving style. To estimate the driving styles, this study considers a problem of the fundamental model under a scenario wherein the target vehicle follows the preceding vehicle. The proposed estimation method is validated through experimental results.

In this paper, we propose a path planning method for the localization of radiation sources using a mobile robot equipped with an imaging gamma-ray detector, which has a field of view in all directions. The ability to detect and localize radiation sources is essential for ensuring nuclear safety, security, and surveillance. To enable the autonomous localization of radiation sources, the robot must have the ability to automatically determine the next location for gamma ray measurement instead of following a predefined path. The number of incident events is approximated to be the squared inverse proportional to the distance between the radiation source and the detector. Therefore, the closer the distance to the source, the shorter the time required to obtain the same radiation counts measured by the detector. Hence, the proposed method is designed to reduce this distance to a position where a sufficient number of gamma-ray events can be obtained; then, a path to surround the radiation sources is generated. The proposed method generates this path by performing principal component analysis based on the results obtained from previous measurements. Both simulations and actual experiments demonstrate that the proposed method can automatically generate a measurement path and accurately localize radiation sources.

Zinc finger E‐box binding protein 1 (ZEB1) and ZEB2 induce epithelial‐mesenchymal transition (EMT) and enhance cancer progression. However, the global view of transcriptional regulation by ZEB1 and ZEB2 is yet to be elucidated. Here, we identified a ZEB1‐regulated inflammatory phenotype in breast cancer cells using chromatin immunoprecipitation sequencing and RNA sequencing, followed by gene set enrichment analysis (GSEA) of ZEB1‐bound genes. Knockdown of ZEB1 and/or ZEB2 resulted in the downregulation of genes encoding inflammatory cytokines related to poor prognosis in patients with cancer, including IL6 and IL8, therefore suggesting that ZEB1 and ZEB2 have similar functions in terms of the regulation of production of inflammatory cytokines. Antibody array and ELISA experiments confirmed that ZEB1 controlled the production of the IL‐6 and IL‐8 proteins. The secretory proteins regulated by ZEB1 enhanced breast cancer cell proliferation and tumor growth. ZEB1 expression in breast cancer cells also affected the growth of fibroblasts in cell culture, and the accumulation of myeloid‐derived suppressor cells in tumors in vivo. These findings provide insight into the role of ZEB1 in the progression of cancer, mediated by inflammatory cytokines, along with the initiation of EMT.

Abstract Epithelial-mesenchymal transition (EMT) is induced by transforming growth factor (TGF)-β and facilitates tumor progression. We here performed global mapping of accessible chromatin in the mouse mammary gland epithelial EpH4 cell line and its Ras-transformed derivative (EpRas) using formaldehyde-assisted isolation of regulatory element (FAIRE)-sequencing. TGF-β and Ras altered chromatin accessibility either cooperatively or independently, and AP1, ETS, and RUNX binding motifs were enriched in the accessible chromatin regions of EpH4 and EpRas cells. Etv4, an ETS family oncogenic transcription factor, was strongly expressed and bound to more than one-third of the accessible chromatin regions in EpRas cells treated with TGF-β. While knockdown of Etv4 and another ETS family member Etv5 showed limited effects on the decrease in the E-cadherin abundance and stress fiber formation by TGF-β, gene ontology analysis showed that genes encoding extracellular proteins were most strongly down-regulated by Etv4 and Etv5 siRNAs. Accordingly, TGF-β-induced expression of Mmp13 and cell invasiveness were suppressed by Etv4 and Etv5 siRNAs, which were accompanied by the reduced chromatin accessibility at an enhancer region of Mmp13 gene. These findings suggest a mechanism of transcriptional regulation during Ras- and TGF-β-induced EMT that involves alterations of accessible chromatin, which are partly regulated by Etv4 and Etv5.

The sit-to-stand motion is a common movement in daily life and understanding the mechanism of the sit-to-stand motion is important. Our previous study shows that four muscle synergies can characterize the sit-to-stand motion, and they have specific roles, such as upper body flexion, rising from a chair, body extension, and posture stabilization. The time-varying weight of these synergies are changed to achieve adaptive movement. However, the relationship between sensory input and the activation of the muscle synergies is not completely understood. In this paper, we aim to clarify how vestibular and visual inputs affect the muscle synergy in sit-to-stand motion. To address this, we conducted experiments as follows. Muscle activity, body kinematics, and ground reaction force were measured for the sit-to-stand motion under three different conditions: control, visual-disturbance, and vestibular-disturbance conditions. Under the control condition, the participants stood without any intervention. Under the visual-disturbance condition, the participants wore convex lens glasses and performed the sit-to-stand motion in a dark room. Under the vestibular-disturbance condition, a caloric test was performed. Muscle synergies were calculated for these three conditions using non-negative matrix factorization. We examined whether the same four muscle synergies were employed under each condition, and the changes in the time-varying coefficients were determined. These experiments were conducted on seven healthy, young participants. It was found that four muscle synergies could explain the muscle activity in the sit-to-stand motion under the three conditions. However, there were significant differences in the time-varying weight coefficients. When the visual input was disturbed, a larger amplitude was found for the muscle synergy that activated mostly in the final posture stabilization phase of the sit-to-stand motion. Under vestibular-disturbance condition, a longer activation was observed for the synergies that extended the entire body and led to posture stabilization. The results implied that during human sit-to-stand motion, visual input has less contribution to alter or correct activation of muscle synergies until the last phase. On the other hand, duration of muscle synergies after the buttocks leave are prolonged in order to adapt to the unstable condition in which sense of verticality is decreased under vestibular-disturbance.

This paper presents a new method to detect lane changes of other vehicles automatically. The main contribution of this work is to propose a new feature using a potential field that changes the distribution depending on the relative number of adjacent vehicles. Previous researches have considered only some of limited situations, for example, that the preceding vehicle is slower than the target vehicle. Therefore, degradation of the detection performance can occur under conditions that were not considered. On the other hand, the new feature we propose is able to describe general lane-changing situations by applying a dynamic potential model. We trained an estimation model and evaluated the performance using a traffic dataset with over 900 lane changes. It was confirmed that the proposed method outperforms previous methods in terms of both accuracy and early detection.

In this paper, we propose a novel method to estimate a goal of surround vehicles to perform a lane change at a merging section. Recently, autonomous driving and advance driver-assistance systems are attracting great attention as a solution to substitute human drivers and to decrease accident rates. For example, a warning system to alert a lane change performed by surrounding vehicles to the front space of the host vehicle can be considered. If it is possible to forecast the intention of the interrupting vehicle in advance, the host driver can easily respond to the lane change with sufficient reaction time. This paper assumes a mandatory situation where two lanes are merged. The proposed method assesses the interaction between the lane-changing vehicle and the host vehicle on the mainstream lane. Then, the lane-change goal is estimated based on the interaction under the assumption that the lane-changing driver decides to minimize the collision risk. The proposed method applies the dynamic potential field method, which changes the distribution according to the relative speed and distance between two subject vehicles, to assess the interaction. The performance of goal estimation is evaluated using real traffic data, and it is demonstrated that the estimation can be successfully performed by the proposed method.

Abstract Rheumatoid arthritis (RA) is a chronic inflammatory synovitis that leads to the destruction of bone and cartilage. The receptor for advanced glycation end products (RAGE) is a multiligand membrane-bound receptor for high-mobility group box-1 (HMGB1) associated with development of RA by inducing production of proinflammatory cytokines such as tumor necrosis factor (TNF)-α, interleukin (IL)-1 and IL-6. We developed a bone-targeting therapeutic agent by tagging acidic oligopeptide to a nonmem-brane-bound form of RAGE (endogenous secretory RAGE [esRAGE]) functioning as a decoy receptor. We assessed its tissue distribution and therapeutic effectiveness in a murine model of collagen-induced arthritis (CIA). Acidic oligopeptide-tagged esRAGE (D6-esRAGE) was localized to mineralized region in bone, resulting in the prolonged retention of more than 1 wk. Weekly administration of D6-esRAGE with a dose of 1 mg/kg to RA model mice significantly ameliorated inflammatory arthritis, synovial hyperplasia, cartilage destruction and bone destruction, while untagged esRAGE showed little effectiveness. Moreover, D6-esRAGE reduced plasma levels of proinflammatory cytokines including TNF-α, IL-1 and IL-6, while esRAGE reduced the levels of IL-1 and IL-6 to a lesser extent, suggesting that production of IL-1 and IL-6 reduced along the blockade of HMGB1 receptor downstream signals by D6-esRAGE could be attributed to remission of CIA. These findings indicate that D6-esRAGE enhances drug delivery to bone, leading to rescue of clinical and pathological lesions in murine CIA.

Robots are expected to be operated in environments where they coexist with humans, such as shopping malls and offices. Both the safety and efficiency of a robot are necessary in such environments. To achieve this, pedestrian behaviour should be accurately predicted. However, the behaviour is uncertain and cannot be easily predicted. This paper proposes a probabilistic method of determining pedestrian trajectory based on an estimation of pedestrian behaviour patterns. The proposed method focuses on the specific behaviour of pedestrians around the robot. The proposed model classifies the behaviours of pedestrians into definite patterns. The behaviour patterns, distribution of the positions of the pedestrians, and the direction of each behaviour pattern are determined by learning through observation. The behaviour pattern of a pedestrian can be estimated correctly by a likelihood calculation. A robot decides to move with an emphasis on either safety or efficiency depending on the result of the pattern estimation. If the pedestrian trajectory follows a known behaviour pattern, the robot would move with an emphasis on efficiency because the pedestrian trajectory can be predicted. Otherwise, the robot would move with an emphasis on safety because the behaviour of the pedestrian cannot be predicted. Experimental results show that robots can move efficiently and safely when passing by a pedestrian by applying the proposed method.