BACKGROUND: Pathway enrichment analysis is a crucial method for the biological interpretation of metabolomic data by identifying associations between altered metabolites and biological pathways. However, such traditional approaches often rely on a limited set of predefined metabolic pathways, resulting in a low likelihood of discovering pathways associated with a given metabolic profile. To overcome this limitation, we extended our previously developed iDMET methodology to incorporate a broader range of metabolite sets, including those derived from differential metabolomic profiles. This enhanced approach, termed iDMET+, significantly expands dataset diversity and size, increasing the likelihood of discovering associated metabolite sets for a given metabolic profile, thereby enables more biological insights to be obtained from the metabolic profile. RESULTS: We validated iDMET+ through case studies on three diseases: clear cell renal cell carcinoma, colorectal cancer, and small cell lung cancer. First, using a clear cell renal cell carcinoma study as input, iDMET+ correctly identified another study of the same disease that involved metabolomic analysis. This pair of studies was identified as relevant in our previous iDMET results, showing the consistency between iDMET+ and iDMET. Second, using the metabolomic profile of colorectal cancer as input, iDMET+ identified not only another metabolomic study of the same cancer but, surprisingly, also metabolomic studies on prostate cancer and a high-fat diet. These studies focused on MYC-driven metabolic reprogramming, which was also a major focus of the input study. In both case studies, related studies were enriched because the differential metabolomic profiles of directly associated studies were part of the metabolite set. In contrast, the small cell lung cancer study highlighted limitations in dataset coverage—the absence of directly relevant differential metabolomic profiles resulted in fewer enriched metabolite sets. Nevertheless, the analysis of commonly altered metabolites still yielded some meaningful results. Metabolite alterations associated with inhibition of the purine salvage pathway were observed, suggesting potential involvement in tumor metabolic reprogramming. CONCLUSIONS: These results demonstrate that iDMET+ offers broader biologically relevant information than the conventional pathway-based approaches and has the potential to uncover biologically significant findings by searching across diverse datasets. This work also identifies areas of improvements for iDMET+.

Infantile epileptic spasm syndrome (IESS) is developmental epileptic encephalopathy occurring in childhood. It is often difficult to predict long-term seizure outcomes at the time of onset, particularly in patients with unknown etiology. Predicting long-term seizure outcomes from electroencephalograms (EEG) at onset may be helpful for decision-making on therapeutic strategy by clinicians and families. This study included twenty-two patients with IESS of unknown etiology with a median follow-up period of 11 years (range 5-14 years). We categorized patients diagnosed with IESS of unknown etiology into good and poor outcome groups according to the presence or absence of seizures at the time of the last follow-up. Fifteen patients were categorized into the good outcome group, and the rest into the poor outcome group. A machine learning (ML) model was developed using scale EEG data during sleep to identify patients in the poor outcome group. A self-attention autoencoder (SA-AE), which is an anomaly detection method for time-series data, was adopted because the numbers of patients in the poor and good outcome groups were unbalanced. The ML model achieved a sensitivity of $1.00\pm 0.05$, specificity of $0.88\pm 0.11$, and accuracy of $0.95\pm 0.06$. In addition, the AUROC was $0.91\pm 0.09$. Thus, the SA-AE model under development enabled highly accurate prediction of long-term prognosis in patients with IESS of unknown etiology. We confirmed that trained attention among the EEG channels could be explained from the viewpoint of pathophysiology of IESS. This study contributes to the determination of appropriate treatment for patients with IESS of unknown etiology.

BACKGROUND: Comprehensive metabolomic analyses have been conducted in various institutes and a large amount of metabolomic data are now publicly available. To help fully exploit such data and facilitate their interpretation, metabolomic data obtained from different facilities and different samples should be integrated and compared. However, large-scale integration of such data for biological discovery is challenging given that they are obtained from various types of sample at different facilities and by different measurement techniques, and the target metabolites and sensitivities to detect them also differ from study to study. RESULTS: We developed iDMET, a network-based approach to integrate metabolomic data from different studies based on the differential metabolomic profiles between two groups, instead of the metabolite profiles themselves. As an application, we collected cancer metabolomic data from 27 previously published studies and integrated them using iDMET. A pair of metabolomic changes observed in the same disease from two studies were successfully connected in the network, and a new association between two drugs that may have similar effects on the metabolic reactions was discovered. CONCLUSIONS: We believe that iDMET is an efficient tool for integrating heterogeneous metabolomic data and discovering novel relationships between biological phenomena.

BACKGROUND: Principal component analysis (PCA) has been widely used to visualize high-dimensional metabolomic data in a two- or three-dimensional subspace. In metabolomics, some metabolites (e.g., the top 10 metabolites) have been subjectively selected when using factor loading in PCA, and biological inferences are made for these metabolites. However, this approach may lead to biased biological inferences because these metabolites are not objectively selected with statistical criteria. RESULTS: We propose a statistical procedure that selects metabolites with statistical hypothesis testing of the factor loading in PCA and makes biological inferences about these significant metabolites with a metabolite set enrichment analysis (MSEA). This procedure depends on the fact that the eigenvector in PCA for autoscaled data is proportional to the correlation coefficient between the PC score and each metabolite level. We applied this approach to two sets of metabolomic data from mouse liver samples: 136 of 282 metabolites in the first case study and 66 of 275 metabolites in the second case study were statistically significant. This result suggests that to set the number of metabolites before the analysis is inappropriate because the number of significant metabolites differs in each study when factor loading is used in PCA. Moreover, when an MSEA of these significant metabolites was performed, significant metabolic pathways were detected, which were acceptable in terms of previous biological knowledge. CONCLUSIONS: It is essential to select metabolites statistically to make unbiased biological inferences from metabolomic data when using factor loading in PCA. We propose a statistical procedure to select metabolites with statistical hypothesis testing of the factor loading in PCA, and to draw biological inferences about these significant metabolites with MSEA. We have developed an R package "mseapca" to facilitate this approach. The "mseapca" package is publicly available at the CRAN website.

This video demonstrates a mixed reality (MR) environment which is constructed for development of autonomous behaviors of robots. Many kinds of functions are required to be integrated for realizing an autonomous behavior. For example, autonomous navigation of humanoid robots needs functions, such as, recognition of environment, localization and mapping, path planning, gait planning, dynamically stable biped walking pattern generation, and sensor feedback stabilization of walking. Technologies to realize each function are well investigated by many research works. However, another effort is required for constructing an autonomous behavior by integrating those functions. We demonstrate a MR environment in which internal status of a robot, such as, sensor status, recognition results, planning results, and motion control parameters, can be projected to the environment and its body. We can understand intuitively how each function works as a part of total system in the real environment by using the proposed system, and it helps solving the integration problems. The overview of the system, projection of each internal status, and the application to an autonomous locomotion experiment are presented in the video clip.

One of the problems in developing a humanoid robot is caused by the fact that intermediate results, such as what the robot perceives the environment, and how it plans its moving path are hard to be observed online in the physical environment. What developers can see is only the behavior. Therefore, they usually investigate logged data afterwards, to analyze how well each component worked, or which component was wrong in the total system. In this paper, we present a novel environment for robot development, in which intermediate results of the system are overlaid on physical space using mixed reality technology. Real-time observation enables the developers to see intuitively, in what situation the specific intermediate results are generated, and to understand how results of a component affected the total system. This feature makes the development efficient and precise. This environment also gives a human-robot interface that shows the robot internal state intuitively, not only in development, but also in operation.

We propose a hybrid camera pose estimation method using an inclination sensor value and correspondence-free line segments. In this method, possible azimuths of the camera pose are hypothesized by a voting method under an inclination constraint. Then some camera positions for each possible azimuth are calculated based on the detected line segments that affirmatively voted for the azimuth. Finally, the most consistent one is selected as the camera pose out of the multiple sets of the camera positions and azimuths. Unlike many other tracking methods, our method does not use past information but rather estimates the camera pose using only present information. This feature is useful for an initialization measure of registration in augmented reality (AR) systems. This paper describes the details of the method and shows its effectiveness with experiments in which the method is actually used in an AR application.

In this paper, we proposed a robot self position identification method by active sound localization. This method can be used for autonomous security robots working in room environments. A system using a AIBO robot equipped with two microphones and wireless network is constructed and is used for position identification experiments. Arrival time differences to the microphones of robot are used as localization cues. To overcome the ambiguity of front-back confusion, a three-head position measurement method was proposed. The robot position can be identified by the intersection of circles restricted by the azimuth differences to different speaker pairs. By localizing three or four speakers as sound beacons positioned on known locations, the robot can identify its self position with an average error of about 7 cm in a 2.5times3.0 m2working space. A robot navigation experiment was conducted to demonstrate the effectiveness of the position identification system.

This paper describes an evaluation system using an industrial robot, constructed for the purpose of evaluating registration technology for Mixed Reality. In this evaluation system, the tip of the robot arm plays the role of the user's head, where a head- mounted display is mounted. By using an industrial robot, we can obtain the ground truth of the camera pose with a high level of accuracy and robustness. Additionally, we have the ability to play back the same specified operations repeatedly under identical conditions. In addition to the system implementation, we propose evaluation methods for motion robustness, relative orientation robustness, relative distance robustness, jitter, and an overall evaluation. We verify the validity of this system through some experiments.

Optical packet switching network is expected to be the most hopeful candidate for future high-speed internetworking since it is characteristic of large capacity and fine granularity. However, due to lack of effective memory in optical domain for buffering, packet loss event happens even when the load of network is relatively low. This phenomenon overthrows the prerequisite assumption of TCP, which regards packet loss event as the sign of network congestion and decreases the sending rate accordingly. As a result, unnecessary slow down will adversely affect the throughput of optical packet switching network if TCP is adopted. In this paper, we propose a novel flow control algorithm which adaptively controls packet size together with window size in order to improve throughput for optical packet switching network. We evaluate it by simulation on throughput improvement and other properties including convergence and fairness

This work describes a new hybrid vision-based registration method with an inclination sensor. In the method, a camera is tracked by solving linear equations under inclination constraint. Linear operations are faster than the nonlinear optimization process, but the output does not usually satisfy the orthonormality constraint. On the contrary, the proposed method calculates the camera position and azimuth directly, thus the result satisfies the orthonormality constraint. Many hybrid approaches using inertia sensors have been proposed for AR/MR. However, such methods still depend on vision-based methods in initialization processes. On the other hand, the proposed method is totally hybrid in that the inclination measured by the sensor is always incorporated in pose calculation process as well as vision information. The method can be used in initialization process of conventional hybrid methods as well as it can be used as an independent registration method. The proposed method can be applied to not only inside-out-style camera tracking but also outside-in-style object tracking.

This paper describes a calibration method of markers which are used for registration in MR applications. There have been many vision-based approaches proposed as registration methods in MR. When multiple markers are utilized in a vision-based method, it is necessary that the geometric information of the marker arrangement such as their positions and orientations be known in advance. In this paper, we propose a hybrid method combining the "bundle adjustment method," which is a photogrammetric technique that calculates the geometric information from a set of images, with some constraints on the marker arrangement which are obtained a priori (e.g. the multiple markers are located on a single plane). After considering marker arrangements seen in many MR systems, we summarize some constraints seen in these arrangements. Then, we explain the basic framework of this method as well as a solution method under some practical constraints. We further describe several experiments and their results in order to show the effectiveness of this method.

This paper describes a new head tracking method utilizing a gyroscope mounted on a head-mounted display (HMD) and a bird's-eye view camera that observes the HMD from a fixed third-person viewpoint. Furthermore, we propose an extension of this method to hybrid registration, combining it with user's view cameras. The HMD is equipped with a gyroscope and a marker. The gyroscope measures the orientation of the user s view camera so that the number of pose parameters to be solved can be reduced. The other parameters are to be estimated by the bird's-eye view camera that observes the marker. This method is an improvement over the conventional outside-in-style vision-based tracker method, which only uses visual information. Hence, it can be thought of as an alternative to a physical head tracker such as a magnetic sensor and to an inside-out-style vision-based tracker. In addition to theoretical discussions, this paper demonstrates the effectiveness of our methods by experiments. We also propose methods for calibrating the gyroscope and the marker on HMD, which are essential in implementing the tracking method.

This paper presents a mixed reality (MR) system with tangible interface as well as visual fusion in an MR space. The sense of touch is given by physical objects on which computer generated imagery is accurately registered and superimposed. The proposed approach is especially useful in industrial design where digital mockups and physical mockups are thoroughly utilized.

This paper describes new vision-based registration methods utilizing not only cameras on a user's head-mounted display but also a bird's-eye view camera that observes the user from an objective viewpoint. Two new methods, the line constraint method (LCM) and global error minimization method (GEM), are proposed. The former method reduces the number of unknown parameters concerning the user's viewpoint by restricting it to be on the line of sight from the bird's-eye view. The other method minimizes the sum of errors, which is the sum of the distance between the fiducials on the view and the calculated positions of them based on the current viewing parameters, for both the user's view and the bird's-eye view. The methods proposed here reduce the number of points that should be observed from the user's viewpoint for registration, thus improving the stability. In addition to theoretical discussions, this paper demonstrates the effectiveness of our methods by experiments in comparison with methods that use only a user's view camera or a bird's-eye view camera.

This paper describes a platform package, called "MR Platform," which we have been implementing for research and development of augmented reality technology and applications. This package includes a parallax-less stereo video see-through HMD and a software development kit (SDK) for a Linux PC environment. The SDK is composed of a C++ class library for making runtime MR applications and related utilities such as a camera calibration tool. By using the SDK, the following functions are available: capturing video, handling a six degree-of-freedom (DOF) sensor, image processing such as color detection, estimating head position and orientation, displaying the real world image, and calibrating sensor placement and camera parameters of two cameras mounted on the HMD.

An image processing workstation called the vision and image engineering workstation, or VIEW-Station, which as developed in order to merge the state-of-the-art software environment of a UNIX workstation and the computing power of the fast image processor, is described. The software system of the VIEW-Station is constructed hierarchically in order to allow device independence in porting to various hardware configurations and to achieve flexibility in developing application systems. An image computing language called V-Sugar, which provides specific data types and a functional notation, allowing image processing algorithms to be expressed simply, is described. The image memory management mechanism, V-server, and the window system extensions, VIEW-Windows, are described. They are integrated to provide an independent basis to hardware configurations.>