SUMMARY: A-liner is a flexible command-line tool for linear visualization of genome-scale sequence alignments, supporting outputs from multiple aligners and integrated visualization of annotations, highlights, quantitative tracks, and coordinate scales. It is applicable to a wide range of organisms, from bacteria to large eukaryotic genomes, and facilitates efficient generation of publication-ready comparative genome visualizations. AVAILABILITY AND IMPLEMENTATION: The source code and example output files for a-liner are available in the GitHub repository: https://github.com/mokuno3430/a-liner. A-liner v1.1.0 has been archived on Zenodo at https://doi.org/10.5281/zenodo.19702001.

BACKGROUND: Dot-plots, along with linear comparisons, are fundamental visualization methods in genome comparisons, widely used for analyzing structural variations, repeat regions, and sequence similarities. However, existing tools often have limitations in visualization flexibility, particularly requiring the concatenation of multiple sequences into a single continuous axis. This constraint can make it difficult to apply highlights or user-defined grid lines effectively, reducing interpretability in comparative genomic analyses. RESULTS: We developed blastn2dotplots, a Python 3-based tool that utilizes the Matplotlib library to generate customizable dot-plots from local blastn results. Unlike traditional approaches, blastn2dotplots treats each alignment as a separate subplot, allowing for independent axis labeling, adjustable spacing between plots, and enhanced visualization flexibility. Users can highlight specific regions of interest, apply custom grid lines, and tailor the display to suit different genomic analyses. This tool is particularly useful for chromosomal structure analyses, detection of horizontal gene transfer events, and visualization of repetitive elements, offering an intuitive and adaptable framework for sequence comparison. CONCLUSIONS: By addressing key limitations of existing dot-plot visualization tools, blastn2dotplots enhances the clarity and flexibility of comparative genomic analyses. Its ability to handle multiple alignments separately while preserving independent axis control and customization options makes it a valuable resource for a wide range of genomic studies. This tool provides a novel and effective solution for researchers needing precise and adaptable visualization of sequence alignments, thereby maximizing the potential of dot-plots in bioinformatics.

It is common to investigate the content of a box or bottle by shaking it. The authors have investigated virtual realization of the shaking interaction using a haptic device that can present inertial force. This paper reports experiments on the discrimination and estimation of content's weight through the shaking interaction. In an experiment, the characteristics of recognition by the subject were evaluated by comparing the real box with solid content and the virtual box with a simulation model. Further, evaluation with liquid content was conducted. The result proved that the device can present solid content in a similar accuracy to the real box. Moreover, it was suggested that the motion of the gravity center of the box has a significant effect on the recognition of the content weight.

This paper addresses the problem of dealing with a collection of negative training documents which is suitable for relatively small number of positive documents, and presents a method for eliminating the need for manually collecting negative training documents based on supervised machine learning techniques. We applied an error correction technique to the results of negative training data obtained by the Positive Example Based Learning (PEBL). Moreover, we used a boosting technique to learn a set of negative data to train classifiers. The results using Japanese newspaper documents showed that the method contributes for reducing the cost of manual collection of negative training documents.

This paper considers an application of cluster validity indexes to automated judgment in Rock-Paper-Scissors (RPS) game. In the task, the number and the locations of hands in RPS pictures are first estimated by c-Means-type clustering in conjunction with cluster validity measures. Then, the judgment of winner/loser is performed considering the difference among compactness of clusters. After preprocessing of extracting hand region coordinates from RPS pictures, Hard c-Means (HCM) and Fuzzy c-Means (FCM) is applied in conjunction with cluster validity measures for searching for the optimal number and locations of hands, and the applicability of noise rejection is compared in several experiments. The compactness of clusters is also measured by the same concept used in cluster validity measures. The experimental results imply that FCM with noise rejection used with cluster validity measures works better in RPS game judgment without following complex image processing.

In order to achieve satisfactory verification for complicated low-power demands in green products, we propose a verification work reduction methodology. It consists of three step, namely virtual, direct actual, and actual model simulations. Virtual low-power simulation inserts low-power cells, such as isolators or level shifters, virtually and simulates logical behavior for design under test (DUT) based on user-defined power mode. Direct actual low-power simulation replaces behavior models without non-logical pins for some of modules with actual models with non-logical pins, which are Vdd and Gnd, and simulates DUT in mixed level. Actual low-power simulation simulates DUT by using actual models with non-logical pins for all cells and hard macros. We introduce techniques which classify the type of the bugs on which we focus at each verification step and prevent the concerned bugs from leaking to the latter verification step as much as possible. We applied our methodology to an actual chip and could reduce the total simulation period until tape-out by 38.8% and the total chip development period by 10%, compared with the conventional methodology.

A linear fuzzy clustering model based on Fuzzy c-Medoids (FCMdd) was proposed for extracting intrinsic local linear substructures from relational data. In the same way with Non-Euclidean Relational Fuzzy (NERF) c-Means, β-spread transformation was also proved to be useful for handling non Euclidean relational data in FCMdd-type linear clustering. In this paper, the FCMdd-type linear clustering model is further modified in order to handle incomplete data including missing values. In several numerical experiments, it is demonstrated that some pre-imputation strategies contribute to properly selecting representative medoids of each cluster.