The Development of a Computer Program to Simplify Complex Knot Diagrams using Global Moves

A mathematical knot is similar in concept to the everyday headphone cable, with the ends closed together to form a continuous loop. These knots are the subject of discussion in molecular biology, mathematics, physics, and chemistry. For example, some enzymes are known to interact with DNA by decreasing the self-entanglement of the genomic material (e.g. after replication). An important tool when it comes to the application of knot theory itself is the unraveling, or simplification, of knots. Every knot has many different two-dimensional representations, called diagrams – the entangled headphone cable can be arranged many different ways on a flat surface. Intuitively, the simpler it looks, the easier it is to identify the characteristics of the knot and untangle the cable. For this project, the number of ‘crossings’ in the cable (diagram) was used as the measure of complexity. Prior research used ‘transformations’ to change the appearance of a diagram without changing the knot itself (cutting the cable to get rid of crossings). However, each transformation involved only one to three crossings at a time. Even with a fast computer, using these transformations to reduce the number of crossings in a complicated diagram proved time-consuming. The purpose of this research project was to investigate whether an additional ‘global slide’ transformation, which usually involves a larger number of crossings, would simplify a diagram faster. The research resulted in the implementation of a computer program that can perform the various transformations on a given diagram. Data collected with the program indicated that, in some instances, the ‘global slide’ transformation lead to a further reduction in the number of crossings in diagrams where other programs would become “stuck.\u27\u27 The results demonstrated the potential of the ‘global’ transformations to simplify knot diagrams and suggested further pursuit of such transformations is justified

Untangling the seasonal dynamics of plant-pollinator communities

Ecological communities often show changes in populations and their interactions over time. To date, however, it has been challenging to effectively untangle the mechanisms shaping such dynamics. One approach that has yet to be fully explored is to treat the varying structure of empirical communities—i.e. their network of interactions—as time series. Here, we follow this approach by applying a network-comparison technique to study the seasonal dynamics of plant-pollinator networks. We find that the structure of these networks is extremely variable, where species constantly change how they interact with each other within seasons. Most importantly, we find the holistic dynamic of plants and pollinators to be remarkably coherent across years, allowing us to reveal general rules by which species first enter, then change their roles, and finally leave the networks. Overall, our results disentangle key aspects of species’ interaction turnover, phenology, and seasonal assembly/disassembly processes in empirical plant-pollinator communities.ISSN:2041-172

A User-Centered Active Learning Approach for Appliance Recognition

Smart homes offer new possibilities for energy management. One key enabler of these systems is the ability to monitor energy consumption at the appliance level. Existing approaches rely mainly on data from aggregated smart meter readings, but lack sufficient accuracy to recognize several appliances. Conversely, smart outlets are a suitable alternative since they can provide accurate electrical readings on individual appliances. Previous approaches for appliance recognition based on smart outlets use passive machine learning, which are deficient in the flexibility and scalability to work with highly heterogeneous appliances in smart homes. In this paper, we propose a stream-based active learning approach, called K-Active-Neighbors (KAN), to address the problem of appliance recognition in smart homes. KAN is an interactive framework in which the user is asked to label signatures of recently used appliances. Differently from previous work, we consider the realistic case in which the user is not always available to participate in the labeling process. Therefore, the system simultaneously learns the signatures and also the user willingness to interact with the system, in order to optimize the learning process. We develop an Arduino-based smart outlet to test our approach. Results show that, compared to previous solutions, KAN achieves higher accuracy in up to 41% less time

ヒト多能性幹細胞を用いた脳幹オルガノイドの開発

The brainstem is a posterior region of the brain, composed of three parts, midbrain, pons, and medulla oblongata. It is critical in controlling heartbeat, blood pressure, and respiration, all of which are life-sustaining functions, and therefore, damages to or disorders of the brainstem can be lethal. Brain organoids derived from human pluripotent stem cells (hPSCs) recapitulate the course of human brain development and are expected to be useful for medical research on central nervous system disorders. However, existing organoid models are limited in the extent hPSCs recapitulate human brain development and hence are not able to fully elucidate the diseases affecting various components of the brain such as brainstem. Here, we developed a method to generate human brainstem organoids (hBSOs), containing midbrain/hindbrain progenitors, noradrenergic and cholinergic neurons, dopaminergic neurons, and neural crest lineage cells. Single-cell RNA sequence (scRNA-seq) analysis, together with evidence from proteomics and electrophysiology, revealed that the cellular population in these organoids was similar to that of the human brainstem, which raises the possibility of making use of hBSOs in investigating central nervous system disorders affecting brainstem and in efficient drug screenings.博士（医学）・乙第1479号・令和2年12月24日Copyright © 2020 Eura, Matsui, Luginbühl, Matsubayashi, Nanaura, Shiota, Kinugawa, Iguchi, Kiriyama, Zheng, Kouno, Lan, Kongpracha, Wiriyasermkul, Sakaguchi, Nagata, Komeda, Morikawa, Kitayoshi, Jong, Kobashigawa, Nakanishi, Hasegawa, Saito, Shiromizu, Nishimura, Kasai, Takeda, Kobayashi, Inagaki, Tanaka, Makinodan, Kishimoto, Kuniyasu, Nagamori, Muotri, Shin, Sugie and Mori. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms

Brainstem Organoids From Human Pluripotent Stem Cells.

The brainstem is a posterior region of the brain, composed of three parts, midbrain, pons, and medulla oblongata. It is critical in controlling heartbeat, blood pressure, and respiration, all of which are life-sustaining functions, and therefore, damages to or disorders of the brainstem can be lethal. Brain organoids derived from human pluripotent stem cells (hPSCs) recapitulate the course of human brain development and are expected to be useful for medical research on central nervous system disorders. However, existing organoid models are limited in the extent hPSCs recapitulate human brain development and hence are not able to fully elucidate the diseases affecting various components of the brain such as brainstem. Here, we developed a method to generate human brainstem organoids (hBSOs), containing midbrain/hindbrain progenitors, noradrenergic and cholinergic neurons, dopaminergic neurons, and neural crest lineage cells. Single-cell RNA sequence (scRNA-seq) analysis, together with evidence from proteomics and electrophysiology, revealed that the cellular population in these organoids was similar to that of the human brainstem, which raises the possibility of making use of hBSOs in investigating central nervous system disorders affecting brainstem and in efficient drug screenings