Cultural biology of the blue mussel, Mytilus edulis (Linnaeus, 1758) in inland saline water in Western Australia

This research investigated the technical feasibility of culturing the blue mussel Mytilus edulis in potassium fortified inland saline water. The results showed that the blue mussels in 100% K+ fortified inland saline water can be successfully cultured. One hundred fortification of inland saline water also improves the settlement rate of mussel larvae and reduces the deformities during early larval life. The mussels are isosmotic at 27 ppt in inland saline water

Bioinformatic tools for analyzing epigenomic profiling data

Epigenetik, die Erforschung der biologischen Information in Genomen ausserhalb der DNA Sequenz, hat durch die rasche Entwicklung der Hochdurchsatz-Techniken besonders viele Impulse bekommen. Deshalb spielt die Bioinformatik eine wichtige Rolle bei der Analyse der ausserordentlich grossen Datenmengen und der Formulierung biologischer Hypothesen in der Epigenetik. DNA Methylierung ist ein wichtiger epigenetischer Parameter in der normalen und pathologischen Entwicklungsbiologie. Genomweite DNA Methylierungsprofile werden hauptsächlich durch Bisulfit-Konversion genomischer DNA erstellt, bei der unmethyliertes Cytosin (C) in Thymin (T) umgewandelt wird, gefolgt von Hochdurchsatz-Sequenzierung (BS-Seq). Die Umwandlung von C zu T erschwert die Zuordnung der Einzelsequenzen zum Referenzgenom in mehrerer Hinsicht. Ausserdem kann mit der herkömmlichen Technik die Heterogenität der DNA Methylierung in Material aus mehreren Zellen oder Geweben nicht berücksichtigt werden. Das beeinträchtigt die Genauigkeit bei der Bestimmung der genomischen Methylierungsmuster. Deshalb sind neue bioinformatische Methoden erforderlich, um zellspezifische DNA Methylierung zu erkennen. Aufgrund der schnell wachsenden Datenmengen ist die gleichzeitige Erfassung mehrerer epigenetischer Parameter in Form von Chromatineigenschaften in verschiedenen Proben, Bedingungen oder Organismen eine weitere Herausforderung und ein wenig bearbeitetes Gebiet der Bioinformatik, jedoch Voraussetzung zur Entdeckung eines chromatin-basierten epigenetischen Codes. Vergleichende bioinformatische Ansätze werden hierbei durch unterschiedliche Verteilung und/oder Spannweite der Parameter erschwert. In dieser Dissertation stelle ich von mir entwickelte bioinformatische Methoden zu diesen Themenbereichen vor und zeige deren Anwendung auf Daten aus dem Modellorganismus Arabidopsis thaliana. Als erstes habe ich ein neues und hochauflösendes Verfahren zur Analyse von BS-Seq Daten entwickelt, welches auf dem „Smith-Waterman local alignment“ Prinzip beruht. Zweitens habe ich einen effizienten Algorithmus konzipiert, um den Grad der Heterogenität in BS-Seq Daten zu bestimmen. Drittens habe ich eine Methode entworfen, mit der man zahlreiche epigenetische Parameter und deren genomweite Profile zusammenfassen, vergleichen und optisch darstellen kann, um die weitere Analyse und Interpretation zu erleichtern.Epigenetics, investigating the biological information of genomes not only encoded in the DNA sequence, has become a hot topic boosted by rapid development of high-throughput technologies. In the light of that, bioinformatics plays an important role in analyzing the massive datasets to further examine the data and to formulate biological hypotheses. DNA methylation is one important epigenetic mark in developmental and disease bi- ology. One widely-used technique to profile genome-wide DNA methylation is based on bisulfite conversion of unmethylated cytosines (C) to thymines (T), followed by deep sequencing technology, called BS-Seq data. The C-T conversion raises a number of challenges in mapping the bisulfite-converted short reads to the reference genome. Besides, the current technology cannot consider the heterogeneity of DNA methylation from mixtures of cells. This affects the accuracy of estimating the DNA methylation patterns in the genome. Hence, new bioinformatics methods are required to estimate the cell-type specific DNA methylation. Integrating multiple datasets of profiling epigenetic/chromatin marks for many different samples, conditions and organisms is also an underdeveloped field in bioinformatics, given the rapid growth of biological data. It is essential for further studies to find epigenomic patterns like a chromatin-based epigenetic code. However, comparative bioinformatics procedure is difficult because of different distributions or different scales of the marks. In this thesis, I have developed bioinformatics tools and applied them to the model organism, Arabidopsis thaliana. First, I have implemented a new and sensitive analysis tool for analyzing BS-Seq data based on Smith-Waterman local alignment mapping. Second, I have developed an efficient algorithm to deal with heterogeneity in DNA methylation data derived from BS-Seq. Finally, I have suggested a method to integrate epigenomic signals from multiple genome-wide profiling data for further data mining purpose, e.g. epigenetic signature discovery

分子プログラミングにおける反応ネットワークの進化的合成フレームワーク

学位の種別: 課程博士審査委員会委員 : (主査)東京大学教授 相澤 清晴, 東京大学教授 伊庭 斉志, 東京大学教授 松浦 幹太, 東京大学准教授 山崎 俊彦, 東京大学准教授 小林 徹也, 東京大学講師 長谷川 禎彦University of Tokyo(東京大学

Laser Graphics in Augmented Reality Applications for Real- World Robot Deployment

Lasers are powerful light source. With their thin shafts of bright light and colours, laser beams can provide a dazzling display matching that of outdoor fireworks. With computer assistance, animated laser graphics can generate eye-catching images against a dark sky. Due to technology constraints, laser images are outlines without any interior fill or detail. On a more functional note, lasers assist in the alignment of components, during installation

TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars

Semantic segmentation is a common task in autonomous driving to understand the surrounding environment. Driveable Area Segmentation and Lane Detection are particularly important for safe and efficient navigation on the road. However, original semantic segmentation models are computationally expensive and require high-end hardware, which is not feasible for embedded systems in autonomous vehicles. This paper proposes a lightweight model for the driveable area and lane line segmentation. TwinLiteNet is designed cheaply but achieves accurate and efficient segmentation results. We evaluate TwinLiteNet on the BDD100K dataset and compare it with modern models. Experimental results show that our TwinLiteNet performs similarly to existing approaches, requiring significantly fewer computational resources. Specifically, TwinLiteNet achieves a mIoU score of 91.3% for the Drivable Area task and 31.08% IoU for the Lane Detection task with only 0.4 million parameters and achieves 415 FPS on GPU RTX A5000. Furthermore, TwinLiteNet can run in real-time on embedded devices with limited computing power, especially since it achieves 60FPS on Jetson Xavier NX, making it an ideal solution for self-driving vehicles. Code is available: url{https://github.com/chequanghuy/TwinLiteNet}.Comment: Accepted by MAPR 202

Experimental and Numerical Evaluation of Concentrically Loaded RC Columns Strengthening by Textile Reinforced Concrete Jacketing

Nowadays, Textile Reinforced Concrete (TRC) has become a very popular strengthening technique for concrete structures. This paper presents an investigation on the applicability of TRC for strengthening reinforced concrete column. Both experimental and numerical studies are conducted to evaluate the confinement effects of various TRC strengthening schemes. The experimental study is performed on a series of six reinforced concrete square columns tested to failure. Two of them were un-strengthened as references, the other four were strengthened by one or two layers of Carbon Textile Reinforced Concrete (CTRC). The results indicated that the application of carbon TRC enhanced the ductility and ultimate strength of the specimens. Failure of all strengthened columns was together with tensile rupture of textile reinforcements at the corners of column. Finite element models of the CTRC strengthened columns based on ATENA software package were developed and verified with the experimental results. The analytical results show that in the specimen corner areas, textile reinforcements are subjected to a 3D complicated stress state and this may be the cause of their premature failure

A low-cost deep-learning-based system for grading cashew nuts

Most of the cashew nuts in the world are produced in the developing countries. Hence, there is a need to have a low-cost system to automatically grade cashew nuts, especially in small-scale farms, to improve mechanization and automation in agriculture, helping reduce the price of the products. To address this issue, in this work we first propose a low-cost grading system for cashew nuts by using the off-the-shelf equipment. The most important but complicated part of the system is its “eye”, which is required to detect and classify the nuts into different grades. To this end, we propose to exploit advantages of both the YOLOv8 and Transformer models and combine them in one single model. More specifically, we develop a module called SC3T that can be employed to integrate into the backbone of the YOLOv8 architecture. In the SC3T module, a Transformer block is dexterously integrated into along with the C3TR module. More importantly, the classifier is not only efficient but also compact, which can be implemented in an embedded device of our developed cashew nut grading system. The proposed classifier, called the YOLOv8–Transformer model, can enable our developed grading system, through a low-cost camera, to correctly detect and accurately classify the cashew nuts into four quality grades. In our grading system, we also developed an actuation mechanism to efficiently sort the nuts according to the classification results, getting the products ready for packaging. To verify the effectiveness of the proposed classifier, we collected a dataset from our sorting system, and trained and tested the model. The obtained results demonstrate that our proposed approach outperforms all the baseline methods given the collected image data. © 2024 by the authors

The Transition in Goods Export Structure in the Northeast Region of Vietnam

The period 2005-2015 is an important stage in the preparation process of bringing Vietnam to basically become an industrialized country by 2020. The economic development of the Northeast with an open and export-oriented economy requires an important role of export activities in the region's socio-economic development. Although it is affected by the global economic crisis, the export value of the region has increased significantly, the structure of exports over the years has positively changed. In more detailed, the rate of raw goods slightly reduced. However, the restructuring of the region's exports has not made a breakthrough and failed to create a suitable structure of export products and made full use of the region’s advantages and potentials. The slow change of exports structure will lead to resource depletion, ecological imbalance, and poor economic efficiency. Keywords: Structure restructuring, Import and export, Northeast of Vietnam, PRODY, export commodities

The role of green finance, eco-innovation, and creativity in the sustainable development goals of ASEAN countries

Recently, sustainable development has become a global requirement. Every country strives to achieve this essential goal, and this attracts the attention of researchers and policymakers. This study investigates the impact of green finance, eco-innovation, and creativity on the sustainable development goals in ASEAN countries. Using CUP-FM and CUP-BC techniques, the study examines the association between variables, and finds that green finance (such as green credit), renewable energy production, eco-innovation, and creativity, have positive associations with sustainable development goals. The control variable, economic growth, has a negative association with sustainable development goals. Based on the evidence, the ASEAN region must increase the quantity of green bonds as a part of green finance. This financial measure would guarantee adequate returns for private investors

Fabrication of silver-nanoparticles-embedded polymer masterbatchs with excellent antibacterial performance

In the present work, a versatile and effective synthesis method of the silver-nanoparticles-embedded polyethylene (PE)-based polymer masterbatchs was demonstrated. Antibacterial investigations revealed that the nano-silver masterbatchs consisting of oleate capped silver nanoparticles dispersed in PE polymer matrix exhibited excellent antibacterial performance against Gram-negative Escherichia Coli (E.coli) and Staphylococcus aureus (S. aureus) bacteria.  A complete inhibition in bacteria growth was found at a silver nanoparticles concentration as low as 600 ppm. The origin of bactericidal effect and interaction mechanism of the stabilized silver nanoparticles with the Gram-negative E. coli and Gram-positive S. aureus bacteria can be understood in the light of electron microscopic observation. These advances make the synthesized nano-silver masterbatchs ideal for mass production of effectively antibacterial green products in medical, biological and industrial sectors. The type of polymer resin and silver concentration can be adjusted depending on the application area