Application of Machine Learning Techniques in Aquaculture

ABSTRACT: In this paper we present applications of different machine learning algorithms in aquaculture. Machine learning algorithms learn models from historical data. In aquaculture historical data are obtained from farm practices, yields, and environmental data sources. Associations between these different variables can be obtained by applying machine learning algorithms to historical data. In this paper we present applications of different machine learning algorithms in aquaculture applications

Immunoglobulin motions and antigen binding effects examined by elastic network models

Immunoglobulin motions are evaluated using Normal Mode Analysis with Elastic Network Models. By employing this approach, we learn about the important motions of the protein, for the domain motions and other internal motions, and see strong evidence of the dominance of the low frequency normal modes. We particularly investigate the CDR motions. The CDR loops tend to move with their attached domains. By finding internal distance changes, we determine which parts of the structure undergo more rigid body like motions and which parts encounter larger internal distance changes. It turns out that Fab undergoes a large extent of changes in its internal distances and the Fc part moves more rigidly. We also investigate the effects of sugar and antigen binding on the IgG structures. The antigen binding effect is highly significant in the CDR regions, since antigen binding seems to enhance the motions in this region, while the sugar effects are more localized to the Fc region. By performing Principal Component Analysis on the numerous available Fab structures, we gather information about motions apparent in this ensemble of conformations, the correspondence of Principal Components to the Normal Modes of the Elastic Network Models, the residue fluctuations in the first few Principal Components and other important information about Immunoglobulin Dynamics

Site fertility drives temporal turnover of vegetation at high latitudes

Experimental evidence shows that site fertility is a key modulator underlying plant community changes under climate change. Communities on fertile sites, with species having fast dynamics, have been found to react more strongly to climate change than communities on infertile sites with slow dynamics. However, it is still unclear whether this generally applies to high-latitude plant communities in natural environments at broad spatial scales. We tested a hypothesis that vegetation of fertile sites experiences greater changes over several decades and thus would be more responsive under contemporary climate change compared to infertile sites that are expected to show more resistance. We resurveyed understorey communities (vascular plants, bryophytes, and lichens) of four infertile and four fertile forest sites along a latitudinal bioclimatic gradient. Sites had remained outside direct human disturbance. We analyzed the magnitude of temporal community turnover, changes in the abundances of plant morphological groups and strategy classes, and changes in species diversity. In agreement with our hypothesis, temporal turnover of communities was consistently greater on fertile sites compared to infertile sites. However, our results suggest that the larger turnover of fertile communities is not primarily related to the direct effects of climatic warming. Furthermore, community changes in both fertile and infertile sites showed remarkable variation in terms of shares of plant functional groups and strategy classes and measures of species diversity. This further emphasizes the essential role of baseline environmental conditions and nonclimatic drivers underlying vegetation changes. Our results show that site fertility is a key determinant of the overall rate of high-latitude vegetation changes but the composition of plant communities in different ecological contexts is variously impacted by nonclimatic drivers over time.Peer reviewe

The galactic scale impact of feedback from individual stars

Feedback from stars is essential for the formation and evolution of galaxies. It is an energy source that drives gas motions and chemically enriches the galaxy by supplying metals. Without this stellar feedback, numerical galaxy simulations result in galaxies with little resemblance to those observed in our Universe. Modern galaxy simulations frequently reach a mass resolution of a few tens of solar masses. Such high-resolution warrants models incorporating individual stars. These models enable a detailed treatment of when and where stars inject feedback. In this thesis, I present such a model and provide a series of papers exploring physical mechanisms unlocked by this model.In paper I, we investigate how runaway stars affect the galactic winds driven by stellar feedback in Milky Way-like galaxies. Massive runaway stars can venture to places where these short-lived stars are otherwise not found (e.g., between spiral arms). In these regions of diffuse gas, supernovae can efficiently incorporate energy into large volumes of gas, thereby boosting the gas outflow rate of the galaxy. Furthermore, the star formation rate is not significantly affected since parts of the feedback budget move away from star-forming gas. The result is a ten-fold boost in the mass loading factor.Paper II is a follow-up investigation of a surprising signal of star formation in spiral galaxies with runaway stars, found in Paper I. The signal is produced by the rapid migration of runaway stars to the galaxy's outskirts. Via direct comparison to observational data, we find that this explains faint far-ultraviolet radiation detected outside the optical radius of nearby spiral galaxies. This radiation manifests as a trend in the star formation relation with a slope similar to one produced by runaway stars escaping to these regions.In paper III, the star-by-star model is upgraded with a more advanced model for feedback and runaway stars. We showcase this model with a suit of simulations of isolated dwarf galaxies, testing a range of parameters for the natal velocity model of individual stars responsible for incorporating runaway stars. In stark contrast to the Milky Way-like galaxy, we find runaway stars play little to no role in determining outflows in dwarf galaxies. We discuss several possible reasons for the different effects in small and large galaxies

A comparison of statistical machine learning methods in heartbeat detection and classification

In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms

行列技術を用いた動的ネットワーク可視化

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 大澤 幸生, 東京大学教授 青山 和浩, 東京大学教授 和泉 潔, 東京大学准教授 森 純一郎, 首都大学東京教授 高間 康史University of Tokyo(東京大学

Topology Reconstruction of Dynamical Networks via Constrained Lyapunov Equations

The network structure (or topology) of a dynamical network is often unavailable or uncertain. Hence, we consider the problem of network reconstruction. Network reconstruction aims at inferring the topology of a dynamical network using measurements obtained from the network. In this technical note we define the notion of solvability of the network reconstruction problem. Subsequently, we provide necessary and sufficient conditions under which the network reconstruction problem is solvable. Finally, using constrained Lyapunov equations, we establish novel network reconstruction algorithms, applicable to general dynamical networks. We also provide specialized algorithms for specific network dynamics, such as the well-known consensus and adjacency dynamics.Comment: 8 page