Enzyme induction in Neurospora crassa

I. Studies on Nicotinamide Adenine Dinucleotide Glycohydrase (NADase)

NADase, like tyrosinase and L-amino acid oxidase, is not present in two day old cultures of wild type Neurospora, but it is coinduced with those two enzymes during starvation in phosphate buffer. The induction of NADase, like tyrosinase, is inhibited by puromycin. The induction of all three enzymes is inhibited by actinomycin D. These results suggest that NADase is synthesized de novo during induction as has been shown directly for tyrosinase. NADase induction differs in being inhibited by certain amino acids.

The tyrosinaseless mutant ty-1 contains a non-dialyzable, heat labile inhibitor of NADase. A new mutant, P110A, synthesizes NADase and L-amino acid oxidase while growing. A second strain, pe, fl;cot, makes NADase while growing. Both strains can be induced to make the other enzymes. These two strains prove that the control of these three enzymes is divisible. The strain P110A makes NADase even when grown in the presence of Tween 80. The synthesis of both NADase and L-amino acid oxidase by P110A is suppressed by complete medium. The theory of control of the synthesis of the enzymes is discussed.

II. Studies with EDTA

Neurospora tyrosinase contains copper but, unlike other phenol oxidases, this copper has never been removed reversibly. It was thought that the apo-enzyme might be made in vivo in the absence of copper. Therefore cultures were treated with EDTA to remove copper before the enzyme was induced. Although no apo-tyrosinase was detected, new information on the induction process was obtained.

A treatment of Neurospora with 0.5% EDTA pH 7, inhibits the subsequent induction during starvation in phosphate buffer of tyrosinase, L-amino acid oxidase and NADase. The inhibition of tyrosinase and L-amino acid oxidase induction is completely reversed by adding 5 x 10^-5M CaCl₂, 5 x 10^-4M CuSO₄, and a mixture of L-amino acids (2 x 10^-3M each) to the buffer. Tyrosinase induction is also fully restored by 5 x 10^-4M CaCl₂ and amino acids. As yet NADase has been only partially restored.

The copper probably acts by sequestering EDTA left in the mycelium and may be replaced by nickel. The EDTA apparently removes some calcium from the mycelium, which the added calcium replaces. Magnesium cannot replace calcium. The amino acids probably replace endogenous amino acids lost to the buffer after the EDTA treatment.

The EDTA treatment also increases permeability, thereby increasing the sensitivity of induction to inhibition by actinomycin D and allowing cell contents to be lost to the induction buffer. EDTA treatment also inhibits the uptake of exogenous amino acids and their incorporation into proteins.

The lag period that precedes the first appearance of tyrosinase is demonstrated to be a separate dynamic phase of induction. It requires oxygen. It is inhibited by EDTA, but can be completed after EDTA treatment in the presence of 5 x 10^-5M CaCl₂ alone, although no tyrosinase is synthesized under these conditions.

The time course of induction has an early exponential phase suggesting an autocatalytic mechanism of induction.

The mode of action of EDTA, the process of induction and the kinetics of induction are discussed.

Advanced Monte Carlo Simulation and Machine Learning for Frequency Domain Optical Coherence Tomography

Optical Coherence Tomography(OCT) is a popular, rapidly growing imaging technique with an increasing number of bio-medical applications due to its noninvasive nature. However, there are three major challenges in understanding and improving an OCT system: (1) Obtaining an OCT image is not easy. It either takes a real medical experiment or requires days of computer simulation. Without much data, it is difficult to study the physical processes underlying OCT imaging of different objects simply because there aren't many imaged objects. (2) Interpretation of an OCT image is also hard. This challenge is more profound than it appears. For instance, it would require a trained expert to tell from an OCT image of human skin whether there is a lesion or not. This is expensive in its own right, but even the expert cannot be sure about the exact size of the lesion or the width of the various skin layers. The take-away message is that analyzing an OCT image even from a high level would usually require a trained expert, and pixel-level interpretation is simply unrealistic. The reason is simple: we have OCT images but not their underlying ground-truth structure, so there is nothing to learn from. (3) The imaging depth of OCT is very limited (millimeter or sub-millimeter on human tissues). While OCT utilizes infrared light for illumination to stay noninvasive, the downside of this is that photons at such long wavelengths can only penetrate a limited depth into the tissue before getting back-scattered. To image a particular region of a tissue, photons first need to reach that region. As a result, OCT signals from deeper regions of the tissue are both weak (since few photons reached there) and distorted (due to multiple scatterings of the contributing photons). This fact alone makes OCT images very hard to interpret.

This thesis addresses the above challenges by successfully developing an advanced Monte Carlo simulation platform which is 10000 times faster than the state-of-the-art simulator in the literature, bringing down the simulation time from 360 hours to a single minute. This powerful simulation tool not only enables us to efficiently generate as many OCT images of objects with arbitrary structure and shape as we want on a common desktop computer, but it also provides us the underlying ground-truth of the simulated images at the same time because we dictate them at the beginning of the simulation. This is one of the key contributions of this thesis. What allows us to build such a powerful simulation tool includes a thorough understanding of the signal formation process, clever implementation of the importance sampling/photon splitting procedure, efficient use of a voxel-based mesh system in determining photon-mesh interception, and a parallel computation of different A-scans that consist a full OCT image, among other programming and mathematical tricks, which will be explained in detail later in the thesis.

Next we aim at the inverse problem: given an OCT image, predict/reconstruct its ground-truth structure on a pixel level. By solving this problem we would be able to interpret an OCT image completely and precisely without the help from a trained expert. It turns out that we can do much better. For simple structures we are able to reconstruct the ground-truth of an OCT image more than 98% correctly, and for more complicated structures (e.g., a multi-layered brain structure) we are looking at 93%. We achieved this through extensive uses of Machine Learning. The success of the Monte Carlo simulation already puts us in a great position by providing us with a great deal of data (effectively unlimited), in the form of (image, truth) pairs. Through a transformation of the high-dimensional response variable, we convert the learning task into a multi-output multi-class classification problem and a multi-output regression problem. We then build a hierarchy architecture of machine learning models (committee of experts) and train different parts of the architecture with specifically designed data sets. In prediction, an unseen OCT image first goes through a classification model to determine its structure (e.g., the number and the types of layers present in the image); then the image is handed to a regression model that is trained specifically for that particular structure to predict the length of the different layers and by doing so reconstruct the ground-truth of the image. We also demonstrate that ideas from Deep Learning can be useful to further improve the performance.

It is worth pointing out that solving the inverse problem automatically improves the imaging depth, since previously the lower half of an OCT image (i.e., greater depth) can be hardly seen but now becomes fully resolved. Interestingly, although OCT signals consisting the lower half of the image are weak, messy, and uninterpretable to human eyes, they still carry enough information which when fed into a well-trained machine learning model spits out precisely the true structure of the object being imaged. This is just another case where Artificial Intelligence (AI) outperforms human. To the best knowledge of the author, this thesis is not only a success but also the first attempt to reconstruct an OCT image at a pixel level. To even give a try on this kind of task, it would require fully annotated OCT images and a lot of them (hundreds or even thousands). This is clearly impossible without a powerful simulation tool like the one developed in this thesis.

Modelization and analysis of the electric arc in low voltage circuit breakers

246 p.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

A Real-Time Sliding Mode Control for a Wind Energy System Based on a Doubly Fed Induction Generator

In this paper, a real time sliding mode control scheme for a variable speed wind turbine that incorporates a doubly feed induction generator is described. In this design, the so-called vector control theory is applied, in order to simplify the system electrical equations. The proposed control scheme involves a low computational cost and therefore can be implemented in real-time applications using a low cost Digital Signal Processor (DSP). The stability analysis of the proposed sliding mode controller under disturbances and parameter uncertainties is provided using the Lyapunov stability theory. A new experimental platform has been designed and constructed in order to analyze the real-time performance of the proposed controller in a real system. Finally, the experimental validation carried out in the experimental platform shows; on the one hand that the proposed controller provides high-performance dynamic characteristics, and on the other hand that this scheme is robust with respect to the uncertainties that usually appear in the real systems.