A fluorescent oil detection device

On April 20th 2010, the largest offshore oil spill in U.S. history happened in the Gulf of Mexico. It is estimated total more than 4 million barrels oil spilled to Gulf of Mexico. More than two million gallons had been used. This had made the threat to coastal and sea ecosystem even greater and long term. Real-time monitoring is also a critical topic for oil spill response. In-situ monitoring devices are needed for rapid collection of real-time data. A new generation of instruments for spilled oil detection is reported in this paper. The main hypothesis in this research is that the sensitivity of the new instrument based on a micro-fluidic-optic chip can be higher than its conventional sized counterparts. The adoption of the micro-fluidic-optic chip helped to miniaturize the sample extraction unit and also to integrate the optical detection on the same chip substrate. Only the monitoring and displaying unit and the power supply were external to the micro-fluidic-optic chip. In this way, the micro-fluidic-optic chip is replaceable and can be disposable. This also helps to eliminate the need for cleaning the fluidic components, which may be very difficult in micro-scales because of surface tension and flow resistances. Liquid-Liquid extraction unit for sample pre-concentration and micro-optic components for fluorescence detection are the key microfluidic components and have been designed and fabricated on a single disposable chip. In the Liquid-Liquid extraction system, different designs are compared and electromagnetically actuated micro-valves and peristaltic pumps have been designed and fabricated to control the aqueous sample fluid and the organic phase solution. In the micro-optic detection system, different designs are compared and an out-of-plane lens was designed, fabricated, and integrated to enhance the measurement sensitivity. The experimental results of the integrated system have proved that the liquid-liquid extraction functioned very well and the overall measurement sensitivity of the system has been increased more than six hundred percent. An overall oil detection sensitivity blow 1ppm has been achieved. The research work presented in this dissertation has proved the feasibility of this novel oil detection instrument based on micro-fluidic-optic chip. This detection system may also be used for detection of other samples that can be measured based on fluoresce principles

Design and fabrication of micronozzles for drug delivery applications

Inhaled drug is an important drug delivery route. It is widely used in medications for respiratory disorders. However, it has some significant limitations, for examples, concentration of drug, output, and particles size, are not accurately controlled in most of the available drug delivery systems today. Various new devices and technologies continue to be developed and introduced. Vibrating orifice is one of them. The critical issue of vibrating orifice technology is the fabrication of micro-nozzles. Traditional technique to fabricate these nozzles is to use dry etching method, which tends to be expensive and inconvenient for mass production. Other two optional fabrication methods are presented in this thesis. The first one was to fabricate grooves using electro-spark etching to make array of micro-needles. This array of needles was then used as pressing mold to make nozzles. The size of these nozzles could be further manipulated using electroplating method. The second approach was to pattern array of micro-columns on a thick photoresist using UV lithography first, metals, such as nickel, are then plated to fill up the open area on the substrate. Finally the remaining SU-8 micro-columns were removed either by chemical stripping or simply burning away. Very thin wires were then inserted through these micro-holes and used in electric discharge process to make arrays of micro-holes on substrate of copper or other metal plates. This method could be used to fabricate large arrays of nozzles matching the pattern of the mold. The experimental results showed that both two methods are feasible. Further work is required to improve the technology

The prescribed Gauduchon scalar curvature problem in almost Hermitian geometry

In this paper we consider the prescribed Gauduchon scalar curvature problem on almost Hermitian manifolds. By deducing the expression of the Gauduchon scalar curvature under the conformal variation, the problem is reduced to solve a semi-linear partial differential equation with exponential nonlinearity. Using super and sub-solution method, we show that the existence of the solution to this semi-linear equation depends on the sign of a constant associated to Gauduchon degree. When the sign is negative, we give both necessary and sufficient conditions that a prescribed function is the Gauduchon scalar curvature of a conformal Hermitian metric. Besides, this paper recovers Chern Yamabe problem, prescribed Chern Yamabe problem and Bismut Yamabe problem

Genetic Analysis of Prostate Cancer with Computer Science Methods

Metastatic prostate cancer is one of the most common cancers in men. In the advanced stages of prostate cancer, tumours can metastasise to other tissues in the body, which is fatal. In this thesis, we performed a genetic analysis of prostate cancer tumours at different metastatic sites using data science, machine learning and topological network analysis methods. We presented a general procedure for pre-processing gene expression datasets and pre-filtering significant genes by analytical methods. We then used machine learning models for further key gene filtering and secondary site tumour classification. Finally, we performed gene co-expression network analysis and community detection on samples from different prostate cancer secondary site types. In this work, 13 of the 14,379 genes were selected as the most metastatic prostate cancer related genes, achieving approximately 92% accuracy under cross-validation. In addition, we provide preliminary insights into the co-expression patterns of genes in gene co-expression networks. Project code is available at https://github.com/zcablii/Master_cancer_project

An Attention-based Multi-Scale Feature Learning Network for Multimodal Medical Image Fusion

Medical images play an important role in clinical applications. Multimodal medical images could provide rich information about patients for physicians to diagnose. The image fusion technique is able to synthesize complementary information from multimodal images into a single image. This technique will prevent radiologists switch back and forth between different images and save lots of time in the diagnostic process. In this paper, we introduce a novel Dilated Residual Attention Network for the medical image fusion task. Our network is capable to extract multi-scale deep semantic features. Furthermore, we propose a novel fixed fusion strategy termed Softmax-based weighted strategy based on the Softmax weights and matrix nuclear norm. Extensive experiments show our proposed network and fusion strategy exceed the state-of-the-art performance compared with reference image fusion methods on four commonly used fusion metrics.Comment: 8 pages, 8 figures, 3 table