A Microfluidic Approach to Selection and Enrichment of Aptamers for Biomolecules and Cells

This thesis presents microfluidic devices for selection and amplification of nucleic acids (aptamers) that bind to specific targets. Aptamers are very attractive molecules in many biological applications due to their interesting properties including high target binding affinities and stability. Using conventional platforms for aptamer generation (SELEX, systematic evolution of ligands by exponential enrichment) is labor-intensive and time consuming. Microfluidic devices have been developed to improve the aptamer enrichment efficiency. However, aptamer generation using these devices is still inefficient because they require complicated flow control components for sample and reagent handling and additional off-chip processes. We developed microfluidic SELEX platforms for rapid isolation of aptamers that possess greatly simplified designs which enable easy chip fabrication and operation. The simplicity of the devices is achieved by utilizing a combination of bead-based selection and amplification of target binding nucleic acids, and gel-based electrokinetic transfer of nucleic acids. In the devices, nucleic acids that bind to targets are isolated on target-functionalized microbeads or target cells in a microchamber and electrokinetically transported to another chamber through a gel-filled microchannel by an electric field. The strands are then hybridized onto reverse primers immobilized on microbeads and amplified via polymerase chain reaction (PCR) using on-chip temperature control. The amplified strands are separated from the beads and electrophoretically transferred back into the selection chamber for subsequent SELEX rounds. Using the devices, we demonstrated enrichment of target-binding nucleic acids against human immunoglobulin E (IgE), the glucose-boronic acid complex, and MCF-7 cancer cells. With the physical and functional integration allowed by the monolithic design realized in our devices, the total process time for selection of aptamers was drastically reduced compared with that required by conventional aptamer selection platforms. Moreover, the binding affinities of the selected strands using our devices are comparable to those of aptamers obtained using the conventional platforms

TRACKING FORMATION CHANGES AND ITS EFFECTS ON SOCCER USING POSITION DATA

This study investigated the application of advanced machine learning methods, specifically k-means clustering, k-Nearest Neighbors (kNN), and Support Vector Machines (SVM), to analyze player tracking data in soccer. The primary hypothesis posits that such data can yield a standalone, in-depth understanding of soccer matches. The study revealed that while k-means and spatial analysis are promising in analyzing player positions, kNN and SVM show limitations without additional variables. Spatial analysis examined each team’s convex hull and studied the correlation between team length, width, and surface area. Results showed team length and surface area have a strong positive correlation with a value of 0.8954. This suggested that teams with longer team length have a more direct style of play with players more spread out which led to larger surface areas. k-means clustering was performed with different k values derived from different approaches. The silhouette method recommended a k value of 2 and the elbow recommended a k value of 4. The context of the sport suggested additional analysis with a k value of 11. The results from k-means suggested natural data partitions, highlighting distinct player roles and field positions. kNN was performed to find similar players with the model of k = 19 showing the highest accuracy of 8.61%. The SVM model returned a classification of 55 classes which indicated a highly granular level of categorization for player roles. The results from kNN and SVM indicated the necessity of further contextual data for more effective analysis and emphasized the need for balanced datasets and careful model evaluation to avoid biases and ensure practical application in real-world scenarios. In conclusion, each algorithm offers unique perspectives and interpretations on player positioning and team formations. These algorithms, when combined with expert knowledge and additional contextual data, can significantly enrich the scope of analysis in soccer. Future work should consider incorporating event data and additional variables to enhance the depth of analytical insights, enabling a more comprehensive understanding of how formations evolve in response to various in-game situations

Network rewiring is an important mechanism of gene essentiality change.

Gene essentiality changes are crucial for organismal evolution. However, it is unclear how essentiality of orthologs varies across species. We investigated the underlying mechanism of gene essentiality changes between yeast and mouse based on the framework of network evolution and comparative genomic analysis. We found that yeast nonessential genes become essential in mouse when their network connections rapidly increase through engagement in protein complexes. The increased interactions allowed the previously nonessential genes to become members of vital pathways. By accounting for changes in gene essentiality, we firmly reestablished the centrality-lethality rule, which proposed the relationship of essential genes and network hubs. Furthermore, we discovered that the number of connections associated with essential and non-essential genes depends on whether they were essential in ancestral species. Our study describes for the first time how network evolution occurs to change gene essentiality

A well-balanced unsplit finite volume model with geometric flexibility

A two-dimensional finite volume model is developed for the unsteady, and shallow water equations on arbitrary topography. The equations are discretized on quadrilateral control volumes in an unstructured arrangement. The HLLC Riemann approximate solver is used to compute the interface fluxes and the MUSCL-Hancock scheme with the surface gradient method is employed for second-order accuracy. This study presents a new method for translation of discretization technique from a structured grid description based on the traditional (i, j) duplet to an unstructured grid arrangement based on a single index, and efficiency of proposed technique for unsplit finite volume method. In addition, a simple but robust well-balanced technique between fluxes and source terms is suggested. The model is validated by comparing the predictions with analytical solutions, experimental data and field data including the following cases: steady transcritical flow over a bump, dam-break flow in an adverse slope channel and the Malpasset dam-break in France

Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature Interactions

Black-box models, such as deep neural networks, exhibit superior predictive performances, but understanding their behavior is notoriously difficult. Many explainable artificial intelligence methods have been proposed to reveal the decision-making processes of black box models. However, their applications in high-stakes domains remain limited. Recently proposed neural additive models (NAM) have achieved state-of-the-art interpretable machine learning. NAM can provide straightforward interpretations with slight performance sacrifices compared with multi-layer perceptron. However, NAM can only model 1$^{\text{st}}$-order feature interactions; thus, it cannot capture the co-relationships between input features. To overcome this problem, we propose a novel interpretable machine learning method called higher-order neural additive models (HONAM) and a feature interaction method for high interpretability. HONAM can model arbitrary orders of feature interactions. Therefore, it can provide the high predictive performance and interpretability that high-stakes domains need. In addition, we propose a novel hidden unit to effectively learn sharp-shape functions. We conducted experiments using various real-world datasets to examine the effectiveness of HONAM. Furthermore, we demonstrate that HONAM can achieve fair AI with a slight performance sacrifice. The source code for HONAM is publicly available