Multiway modeling and analysis in stem cell systems biology

<p>Abstract</p> <p>Background</p> <p>Systems biology refers to multidisciplinary approaches designed to uncover emergent properties of biological systems. Stem cells are an attractive target for this analysis, due to their broad therapeutic potential. A central theme of systems biology is the use of computational modeling to reconstruct complex systems from a wealth of reductionist, molecular data (e.g., gene/protein expression, signal transduction activity, metabolic activity, etc.). A number of deterministic, probabilistic, and statistical learning models are used to understand sophisticated cellular behaviors such as protein expression during cellular differentiation and the activity of signaling networks. However, many of these models are bimodal i.e., they only consider row-column relationships. In contrast, multiway modeling techniques (also known as tensor models) can analyze multimodal data, which capture much more information about complex behaviors such as cell differentiation. In particular, tensors can be very powerful tools for modeling the dynamic activity of biological networks over time. Here, we review the application of systems biology to stem cells and illustrate application of tensor analysis to model collagen-induced osteogenic differentiation of human mesenchymal stem cells.</p> <p>Results</p> <p>We applied Tucker1, Tucker3, and Parallel Factor Analysis (PARAFAC) models to identify protein/gene expression patterns during extracellular matrix-induced osteogenic differentiation of human mesenchymal stem cells. In one case, we organized our data into a tensor of type protein/gene locus link × gene ontology category × osteogenic stimulant, and found that our cells expressed two distinct, stimulus-dependent sets of functionally related genes as they underwent osteogenic differentiation. In a second case, we organized DNA microarray data in a three-way tensor of gene IDs × osteogenic stimulus × replicates, and found that application of tensile strain to a collagen I substrate accelerated the osteogenic differentiation induced by a static collagen I substrate.</p> <p>Conclusion</p> <p>Our results suggest gene- and protein-level models whereby stem cells undergo transdifferentiation to osteoblasts, and lay the foundation for mechanistic, hypothesis-driven studies. Our analysis methods are applicable to a wide range of stem cell differentiation models.</p

Image Based View Synthesis

This dissertation deals with the image-based approach to synthesize a virtual scene using sparse images or a video sequence without the use of 3D models. In our scenario, a real dynamic or static scene is captured by a set of un-calibrated images from different viewpoints. After automatically recovering the geometric transformations between these images, a series of photo-realistic virtual views can be rendered and a virtual environment covered by these several static cameras can be synthesized. This image-based approach has applications in object recognition, object transfer, video synthesis and video compression. In this dissertation, I have contributed to several sub-problems related to image based view synthesis. Before image-based view synthesis can be performed, images need to be segmented into individual objects. Assuming that a scene can approximately be described by multiple planar regions, I have developed a robust and novel approach to automatically extract a set of affine or projective transformations induced by these regions, correctly detect the occlusion pixels over multiple consecutive frames, and accurately segment the scene into several motion layers. First, a number of seed regions using correspondences in two frames are determined, and the seed regions are expanded and outliers are rejected employing the graph cuts method integrated with level set representation. Next, these initial regions are merged into several initial layers according to the motion similarity. Third, the occlusion order constraints on multiple frames are explored, which guarantee that the occlusion area increases with the temporal order in a short period and effectively maintains segmentation consistency over multiple consecutive frames. Then the correct layer segmentation is obtained by using a graph cuts algorithm, and the occlusions between the overlapping layers are explicitly determined. Several experimental results are demonstrated to show that our approach is effective and robust. Recovering the geometrical transformations among images of a scene is a prerequisite step for image-based view synthesis. I have developed a wide baseline matching algorithm to identify the correspondences between two un-calibrated images, and to further determine the geometric relationship between images, such as epipolar geometry or projective transformation. In our approach, a set of salient features, edge-corners, are detected to provide robust and consistent matching primitives. Then, based on the Singular Value Decomposition (SVD) of an affine matrix, we effectively quantize the search space into two independent subspaces for rotation angle and scaling factor, and then we use a two-stage affine matching algorithm to obtain robust matches between these two frames. The experimental results on a number of wide baseline images strongly demonstrate that our matching method outperforms the state-of-art algorithms even under the significant camera motion, illumination variation, occlusion, and self-similarity. Given the wide baseline matches among images I have developed a novel method for Dynamic view morphing. Dynamic view morphing deals with the scenes containing moving objects in presence of camera motion. The objects can be rigid or non-rigid, each of them can move in any orientation or direction. The proposed method can generate a series of continuous and physically accurate intermediate views from only two reference images without any knowledge about 3D. The procedure consists of three steps: segmentation, morphing and post-warping. Given a boundary connection constraint, the source and target scenes are segmented into several layers for morphing. Based on the decomposition of affine transformation between corresponding points, we uniquely determine a physically correct path for post-warping by the least distortion method. I have successfully generalized the dynamic scene synthesis problem from the simple scene with only rotation to the dynamic scene containing non-rigid objects. My method can handle dynamic rigid or non-rigid objects, including complicated objects such as humans. Finally, I have also developed a novel algorithm for tri-view morphing. This is an efficient image-based method to navigate a scene based on only three wide-baseline un-calibrated images without the explicit use of a 3D model. After automatically recovering corresponding points between each pair of images using our wide baseline matching method, an accurate trifocal plane is extracted from the trifocal tensor implied in these three images. Next, employing a trinocular-stereo algorithm and barycentric blending technique, we generate an arbitrary novel view to navigate the scene in a 2D space. Furthermore, after self-calibration of the cameras, a 3D model can also be correctly augmented into this virtual environment synthesized by the tri-view morphing algorithm. We have applied our view morphing framework to several interesting applications: 4D video synthesis, automatic target recognition, multi-view morphing

Estimating Continuous Treatment Effects in Panel Data using Machine Learning with a Climate Application

This paper introduces and proves asymptotic normality for a new semi-parametric estimator of continuous treatment effects in panel data. Specifically, we estimate the average derivative. Our estimator uses the panel structure of data to account for unobservable time-invariant heterogeneity and machine learning (ML) methods to preserve statistical power while modeling high-dimensional relationships. We construct our estimator using tools from double de-biased machine learning (DML) literature. Monte Carlo simulations in a nonlinear panel setting show that our method estimates the average derivative with low bias and variance relative to other approaches. Lastly, we use our estimator to measure the impact of extreme heat on United States (U.S.) corn production, after flexibly controlling for precipitation and other weather features. Our approach yields extreme heat effect estimates that are 50% larger than estimates using linear regression. This difference in estimates corresponds to an additional $3.17 billion in annual damages by 2050 under median climate scenarios. We also estimate a dose-response curve, which shows that damages from extreme heat decline somewhat in counties with more extreme heat exposure

Multithreaded Multiway Constraint Systems with Rust and WebAssembly

User interfaces are difficult to get right, and implementing and maintaining them takes up a significant portion of development time. Ensuring that all dependencies between Graphical User Interface (GUI) widgets are maintained, such as the value of one being computed from another, can be challenging and prone to bugs with a standard callback-based approach. The dependency graph formed from relations and constraints between variables quickly becomes unwieldy for humans, especially with multi-directional dataflow and transitive dependencies. HotDrink is a library for declaratively modeling constraints between widgets as a constraint system. This model includes information about how to enforce the constraints, which the library can use to automatically enforce them when values are changed, a process called solving. The programmer can thus focus on individual constraints without being distracted by their effect on the rest of the system. Previous implementations of HotDrink have been written in TypeScript and Flow, but they sometimes suffer from poor performance in larger constraint systems. In this project, we have explored the design space of constraint-based GUI programming for web applications, with a focus on static typing and multithreading. We have developed the library hotdrink-rs, a version of HotDrink implemented in Rust. To improve the performance of the planning step of solving, we have used an optimization technique called pruning that can speed up planning by several orders of magnitude. This enables use of the library for modeling larger systems, and for more performance-sensitive tasks. Our implementation falls short in systems where this optimization is not effective, which suggests that experiments with further optimizations, e.g., incremental planning algorithms, should be done. The library also supports multithreaded execution of plans, which both speeds up solving and guarantees GUI responsiveness in the face of long-running computations. The GUI is thus also more resilient to programmer mistakes that cause long-running or non-terminating computations. We have also developed hotdrink-wasm, a library that wraps data structures from hotdrink-rs to allow the library to be compiled to WebAssembly. hotdrink-wasm supports the use of Web Worker-based threads for multithreaded constraint system solving with cancelable computations in web applications. Finally, we present more memory-efficient data structures for constraint systems by representing variable indices with individual bits. In addition to saving memory, it may also provide performance benefits by being more cache-friendly.Masteroppgave i Programutvikling samarbeid med HVLPROG399MAMN-PRO

Alternative reproductive tactics in the queen size dimorphic ant Leptothorax rugatulus (Emery) and population genetic consequences

We report the results of a comprehensive investigation of the queen size dimorphism in the North American ant Leptothorax rugatulus. Employing allozymes and microsatellites as genetic markers, we found no evidence that the gene pools of large (macrogynes) and small (microgynes) queens are distinct. Queens in polygynous colonies are related to each other, supporting the hypothesis that colonies with more than one queen commonly arise by the adoption of daughter queens into their natal colonies. The higher fat content of macrogynes, their predominance in monogynous societies and in small founding colonies, and their greater flight activity favor the view that macrogynes predominantly found colonies independently, while microgynes are specialized for dependent colony founding by readoption. When comparing the genetic structure of three different subpopulations, we found that the alternative life histories had no significant effect on population viscosity at the scale investigated