Algorithms to Explore the Structure and Evolution of Biological Networks

High-throughput experimental protocols have revealed thousands of relationships amongst genes and proteins under various conditions. These putative associations are being aggressively mined to decipher the structural and functional architecture of the cell. One useful tool for exploring this data has been computational network analysis. In this thesis, we propose a collection of novel algorithms to explore the structure and evolution of large, noisy, and sparsely annotated biological networks. We first introduce two information-theoretic algorithms to extract interesting patterns and modules embedded in large graphs. The first, graph summarization, uses the minimum description length principle to find compressible parts of the graph. The second, VI-Cut, uses the variation of information to non-parametrically find groups of topologically cohesive and similarly annotated nodes in the network. We show that both algorithms find structure in biological data that is consistent with known biological processes, protein complexes, genetic diseases, and operational taxonomic units. We also propose several algorithms to systematically generate an ensemble of near-optimal network clusterings and show how these multiple views can be used together to identify clustering dynamics that any single solution approach would miss. To facilitate the study of ancient networks, we introduce a framework called ``network archaeology'') for reconstructing the node-by-node and edge-by-edge arrival history of a network. Starting with a present-day network, we apply a probabilistic growth model backwards in time to find high-likelihood previous states of the graph. This allows us to explore how interactions and modules may have evolved over time. In experiments with real-world social and biological networks, we find that our algorithms can recover significant features of ancestral networks that have long since disappeared. Our work is motivated by the need to understand large and complex biological systems that are being revealed to us by imperfect data. As data continues to pour in, we believe that computational network analysis will continue to be an essential tool towards this end

Women in Science 2017

Ever since its 1967 start, SURF has been a cornerstone of Smith’s science education. Women in Science 2017 summarizes research done by Smith College’s SURF Program participants during the summer of 2017. 151 students participated in SURF (144 hosted on campus and nearby eld sites), supervised by 58 faculty mentor-advisors drawn from the Clark Science Center and connected to its eighteen science, mathematics, and engineering departments and programs and associated centers and units. At summer’s end, SURF participants summarized their research experiences for this publication.https://scholarworks.smith.edu/clark_womeninscience/1006/thumbnail.jp

Lithium-Ion Batteries

Lithium-ion batteries (LIBs), as a key part of the 2019 Nobel Prize in Chemistry, have become increasingly important in recent years, owing to their potential impact on building a more sustainable future. Compared with other batteries developed, LIBs offer high energy density, high discharge power, and a long service life. These characteristics have facilitated a remarkable advance of LIBs in many frontiers, including electric vehicles, portable and flexible electronics, and stationary applications. Since the field of LIBs is advancing rapidly and attracting an increasing number of researchers, it is necessary to often provide the community with the latest updates. Therefore, this book was designed to focus on updating the electrochemical community with the latest advances and prospects on various aspects of LIBs. The materials presented in this book cover advances in several fronts of the technology, ranging from detailed fundamental studies of the electrochemical cell to investigations to better improve parameters related to battery packs

Down-Conditioning of Soleus Reflex Activity using Mechanical Stimuli and EMG Biofeedback

Spasticity is a common syndrome caused by various brain and neural injuries, which can severely impair walking ability and functional independence. To improve functional independence, conditioning protocols are available aimed at reducing spasticity by facilitating spinal neuroplasticity. This down-conditioning can be performed using different types of stimuli, electrical or mechanical, and reflex activity measures, EMG or impedance, used as biofeedback variable. Still, current results on effectiveness of these conditioning protocols are incomplete, making comparisons difficult. We aimed to show the within-session task- dependent and across-session long-term adaptation of a conditioning protocol based on mechanical stimuli and EMG biofeedback. However, in contrast to literature, preliminary results show that subjects were unable to successfully obtain task-dependent modulation of their soleus short-latency stretch reflex magnitude

Alfvén waves underlying ionospheric destabilization: ground-based observations

During geomagnetic storms, terawatts of power in the million mile-per-hour solar wind pierce the Earth’s magnetosphere. Geomagnetic storms and substorms create transverse magnetic waves known as Alfvén waves. In the auroral acceleration region, Alfvén waves accelerate electrons up to one-tenth the speed of light via wave-particle interactions. These inertial Alfvén wave (IAW) accelerated electrons are imbued with sub-100 meter structure perpendicular to geomagnetic field B. The IAW electric field parallel to B accelerates electrons up to about 10 keV along B. The IAW dispersion relation quantifies the precipitating electron striation observed with high-speed cameras as spatiotemporally dynamic fine structured aurora. A network of tightly synchronized tomographic auroral observatories using model based iterative reconstruction (MBIR) techniques were developed in this dissertation. The TRANSCAR electron penetration model creates a basis set of monoenergetic electron beam eigenprofiles of auroral volume emission rate for the given location and ionospheric conditions. Each eigenprofile consists of nearly 200 broadband line spectra modulated by atmospheric attenuation, bandstop filter and imager quantum efficiency. The L-BFGS-B minimization routine combined with sub-pixel registered electron multiplying CCD video stream at order 10 ms cadence yields estimates of electron differential number flux at the top of the ionosphere. Our automatic data curation algorithm reduces one terabyte/camera/day into accurate MBIR-processed estimates of IAW-driven electron precipitation microstructure. This computer vision structured auroral discrimination algorithm was developed using a multiscale dual-camera system observing a 175 km and 14 km swath of sky simultaneously. This collective behavior algorithm exploits the “swarm” behavior of aurora, detectable even as video SNR approaches zero. A modified version of the algorithm is applied to topside ionospheric radar at Mars and broadcast FM passive radar. The fusion of data from coherent radar backscatter and optical data at order 10 ms cadence confirms and further quantifies the relation of strong Langmuir turbulence and streaming plasma upflows in the ionosphere with the finest spatiotemporal auroral dynamics associated with IAW acceleration. The software programs developed in this dissertation solve the century-old problem of automatically discriminating finely structured aurora from other forms and pushes the observational wave-particle science frontiers forward

Explosive Welding of Aluminum Plates: Experiments, Evaluation, and Modeling

Explosive welding is a field with a wide variety of applications of great value, such as corrosion resistant cladding and bi-metallic joints. It occupies a special place in the available metal joining techniques. Dissimilar metal welding is possible in metal pairings that don’t support other conventional bonds, and it can produce superior area welds regardless of the metal parts to be joined. The objectives of this dissertation were to further the understanding of explosive welding in general, as well as the empirical understanding of welding of Aluminum 6061-O, and to investigate the use of LS-DYNA’s Multi-Material Arbitrary Lagrangian-Eulerian formulation as a potential tool for the design of explosive welds. In the course of the work, the theory on formation of bond interfacial waves was identified as an area where there was not an apparent consensus, and this was addressed in light of both recent works and information from this study. For this dissertation, an experimental program of explosive welding tests, mechanical weld verification, and metallurgical observation were undertaken in order to add to the data available for this type of welding. Nine different explosive welding tests were conducted covering four scenarios, which were combinations of different explosive thicknesses and flyer inclination angles. Tensile shear tests with digital image correlation were used to test the welds, and optical microscope, Scanning Electron Microscope, and Transmission Electron Microscope images were used to investigate the nature of the bond. The numerical investigation was conducted and compared to both experiment and initial modeling results. The results reinforce the need for well-developed and material specific welding windows, adding additional data for the joining of Aluminum 6061-O. The endorsement of the continuous Kelvin-Helmholtz jet wake as the source of instability was supported with modeling results. The Multi-Material Arbitrary Lagrangian Eulerian modeling with Euler-Lagrange Coupling was demonstrated to yield results comparable to research codes for welding parameters, to be able to capture jetting, and provide meaningful temperature results. Bond interfacial waves were characterized with some success as well, concluding that this modeling technique is a viable means to assist in the design of explosive welds