Dartmouth Institute for Health Policy and Clinical Practice
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Computational Drug Repositioning and 3D Skin-Like Tissues Identify Anti-Fibrotic Targets for Systemic Sclerosis (SSc)
Get PDFSystemic Sclerosis (SSc) is a rare autoimmune disease characterized by dermal and internal organ fibrosis, including heart, lungs, and gastrointestinal tract, and autoantibody formation. Although disease etiology is currently unknown, like other autoimmune diseases, SSc likely develops due to environmental factor exposure in genetically susceptible individuals. Fibrotic diseases are notoriously difficult to treat. Coupled with the autoimmune aspect, SSc is difficult to study scientifically due to the lack of complex disease models that can recapitulate the immune-fibrotic axis of the disease. Due to this, there are only two FDA approved medical treatments for SSc approved for symptomatic treatment of SSc interstitial lung disease (SSc ILD).
Gene expression heterogeneity is seen within patients with SSc when comparing microarray or RNA-seq data. We have identified four reproducible gene expression subsets replicated across multiple datasets. The inflammatory subset, with upregulated gene expression pertaining to the immune system and inflammatory related genes. The fibroproliferative subset, with upregulation of extracellular matrix and cell cycle related genes. The limited subset, which consists solely of limited cutaneous SSc patients, and the normal-like subset, which is comprised of SSc patients clustering alongside healthy controls.
In the second chapter of this thesis, I describe the ability to use gene expression analyses to identify biologically relevant pathways specific to SSc patient subsets for potential therapeutic targeting. Using data from SSc cohorts and Connectivity Map, we found multiple small molecule inhibitors, which we tested in our 3D tissue culture system. SSc tissues treated with these molecules showed disease-specific effects, particularly thinner tissue growth, when compared to healthy control tissues.
In chapter three, studies focused on identifying commonalities between SSc and Morphea patients. Morphea, also known as localized scleroderma, is a fibrotic disease resulting in fibrotic patches of skin. Contrasting SSc, affected and unaffected skin showed different gene expression. Morphea patients clustered with SSc patients and had striking similarities in inflammatory gene expression signatures.
These studies were performed to elucidate mechanisms of SSc that may lead to potential therapeutic targeting. More complex models will allow for safer, more targeted and biologically relevant therapeutic testing, which is imperative to curing complex diseases
COMPUTATIONAL MODELLING AND DESIGN OF ANTIBODIES: BENEFITS FROM ANALYSIS OF THEIR UNIQUE STRUCTURAL MOTIFS
Get PDFThe complexes that antibodies make with their binding partners, or antigens, are especially valuable to be able to predict and modify due to their unique role in the immune system. Yet, they present significant challenges to computational methods of both modelling and design. Antibodies are unlike most other proteins in that they are composed of a scaffold region, which is a highly conserved structure that is largely the same between antibodies of the same class, and Complementary Determining Regions (CDRs), which are comprised of hypervariable loops that largely determine their binding motifs. Additionally, antibodies and their antigens do not co-evolve to bind each other. Thus the challenges antibody-antigen complexes present are threefold: modern machine-learning methods for modelling protein complexes rely on both co- evolution signals from Multiple Sequence Alignments (MSAs) and overall-homologous structural matches to predict general protein binding motifs, neither of which are very useful for predicting antibody-antigen interactions, while the binding motifs are determined solely by the highly unstructured CDR loops, which have unique sequence preferences.
In this thesis, the modelling and redesign of antibody-antigen complexes are ap- proached with particular sensitivity to these issues. A novel score is developed to quantify the significance of antibody-antigen models, which considers the restricted space of reasonable binding motifs involving CDR-antigen contacts. Then, this met- ric—along with DOCKQ score—is used to benchmark antibody-antigen models produced by six diverse methods. It reveals that methods relying on MSAs and ho- mologous matches, like AlphaFold-Multimer and RoseTTAFold, perform far worse in comparison to their accuracy at predicting general protein complexes. Moreover, when their interaction motifs are analyzed, it is shown that high quality AlphaFold- Multimer models have much more common interaction motifs than low-quality mod- els. This suggests that limited interfacial geometry data in AlphaFold-Multimer’s training set is limiting its performance, but also allows for better discrimination of low and high quality AlphaFold-multimer models via a novel confidence score based on commonness of the interaction motifs. Finally, statistical analysis of the interface be- tween an antibody and Venezuelan Equine Encephalitis Virus is used to redesign that antibody, relying solely on sequence preferences of similarly structured loop-heavy in- teraction motifs from general proteins. The suggested mutations were combined with other mutations in a collaborator’s directed library, which generated a variant that bound 60 times more strongly than the original
Shader-based Real-time Image Tracking for Mobile Augmented Reality
Get PDFImage target tracking is a technique widely used in a variety of augmented reality (AR) applications to trigger AR interaction and accurately locate virtual objects relative to physical space. This project is a Unity image tracking pipeline based on the ORB feature detection and description technique that seeks to be robust enough to track images despite partial occlusion, uneven lighting, and image target depth. This pipeline employs compute shader code to conduct image tracking computations on the GPU to track images in real-time for mobile AR apps
SPACE BOUNDS FOR ESTIMATING MINIMUM NORM OF SOLUTIONS IN UNDERCONSTRAINED SYSTEMS
Get PDFIn this work, we wish to investigate the following situation: suppose we are in an underconstrained linear system where observations are constant but predictors are streaming in. That is, the number of predictors—and therefore the dimensionality of our solution—is changing. How hard is it for a streaming algorithm to maintain the ”size” or norm of the solution if we are constrained in space? More informally, can we keep track of the norm of the solution as new data is streaming in without naively memorizing all data and computing the solution directly? We first show a lower bound that any streaming algorithm which returns the minimum l0 norm must use at least linear space. We then follow this up with a streaming algorithm that can return the minimum l2 norm in sublinear space when the system is highly underconstrained
Implementing Selective Signature Scanning to Optimize Malware Detection
Get PDFSignature scanning is one of the oldest types of malware detection, and it remains an essential lightweight detection method for many antivirus programs. However, signature scanning has unavoidable limitations, including an inevitably increasing runtime as malware signature databases continually expand. In this paper, we discuss the current state of signature scanning, including usage of the open-source signature scanning tool YARA. We test Zemlyanaya et al’s assertion that scanning only the beginning and end of files can reduce the runtime cost of signature database expansion — while maintaining a high level of accuracy — and find it inaccurate in the case of general scanning. However, by examining the behavior of specific rules during head-and-foot scanning, we argue that head-and-foot scanning can provide large runtime improvements with minimal accuracy loss, but only for a specific subset of malware signatures. Finally, we argue for further investigation into the prevalence of malware signatures amenable to head-and-foot scanning, as this may enable analysts to improve the runtime of malware detection tools
Appalachia Summer/Fall 2024: Complete Issue
Get PDFSummer/Fall 2024 - Volume LXXV, Number 2 - Issue #258. Dark Places: Exploring pitch black skies, a forest, and a storm
Moths to a Flame: The Light and Dark of Reading Accidents in \u3ci\u3eNorth American Mountaineering\u3ci\u3e
Get PDFA climber finds two issues of the American Alpine Club’s annual accidents reports from 2013 and 2014 and ruminates on his obsessions
Caught in a Vortex: Two Hikers Stumble upon a Private Ceremony
Get PDFA mother and daughter hike past a wedding in progress in Sedona, Arizona
Accidents: Analysis from the White Mountains of New Hampshire and Occasionally Elsewhere
Get PDFReports of rescues and accidents in New Hampshire’s White Mountains from spring and fall 2023. A long-distance hiker on the Appalachian Trail was knocked unconscious after one her trekking poles snapped crossing a brook on the Rattle River Trail. A woman tries and fails to hike down from Mount Washingtron after taking the Cog Railway up. A hiker falls into a stream, soaking all of her gear, and calls for help below Mount Passaconaway. A couple battles hypothermia after starting up Little Haystack Mountain late on a cold June day. A 21-year-old falls 30 feet off a ledge on Cannon Mountain. A novice hiker is led astray on East Osceola by relying on the Alltrails app in an area of poor cell service. A man in a group of nine collapses on a hot July day on Mount Moriah. A 21-year-old man dies on a cold August day on Mount Madison. A mother drowns in Franconia Falls after trying to save her son, who survived
