GRID3 COD - Roads v1.0

The GRID3 COD - Roads v1.0 spatial dataset consists of road and path locations in the Democratic Republic of the Congo. Keywords: Road

Lucerna Extincta: Spiritual Promiscuities in Performance and Installation Art, 1970s-80s

This dissertation examines how artists in Bogotá, Port of Spain, London, and Los Angeles during the 1970s and 1980s engaged in what I term spiritual promiscuities—the purposeful synthesis of Indigenous, Afro-Diasporic, Asian, and European belief systems—as creative platforms to challenge modernist, colonial hierarchies and forge subaltern social coalitions. By analyzing key yet understudied performance and installation works, I argue that these artists navigated sacred and profane expressions of the otherworldly, complicating and resisting aesthetic distinctions, state repression, racialized exclusion, and, to varying degrees, the commodification of spiritual traditions. Drawing on archival research, oral histories, comparative religious studies, and art historical analysis—as well as feminist, queer, and performance theory—this study situates these artistic interventions within the broader sociopolitical transformations of the countercultural, post-Civil Rights, and postcolonial era. The selected case studies reveal how these artists transformed ritualistic practices, community engagement, and site-specific installations into strategies of artistic and social resistance that blurred the boundaries between public and private devotion, as well as trained and self-taught artistic traditions. By foregrounding the intersections of spirituality, corporeality, and performative agency, this dissertation contributes to discourses on decolonial aesthetics while critically engaging spirituality within modern and contemporary art historiography. In doing so, it illuminates how alternative and hegemonic epistemologies coalesce in spiritual, artistic, and sociopolitical conversations across the Americas and the Caribbean in the second half of the twentieth century

Biomechanical Testing of Human Uterine and Cervical Tissues – Literature Review Dataset

This dataset compiles peer-reviewed studies reporting experimental ex vivo biomechanical tests of human uterine and cervical tissues across reproductive states. It is intended as a reusable reference for academic applications. Users should still consult original publications for detailed methodology and interpretation. This dataset accompanies the following book chapter (in press): Louwagie, E. and Fodera, D. (2026) Biomechanics of the Uterus and Cervix Across Pregnancy and Disease: Considerations for Biomaterial Design. In: Biomaterials for Women’s Health Engineering. Springer Nature

Gene-Agnostic Metabolic Engineering in Inherited Retinal Degenerations

Inherited retinal degenerations (IRDs) culminate in non-cell-autonomous cone loss following rod failure and destabilization of outer-retinal metabolism. This dissertation tests whether compartment-specific metabolic reprogramming in rods, cones, and the retinal pigment epithelium (RPE) can preserve cone structure and function independent of genotype in etiologically diverse mouse models, including phosphodiesterase 6B (6), rhodopsin (), and membrane frizzled-related protein () mutant lines. The studies herein establish that metabolism can be therapeutically redirected across these compartments, supporting a strategy that complements gene-specific augmentation while extending protection to most patients without access to tailored genetic therapies. By reframing retinal degeneration as a disorder of metabolic ecosystem collapse, this work lays the conceptual and experimental foundation for therapies that are both mutation-agnostic and scalable, with potential relevance to common degenerative conditions such as age-related macular degeneration. Aim 1 (rods – 1) used 6⌃(⁶²⁰/⁶²⁰) and ⌃(¹¹⁰/⁺) mice to modulate rod metabolism via conditional prolyl-hydroxylase disruption(PHD) [6-CreERT2] and dual-AAV CRISPR editing of 1. Outcomes combined electroretinography (ERG), optical coherence tomography (OCT), histology, cone flatmounts, lactate assays, and [U-¹³C]glucose tracing. PHD2 disruption induced a Warburg-like shift via enhanced ¹³C labeling of glycolytic intermediates with increased phospho-pyruvate dehydrogenase 1 (Ser293), without elevating bulk lactate, preserving cone morphology, and improving cone-mediated ERG across recessive and dominant models. One-year fluorescein angiography showed no neovascularization. These results demonstrate that rod-specific glycolytic reprogramming through PHD2 disruption preserves cones by stabilizing carbon flux and redox balance, establishing 1 as a dominant lever for mutation-agnostic metabolic rescue in murine rods. Aim 2 (cones – 1) targeted cones in 6⌃(¹/¹) and 6⌃(⁶²⁰/⁶²⁰) mice with 1 deletion [3-CreERT2] and evaluated photopic ERG, cone flatmounts, and whole-retina qPCR/immunoblotting of canonical nuclear factor erythroid 2-related factor 2 (NRF2) targets. 1 loss yielded consistent structural rescue, higher opsin-positive cone counts, and healthier segment morphology. While functional gains were modest, bulk assays showed minimal induction of canonical NRF2 targets. This suggests cone protection may arise through subtle redox stabilization or structural preservation rather than broad transcriptional reprogramming, refining the paradigm of NRF2-mediated rescue. Aim 3 (RPE – ) reprogrammed the RPE in ⌃(⁶/⁶) mice by deleting [65-CreERT2] and assessed longitudinal ERG/OCT, cone flatmounts, RPE whole-mount morphology, and [U-¹³C]palmitate tracing to -hydroxybutyrate (BHB). loss increased ¹³C-palmitate incorporation into BHB and qualitatively preserved RPE architecture, with a mid-course plateau in outer nuclear layer thinning, late-stage scotopic ERG improvements, and significant peripheral cone preservation. This indicates that mutation-agnostic RPE reprogramming can secondarily stabilize photoreceptors in the background of IRD. These experiments demonstrate that tuning rod glycolysis, cone redox balance, and RPE fattyacid -oxidation provide complementary, mutation-agnostic protection of cone vision, establishing retinal metabolism as a practical therapeutic axis alongside gene-specific repair. By showing that interventions in distinct retinal compartments converge on the shared goal of cone preservation, this work reframes IRDs not only as collections of genetic defects but as disorders of metabolic interdependence. This perspective expands the therapeutic landscape beyond mutation-specific augmentation, highlighting metabolism as a scalable axis applicable across the genetically heterogeneous spectrum of IRDs. Moreover, by identifying nodal regulators that can be targeted with genetic or pharmacologic tools, these findings create a translational bridge to common late-stage diseases such as age-related macular degeneration, where metabolic instability is a central driver of pathology. Together, this dissertation establishes metabolism-centered therapy as a unifying framework that can complement precision medicine and reshape how retinal degeneration is treated in both rare and common contexts

Sculpting Visual Cortex: How Recurrent Structure, Modulatory Signals, and Development Shape V1 Responses

The primary visual cortex (V1) is the first cortical area to process retinal signals, and the classical picture featuring feedforward orientation-selective inputs and recurrent amplification is well established. However, this view is incomplete: V1 receives modulatory inputs from non-visual sources and orientation selectivity emerges even before visual experience. My thesis explores how modulation and development shape V1 responses across three complementary projects. In my first project, I show that optogenetic stimulation of macaque V1 excitatory neurons produces diverse single-cell responses yet preserves the population statistics of neurons whose feature preferences match the visual stimulus – a phenomenon we term "rate reshuffling." While randomly connected networks can reproduce this effect, they require strong coupling and tight excitatory-inhibitory cancellation inconsistent with observations. In contrast, I show that networks with feature-dependent structure generate reshuffling robustly under more biologically plausible conditions. My second project explores the underlying mechanisms by developing a mean-field framework for networks with feature-dependent structure in which decomposing global activity into tuned and untuned components reveals effective interactions between the visual-stimulus-matched and baseline populations. A linear response analysis then shows that strongly-coupled, feedback-inhibition-dominated networks exhibit suppressive baseline-to-matched interactions, robustly explaining the lack of matched responses to the optogenetic stimulus. In my final project, I characterize how endogenous mechanisms generate orientation selective and spatially organized visual responses in ferret V1 before the onset of vision. I propose that strong receptive field biases drive recurrent interactions to form phase-insensitive responses in layer 4, which are transformed into orientation-selective and spatially periodic activity in layers 2/3. This structure differs from the mature architecture but naturally emerges from activity-dependent plasticity driven by geniculate activity prior to eye-opening. My findings reveal mechanisms by which visually and behaviorally relevant signals can coexist in separate populations and demonstrate that structured visual representations can emerge without prior visual experience

Dendritic mechanisms of memory encoding in the hippocampus

The mammalian brain learns and forms memories continuously throughout an individual’s lifetime, with an astonishing capacity to acquire, retain, and retrieve relevant new information while simultaneously filtering and forgetting behaviorally irrelevant experiences. Memories are thought to be encoded during ‘online’ periods of awake exploration and subsequently consolidated into stable memories during ‘offline’ periods of sleep; otherwise, memories are forgotten. Both the rapid encoding of spatial and episodic memories and their subsequent consolidation rely critically on the CA1 region of the hippocampus. Pyramidal neurons in CA1 rapidly form spatially selective firing fields called place fields, which serve as the cellular basis for memory encoding. The primary neural basis for these memory processes is thought to be synaptic plasticity, which underlies changes in the functional connectivity of neuronal circuits in the brain. Various forms of experience-dependent synaptic modifications, particularly at excitatory glutamatergic synapses, are widely considered to be the primary substrates of memory encoding and consolidation. However, causal links have yet to be made in vivo between synaptic plasticity and memory formation due to the difficulty of monitoring and manipulating plasticity at the single-neuron resolution in awake behaving animals. To address this, we combined high-resolution in vivo single-cell labeling (Chapter 1), 3D real-time motion correction (Chapter 2), and multicompartment two-photon dendritic glutamate, calcium, and voltage imaging to examine the subcellular plasticity mechanisms supporting hippocampal-dependent memory formation (Chapter 3)

Privacy-Preserving Techniques for Genotype-Phenotype Association Studies

Genotype-phenotype association studies play a central role in precision medicine by enabling researchers to identify genetic variants that influence complex traits and diseases with applications ranging from disease risk prediction to treatment stratification. Notable approaches include genome-wide association studies (GWAS), quantitative trait loci (QTL) mapping, and increasingly, machine learning models that learn predictive relationships between genotype and phenotype data. The power of these approaches grows substantially in collaborative research settings, where pooling data across cohorts increases the statistical power for variant discovery, and sharing trained machine learning models enables institutions with limited computational resources to perform inference on their own cohorts. However, these collaborative gains come with a fundamental privacy-utility tradeoff: sharing raw genomic and phenotypic data can expose sensitive information about study participants, and sharing machine learning models introduces the risk of information leakage through model parameters. These concerns are amplified by strict data governance policies and institutional silos that restrict data movement, underscoring the urgent need for privacy-preserving frameworks to enable secure, collaborative analysis while maintaining statistical rigor and practical scalability. This dissertation develops end-to-end, cryptography-based solutions for privacy-preserving genotype-phenotype association studies, and addresses the entire analysis pipeline from preprocessing and harmonization of phenotype data to secure multi-site eQTL mapping to inference on sensitive data using machine learning models. The overarching goal is to design frameworks that are designed to (i) provide strong cryptographic security guarantees, (ii) be computationally scalable to large multi-institution cohorts, and (iii) be statistically robust by preserving accuracy comparable to non-secure baselines. In Chapter 3, we design a privacy-preserving gene expression preprocessing framework that addresses key bottlenecks in federated transcriptomic studies. Our framework provides two secure normalization options — quantile normalization (QN) and relative log expression (RLE) — to allow flexibility depending on data sharing standards, a secure multiparty computation (MPC)-based protocol for inverse normal transformation, and a scalable local principal component analysis (PCA)-based hidden covariate correction strategy. We validate our approach using both simulated multi-institution datasets and real-world gene expression data to show that our methods achieve phenotype correction accuracy comparable to centralized, non-secure pipelines while maintaining privacy of individual-level data. These results demonstrate that federated preprocessing with local computation is feasible and effective for collaborative studies. Building on this foundation, Chapter 4 introduces privateQTL, a secure and scalable framework for multi-center eQTL mapping. privateQTL implements practical genotype and phenotype correction strategies, including genotype population stratification via projection on public reference panels and the privacy-preserving gene expression preprocessing discussed in Chapter 3. We further propose a one-shot matrix multiplication approach that enables efficient nominal association testing and permutation-based false discovery control without repeated communication rounds, significantly reducing runtime. Our evaluation compares privateQTL against meta-analyses and centralized pipelines across multiple axes — eGene and eVariant discovery rates, robustness to batch effects, statistical power, runtime, and memory footprint — using both simulated federated datasets and real-world multi-site data with known batch heterogeneity. Our results demonstrate that privateQTL achieves superior discovery rates compared to meta-analyses, particularly in heterogeneous data settings, while maintaining strong privacy guarantees under a semi-honest adversary model. Finally, in Chapter 5, we address the emerging challenge of using secure machine learning inference for sensitive genomic data. We present two HE-based frameworks: (i) a secure inference method for linear models where both inputs and model weights are encrypted, enabling end-to-end confidential inference without compromising predictive performance, and (ii) a method for secure inference on transformer architectures using approximations for non-linear functions. For linear model inference, we introduce an efficient encoding method that improves computational efficiency during encrypted dot product computation, and for transformer inference, we develop polynomial approximations for nonlinear functions such as softmax, ReLU, and layer normalization to balance computational feasibility with model accuracy. We validate our linear model inference framework on both continuous and binary phenotype prediction tasks using simulated and real data, achieving performance comparable to plaintext inference. For transformer inference, we discuss the challenges of implementing our approximations in a practical and scalable setting for large scale transformer inference and lay the grounds for future work. Collectively, this dissertation makes significant contributions to the field of privacy-preserving biomedical informatics. By providing scalable, modular, and cryptographically sound methods for phenotype preprocessing, federated eQTL mapping, and secure machine learning inference, this work enables collaborative genomic research while rigorously protecting sensitive participant data. The frameworks and findings presented here create a foundation for future developments in privacy-aware collaborative studies, advancing the realization of precision medicine in a manner that respects individual privacy, complies with regulatory requirements, and preserves scientific reproducibility

Living the Part: Stanislavski’s Acting Method Bringing Life to Shakespeare’s Plays through the Art of Reading

This teacher research project explored the use of Stanislavski’s Method Acting as an interpretive reading strategy, specifically for reading Shakespeare’s plays. Drawing on both traditional and contemporary teaching artists, the study examined how the Method was applied to reading instruction with preservice educators in The Teaching of Shakespeare 4551 course at Teachers College, Columbia University, in New York City. By considering the perspectives of both the Method actor and the preservice English educator, the research focused on reflections of activities adapted from Drama Education and Method Acting, applying them to the teaching of reading in English Education. Through dramatically reconstructed retellings of my teaching workshops and an analysis of practice, I sought to answer the following question: What happens when the Method Acting approach is used as a reading strategy for studying dramatic texts? My analysis of the collected data (observations, student writing, and interviews) revealed that through my adaption of the Method as an approach to Shakespearean drama study, my students connected with the text, both intellectually and emotionally, and intended to adopt a similar approach in their own teaching of Shakespeare and perhaps other literary works

RNA-templated DNA synthesis in antiviral immunity and genome evolution

The conventional flow of genetic information proceeds from DNA to RNA to protein, and yet reverse transcriptase (RT) enzymes that reverse this flow are widespread across all three domains of life. Many RTs are associated with RNA-based mobile genetic elements (MGEs), promoting their maintenance and propagation by copying them for long-term storage in DNA genomes. However, expanded surveys of RT diversity have revealed that a substantial fraction of these enzymes lack any apparent connection to MGE mobility, implying that they have been repurposed for distinct cellular roles. In this work, I investigate the mechanisms and biological functions of an enigmatic bacterial RT family, termed defense-associated RTs (DRTs), and uncover a remarkable diversity of pathways through which reverse transcription mediates immunity against bacteriophages. By integrating high-throughput sequencing, genetics, biochemistry, microbiology, and bioinformatics approaches, I demonstrate that DRT enzymes catalyze the synthesis of de novo genes, DNA homopolymers, and tandem-repeat products — each functioning in unique ways to confer phage defense. Furthermore, elucidation of the repetitive DNA synthesis mechanism employed by DRT enzymes reveals an evolutionary link between this bacterial RT family and eukaryotic telomerase. This finding points to an ancient bacterial origin for the DNA repeat addition mechanism that safeguards genome integrity across nearly all eukaryotes. Collectively, this work expands the conceptual boundaries of the genome, highlights novel noncoding functions of DNA, and uncovers a striking diversity of previously unrecognized cellular roles for RNA-templated DNA synthesis

GRID3 COD - Travel Time Friction Surface v1.0

The dataset consists of two surfaces: - Walking cost surface: Applies walking speeds across the entire surface, adjusted for slope and elevation. - Mixed-mode cost surface : Applies motorized speeds on roads and walking speeds in all other areas. Keywords: Travel tim