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DIRECT AIR CAPTURE ENABLED BY ELECTROSYNTHESIS OF ACID AND BASE
No full textGlobal efforts to mitigate climate change have accelerated the development of carbon capture and storage technologies, particularly for dilute sources such as ambient air. This thesis presents the design, implementation, and evaluation of an integrated electrochemical direct air capture system that continuously captures and releases CO₂ relying on electricity and salt solutions. The system comprises a digitally controlled electro-synthesizer for in-situ acid and base generation, an air contactor for CO₂ absorption, and a neutralization unit for CO₂ release. All modules were designed for compatibility with ambient operating conditions and optimized for efficient integration.
Comprehensive analytical methods, including real-time infrared gas analysis and titration-based quantification, were employed to assess capture efficiency, alkaline sorbent conversion, and overall system mass balance. Under various operating conditions, the system consistently achieved over 40% capture efficiency and maintained a mass balance closure above 90%, indicating reliable operation, robust system performance, and practical scalability. Notably, the platform was scaled from a lab-scale capture rate of ~10 grams to ~1 kilogram of CO₂ per day without compromising system stability or performance, demonstrating practical scalability for real-world deployment.
By eliminating the need for thermal regeneration and enabling electrochemical regeneration of sorbents under ambient conditions, this approach offers a more energy-efficient and modular pathway for direct air capture. The use of electrical input enables integration with renewable energy sources, and the system's modularity facilitates distributed carbon capture and storage applications. These findings demonstrate the feasibility of an electrically driven, scalable, and field-deployable CO₂ removal system and establish a foundation for future development of carbon capture and storage infrastructure
INDUSTRIAL DECARBONIZATION: WHAT FACTORS MOTIVATE THE DECISION TO IMPLEMENT TECHNOLOGY SOLUTIONS?
Full text linkThe industrial sector in the United States and around the world needs to reduce overall energy usage and greenhouse gas emissions to mitigate the destructive impacts of global climate change. The research questions posed in this report include What factors motivate the decision to decarbonize an industrial facility? Are all these factors purely economic? If not, could any of those factors be categorized as “social” drivers? This narrative literature review consists of a comprehensive review of 21 scholarly articles, government publications, and limited relevant grey literature to identify driving factors of industrial decarbonization. A total of 12 unique factors were identified and subsequently categorized into “economic” “social” or “regulatory” depending on the primary area of influence. While more factors were categorized as economic than any other category, it became clear that there are many motivating factors in each of these three categories and they are not all categorized as economic. A quantitative assessment of 117 industrial decarbonization project sites in the U.S. was completed to identify whether the social driver of “industrial clusters” has already impacted the likelihood of decarbonization taking place. Industrial clusters refer to the proximity of industrial facilities to one another. There are indeed several motivating factors that drive industrial decarbonization through primarily social or regulatory means. This report can be used by any entity, public or private, seeking to increase the speed and adoption of industrial decarbonization solutions. A robust and multi-faceted
approach is required in which all three categories of factors are understood and used
CIRCULATING BIOMARKERS OF SENESCENCE REVEAL HIGH RESOLUTION HEALTH STATUS AND TRAJECTORIES IN HUMAN LONGITUDINAL STUDIES
No full textCellular senescence becomes more common with age and is thought to be a key contributor to age and age-related disease. One key aspect of senescence that facilitates its deleterious effect is the pro-inflammatory cytokines, among other proteins that are expressed and secreted by senescent cells, known as the senescence-associated secretory phenotype (SASP). In recent years, the proteome has been tracked in circulating plasma and showed associations with clinical traits and disease. These findings present the captivating possibility that the plasma proteome could be leveraged as a noninvasive diagnostic tool in clinical settings. Considering that senescence is a key hallmark of aging, the SASP could hold unique clinical relevance in circulation. One key challenge in similarly assessing senescence burden in circulation is that SASP are numerous and heterogeneous by cell type and induction method. Though some tissue-specific and canonical SASP have been examined in circulation, clinical associations of the broad range of tissue-specific SASP have been largely uncharacterized. This study undertakes a comprehensive investigation into clinical associations of tissue-specific senescence proteins in circulating plasma in two longitudinal studies, including 1275 individuals (ages 22-96) from the Baltimore Longitudinal Study of Aging (BLSA) and 997 individuals (ages 21-98) from the Italian Invecchiare in Chianti study (InCHIANTI). Tissue-specific senescence markers are identified from 15 total cell types including monocytes, astrocytes, and renal endothelial cells, among many others. These senescence signatures were then assessed for associations with a broad range of age-related clinical parameters. Senescence signatures were associated with a broad range of clinical traits in both the BLSA and InCHIANTI, and tissue-unique senescence signatures showed unique clinical relevance. For instance, kidney senescence signatures showed the strongest association with kidney disease. Additionally, PBMC senescence burden showed strong associations with multiple clinical parameters. These results suggest that senescence in immune cells may play an important role in age-related decline and demonstrate that individual senescence burden can be modeled with higher resolution on the organ-specific level than previously determined. This study extensively characterizes associations between circulating senescence signatures and clinical traits in humans and identifies biomarkers of senescence that can inform future clinical study
A BRIEF DIP IN THE SCIENCE POOL A THESIS
No full textThis thesis is a collection of works by a writer with no background in “hard” sciences. Instead, the author was a student in the humanities and social sciences, with an interest in bringing skills from science writing to those fields. This interest morphed over time into a passion for accessibility of information and its ability to create informed citizens in a time of rampant misinformation. This thesis is made up of a collection of profiles, essays, and articles on people and their contributions to science and communication in their various fields
Exploring the Epigenetic Regulation Using a CRISPR Edited Cell Line
No full textThe ATRX (Alpha Thalassemia/Mental Retardation X-linked) protein is a critical chromatin remodeler with multifaceted roles in genome maintenance. This study explores ATRX’s functional domains, emphasizing its ability to recognize repressive histone marks and restructure chromatin through its SWI/SNF helicase activity. Following sessions highlight ATRX’s role in silencing transposable elements via H3.3 deposition, as well as its contributions to replication stress resolution and DNA repair, where it stabilizes stalled forks and suppresses aberrant recombination. To investigate ATRX’s biological significance, I generated an ATRX knockout (KO) cell line, validated by Western blot (WB) and immunofluorescence (IF). These findings underscore ATRX’s importance in maintaining genomic stability and cellular homeostasis. Our work provides insights into the molecular mechanisms underlying ATRX-related disorders, including neurodevelopmental syndromes and cancer
Mechanisms underlying naturalistic event encoding and retrieval in the posterior midline default mode network
No full textEvent segmentation, the process of chunking continuous experience into discrete units (events) in the mind, shapes memory organization. It engages the hippocampus, a key subcortical structure for episodic memory, at event boundaries where one segment transitions to the next. However, our understanding of the cortical mechanisms underlying event encoding and the spoken recall of these events remains limited. This dissertation investigates these mechanisms using two naturalistic paradigms: passive movie-viewing and volitional web-browsing. The default mode network, positioned at the apex of a cortical hierarchy organized by processing timescales, integrates information over long timescales. These timescales correlate with the level of abstraction in cortical representations. Additionally, research suggests that transitions between stable neural states in the default mode network align with human-identified event boundaries. In the first study, I examined how cortical areas with different timescales respond to event boundaries when those boundaries are perceived with varying degrees of salience. In sensory regions with short timescales, which serve as the initial entry point for event segmentation, boundary-triggered shifts in spatial activity patterns were strongly modulated by boundary strength. However, this effect diminished in the default mode network, which operates on longer timescales. Next, I investigated the spatial structure of activity patterns in the posterior midline default mode network during encoding and recall. These regions showed event-specific activity patterns that were distinct across events and similar between matching encoding and recall pairs, consistent with recent literature. However, a data-driven analysis that grouped individual time points based on the similarity of spatial activity patterns revealed that a small set of spatial activity states (16 across hemispheres) was sufficient to capture memory reactivation of event-specific patterns when subjects verbally recalled events from 10 different movies. These neural states were shared across individuals and linked to stimulus features during encoding, supporting the idea of a low-dimensional neural code in these regions. Lastly, preliminary analyses showed that in the posterior midline default mode network, major context transitions elicited similar response profiles between movie-viewing and web-browsing. Collectively, this dissertation advances understanding of the neural mechanisms underlying the encoding and retrieval of naturalistic experiences
Developing and Evaluating Spatial Statistical Models for Predicting Presence: Application to the State of New York
Full text linkAccurate forecasting of vector abundance is essential for anticipating disease risk and implementing timely public health interventions for all vector-borne diseases. In this study, we analyzed spatial occurrence patterns of nymphal Ixodes scapularis ticks using a data set of field-collected tick counts from 614 locations across New York State (NY), USA. We explored tick abundance, employed statistical models to predict tick occurrence, and assessed predictive accuracy. Our results show marked spatial heterogeneity in tick populations, with a higher abundance concentrated in southeastern and central NY, and expanding into previously low-density regions in the north and west. Comparing Generalized Linear Model (GLM) and Generalized Additive Model (GAM), we found that GAM more accurately predicts tick occurrence, though there are locations where GLM performs better. Ordinary kriging results further confirmed GAM’s superior performance, highlighting its utility for forecasting tick distributions at fine spatial scales. These predictive frameworks offer valuable tools for risk assessment, guiding public health outreach, vector control efforts, and individual prevention behaviors to mitigate tick-borne disease transmission
The Maternal Effect of SKN-1B and DAF-7 on Intergenerational Pathogen Avoidance Learning in C. elegans
Full text linkOrganisms continuously adapt to their dynamic environments by modifying behaviors that enhance survival, a process driven by changes in the nervous system. Maternal influences play a critical role, as environmental exposures can reprogram gene expression in the germline, facilitating intergenerational transmission of adaptive responses. These maternal effects have significant implications for health, adaptation, and evolution. Maternal provisioning shapes many biological processes, from transposon silencing to stress responses such as starvation, heat, and osmotic stress. It also influences behavior, with mouse models demonstrating inherited sensitivities to environmental cues like fear learning. In humans, ancestral factors, including nutrition, psychosocial stress, and exposure to environmental toxins, have been linked to altered health outcomes in descendants, underscoring the impact of maternal effects on physiology and behavior across generations. However, investigating maternal contributions to adaptive responses in human offspring is confounded by ecological, genetic, and cultural variability. The nematode Caenorhabditis elegans serves as an ideal model for maternally mediated adaptive inheritance, offering genetic tractability, a short generation time, and a mapped neuronal connectome. These advantages enable precise spatial and temporal control of critical molecular players, providing insight into the mechanisms governing behavioral inheritance.
Recently, studies using C. elegans have identified key regulators of maternally transmitted adaptive responses through the paradigm of pathogen avoidance behavior in response to Pseudomonas aeruginosa (PA14). This inheritance requires the TGF-β ortholog DAF-7, a neuroendocrine ligand secreted by ASI sensory neurons. However, its regulation remains unclear.
We demonstrate through behavioral assays and fluorescence microscopy that the Nrf2 transcription factor SKN-1 suppresses daf-7 expression, and its loss in germline-deficient mutants restores pathogen avoidance behavior. SKN-1 regulates homeostasis, detoxification, immunity, proteostasis, and metabolism, with one isoform, SKN-1b, localized to ASI neurons. The co-expression of SKN-1b and DAF-7 suggests a functional interaction in modulating pathogen avoidance. This dissertation advances our understanding of the molecular mechanisms governing acquisition and transmission of behavioral adaptations in response to environmental stressors
INTEGRATING MOBILITY AND GENETIC DATA IN MALARIA MODELING: A FRAMEWORK FOR UNDERSTANDING TRANSMISSION HETEROGENEITY
Full text linkAbstract
Due to the interaction between human mobility and the genetic complexity of Plasmodium falciparum, malaria elimination faces ongoing challenges. While genomic surveillance has made progress in tracking viral pathogens, parasitic diseases like malaria face unique challenges, that prevent the direct translation of these methods including complex infections and recombination. This study presents a novel modeling framework that combines individual-based epidemiological dynamics while directly recording the genetic haplotype of parasites during simulated transmission events. Further, the individual human and vector cover multiple spatial units that allow for interaction between different transmission settings. Here we used this model to explore how mobility between patches can drive genetic relatedness between populations. We considered two mobility scenarios, uniform and skewed travel patterns, which described the different distributions in the probability of travel for infected individuals. We then investigated how these behaviors influence local transmission and the genetic structure of infections. Parasite genomes are explicitly tracked in the simulation, allowing inference of transmission relationships to be subsequently inferred from genetic data. Simulation results indicate that increased migration from rural to urban areas amplified genetic mixing. Furthermore, when the probability of travel is skewed, i.e. there are few individuals who take the majority of trips ,genetic patterns between the populations are more distinct. By linking observable genetic markers to underlying transmission processes, this study provides a mechanistic foundation for interpreting genomic data in malaria epidemic contexts. This framework offers a practical tool for assessing the impact of interventions, optimizing monitoring strategies, and identifying hotspots for reintroduction risks. The integration of genetic and mobility data lays the foundation for more sensitive and tailored malaria elimination efforts
TARGETING PTPN22 FOR CANCER IMMUNOTHERAPY
No full textImmunotherapies have shown remarkable success in cancer treatment. However, diverse
tumor-specific immunosuppressive mechanisms dampen response rates highlighting the need
for novel immunotherapeutic targets. Protein Tyrosine Phosphatase Non-Receptor Type 22
(PTPN22) is a phosphatase specific to hematopoietic cells that negatively regulates immune
responses. For example, it functions as an early break on T cell activation by dephosphorylating
tyrosine residues on Lck and ZAP70 in the T cell receptor signaling cascade. We previously
demonstrated that PTPN22 abrogation can enhance antitumor efficacy against immunogenic
MC38 tumors. However, it is unclear whether PTPN22 can serve as an immunotherapeutic
target in more immune-resistant cancer types. Here, we evaluate the role of PTPN22 in
antitumor immunity across tumor models of varying immunogenicity. We utilized tumor mouse
models of varying immunogenicity ranging from highly immunogenic lung carcinoma (CMT167),
and moderately immunogenic models of hepatocellular carcinoma (HCC) (Hep53.4 and RIL
175), to immune-restricted lung models (LLC1) and triple negative breast cancer (TNBC)
models (E0771.LMB and 4T1). Tumors were harvested for tumor microenvironment (TME)
analysis using immunohistochemistry, high-parameter suspension and imaging mass cytometry
(IHC, CyTOF and IMC, respectively). Monotherapeutic PTPN22 targeting led to a substantial
reduction in tumor growth as expected in the immunogenic models, CMT167, Hep53.4, but also
in the relatively immunosuppressed models, RIL-175, E0771.LMB, and 4T1. Analysis of tumor
infiltrating immune cells using high-parameter mass cytometry revealed that T cell subsets
exhibited increased expression of Granzyme B, PD-1, and co-stimulatory molecules such as 4
1BB, and ICOS indicating improved activation. Similarly, myeloid subsets including tumor
associated macrophages (TAMs) displayed upregulation of CD86 and OX40L suggesting
enhanced capacity for co-stimulation and repolarization of TAMs into a more pro-inflammatory
phenotype. Additionally, spatial analysis of orthotopic models revealed reorganization of immune
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cells in the TME creating T cell-TAM interactions that are more favorable to antitumor immune
responses. Collectively, our results show that abrogation of PTPN22 yields improved anti-tumor
efficacy in a range of tumor models. Furthermore, PTPN22 abrogation modulates T cell and
myeloid populations in the TME of multiple cancer models towards a more activated and
proinflammatory phenotype. Further work to identify PTPN22-dependent signaling pathways
involved in antitumor efficacy is warranted