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A Collaborative Data Integration and Visualization Platform for Planetary Exploration: The Mars 2020 Campfire and Mars Mission Minder
No full textThe Mars Perseverance Rover is critical to a decades-long investigation of Mars’ habitability and NASA’s Mars Sample Return campaign. The Perseverance instrument payload, including SHERLOC, LIBS, and PIXL, is capturing an unprecedented wealth of data about Mars’ surface. We introduce two new tools for data exploration and analytics. Firstly, we apply the Rensselaer Campfire, a multi-user, collaborative, immersive computing interface, to facilitate interactive data analytics with real-time, publicly-available planetary observational data. “Mars 2020 on the Campfire” transcends traditional data presentation methods by integrating a geolocated, multi-faceted dataset atop an interactive map of the rover’s journey. Secondly, through a course-based data analytics research program, we have engaged undergraduate teams to develop an interactive application, the “Mars Mission Minder”, which enables data exploration on personal computing platforms. Both the Campfire and Mars Mission Minder can be shared interactively and virtually across institutions.
The Campfire [1,2] is a desk-height, six-foot panoramic screen and floor projection (Figure 1), connected to two large displays that host data analytics content. The system integrates high-resolution panoramic and aerial imagery, and 3D terrestrial models with instrument data. The Mars Mission Minder is an interactive app that allows users to choose data sets and analytical techniques based on a suite of scientific questions. By leveraging cutting-edge data analytics and sophisticated visualization tools, our project offers a comprehensive, intuitive data exploration. Participating scientists can select data by geospatial location, sampling metadata category, or even PIXL, LIBS, and SHERLOC data class. The data analytics platform allows users to explore the relationship between organic compounds, mineralogy, and inorganic compositions. Results highlight mineral/organic compound co-location, and enable prediction of most likely locations for abiotic organic synthesis. Integrating a comprehensive data analytics suite emphasizes analytical rigor with dynamic visualizations. This platform enables hypothesis testing and can accelerate discovery, foster cross-disciplinary collaborations, and serve as an example for ongoing and future astrobiology and planetary science missions.
[1] EL Ameres, Ph.D. diss., RPI, USA, 2018; [2] EL Ameres and GP Clement, U.S. Patent 10,996,552 B2. May 2021
Characterization of poly(pro-curcumin) polymer thin films and assessment of neuroprotective capabilities in vitro
No full textMay 2024School of EngineeringIntracortical microelectrodes are utilized in brain-machine interfaces to help restore motor and sensory function in patients who have suffered spinal cord injury. They allow for the potential to observe and record neuronal activity, as well as unite brain-computer interfaces for neuroprosthetics, such as prosthetic limbs. While promising for use in neuroprosthetic technologies, electrode functionality is compromised by biological mechanisms, such as inflammatory cascades, that result from electrode insertion into neural tissue. Curcumin, a naturally occurring compound found in turmeric, is antioxidative and anti-inflammatory and can dampen phenomena related to secondary injury. Curcumin is highly hydrophobic and has low bioavailability, limiting it in clinical applications; thus, it is often conjugated into a polymeric drug delivery system to overcome these limitations. This work aims to characterize different formulations of a poly(curcumin-co-polyethylene glycol) (PEG) polymer, referred to as poly(pro-curcumin), at several concentrations to assess their neuroprotective abilities following stimulated electrode implantation neural injury. The two formulations, P50 (50 mol% curcumin:50 mol% PEG) and P75 (75 mol% curcumin:25 mol% PEG), were cast into polymer films at varying weight percentages. Degradation studies, analyzed via mass loss and scanning electron microscopy, revealed that P50 films at lower concentrations (6% and 8%) degraded almost entirely, while the highest concentration film, 10%, degraded to about half of its mass. Additionally, these studies revealed that the P75 films across all concentrations exhibited very little to no degradation. Overall, these findings demonstrate that polymer hydrophilicity plays a greater role in degradation kinetics than polymer concentrations within the selected range. Finally, the polymer formulations were investigated for their neuroprotective and antioxidant abilities following a stimulated injury to neuronal cultures in vitro. Assessment of the cultures revealed that the P50 and P75 films across all concentrations exhibited neuroprotective effects by reducing cell death, with the P75 films outperforming the P50 films. These results provide evidence that solubilized polymer is not necessary to provide neuroprotective effects; the films are able to quench free radicals at their surface. Collectively, this work is the first to characterize P50 and P75 over several concentrations and to assess their neuroprotective abilities. The findings detailed in this work reveal the degradation kinetics and neuroprotective mechanisms of P50 and P75, and provide insight into the selection of specific poly(pro-) material compositions for future applications.M
Drug discovery and molecular mechanisms in alzheimer's disease and hedgehog signaling
No full textJune 2024School of ScienceThe first half of this dissertation focuses on Alzheimer's disease (AD), a neurodegenerative disease that was the 7th leading cause of death in the US in 2021. Characterized by intracellular abnormal tau deposits in the brain, tau aggregates can propagate from one neuron to another in a prion-like manner, mediated by interactions with heparan sulfate (HS) proteoglycans and low-density lipoprotein 1 (LRP1). Here I report the development of two novel AlphaScreen assays, that target the tau-HS and tau-LRP1 interface. From tau-HS AlphaScreen, a small-molecule compound was discovered to disrupt the tau-heparin interaction by binding to heparin with micromolar affinity; and further characterization showed that this compound can disrupt the tau-glycan interface in both in vitro molecular and cellular environments. In our tau-LRP1 AlphaScreen assay, we identified a low micromolar inhibitor, which binds to tau in vitro and inhibits tau uptake in cellular studies.
The second half of this dissertation focuses on Hh autoprocessing and cholesterol binding. The Hedgehog (Hh) signaling pathway plays fundamental roles in embryonic development while abnormal activation of Hh signaling in adults is associated with many types of cancer. Hh signaling is initiated by the Hh ligand, generated from the autoprocessing of Hh precursor protein that undergoes a unique cholesterol autoprocessing reaction called cholesterylation.
Here I report the novel catalytic role of a highly conserved cysteine residue C143 in Hh autoprocessing which mediates the formation of a branched intermediate (BI) thioester which unifies the catalytic mechanisms of Hh autoprocessing with intein splicing, its likely evolutionary predecessor. A novel surface plasmon resonance (SPR) method was developed to study protein-cholesterol binding with biotinylated cholesterol immobilized onto a SPR sensor chip. 6-CF3 modified cholesterol and a non-steroidal Hh inhibitor, PAPP was utilized in 19F NMR to characterize the C-terminal autoprocessing domain of Hh (HhC) binding to cholesterol and that also revealed a surprising ternary interaction among HhC, PAPP and cholesterol.Ph
Machine learning interatomic potentials for first-principles electrochemistry
No full textMay 2024School of EngineeringComputational predictions of material properties are growing in reliance on machine learning tools to unlocking the calculations at the timescales and sizes to compute dynamic properties (e.g., melting temperature, solvation, diffusion, reaction rates).Machine learning methods have mostly been trained and used on the homogeneous materials with a high degree of accuracy, but have not been readily applied to mixed systems of solutes, solvents, and interfaces.
Our research explores use of machine learned interatomic potentials (MLP) on complex systems (molten salts, water, and polymer catalysts) to discover first-principles based properties. First, we study homogeneous molten salts and apply techniques in MLPs to compute phase diagrams. We apply techniques to predict uncertainties of data and MLPs as they project through these calculations to find an optimal density functional theory exchange correlation functional. Then, we develop and characterize MLP performance in inhomogeneous water and molten salt critical to developing solvation models.Inhomogeneous and heterogeneous materials push MLPs to their limit and beyond, requiring new methods for developing custom training data and quality validation.
We show how standard homogeneous potentials fail to capture these environments and the need to design custom training data to teach the potentials properly.
We also develop an alternate physically-informed delta MLP that can similarly capture the inhomogeneous environment of surface tension. Lastly, we study a heterogeneous polymer catalyst system (in fuel cells) to characterize structure and dynamic properties.With the most complex of interfaces, we explore advanced machine learning techniques and new MLPs.
These include on-the-fly learning, delta learning, and state-of-the-art message passing neural networks with equivariance to build a stable and accurate MLP.
This enabled us to determine proton diffusion differences between bulk and Pt surface, surface reactions, and early polymer structuring.Ph
Design and simulation of cryogenic systems and calibrations techniques for the nexo neutrinoless double beta decay experiment
No full textAugust 2024School of ScienceThis dissertation presents a study of the dynamics of cryogenic plants and the implementation of radon injection as a calibration strategy for the nEXO neutrinoless double beta decay experiment. The research focused on three main areas: the design and optimization of cryogenic systems, the simulation of \textsuperscript{220}Rn progeny propagation in a xenon flow, and the development of a laboratory test stand to validate these simulations. Simulations of the EXO-200 xenon plant were developed using the Aspen engineering software suite, validating the modeling strategy for application to the nEXO refrigerant plants. Performance evaluations, conducted with Aspen Plus, included steady-state simulations and dynamic modeling of dual-stage heater and condenser systems based on those in EXO-200. These simulations assessed their ability to regulate pressure and temperature during and after transient upset conditions. Ramped pump speed changes were simulated to reflect typical operational variations, with both heater and condenser models utilizing PID controllers for temperature and pressure regulation. The dual-stage heater mitigated temperature excursions to 0.2 K while ensuring full xenon evaporation, achieving a temperature differential of just 0.001 K. The condenser model limited pressure excursions to bar, well within the 0.35 bar TPC wall limit. Power consumption for the simulated condenser and heater closely matched theoretical values, confirming the reliability of this simulation strategy. Additionally, data from the LXTS slow control system was used to validate the modeling strategy, further demonstrating its applicability to simulating heat exchanger dynamics in xenon recirculation plants. Models were then created of the nEXO Novec-7000 refrigerant system to explore the viability of different operating conditions and plant configurations. These analyses revealed that using 1-inch piping for the entire Novec-7000 plant was inadequate for meeting the circulation needs of the nEXO system. Moreover, the ``top deck'' configuration, where the xenon circulation pump is placed above the cryostat, was found to be unsuitable due to cavitation issues in the pump caused by insufficient inlet pressure. The effect of incorporating valves with varying flow coefficients was analyzed, ruling out the use of valves with flow coefficient , while indicating that valves with work under a variety of configurations. Results of these simulations allowed estimates to be placed on the Novec-7000 pump inlet pressure under these different system conditions, specifying which configurations ensure recirculation of refrigerant and which do not. For the unsuitable ``top deck'' configuration, required Novec-7000 vessel operating pressures to prevent recirculation pump cavitation are instead presented. A radon injection simulation model was developed in this study to leverage the entire decay chain of Rn down to stable Pb to understand the flow of calibration radioisotopes through the nEXO xenon plant. Statistical analyses, including z-tests, p-tests, and Kolmogorov-Smirnov tests, demonstrated the infeasibility of using late-chain isotopes for determining velocity and diffusion coefficients in a small-scale test stand. Fitting procedures applied to the model using krypton tracer data yielded best-fit velocity and diffusion parameters, which were and , respectively. These parameters were then used to estimate radon arrival times in the nEXO radon injection design, with an estimated arrival time to the TPC of s in agreement with the calculated upper limit of 145 s. The developed simulation code will be made available to the collaboration, ensuring its utility in ongoing and future research. A concentration vs. time trend was determined for an updated TPC model with four outlet lines and a tangentially oriented inlet line using SolidWorks flow simulation. These results were coupled with outputs from the radon injection simulation code to generate a plot of activity vs. time in the TPC. From this plot, it is determined that activity in the TPC reaches a local maximum at 0.08 days, after which point total activity drops to 10\% after 1.41 days and to 1\% after 2.67 days. The updated TPC model is shown to promote effective mixing similar to a previously studied model with tangential inlet and outlet lines called Orientation 6. In contrast, Orientation 2, with radially positioned supply and return lines, demonstrates less effective mixing. Activity trends were determined for each of these configurations, with those encouraging better mixing correlating to higher activites in the TPC. These trends confirm that calibration with Rn is feasible using a standard 20 kBq source as 44\% of the Rn isotopes emanated from the calibration source reach the TPC. A laboratory test stand was constructed to experimentally validate the radon injection simulation model in a dual-phase system. This test stand was designed to be sensitive to xenon scintillation produced by alphas emitted along the Rn decay chain. The characterization of the QDrive and PT-100 components was detailed, ensuring their reliability for experimental use. Helium leak testing was performed to guarantee the integrity of the system, with a global leak rate of mbar L/s placed on the test stand, with no detection of localized leakage. The leak rate is below the approximate threshold of mbar L/s which indicates that xenon leakage is dominated by molecular rather than bulk fluid flow, confirming the system's integrity and suitability for commissioning with xenon \cite{pfeiffer2013leak}. Cooling of the xenon condenser was initially achieved with a copper braid in the original design, which was upgraded to a custom thermal link with enhanced cooling capacity calculated to be 67 W for 40-Kelvin temperature differentials across the link. Custom data acquisition software was developed to support the robust operation of the test stand, facilitating efficient data collection and analysis. Argon was studied as a potential inexpensive proxy to xenon for research and development purposes, which necessitated exploring strategies to shift its short-wavelength scintillation light to wavelengths detectable by the photodiode in the test stand. Tetraphenyl butadiene (TPB) spectroscopy studies were conducted to explore the wavelength-shifting efficiency of TPB dissolved in ethanol and toluene for this application. The studies indicated that TPB dissolved in toluene showed markedly higher degrees of wavelength shifting compared to TPB dissolved in ethanol, with TPB coating thickness found to correlate to wavelength shifting efficiency. The TPB-coated slides exhibited flaking over time, making them unsuitable for incorporation in a high purity experimental appartus, indicating that future work must be done to refine the deposition strategy before this material can be used in the test stand.Ph
Towards a Generative Approach for Emotion Detection and Reasoning
No full textLarge language models (LLMs) have demonstrated impressive performance in mathematical and commonsense reasoning tasks using chain-of-thought (CoT) prompting techniques. But can they perform emotional reasoning by concatenating `Let's think step-by-step' to the input prompt? In this paper we investigate this question along with introducing a novel approach to zero-shot emotion detection and emotional reasoning using LLMs. Existing state of the art zero-shot approaches rely on textual entailment models to choose the most appropriate emotion label for an input text. We argue that this strongly restricts the model to a fixed set of labels which may not be suitable or sufficient for many applications where emotion analysis is required. Instead, we propose framing the problem of emotion analysis as a generative question-answering (QA) task. Our approach uses a two step methodology of generating relevant context or background knowledge to answer the emotion detection question step-by-step. Our paper is the first work on using a generative approach to jointly address the tasks of emotion detection and emotional reasoning for texts. We evaluate our approach on two popular emotion detection datasets and also release the fine-grained emotion labels and explanations for further training and fine-tuning of emotional reasoning systems
Feeding the masses: animal experimental farms, nutrition science, and the politics of metabolism
No full textAugust 2024School of Humanities, Arts, and Social SciencesThis dissertation investigates how agricultural animal science research influenced theimplementation of population-level policies around human mass feeding and nutrition in the
early- to mid-twentieth century United States. Following ‘experimental mass feeding’ projects
and theories for both humans and nonhumans, this dissertation shows how developments in
nutrition science and administrative theories of mass feeding interpolated social arenas not
always related to agriculture itself. This project begins at the USDA’s experimental farm in
Beltsville, Maryland, in the early-twentieth century. Unlike other experiment stations attached to
land-grant universities, Beltsville was the only national experimental farm, later earning the
moniker of ‘Uncle Sam’s Proving Ground.’ Focusing on metabolic experimentations, I trace how Beltsville changed within an early twentieth century context of an imperialist United States that was also experimenting with the implications and promises of an increasingly scientized agricultural system. Interested in how agricultural knowledge production travels, I then follow one of Beltsville’s prominent animal nutrition scientists, Paul E. Howe, as he moved from the USDA to the Bureau of Prisons (BOP). I outline how Howe used the scientism of emerging nutritional science to establish nutritional baselines that would protect the prison’s authority and legitimacy, as well as shore up administrative and managerial expertise. This dissertation then analyzes federal massfeeding programs like ‘nutritional engineering’ and ‘nutritional anthropometry’ that gained footholds in a post-WWII era largely due to the significant amounts of federal money supporting feeding experiments through the Office of Scientific Research and Development (OSRD). I show how these federal frameworks of ‘nutritional engineering’ and ‘nutritional anthropometry’ contained within them distinctly agricultural origins, even if those who drew inspiration from
agricultural practices often waxed romance about the bucolic ease of agricultural feeding. This dissertation ends with contemporary examples of experimental mass feeding by way of ethnographic participant observations at industrial trade-shows. ‘Nature-based’ methods of industrial agricultural feeding has emerged in a long line of industrial agricultural techno-fixes that promises to meet global food demand and preserve existing infrastructures in place. I map how hype in both industrial agricultural spaces and human health are looking to the space of the gut microbiome and the gut- brain axis to absorb larger social ills. Ultimately, I demonstrate how my historical scholarship on American experimental mass feeding is crucial to understanding these trends today and their implications for planetary and human health.Ph
Psychometric Assessment Issues and Potential Opportunities
No full textPsychometric assessment is essential for mental health care. However, which assessment instruments are best suited for particular use cases remains largely opaque to researchers, clinicians and policy makers alike. Although there have been metrics proposed to indicate the strength of evidence for assessment resources (SOEFA), the reporting of research evidence needed for these metrics is currently inconsistent and highly fragmented, which complicates evaluation of SOEFA. In efforts to improve the systematic collection and reporting of SOEFA, Hunsley and Mash (2008) and Youngstrom et al. (2017) have proposed a standard set of criteria to evaluate the SOEFA. Twelve categories, including norms, internal consistency, interrater reliability, test-retest reliability (stability), repeatability, content validity, construct validity, discriminative validity, prescriptive validity, validity generalization, treatment sensitivity, and clinical utility, are suggested to evaluate mental health assessment resources as adequate, good or excellent. In an effort to apply these criteria to a widely used measure of youth anxiety and depression, the Revised Child Anxiety and Depression Scale (https://rcads.ucla.edu/; Chorpita et al., 2000), we encountered a variety of challenges due to the fit between standards and the knowledge base represented in published research papers. First, it is difficult to map and connect the proposed criteria to the inconsistent, disintegrated and fragmented research evidence, such as varied use of criteria to determine validity or accuracy of measurement. Second, many assessment instruments exist in different versions, such as translations in different languages, derivatives (e.g., short forms) or respondent formats (e.g., youth or caregiver forms). The provenance of different versions (e.g., which items are newly created or are reused from already existing instruments) is highly opaque, and there is minimal guidance about how SOEFA metrics (indicators about the degree of uncertainty in the expected performance of a specific version) can be applied across resources with shared provenance.
For example, one could assume that different versions inherit the SOEFA (1) of the parent class of instruments or (2) from sibling classes or (3) or not at all. Third, psychometric assessment instruments are always used in a specific context, i.e., with a specific cohort (with a specified age, gender, language, nationality, etc.) and with a specific purpose (of assessment), i.e., screening, supporting diagnosis, or treatment progress monitoring. To inform end users of the potential suitability of an assessment resource therefore requires marshaling large amounts of meta-data about the contexts and cohorts in which the SOEFA metrics were established for each measure. Thus, despite a laudable aim to apply standardized metrics to inform users about the evidence supporting specific assessment, the practical implementation of these standards (or their evolution) requires a significant change to the knowledge infrastructure of psychometric assessment. In our joint work that includes psychological clinical assessment experts along with semantic technology experts, we are exploring a semantic infrastructure that supports the encoding of SOEFA metrics in the context of mental health screening questionnaires. Our current effort involves the modeling of statistical evidence using standardized terminology from established ontologies (such as HAScO, STATO, and ECO). Our goal is to provide a provenance-aware, semantics-powered data portal that can be used by a broad set of users to help understand some of the important nuances of assessment instruments, to guide which instruments are best suited to which purpose, and to expose the reasons for (or against) such choices, in a way that is aligned with the guidance of the best scholars in mental health assessment.National Institute of Mental Health (NIMH
Thermal-mechanical modeling of laser powder bed fusion inconel-718 towards relating processing to properties
No full textDecember 2023Additive manufacturing (AM) offers a plethora of advantages to modern industry, such as the ability to produce complex geometries and offer on-site part replacement. The drawback of incorporating AM techniques for metal structural components is the uncertainty in the integrity of the parts due to the intense and cyclic thermal-mechanical work to which the part is exposed. This work aims to increase the understanding of the linkage between the processing parameters in a laser powder bed fusion (LPBF) AM process of Inconel-718 (IN-718) to the resultant mechanical properties and residual stress field. As a part of this research, a small number of experimental tests are performed to inform and test the performance of the models developed. An initial set of tensile samples of IN-718 is manufactured with varying processing parameters but maintaining the same energy per unit area. These samples undergo tensile testing, density measurements, and microscopy imaging. The experimental work in conjunction with thermal simulations and relations from the literature are used to develop a porosity indicator parameter. Conclusions derived from the tensile samples are used to guide the processing of an additional set of IN-718 sample cubes with different processing parameters and energy densities from that used with the tensile sample set. The porosity indicator parameter is then computed for the cube sample processing conditions and compared to the measured porosity of the cube samples. The results show the expected correlation between the predicted porosity indicator and the measured porosity levels. The porosity indicator parameter is then made non-dimensional through a relation of meltpool geometry to the linear energy density. The simulation work focuses on developing and implementing a temperature-dependent elastic-viscoplastic model, capable of modeling the mechanical behavior from the melt to room temperature, into an in-house finite element code to allow for the prediction of residual stresses due to LPBF processing. The mesoscale model takes the temperature history from LPBF thermal simulations for its input and predicts the resulting stress field after the process. The residual stress fields predicted with the elastic-viscoplastic framework are verified against semi-analytical solutions for simplified test problems. Simulations forming portions of an IN-718 part and predictions of accumulated residual stresses are performed for one layer of powder. This code is used to examine the effects of individual processing parameters on the residual stress field for a limited amount of test cases. The connections explored in this work bring AM IN-718 one step closer to inclusion in wide-scale production with its increased prediction of failure and performance with respect to processing parameters.Ph
Evaluating strategic decision-making with iterative voting
No full textMay 2024School of ScienceSocial choice theory is prolific with paradoxes and impossibilities that prevent cogent justifications for decisions made by groups of people. For example, Gibbard and Satterthwaite proved that no reasonable voting procedure exists that is non-dictatorial and immune to agents misrepresenting their preferences. Significant work has sought to overcome this impossibility by either restricting the domain of agents' preferences or dissuading this form of strategic behavior through computational hardness. The recent approach of iterative voting (IV), rather, aims to characterize the complex interactions ensuing from agents reporting their preferences strategically. In particular, agents may update their votes, given information about other agents' reports, prior to finalizing the group decision. Prior work has documented properties about IV equilibrium and conditions for convergence according to various social choice rules, information agents have access to, and agents' behavioral schemes. Still, only preliminary work has studied the effect IV has on social welfare of equilibrium outcomes relative to the truthful vote. This thesis advances our understanding of strategic behavior in social choice, via IV, on two fronts. First, we study the effect iterative plurality has on the social welfare of the chosen outcome with respect to the worst-case preference profile and as agents have arbitrary rank-based utility. To overcome a poor worst-case result, we study expected performance when agents' preferences are independent and identically distributed according to the impartial culture. Our finding surprises us in that IV helps agents choose higher quality alternatives on average, regardless of the order of their strategic manipulations. We go on to characterize certain classes of preference distributions for which IV improves or degrades social welfare, thus helping to explain why prior experiments attained varying results. Second, we generalize iterative plurality to multiple issues while agents have uncertainty about each alternative's score. In this setting, we identify sufficient conditions for convergence, including O-legal preferences and a novel model about what information agents have access to. Our study through both fronts characterizes agents' behavior given the opportunity to deliberate their votes. Our results provide insight for mechanism designers choosing whether or not to encourage such deliberation, and call for further study of non-incentive compatible mechanisms.Ph