Plants are generally unable to move reasonable distances as single adult organisms. This lack of mobility necessitates robust pathways that allow for response to environmental stimuli which can cause changes in the plant’s morphology to adapt to their changing environment. One such trait is flowering, and it is crucial for plant survival because it allows for plants to reproduce. The vast majority of agricultural plants are flowering plants, and robust growth and reproduction of these plants is especially important for the growing human population. This experiment aims to understand the interaction between an Arabidopsis thaliana nitrogen response gene named NITRATE-INDUCIBLE GARP-TYPE TRANSCRIPTIONAL REPRESSOR 1 (NIGT1), and an Arabidopsis gene which controls flowering called FLOWERING LOCUS T (FT). NIGT1 is known to modulate plant flowering despite being a nitrogen response gene. Here, I used a Dual-Luciferase Reporter System to test whether NIGT1 proteins directly interact with the FT promoter to regulate FT gene expression. In this system, the FT promoter is used to drive expression of Firefly Luciferase rather than FT. Separately, the NIGT1 gene coding region will be highly expressed under a constitutive 35S promoter. It will also be fused to the transactivation domain of the viral protein VP16 which converts transcriptional repression into transcriptional activation, as NIGT1 is known to be a transcriptional repressor. The VP16 transactivation domain will be repeated as four tandem repeats, forming the construct called VP64. The gene constructs of interest will be inserted into Nicotiana benthamiana using agrobacterium infiltration, then the leaf material from N. benthamiana will be used for the Luciferase assay. I hypothesize that the binding of NIGT-VP64 to the FT promoter will cause increased in Firefly Luciferase production compared to the absence of NIGT1-VP64. Higher amounts of Firefly Luciferase will result in greater luminescence, which we will quantify with a luminometer.

Family medicine requires effective dissemination of its growing research base to inform practice, education, and policy. The new Consensus Reporting Items for Studies in Primary Care (CRISP) guidelines may contribute to success. We will describe the pathways primary care (PC) research follows from presentation to publication and test if encouragement to use the CRISP guidelines is associated with increased acceptance and publication rates. We are conducting a confidential online survey in two phases of everyone who presented original research at the November 2023 meeting of NAPCRG (North American Primary Care Research Group), using the Qualtrics platform. Currently in progress, Phase 1 collects data on presenters, studies, research reports, author teams, submission processes, acceptance rates, and publication outcomes. Bivariate and multivariate analyses will identify factors associated with submission, acceptance, and publication. In Phase 2, a randomized controlled trial (RCT) will assign participants to either an observation-only control group or an intervention group receiving the CRISP guidelines. A follow-up survey at 6-9 months will assess presenters’ experiences with acceptance and publication of their written reports. The ongoing Phase 1 survey of 659 presenters worldwide includes diverse professions, specialties, scientific disciplines, and research roles. The presented studies include a broad range of research methods, study designs, problems, populations, and research questions. We will describe presenter experience with the submission, acceptance, and publication of these studies and examine associations with characteristics of the researchers, studies, and reports. The later Phase 2 RCT and follow-up survey will test for differences between the CRISP guideline group and the observation-only control group in success with acceptance and publication. Study results will describe the current practices and patterns of submitting and publishing reports of PC research to guide the dissemination and implementation of research findings to help improve patient care and population health.

Existing studies show the importance of migration in ensuring both the livelihood and safety of migrants around the world, and that factors such as ethnicity and caste have a significant impact on an individual’s ability to migrate. My study aims to further explore the association between caste/ethnicity and the order of starting, intermediate, and most recent destinations a migrant passes through. To do so, I perform a sequence analysis on places traveled to by participants of the Chitwan Valley Family Study (CVFS) dataset. CVFS focuses on the Chitwan Valley region in Nepal, a country that hosts a variety of ethnic groups and has a wide range of destinations that its inhabitants migrate towards, with a much higher rate of human migration than other countries of similar population size or GDP. Using the dataset, I also search for common factors between caste groups, such as the kinds of intermediate locations they may travel to before heading to a more permanent destination, and how long certain castes take to migrate either due to legal issues, their own caution or the resources available to them. I expect people of lower caste to face more difficulty in migrating due to how caste profoundly influences social life in the region. This transforms how we view the impact of caste or ethnicity on migration, not just isolated to the Chitwan region but across any region involving the migration of multiple ethnic groups. It is also relevant to note that sequence analysis has little pre-existing use in migration sociology, especially when focusing on migration across multiple continents. Therefore, this study presents a new method of studying migration along with a better understanding of what goes on between the assumed starting and most recent point of a migrant’s journey.

The lithium isotopic composition of metasomatized rocks preserves a history of fluid movement that may be related to large subduction zone earthquakes. High pressure and temperature conditions within subduction zones cause the dehydration of hydrous minerals, and the resulting fluid can raise the pore pressure and trigger slip along the plate interface. The role of fluid movement in subduction zone processes can be better understood by constraining the duration of these events. These relatively fast and periodic fluid increases are recorded by chemical diffusion between the fluid and rock. Fluid containing lithium within a subduction zone can drive the diffusion of lithium into the surrounding rock. Lithium has two stable isotopes that diffuse at different rates, ⁶Li diffusing faster than ⁷Li, creating spatial heterogeneity in the isotopic composition of the reacted rock. Metamorphic rocks from the Western Alps have a reacted rind structure where fluid interaction occurred within an extinct subduction zone. The period of this interaction was examined using lithium isotopes. I prepared these samples for isotopic analysis, first weighing rock powders, then digesting them in acid, and finally separating the lithium with cation exchange columns. Lithium isotopes were measured on a multi-collector inductively coupled mass spectrometer. The spatial distribution of lithium isotopes along the profile from the reaction rind to the unreacted core, together with a thermodynamic model of Li diffusion through the rock, constrain the duration of the fluid interaction and provide insight into the role of fluid in catalyzing slip along the plate interface. I expect the duration of this fluid contact to be short, consistent with pulsed fluid movement within subduction zones. Modern subduction zones, including the Cascadia Subduction Zone that underlies Seattle, pose seismic hazards that can be better understood by examining the relationship between fluid movement and slip in extinct subduction zones.

One of the core problems in the analysis of protein tandem mass spectrometry data is the peptide assignment problem: determining, for each observed spectrum, the peptide sequence that was responsible for generating the spectrum. Two primary classes of methods are used to solve this problem:database search and de novo peptide sequencing. State-of-the-art methods for de novo sequencing employ machine learning methods, whereas most database search engines use hand-designed score functions to evaluate the quality of a match between an observed spectrum and a candidate peptide from the database. We hypothesize that machine learning models for de novo sequencing implicitly learn a score function that captures the relationship between peptides and spectra, and thus may be re-purposed as a score function for database search. Because this score function is trained from massive amounts of mass spectrometry data, it could potentially outperform existing, hand-designed database search tools. To test this hypothesis, we re-engineered Casanovo, which has been shown to provide state-of-the-art de novo sequencing capabilities, to assign scores to given peptide-spectrum pairs. We then evaluated the statistical power of this Casanovo score function, Casanovo-DB, to detect peptides on a benchmark of three mass spectrometry runs from three different species. Our results show that, at a 1% peptide-level false discovery rate threshold, Casanovo-DB outperforms existing hand-designed score functions by 35% to 88%. In addition, we show that re-scoring with the Percolator post-processor benefits Casanovo-DB more than other score functions, further increasing the number of detected peptides.

Understanding how DNA folds in three dimensions is crucial for deciphering cellular function. Chromosomal contacts are interactions between different DNA regions. These contacts hold key information about tissue-specific characteristics, such as gene expression and regulation. However, current predictive models for genome folding primarily focus on within-chromosome interactions, largely ignoring variations across tissues and the role of interactions between chromosomes (trans-contacts). To address these issues, we developed TwinC, a machine learning model that predicts trans-contact maps from pairs of nucleotide sequences. To build TwinC, we used a convolutional decoder coupled with an encoder architecture that can be configured to employ transformers, convolutional networks, or a hybrid approach. Preliminary results suggest that the convolutional architecture achieves performance comparable to Orca, the current state-of-the-art in sequence-to-contact predictions. TwinC is trained and evaluated on contacts measured in two human tissues and one mouse tissue. We are experimenting further with other encoder architectures, fine-tuning the model, and investigating how it generates its predictions. This research will provide valuable insights into the underlying biological mechanisms responsible for chromosomal contacts and lead to an improved, high-performance model for predicting trans-contacts.

De novo sequencing of peptides is a key technique in proteomics mass spectrometry for detecting and quantifying proteins from biological samples without the use of a reference protein database. This is necessary when working with organisms with low proteome coverage in extant databases, such as in metaproteomics, and in circumstances where the space of possible peptide sequences is extremely large, as in immunopeptidomics. Currently, all state-of-the-art de novo sequencing methods employ deep learning models. However, the majority of the available data used to train these models comes from mass spectrometry experiments that use the enzyme trypsin to digest proteins into peptides. Consequently, these methods exhibit a strong learned bias toward peptides generated by tryptic digestion, and degraded performance when applied to spectra generated by alternative digestion enzymes. This bias limits the models’ ability to generalize to other settings, where the use of alternative digestion enzymes can preserve sequence features that are lost from tryptic digestion. We approach this problem by modifying the state-of-the-art deep learning model, Casanovo, to incorporate an explicit representation of the digestion enzyme as part of its input. We then train Casanovo on a set of spectra digested using a wide variety of enzymes, providing the enzymatic context to the model to facilitate learning the diverse spectral and peptide patterns inherent in non-tryptic digests. We observe that our enzyme-aware model learns enzyme-specific digestion rules and shows significant improvements over the control model on enzymatically diverse data. The enhanced generalizability of our model will enable proteomics researchers to improve the robustness of their de novo sequencing workflows in settings that employ non-tryptic digestion.

Land plants have evolved the ability to uptake silicon from the soil and deposit it as silica-based structures called phytoliths in their tissues. Phytoliths are hypothesized to play a variety of roles in plants including contributing to structural support, defense against abiotic stressors such as drought, and herbivore deterrence. Grasses (Family Poaceae) in particular are known for their high silica concentrations of up to 40% of dry mass. We are investigating whether or not high silica concentrations are more common in grassland species and are associated with C4 photosynthesis. This directly tests the “C4-grazer hypothesis,” which states that, compared to other species, C4 grasses (i.e., those with adaptations allowing more efficient photosynthesis under hot and dry conditions relative to the ancestral C3 photosynthesis) have evolved increased silica accumulation as a response to ungulate herbivory in grassland environments. Using material from herbariums across the world, we have prepared 482 grass leaf samples for analysis. Our samples derive from approximately 200 species from all 12 subfamilies of Poaceae. These samples are analyzed using a portable X-ray fluorescence spectrometer to measure silicon concentration, which serves as a proxy for accumulated silica. To do so, we have established a custom calibration and protocol for measurements. Preliminary results show that high silica concentrations have evolved in a range of grasses, including species occupying both open grasslands and shady forest habitats, and both C3 and C4. This suggests that the mechanisms and influences on silica accumulation may depend not only on photosynthetic pathway and herbivory pressure, but may also depend on other evolutionary and environmental factors. Overall, this research will improve our understanding of how grasses adapt to stressors that will worsen under climate change, and how grasses might contribute to global carbon-silicon cycling.

Local Group galaxies are the closest ones we can study in detail to decipher the processes that shape the universe around us. An interesting property of these galaxies is their star formation history (SFH), which provides a fossil record of when stars were formed in a galaxy. The process by which this occurs is a complex interplay between the gas, the interstellar medium (ISM), and the energy from newly formed stars. By pairing SFH measurements with data on the galaxy’s gas content, we can investigate the timescales on which young massive stars affect the structure of the surrounding gas in the (ISM) as well as its ability to form more stars. Furthermore, since star formation is closely linked to the properties of the gas in a galaxy, such as metallicity and extinction, the SFH also probes these properties. By utilizing resolved stellar photometry from the Hubble Space Telescope (HST), I measured the SFH for four Local Group galaxies (IC10, IC1613, WLM, and NGC 6822) that already have detailed imaging of their gas content from radio observations. First I measured the colors and brightnesses of resolved stars in each galaxy from the HST imaging. Next, I generated and processed a set of artificial stars using the same photometry pipeline as the real observations to provide statistical measures of our data quality. With the processed artificial stars and the original photometry, I then fitted a series of model Hess diagrams for a range of ages and metallicities to obtain each galaxy’s SFH. These measurements allow us to pair this SFH with other observational data. For example, we can map star formation and compare it with observations such as supernovae locations, and we can explore links between the star formation and the ISM as measured through emission from neutral and ionized hydrogen.

The expected launch of the Nancy Grace Roman Space Telescope (Roman), a next-generation space-based infrared observatory, will soon allow us to observe fields in minutes that would previously have taken months to cover. As such, it will advance our knowledge of galactic structure and evolution at a rapid rate. To better leverage the influx of science that will come out of the launch and commissioning of Roman, we are developing a pipeline that simulates observational images taken by Roman and then performs photometry on the images. As a first step, we are testing methods for recovering dwarf galaxies from photometry catalogs that contain both a dwarf and a surrounding stellar halo. In order to produce mock Roman observations, we use the Space Telescope Science Institute's Space Telescope Image Product Simulator (STScI-STIPS) software tools, which are able to add background galaxies, realistic background levels, and noise along with source catalogs to produce simulated images. Our early testing uses input catalogs designed to simulate typical dwarf galaxy characteristics added to stellar catalogs generated from numerical simulations that mimic the nearby spiral galaxy M81. The ultimate goal of this pipeline software is to determine the observational strategy to resolve structures in extended stellar halos of nearby galaxies. Such measurements would allow us to distinguish between formation and evolution scenarios for these halos. This work is part of the Roman Infrared Nearby Galaxies Survey (RINGS), a large Roman Wide-Field Science (WFS) program funded by NASA under grant 80NSSC24K0084.