Schedule of Events

Microplastic pollution is a significant environmental concern, particularly incoastal and agricultural regions where human activities contribute tocontamination. This study compares microplastic and microfiber pollutionbetween Channel Islands Harbor, a recreational and commercial hub, and theSanta Clara Riverbed, which runs through agricultural land before emptyinginto the Pacific Ocean. By analyzing sediment samples from both sites, weaim to identify the sources and distribution of microplastic contamination inthese contrasting environments.Sediment samples were collected at high tide marks from both locations andprocessed using vacuum filtration and Rose Bengal staining to distinguishsynthetic microplastics from organic material. Samples were examined undera Nikon dissecting microscope for quantification, and Fourier TransformInfrared Spectroscopy (FTIR) was used for material identification.Preliminary results indicate higher concentrations of microfibers in theharbor, likely due to boating activity, urban runoff, and marineinfrastructure, whereas the riverbed samples contained more microplastics,likely originating from agricultural runoff, textiles, and irrigation systems.The findings highlight distinct pollution sources in coastal and inlandenvironments, emphasizing the need for further research on pollutionmitigation and regulatory measures.

To search for magnetic fields in evolved OBA stars, the Large Impact of magnetic Fields on the Evolution of hot stars (LIFE) project used the spectropolarimeter ESPaDOnS@CFHT to observe the B8 II star, HR 3042. Initial observations showed the average line shape was asymmetric.  Such asymmetries can be caused by chemical inhomogeneities, pulsations, and/or binary companions. An additional 16 spectropolarimetric observations were taken and the data were normalized.  The equivalent width (EW) was calculated using python for several lines of He, H, Fe, Si, Mg, and C.  For single elements, spectral lines for different transitions sometimes matched well, while others matched poorly.  For example,  Fe II 4584 Å and Fe III 5127 Å experience a peak at a rotational phase of 0.5, while Mg II 4481 Å experiences a peak and Mg II 6347 Å experiences a minimum at a rotational phase of 0.5.  We used Specpolflow and the multi-line technique Least Squares Deconvolution (LSD) to determine average Stokes I and Stokes V line profiles from which the surface-averaged longitudinal magnetic field strength (Bl) was calculated.  We observed both the positive and negative poles of the magnetic field with the maximum value of the dipolar magnetic field strength, Bd, of estimated value of 629.4 G occurring at a rotational phase of ~0.5.  We note that the EW extremes generally occur when the magnetic field is at its maximum; thus, the variation of chemical abundance and magnetic field are likely connected.

The Ventura - Santa Barbara region is one of California's most seismically active areas, characterized by a complex geomorphological setting of highly active thrust faults that have shaped the east-west Transverse Ranges. This region has a long history of seismic activity, including large M7.0+ earthquakes, triggering subsequent hazards such as mass wasting events, which historically have also resulted in regional tsunamis. This study consolidates recent and historical scientific data, as well as accounts from local Indigenous peoples and Spanish missionaries, to assess seismic hazards. Using historical data and USGS projections, a data analysis was conducted to better understand historic seismicity, potential faults of high concern, and future earthquake scenarios. Data analysis was performed using R programming in Collab constructed by Anthony D. Salas, which focused on the distribution, frequency, potential fault origins, and locations of earthquakes in the region since 1800. These findings were then compared to historical seismic events to identify seismic “hotspots” and assess future risks. A review and analysis of multiple high-risk fault systems in the Ventura-Santa Barbara region was conducted alongside the examination of historical seismic activity to more precisely assess earthquake potential and narrow realistic future earthquake scenarios. Due to a lack of reliable records prior to 1800, many seismic events and their associated hazards remain poorly constrained, underscoring the need for further paleoseismic research to enhance our understanding of the region’s seismic history and the extent of existing hazards.

This research project focuses on developing transparent and generalizable neural networks for accurate chemical toxicity prediction using MATLAB and Python. By leveraging an expanded dataset of over 50 xenobiotics and reactive metabolites, the study addresses the limitations of commercial "black box" models, which often lack interpretability and struggle with diverse chemical groups. The enhanced framework incorporates automated data filtering, MinMax normalization, and modular neural networks with three hidden layers. MATLAB employs Levenberg-Marquardt optimization, while Python utilizes Keras/Adam. Results show that Python models achieve slightly better generalizability (0.91 vs. 0.89), while MATLAB trains 30% faster, with both outperforming commercial tools in transparency and achieving low MSEs (near 1e-9). The study provides accessible open-source tools and visualizations, enabling researchers to predict toxicity across a wide range of chemical structures. This work bridges the gap between performance and interpretability, offering a valuable resource for future environmental and biological analyses

Given the rapid increase in mutual funds and ETFs, understanding fund similarities is essential for tailored investment strategies, effective risk management, and informed decision-making. Traditional methods of quantifying fund similarity often fall short due to subjectivity and the complexity of nonlinear relationships among assets.
 
Introducing Fund2Vec: a graph machine learning approach to evaluate mutual fund similarity. By representing funds and assets as a weighted bipartite graph and using the node2vec algorithm, we gain a nuanced understanding of fund similarities. The previous authors used a k-means clustering approach to tune the hyperparameters of the embedding. We proposed to replace k-means clustering with Gaussian model-based clustering for better separation of funds and assets in the embedding space. Our method outperforms traditional k-means clustering in identifying anomalous funds, aiding in risk management and investment strategy.

~10% of OBA type stars (~3x Msun) exhibit strong, large-scale, stable magnetic fields, which can significantly impact both their internal dynamics and surrounding galactic environments. This study focuses on the magnetic B-type star HD 23478, whose geometry complicates the analysis of its dynamics and magnetic field. 10 archival and 18 new high-precision spectropolarimetric observations of the star were obtained using ESPaDOnS@CFHT and Narval@TBL. These data were analyzed using custom Python scripts that normalized individual elemental absorption lines, calculated their equivalent widths and corresponding errors, and plotted these against the star’s rotational phase. Our analysis focused primarily on Hα, Hβ, HeI 4713 Å, HeI 5876 Å, along with Si 4553 Å, and C 4267 Å.

Microplastic contamination is a global issue that is harmful to the environment, marine life and even humans. Our study focused on the potential microplastic pollution of fish in the area of Channel Islands Harbor in Ventura County. Our method was to dissect and analyze the tissues of several fishes, Scomber japonicus (Pacific mackerel) and Atherinopsis californiensis (Jacksmelt), using a dissecting microscope and Fourier transform infrared spectroscopy (FTIR) for quantification and identification of the micro- contaminants. The previously frozen fish had been caught in May of 2024 as future lobster bait four months prior to the start of this study. Our research identified a number of microplastics found deeply embedded in the tissue walls of the fish including the presence of polyacrylamides (PAMs) in the oviduct of a female Pacific mackerel and in the G.I tract of another Pacific mackerel. This study suggests that remediation of micropollutants in the marine environment is needed to protect marine species and the humans that consume them.

Microplastic contamination of the world’s ecosystem has been documented in many studies overthe past two decades. This study focused on the possibility that microplastics could make theirway into root vegetables that are constantly in contact with the soil. The presence ofmicroplastics in root vegetables was proven using Fourier transform infrared spectroscopy onsamples collected from three, organically grown, root vegetables from various sources. Thisstudy strongly suggests the prevalence of micro-contaminants in a common component of thehuman diet. The spectra were compared with a commercial library of standards from AgilentTechnologies. Peaks were assigned for functional group recognition. These spectra matchedmaterials such as alginic acid, polystyrene, fiberglass, and butadiene rubber. Anthropogeniccontamination of these food groups could signify a major cause of disease now and in the future.

Studying the structure and function of oxidized microplastics is vital to correctly identifying them in their oxidized form in the environment (beach, ocean, etc.). The current limitation of microplastic analysis is that FT IR Spectroscopy reference libraries only show the unoxidized form of these plastics, making it challenging to identify aged and weathered plastics. This study aimed to bridge that gap by creating a library of artificially oxidized polymers. We hypothesized that exposure to UV light would induce oxidative changes in the microplastics, leading to the addition of peaks in their FT IR spectra. First, we established a baseline of the three most commonly found plastics in nature–polypropylene (PP), polyethylene (PE), and polystyrene (PS)–and confirmed their spectra to current reference libraries. The samples were then subjected to three separate oxidation techniques: heat, UV, and an electrophoresis salt solution. We then took weekly scans of each sample and analyzed changes in their spectra. Our results indicate that oxidation introduced new spectra peaks and shifts, specifically adding a 1700 cm-1 carbonyl functional group. We then hypothesized an oxidation mechanism for the three plastic structures. The development of this FT IR reference library for oxidized microplastics will benefit the field-based identification research of microplastics. This has implications for our understanding of identifying which microplastics are in nature and their true origins. This study impacts further understanding of microplastic degradation pathways and improves analytical methods for environmental research.

The presence of microplastics in the environment has become an increasing concern, mainly in regions where coastal and agricultural activities contribute to contamination. This study investigates and compares the impact and extent of microplastic and microfiber pollution in two different locations.  We choose to compare the Channel Islands Harbor, a center for recreational and commercial maritime activity and the Santa Clara Riverbed, which passes through farmland right before draining into the Pacific Ocean near the Ventura Harbor.   Our goal in examining sand sediment from the two sites is to identify the quantities and type of the micro contaminants found.
    Sediment samples were collected at high tides at both sites using Rose Bengal stain to differentiate between synthetic and organic microfibers.  Samples were then vacuum filtered to isolate the contaminants and analyzed with a Nikon dissecting microscope and Fourier transform infrared spectroscopy.
    Initial results indicate that there is a greater concentration of microplastics in the Santa Clara Riverbed, suggesting that they are likely brought in by irrigation systems, textile waste, and agricultural runoff. In contrast, microfiber contamination was more prevalent in the Channel Islands Harbor, potentially due to boating activity, urban runoff and marine infrastructure. The findings draw attention to the different sources of pollution that impact inland river systems as opposed to coastal ones, highlighting the necessity of more studies to create efficient mitigation plans and legislative actions.

Microplastic contamination is truly an escalating environmental concern, specifically in sedimentary environments where accumulation raises risks to marine ecosystems and human health. Traditional methods for extracting microplastics, as well as microfibers, from sand sediments include mixing the sand with artificial seawater at a salinity of 35 ppt (3.5% NaCl), and then by decanting the supernatant onto filter paper. The saltwater helps with buoyancy, allowing the microplastics to remain suspended after mixing. However, it is not very convenient and improving efficiency and scalability in microplastic extraction remains a challenge. This study aims to use sand sediment samples extracted from the estuary of the Santa Clara riverbed that empties into the Pacific Ocean. We plan to compare different extraction methods for retrieving microplastics from sand sediments optimizing the microfibers/microplastic extraction by testing three different solutions, deionized (DI) water, 3.5% NaCl, and a 2% sodium dodecyl sulfate (SDS) against two agitation techniques: mechanical stirring and vibrational energy. We hypothesize that SDS, because of its ability to interact with hydrophobic particles, will enhance microplastic recovery compared to NaCl and DI water. Also, we explore vibrational energy as an alternative to stirring by using a standard laboratory vortexer. If vibrational extraction demonstrates comparable or superior efficiency, scaling up sediment processing using vibrational energy is possible. Fourier Transform Infrared (FTIR) spectroscopy will be used for polymer identification after filtering. Our results aim to optimize microplastic recovery techniques, providing a finding for more effective sediment contamination assessments and potential large-scale investigations.

Immunity is plastic and varies with many internal and external factors, including sex. Immunity can be lower in males than females, potentially due to differences in circulating immunomodulatory hormones. However, sex-specific differences in immunity remain poorly understood in juvenile wild birds. Avian malaria has negatively affected many endemic Hawaiian songbirds, but the Hawaii Amakihi is one of the only native species with tolerance to the disease. Increased tolerance may be related to increased immunity because Amakihi populations with greater tolerance of avian malaria have higher levels of some measures of innate immunity.  Sex-specific differences in immunity could have important implications for individual susceptibility to infection. However, these relationships between immunity and sex remain wholly unexplored in juvenile Amakihi. The goal of our project was to determine whether immunity varies by sex in juvenile Amakihi. We hypothesized that females would have greater immunity than males due to differences in immunomodulatory hormones. To test this hypothesis, we collected blood samples from free-living juvenile Amakihi for molecular sexing and to measure innate immunity and circulating prolactin. In support of our hypothesis, the number of WBCs was significantly higher in females than males (p = 0.0375), while no sex-specific differences were observed for agglutination (p = 0.629). Prolactin levels tended to be higher in females than males (p = 0.206) and correlated positively with number of WBCs (p = 0.317), but these results were not statistically significant, suggesting that prolactin is unlikely to be the mechanism directly mediating differences in WBCs across sex.

We analyze the rotation curves of spiral galaxies to quantify their dark matter component and better understand the impact it has on spiral galaxy dynamics. Previous observations show that the stellar mass alone cannot account for the gravitational effects seen in rotation curves, indicating the presence of large dark matter halos surrounding these galaxies. We calculate rotational velocities from spectra observed during the first year of the Dark Energy Spectroscopic Instrument (DESI) Peculiar Velocity Survey, and model the rotation curves of those spiral galaxies that pass visual inspection. Parameterizing the rotation curves allows us to estimate the total mass of the galaxies within R26 (the radius of the 26 mag/arcsec2 isophote) We find a strong correlation between this total mass and the galaxy’s luminosity, which is to be expected from the Tully-Fisher relation and confirms the validity of our total mass calculations. Additionally, the relationship between total mass and stellar mass provide insights into the distribution of dark matter within spiral galaxies, allowing us to better constrain the properties of dark matter.

Microplastics are pervasive environmental pollutants that often go unnoticed due to their small size. Traditional extraction methods from sand or soil sediment involve creating a slurry in a 3.5% salt solution, allowing sediments to settle while keeping microplastics suspended. The supernatant is then decanted onto a filter to collect contaminants for analysis.
This study presents a novel extraction method using electrolysis in a solution of sand, water, and 0.04% sodium dodecyl sulfate (SDS). SDS facilitates microplastic binding and migration through an electric field. Sand sediment samples were collected from the Santa Clara Riverbed. A slurry of sand, water and SDS solution was placed in the well associated with the negative electrode of an electrophoresis box. The box was then filled with buffer solution, and electrolysis was conducted at 100 volts for 30 minutes. Afterward, the aqueous solution near the positive electrode was removed and filtered.
Results were compared to the traditional decanting method using sand from the same location. The electrolysis method extracted significantly more microplastics than the conventional approach.
 

RNA thermometers are specialized regulatory elements that are used to allow adjustment of gene expression in response to changes in temperature. These RNA molecules are highly structured and located in the 5’-untranslated region (5’-UTR) of a gene. These molecular thermo-sensors are usually associated with heat-shock, cold-shock, and virulence genes (Chowdhury et al., 2006). Furthermore, these structured RNAs sequester the ribosome binding site (Shine Dalgarno) at colder temperatures. Increases in temperature result in an alternation to the structure, which allows ribosome-binding access and the initiation of translation (Righetti & Narberhaus, 2014). FtsH is a bacterial AAA+ protease that was found, in past studies, to be associated with many cellular activities such as playing a role in the regulation of the E. coli heat shock response. (Langklotz et al., 2012). Previous research conducted in Dr. Abdelsayed’s lab found FtsH to be a candidate for an RNA thermometer; however, further testing must be done to ensure the RNA thermometer is a real RNA thermometer that works. The aim of this research is to further characterize FtsH and to biochemically validate that it is an RNA thermometer.

Female choice, a form of sexual selection, can drive the evolution of male ornaments or behaviors. Squirrel monkeys (genus Saimiri) are small, neotropical primates that live in large groups, characterized by seasonal breeding, polygamy, and female dominance. Adult males undergo hormonally induced seasonal fattening, gaining up to 20% of their body weight during the 2-month mating season. We examined the possible relationship between male fattening and female choice in Saimiri collinsi. We hypothesized that female squirrel monkeys prefer to mate and associate with more robust (“fatter”) males. We collected behavioral data on two squirrel monkey groups, in Eastern Amazonia, Brazil during two mating seasons (N= 272 contact hours). Groups averaged 33 individuals, with 9 adult males per group. Each adult male was classified visually between 1-3 (1= least fattened; 3=fattest). We collected adult focal data by documenting nearest neighbors (within 3m) and recorded male-female sociosexual interactions, noting female receptivity. Females copulated mostly with grade 3 males (90% of 20 copulations) and were more frequently observed near these males (χ2= 55.05; p<0.0001). Grade 3 males also spent more time near adult females (H=7.61, df= 2; p=0.02) and associated with more individual females than grade 1 and 2 males. Grade 3 males initiated 73% of 881 male sexual solicitations and had the highest female receptivity (78% of observations). These results indicate that fatter males have higher mating success, and that female choice likely plays a role in the evolution of male fattening in squirrel monkeys. NSF funded this research (Award no. BCS-2140666).

Reinforcement learning, specifically DQN models, have been created to play games from the 1970s and 1980s, such as Space Invaders (Mnih et al., 2013). This study attempts to create a DQN model to play the game of Tetris and reach a score of at least 1 million points. In this model, the Q-learning algorithm was used in tandem with a deep neural network to maximize the rewards, in this case points, obtained from interacting with the Tetris environment. Every time a new piece was chosen by a randomizer, the agent chose an action to take by ranking all possible moves based on the rewards gained from the resulting state observation. This was done by passing all state observations through a neural network with two hidden layers each with 42 artificial neurons and comparing the ranks from the single node output layer. Each state observation consists of five features which describe the maximum height reached on the board, the minimum height on the board, the number of holes between placed pieces, the number of filled rows, and the difference in adjacent column heights. After successive training sessions and iterative adjustments to gamma and to other learning constants, training stopped at a loss of 185 and a high score of 5.265 billion points, played in real time with the use of the hold move, where a secondary piece is evaluated alongside the main piece. Results suggest that the agent gained a fundamental understanding of the complexities of Tetris, mirroring other studies’ results.

Many high-temperature superconducting materials are characterized by high density of states (DOS) near the Fermi level and  strong magnetic fluctuations. In this project we developed Python code to fetch tight-binding parameters from the Jarvis materials database (https://jarvis.nist.gov/) and then calculate the DOS and bare magnetic susceptibility. Using these calculations, we  hope  to identify potential candidate materials, either in their pure or doped forms, having desired properties such as superconductivity or magnetism. The standardized tight-binding format used in the Jarvis database, which is derived from the Wannier-90 project (https://wannier.org), enables rapid searches through the Jarvis database and provides a demonstration for accelerating research in materials physics through standards-based modeling and simulation.