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Dataset for Cholesterol and Q147E Deamidation Modulates aA-Crystallin Membrane Binding Elucidating Protective Role of Lens Membrane Composition Changes with Aging
No full textPurpose: αA-Crystallin (αAc) binding with lens membranes increases with age and cataract formation. However, the role of lipids and cholesterol (Chol) in Q147E-αAc membrane binding remains unclear, which we aim to elucidate in this study.
Methods: We have used the electron paramagnetic resonance spin-labeling method to probe the Chol/ 1-palmitoyl-2-oleoylphosphatidylcholine (POPC) and Chol/ sphingomyelin (SM) membranes binding with wild-type (WT) and Q147E-αAc.
Results: Compared to WT-αAc, the Q147E mutant had increased binding to POPC and decreased binding to SM membranes without Chol. Adding 33 mol% Chol to the POPC and SM membranes decreased the binding of WT and, to a lesser degree, decreased the binding of Q147E-αAc to the membranes. Adding 60 mol% Chol completely inhibited Q147E mutant and WT binding to POPC membranes. However, 33 and 60 mol% Chol completely inhibited WT and Q147E mutant binding to SM membranes, respectively. WT and Q147E-αAc membrane binding decreased membrane mobility while increasing order and hydrophobicity near the headgroup.
Conclusions: In Chol-free membranes, the deamidated Q147E-αAc binds significantly more to the POPC membranes compared to WT, whereas WT binds significantly more to the SM membranes compared to Q147E-αAc. In contrast, for 33 mol% Chol-containing membranes, the deamidated Q147E-αAc binds significantly more to POPC and SM membranes than WT. Conversely, 60 mol% Chol-containing membranes completely inhibit WT and deamidated Q147E-αAc binding to POPC and SM membranes. These results suggest that increased Chol content of the lens membranes during aging protects against accumulation of modified proteins on the membrane associated with cataracts
Alternative Wood Composite Using Copper (II) Oxide Nanoparticle and Cinnamaldehyde Adhesive
No full textOver 4.4 million cubic meters of particleboard were produced in the United States in 2023. Currently, urea-formaldehyde is one of the most common adhesives used in the creation of particleboard due to its short curing time and strong bonding ability. However, health concerns surrounding formaldehyde in construction materials have led to research to develop a safer and more sustainable adhesive to use in wood composites. In attempting to create a safer alternative, wood composite panels were made using an adhesive resin composed of copper (II) oxide (CuO) nanoparticles and trans-cinnamaldehyde (CN), chosen for their antifungal properties, in a 1:1 ratio by mass. Composite panels were made with 10% and 20% concentrations of the adhesive with unsifted wood fiber and wood fiber sifted through a 40-mesh screen. The composite panels were tested for mechanical properties, and the adhesive was analyzed using Fourier Transform Infrared spectroscopy (FTIR) and Thermogravimetric Analysis (TGA). The wood composite created with 10% adhesive and sifted wood fiber displayed the best mechanical properties with average internal bond strength of 0.230 MPa, average maximum bending stress of 4.777 MPa, and average Young’s modulus of 1454.923 MPa. The 20% adhesive panels were too weak to test. Although safer than the composites utilizing urea-formaldehyde, the composite made using CuO and CN did not display adequate mechanical properties to serve as a suitable replacement wood composite without further study
Complex-Valued Neural Networks in Federated Learning: Performance Analysis and Investigating Privacy Attacks
No full textComplex-Valued Neural Networks (CVNNs) have shown significant potential in recent years due to their enhanced expressiveness and inherent ability to encode phase information, making them promising for fields such as signal and image processing. While research into CVNNs is limited, studies exploring their application in Federated Learning (FL)—a decentralized machine learning approach designed to address paramount user data privacy concerns—are even scarcer. This research explores the use of CVNNs within the FL framework, providing a direct performance comparison against their Real-Valued Neural Network (RVNN) counterparts in both federated and standalone settings. For our experiments, we propose and train a condensed version of the Complex-Valued Residual Network (CV-ResNet) architecture on the CIFAR-10 dataset. We evaluate various permutations of this network configuration, different hyperparameters, and also utilizing several activation functions including complex cardioid. In addition to performance comparisons, we investigate potential privacy vulnerabilities inherent in this approach, such as gradient inversion attacks
Does Dorsal Fin Size Matter?: Assessing Dorsal Fin Characteristics to Evaluate Hierarchy Formation in a Sexually Plastic Fish
No full textIn species that organize into social hierarchies, specific morphological traits combined with aggressive behavioral displays are often used to establish and maintain status. The size-advantage model states that individuals with a larger body size attain higher social ranks than smaller individuals. However, body size is only one aspect of communication between conspecifics and additional morphological features should be considered. The bluebanded goby, Lythrypnus dalli, is a bidirectional sexually plastic fish that organizes into social hierarchies composed of one dominant male and several subordinate females. Dominant L. dalli perform low intensity aggressive displays utilizing their dorsal fin to demonstrate size. While there is a positive relationship between the standard length of L. dalli and higher social status, we hypothesized that dorsal fin size may be an additional anatomical feature used in hierarchy formation. We predicted that dominant fish will have a larger fin area. Using ImageJ, we measured the fin area and longest fin ray length and compared the dominant male to a subordinate female. These data will help us understand whether multiple characteristics are important in determining social status
Reducing Observational Astronomy Time Costs with Multi-Slit Spectrographs
No full textIn observational astronomy, limited telescope time and high operational costs constrain our ability to assemble large spectroscopic data sets. A multi-object spectrograph (MOS) improves efficiency by allowing observers to observe multiple stars simultaneously. We used the KOSMOS spectrograph on the ARC 3.5 m telescope at Apache Point Observatory to observe star clusters. Because no existing data reduction package supports KOSMOS, we adapted John Holtzman’s Python-based PyVista reduction module into a Jupyter Notebook pipeline that automates bias subtraction, flat‐field correction, cosmic‐ray removal, wavelength calibration, and spectral extraction. This streamlined workflow will enable near–real-time quality assessment, minimizing reduction time.
We are currently validating our results using data from seven stars, which have radial velocities reported in the APOGEE catalog. By comparing our calculated radial velocities with the APOGEE values, we assess the accuracy of our reduction method. The discrepancy observed between our results and the APOGEE catalog is ~17 km/s, indicating our reduction software is producing well-calibrated spectra
Field Evidence of the Spatial and Temporal Variation in Bed Shear Stress in a Gravel-Bedded River
No full textUnderstanding how bed shear stress varies through time and space is essential for predicting sediment transport and channel stability in gravel bedded rivers. This project will collect high resolution field measurements of water depth, flow velocity, and discharge along repeated cross sections during low flow, rising, and falling limbs of a spring–summer hydrograph. Velocity profiles will be used to compute point estimates of shear stress, which, together with measured channel geometry, will drive two dimensional river analysis modelling and produce spatial shear stress maps. Comparing these maps through successive flow stages will quantify how shear stress magnitude and distribution respond to changing discharge and help identify transient high stress zones that influence bed mobility, thereby informing better river management decisions. The work will also train undergraduate researchers in modern hydraulic instrumentation, data processing, and modelling workflows. Resulting insights will strengthen the empirical basis for sediment transport thresholds and advance the Idaho NSF I CREWS objective of improving resilience in coupled energy–water systems under climate driven hydrologic variability
Investigating the Groundwater Microbiome in Sulfate-Rich Aquifers of the Reynolds Creek Critical Zone Observatory, Southwestern Idaho
No full textIn the Earth’s subsurface life is comprised exclusively of microorganisms, and estimates indicate that approximately 12-20% of the global prokaryotic biomass is found in the continental subsurface. In this realm, groundwater systems serve as prominent microbial habitats, hosting non-photosynthetic chemotrophic microbial communities. Sulfate-reducing microorganisms are common in Earth\u27s subsurface, often using hydrogen derived directly from radiolysis of pore water and sulfate derived from oxidation of rock-matrix-hosted sulfides by radiolytically derived oxidants. During the summers of 2022 and 2023, we sampled groundwater from two adjacent wells within the Reynolds Creek Critical Zone Observatory in Southwestern Idaho including a newly dug nested well with surprisingly high sulfate concentrations, varying seasonally between 1000-2500 mg/L. Microbial diversity in the groundwater samples was investigated using high throughput 16S ribosomal RNA gene amplicon sequencing conducted at Idaho State University’s Molecular Research Core Facility. Microbial communities in the sulfate-rich waters were observed to be dominated by known sulfate-reducing bacterial genera e.g. Desulfosporosinus. Beta diversity analysis further demonstrated that the microbial communities in the sulfate-rich aquifer were significantly different from those in the adjacent well with low sulfate concentrations. These results provide initial evidence of microbially-mediated sulfur cycling within sulfate-rich aquifers in the Reynolds Creek watershed
Mechanistic Insights Into the Toxic Effects of Profenofos and Mancozeb, Common Agricultural Pesticides in Rwanda
No full textThis research examines the health effects of two pesticides: profenofos and mancozeb, focusing on their biological mechanisms of toxicity. Our lab group identified profenofos and mancozeb as two of the most widely used pesticides in Rwanda, despite profenofos no longer being registered for use in the US, and mancozeb being banned in the EU. We conducted a literature review, drawing on scientific articles from PubMed, Google Scholar, and ScienceDirect; government documents from state, federal, and international agencies; and toxicology databases, which described the acute and chronic health impacts of pesticides. Profenofos, an insecticide, has been linked to short-term symptoms including miosis, urination, diarrhea, diaphoresis, lacrimation, central nervous system excitation, and salivation, as well as long-term risks such as neurological damage and endocrine disruption. Mancozeb, a fungicide, has been linked to short-term symptoms including skin irritation, coughing, sneezing, sore throat, and bronchitis, and long-term risks including thyroid dysfunction, reproductive toxicity, and neurotoxicity. This research is important because farmers in places like Rwanda often use pesticides that are banned or restricted in other countries without access to proper training or protective equipment, increasing their risk of exposure. Understanding the toxic profiles of these pesticides can help design interventions that reduce poisoning events
Targeting and Detection of Collagen Proteins in 3-D Cancer Cell Lines
No full textThree-dimensional (3D) cell culture models are a highly relevant platform for mimicking tissue biology and studying disease. These models enable the identification of novel proteins that can serve as biomarkers or therapeutic targets. However, proteomic studies can be technically challenging due to difficulties in extracting sufficient quantities of high-quality protein. Our long-term goal is to develop non-animal 3D cell culture models to test targeted cancer therapies. The goal of this project is to demonstrate that minor collagen proteins are expressed in 3D glioblastoma cell culture models. We hypothesize that the expression of developmentally regulated collagens increases during stages of tumor growth. To support this, we developed methods to improve protein yields for downstream targeted analysis. Our approach was tested using spheroids composed of 5,000–30,000 glioblastoma-derived cells. We found that incorporating a sonication procedure with a cell lysis buffer enhanced protein yields, improving our ability to detect target proteins in downstream applications. We use western blot analysis with unique, collagen-specific antibodies to verify collagen expression in glioblastomas