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Ovalbumin as a PFAS carrier protein in aquatic environments
No full textPer- and polyfluoroalkyl substances (PFASs) are persistent environmental contaminants, necessitating sustainable and scalable solutions for sensing and remediation. Leveraging abundant natural proteins as bio-adsorbents offers an environmentally benign approach. Here, we present a comprehensive investigation of the binding mechanisms between seven structurally diverse PFAS compounds and ovalbumin (OVA), the predominant egg white protein. Using integrated experimental measurements and molecular modeling, we show that OVA binds PFASs spontaneously and rapidly, with affinities strongly dependent on perfluoroalkyl chain length and head group chemistry. Hydrophobic interactions emerge as the primary thermodynamic driver, while molecular simulations identify key basic residues, including arginine and lysine, as critical contributors to binding stability. Thermodynamic analyses confirm that binding is enthalpically favored and strongly modulated by environmental factors: elevated temperature enhances binding, whereas higher salinity generally attenuates it. Together, these findings establish OVA as a robust and versatile biomolecular platform for next-generation PFAS sensing and remediation technologies.This article is published as Arshad, Amara, Shirsa Mazumdar, Jimli Goswami, Mohiuddin Quadir, Mallikarjuna Nadagouda, Achintya N. Bezbaruah, and Wenjie Xia. "Ovalbumin as a PFAS carrier protein in aquatic environments." Cell Reports Physical Science (2026). doi: https://doi.org/10.1016/j.xcrp.2025.103089.The National Science Foundation (NSF) grant (CBET-1707093, A.N.B.) is acknowledged. S.M. received funding from the North Dakota Water Resources Research Institute (NDWRRI) during this work. The authors acknowledge support from the Department of Civil, Construction, and Environmental Engineering and the Department of Coatings and Polymeric Materials at North Dakota State University (NDSU). W.X. acknowledges support from the NSF under award no. 2331017 and the Department of Aerospace Engineering at Iowa State University (ISU)
Topology-preserving dimensionality reduction
No full textAs data becomes increasingly central across scientific and engineering disciplines, effective
visualization is essential for interpreting complex, high-dimensional structures. Dimensionality
reduction techniques are widely used to project high-dimensional data into lower dimensions while
preserving structural properties such as distances and local neighborhoods. In this paper, we
extend landmark-based dimensionality reduction to improve homological preservation, that is, the
preservation of homological features of the data, which are critical for maintaining global shape
and continuity. We first introduce AdaMapper, a Mapper-based algorithm that leverages
persistence diagrams to guide skeleton construction and landmark selection. AdaMapper is
parameter-free and adaptively refines covers in regions where topological loops occur. We then
propose AdaHIsomap, which integrates landmark Isomap with homology-informed landmark
selection and introduces random anchor points to balance distance and homology preservation.
We evaluate both methods on a variety of datasets—including high-dimensional point clouds,
scientific simulations, networks, and images—and benchmark them against state-of-the-art
approaches
Advances in data-driven learning of spatial processes
No full textThe dissertation investigates three interrelated research problems focused on governing mechanisms for data on spatial domain with the goal of estimating key governing functions for data point generation. Specifically, the problems entail estimating the density function of independent spatial data points, covariance parameters and microergodic functions of dependent Gaussian processes on regular grids, and governing parameters and microergodic functions of Gaussian random fields in replicated datasets. The chapters progress from independent data points to dependent observations considered over independent replications.
Chapter 2 introduces a nonparametric density estimation technique using bivariate penalized spline smoothing on triangulated data in irregular domains. This likelihood-based method includes a regularization term to reduce logarithmic density roughness, enhancing efficiency and flexibility in complex domains. Theoretical asymptotic convergence rates are established, supported by simulations and a case study on motor vehicle theft data in Portland, Oregon, showcasing improvements over existing techniques.
Chapter 3 presents a data-driven method for assessing nonstationary spatial processes using higher-order quadratic variations to estimate functions of two-dimensional random fields. A model based on the isotropic Matern covariance function is developed, focusing on smoothness parameter estimation. A two-stage estimation process leverages a hybrid grid framework for local stationary parameter estimation, followed by nonparametric kernel smoothing for global estimation. Theoretical convergence rates and validation through simulations demonstrate its accuracy and time efficiency compared to traditional models.
Addressing the challenges of analyzing nonstationary spatial data, traditional likelihood-based methods often become impractical, while likelihood-free approaches like Approximate Bayesian Computation (ABC) struggle with calibration and efficiency. Recent advances in neural point estimators offer a viable alternative by mapping data directly to parameter estimates. However, standard neural networks may introduce bias in replicated spatial data. Chapter 4 extends neural Bayes estimators by using permutation-invariant network architectures, inspired by the DeepSets framework, to accurately estimate parameters from replicated nonstationary Gaussian fields. This approach accounts for the exchangeability of replicated spatial fields, employing a two-grid method for estimating constant covariance parameters under local stationarity, followed by a nonparametric smoothing technique
Porcine reproductive and respiratory syndrome virus surveillance in breeding herds using postmortem tongue fluids
No full textPorcine reproductive and respiratory syndrome virus (PRRSV) remains one of the most significant health and economic challenges for the global swine industry. Current monitoring and surveillance systems rely on a combination of sample types and diagnostic tools; yet, each has limitations, particularly in detecting the virus in low-prevalence scenarios, in terms of applicability across production stages, and in the inability to capture vertical transmission events in a practical manner. In this context, postmortem tongue fluids have recently been proposed as a population-based sample type that may complement existing approaches, but knowledge on their sampling, storage conditions, and application in United States swine breeding herds remains limited. The issue addressed in this dissertation is the evaluation and improvement of tongue fluids as a sample type to be used in PRRSV surveillance programs, with the following chapters summarizing previously described concerns.
Chapter Two is entitled “Porcine reproductive and respiratory syndrome virus RNA detection in tongue tips from dead animals.” This chapter compared PRRSV RNA detection in postmortem tongue fluids to serum, processing fluids, and family oral fluids collected from suckling piglets of three age groups from the farrowing room population across three breeding herds. Tongue fluids consistently showed high positivity across the sampled groups in two herds, with findings highlighting the potential value of this sample type for PRRSV monitoring and surveillance in breeding herds.
Chapter Three is entitled “Effect of time and temperature on the detection of PRRSV RNA and endogenous internal sample control in porcine tongue fluids.” This chapter investigates the effect of time and temperature storage conditions on PRRSV RNA and internal sample control RNA detections in tongue fluids. This chapter is organized into three studies: (i) fresh and freeze-thaw tongue fluid comparison; (ii) 15 combinations of time and temperature storage conditions with an initial high viral load sample; and (iii) 15 combinations of time and temperature storage conditions with an initial low viral load sample. In study (i), fresh samples showed slightly lower Ct values than freeze–thawed samples, and across studies (ii) and (iii), PRRSV and internal sample control RNA detections remained stable for up to 14 days when stored at 4°C. In contrast, higher temperatures led to RNA degradation and increased Ct values. Overall, these findings demonstrate that proper storage at a temperature of ≤ 4°C maintains the quality of tongue fluid samples and ensures reliable PRRSV RNA detection results.
Chapter Four is entitled “PRRSV vertical transmission evaluation using tongue fluids from stillborn piglets.” This chapter investigates PRRSV vertical transmission using stillborn piglet tongue fluids in relation to sow tonsil scrapings. Samples were collected within 12 hours post-farrowing in two PRRSV-positive herds and tested by RT-qPCR. Half of the litters were PRRSV-positive, with moderate agreement between stillborn tongue fluids and stillborn serum. Sow tonsil scraping results were not associated with stillborn tongue fluid positivity, indicating that sow status did not predict litter infection. Thus, stillborn tongue fluids proved to be a practical indicator of vertical transmission events, whereas sow-based testing alone may not accurately reflect the piglet's infection status.
Chapter Five is entitled “Evaluating stillborn and litter size as indicators of PRRSV detection in live piglets and the use of stillborn tongue fluids as risk-based samples for PRRSV monitoring”. This chapter describes risk-based indicators for PRRSV detection in live piglets. Samples from two breeding herds were collected within 12 hours post-farrowing and tested by RT-qPCR. Results showed that litters with stillborns or small litter sizes had significantly higher odds of PRRSV detection, and positive stillborn tongue fluids were a strong predictor of the presence of viremic liveborn piglets within the litter. These findings support approaches for targeting high-risk litters to improve PRRSV RNA detection within litters.
Chapter Six is entitled “Evaluating tongue fluids for PRRSV monitoring in breeding herds and the effect of pooling on PRRSV RNA detection by RT-qPCR.” This chapter evaluates tongue fluids as a monitoring tool for PRRSV in breeding herds and their pooling effect on PRRSV RNA detection. Tongue fluids were daily collected from deceased piglets at two different ages, and the results were compared with processing fluids across three herds undergoing PRRSV stabilization. Results showed no differences in positivity between sample types, with tongue fluids consistently having the lower Ct values than processing fluids. To consistently extract at least 1 mL of fluid, more than 20 tongue tips should be sampled in one disposable bag, followed by one freeze-thaw cycle. Moreover, pooling tongue fluids significantly increased detection probabilities, achieving 85% detection at a 1:7 dilution (daily collections with weekly pooling), compared to 14% when testing a single day’s tongue fluids from a week with one positive day. Altogether, the results of this study support daily collection of tongue tips and highlight tongue fluids as a practical and cost-effective sample type for PRRSV monitoring, particularly when combined with pooling strategies.
Chapter Seven is entitled “PRRSV stabilization in breeding herds: field implementation of multiple diagnostic tools.” This chapter describes the field implementation of multiple PRRSV diagnostic tools in two breeding herds undergoing stabilization through load-close-exposure with live virus inoculation. Monitoring strategies combined stillborn tongue fluids, postmortem newborn tongue fluids, processing fluids, serum, family oral fluids, environmental sample types, as well as key performance indicators analyzed with exponentially weighted moving average (EWMA) statistical process control charts. Results demonstrated that tongue fluid positivity was comparable to that of processing fluids and enabled partial recovery of the PRRSV genome sequencing (12,392 nucleotides), which was useful during an epidemiological investigation conducted in the second herd, suggesting a second PRRSV lateral introduction. Environmental samples had limited detection. The EWMA charts identified early outbreak signals, including pre-weaning mortality and neonatal losses, up to four weeks before farm personnel first noticed clinical changes indicative of PRRSV circulation.
Chapter Eight provides an overall conclusion of the dissertation. It highlights the findings from the seven research chapters, emphasizing the role of population-based samples, particularly tongue fluids, in PRRSV surveillance programs for breeding herds undergoing virus stabilization, as well as the practical application of this tool under field conditions
Full Factorial Comparison of the Diagnostic Performance of Three Nucleic Acid Extraction Kits and Three PRRSV RT-qPCR Assays Using Swine Oral Fluids of Known Status
No full textPorcine reproductive and respiratory syndrome (PRRS) is one of the costliest diseases in swine production, causing >$1.2 billion USD in annual losses in the United States. Oral fluids are widely used for PRRS virus (PRRSV) surveillance, accounting for 42% of nearly 480,000 PRRSV RT-qPCR cases submitted to six Midwestern U.S. laboratories between 2020 and 2025. Despite this reliance, few studies have applied appropriate methodological approaches to compare the performance of commercial extraction and PRRSV RT-qPCR protocols for oral fluid specimens. In this study, we evaluated nine extraction-amplification protocols for PRRSV RNA detection, based on three commercial extraction kits and three commercial RT-qPCR assays. For each protocol, performance was evaluated using 314 oral fluid samples of known status (215 positive, 99 negative), collected under controlled conditions from 72 pigs assigned to five groups inoculated with contemporary PRRSV isolates and from one negative control group. The Cq values were normalized as efficiency standardized Cqs (ECqs) and then analyzed by receiver operating characteristic (ROC) analysis. The mean amplification efficiencies ranged from 67 to 92%, repeatability from 0.98 to 0.99, and overall reproducibility was 0.91. The ROC AUCs ranged from 0.916 to 0.986, with significant pairwise differences (p < 0.05). At optimal ECq cutoffs, sensitivities ranged from 83 to 98.1% with 100% specificity. Normalization enabled objective protocol comparisons and statistically valid diagnostic cutoffs and supports future improvements in PRRSV diagnostics.This article is published as Armenta-Leyva, Betsy, Gaurav Rawal, Jianqiang Zhang, Berenice Munguía-Ramírez, Grzegorz Tarasiuk, Danyang Zhang, Rolf Rauh, Kyoung-Jin Yoon, Luis G. Giménez-Lirola, and Jeffrey J. Zimmerman. "Full Factorial Comparison of the Diagnostic Performance of Three Nucleic Acid Extraction Kits and Three PRRSV RT-qPCR Assays Using Swine Oral Fluids of Known Status." Microorganisms 14, no. 2 (2026): 282. doi: https://doi.org/10.3390/microorganisms14020282.This research was funded by Iowa State University Veterinary Diagnostic Laboratory
Statistical Examination of the Shear Strength of Frozen and Unfrozen Fine-Grained Soils
No full textClimate change causes an increase in the temperature of permafrost. This increases the amount of unfrozen water in the permafrost soils, which will reduce their shear strengths. Such reductions may result in the failure of foundations, buildings, pipelines, and other earth structures. Many factors, including temperature, normal stress, plasticity characteristics of soil, and type of soil, impact the shear strength of frozen and unfrozen soils. This study leverages a statistical design of experiments (DoE) to investigate the impact of temperature, normal stress, and soil type on the peak shear strength of frozen soils. The DoE includes input parameters consisting of temperatures ranging from -10℃ to +4℃ in increments of 2℃, normal stresses of 50 kPa, 100 kPa, 200 kPa, and 300 kPa, and a selection of ten finegrained laboratory-prepared soils. The design optimized the 960 experiments required to explore each factor individually to 37 randomized experiments. These experiments were then conducted using a custom temperaturecontrolled direct shear device. The findings from the DoE were compared with the traditional one-variable-at-a-time (OVAT) approach. It was determined that DoE offers a valuable understanding regarding the importance of individual parameters affecting peak shear strength. Among these parameters, temperature was found the most influential factor, followed by soil type and normal stress. An increase in the temperature decreased the peak shear strength for temperatures between -10℃ and -2℃. Further increases in temperature from -2℃ to +4℃ densifies the soil, leading to a slight increase in the peak shear strength. An increase in liquid limit was seen to decrease the peak shear strength. While the DoE approach provides an acceptable analysis of the significance of the various parameters on the peak shear strength, the predicted values were not reliable due to limitations, such as an oversight of critical boundaries and inability to capture the fundamental differences between the behavior of frozen and unfrozen soil.This is a manuscript of an article published as Emami Ahari, Hossein, Beena Ajmera, and Yuderka Trinidad Gonzalez. "Statistical Examination of the Shear Strength of Frozen and Unfrozen Fine-Grained Soils." Journal of Geotechnical and Geoenvironmental Engineering 152, no. 4 (2026): 04026003. doi: https://doi.org/10.1061/JGGEFK.GTENG-13823.The authors would like to acknowledge the generous financial support provided by the Iowa State University College of Engineering Exploratory Research Program and the Department of Civil, Construction and Environmental Engineering to undertake the work described in this paper
Optical Deep Space Navigation and Sensor Alignment with an Adaptive Cascade Filter for Small Satellites
No full textAs deep space missions for CubeSats are now possible, new autonomous technologies are required to navigate the Solar System. The low cost and low power consumption of off-the-shelf cameras make optical navigation an attractive technology for CubeSats. Therefore, this work proposes an adaptive cascade filter for the attitude and orbit determination of CubeSats. The attitude determination system (ADS) processes star tracker attitude measurements and gyroscopic rate measurements to estimate the spacecraft inertial attitude, gyroscope bias, gyroscope scale factor, and camera misalignments. The gyroscope Angle Random Walk (ARW) is additionally estimated through mutating Multiple Model Adaptive Estimation (MMAE). The ADS state estimate is then input to the Orbit Determination System (ODS) in a cascade filter. The ODS uses the attitude information and ephemeris data to filter the spacecraft position from bearing measurements to planets. Monte Carlo simulations show that position accuracy is attainable on the order of 10000 km with low-grade sensors, or 1000 km with mid-grade sensors. The MMAE scheme improves the ADS performance when the gyroscope ARW is unreliable.This is a manuscript of a proceeding published as Perruci, Alexander, David Lee, Ossama Abdelkhalik, and Simone Servadio. "Optical Deep Space Navigation and Sensor Alignment with an Adaptive Cascade Filter for Small Satellites." In AIAA SCITECH 2026 Forum, p. 0173. 2026. doi: https://doi.org/10.2514/6.2026-0173.This work was supported by NASA Established Program to Stimulate Competitive Research, "Non-GPS Navigation System Using Dual Star/Planetary Cameras for Lunar and Deep-Space CubeSat Missions", Award No. 80NSSC24M0110
Effects of feedstock moisture content on biochar yield and carbon recalcitrance during autothermal pyrolysis of digestate
No full textThis study investigates the influence of feedstock moisture content on biochar yield and carbon recalcitrance during autothermal pyrolysis of anaerobic digestate. Biochar is a product of pyrolysis that can serve as a carbon sequestration agent – the fraction of recalcitrant carbon in the biochar contributes to its stability and allows it to store carbon in the soil long term. Digestate samples with moisture contents of <5%, 10%, 20%, 30%, and 40% were tested in duplicate. The resulting product distribution and composition of biochars produced were analyzed for yield and carbon stability. Increasing feedstock moisture content had no significant effect on biochar yields produced but significantly affected the yield and composition of the bio-oil and noncondensable gases (NCGs). The Van Krevelen Diagram and the volatile matter-to-fixed carbon (VM/FC) diagram were used to evaluate biochar’s composition to determine its degree of carbonization and stability. Analysis of biochar composition indicates that an increase in moisture content had no significant effect on biochar composition, and thus, its carbon recalcitrance
The global extent of the grassland biome and implications for the terrestrial carbon sink
No full textLand cover data are commonly used to model the terrestrial carbon (C) sink, yet these data have wide margins of error that significantly alter estimates of global C storage. Here we demonstrate this data vulnerability in grasslands, which are critical to C cycling but whose estimated distribution has varied by >50 million km2 (3.5–42% of the Earth’s terrestrial surface). Comparing multiple high-resolution land cover products with expertly annotated grassland data from six continents, we show sources of mapping error and discuss C implications based on 2023 United Nations (UN) FAO estimates. Past misidentification arose from inconsistent definitions on grassland identity and classification flaws especially relating to woody plant cover. Correcting these errors adjusted grassland coverage to 22.8% of the terrestrial land base (30.1 million km2), elevating UN projections of soil C stocks to 155.02 Pg (0–30 cm depth). These findings underscore the challenges of biome mapping for ecosystem accounting and policy, when lacking field-validated remotely sensed data.This article is published as MacDougall, A.S., Vanzant, B., Sulik, J. et al. The global extent of the grassland biome and implications for the terrestrial carbon sink. Nat Ecol Evol (2026). https://doi.org/10.1038/s41559-025-02955-
Compensating Star-Trackers Misalignments with Adaptive Multi-Model Estimation
No full textThis paper presents an adaptive multi-model framework for jointly estimating spacecraft attitude and star-tracker misalignments in GPS-denied deep-space CubeSat missions. A Multiplicative Extended Kalman Filter (MEKF) estimates attitude, angular velocity, and gyro bias, while a Bayesian Multiple-Model Adaptive Estimation (MMAE) layer operates on a discrete grid of body-to-sensor misalignment hypotheses. In the single-misalignment case, the MEKF processes gyroscope measurements and TRIAD-based attitude observations, and the MMAE updates a three-dimensional grid over the misalignment vector. For a dual-misalignment configuration, the same MEKF dynamics are retained, and the MMAE bank is driven directly by stacked line-of-sight measurements from two star trackers, forming a six-dimensional grid over the two misalignment quaternions without augmenting the continuous-state dimension. A novel diversity metric, is introduced to trigger adaptive refinement of the misalignment grid around a weighted-mean estimate, thereby preventing premature collapse of the model probabilities and concentrating computation in the most likely region of the parameter space. Monte Carlo simulations show arcsecond-level misalignment estimation and sub-degree attitude errors for both estimation problems, with estimation errors remaining well-bounded, proving robustness and consistency. These results indicate that the proposed MEKF--MMAE architecture enables accurate, autonomous, and computationally efficient in-flight calibration for resource-constrained spacecraft, and establishes dual star-tracker misalignment estimation as a practical option for deep-space CubeSat missions.This is a preprint from Ganganath, Ridma, Simone Servadio, and David Daeyoung Lee. "Compensating Star-Trackers Misalignments with Adaptive Multi-Model Estimation." arXiv preprint arXiv:2601.01130 (2026). doi: https://doi.org/10.48550/arXiv.2601.01130.The authors wish to acknowledge the support of this work through the National Aeronautics and Space Administration (NASA) Established Program to Stimulate Competitive Research (EPSCoR) under grant number 80NSSC24M0110